TY - JOUR A1 - Abbaspour-Gilandeh, Yousef A1 - Molaee, Amir A1 - Sabzi, Sajad A1 - Nabipour, Narjes A1 - Shamshirband, Shahaboddin A1 - Mosavi, Amir T1 - A Combined Method of Image Processing and Artificial Neural Network for the Identification of 13 Iranian Rice Cultivars JF - agronomy N2 - Due to the importance of identifying crop cultivars, the advancement of accurate assessment of cultivars is considered essential. The existing methods for identifying rice cultivars are mainly time-consuming, costly, and destructive. Therefore, the development of novel methods is highly beneficial. The aim of the present research is to classify common rice cultivars in Iran based on color, morphologic, and texture properties using artificial intelligence (AI) methods. In doing so, digital images of 13 rice cultivars in Iran in three forms of paddy, brown, and white are analyzed through pre-processing and segmentation of using MATLAB. Ninety-two specificities, including 60 color, 14 morphologic, and 18 texture properties, were identified for each rice cultivar. In the next step, the normal distribution of data was evaluated, and the possibility of observing a significant difference between all specificities of cultivars was studied using variance analysis. In addition, the least significant difference (LSD) test was performed to obtain a more accurate comparison between cultivars. To reduce data dimensions and focus on the most effective components, principal component analysis (PCA) was employed. Accordingly, the accuracy of rice cultivar separations was calculated for paddy, brown rice, and white rice using discriminant analysis (DA), which was 89.2%, 87.7%, and 83.1%, respectively. To identify and classify the desired cultivars, a multilayered perceptron neural network was implemented based on the most effective components. The results showed 100% accuracy of the network in identifying and classifying all mentioned rice cultivars. Hence, it is concluded that the integrated method of image processing and pattern recognition methods, such as statistical classification and artificial neural networks, can be used for identifying and classification of rice cultivars. KW - Maschinelles Lernen KW - Machine learning KW - food informatics KW - big data KW - artificial neural networks KW - artificial intelligence KW - image processing KW - rice Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200123-40695 UR - https://www.mdpi.com/2073-4395/10/1/117 VL - 2020 IS - Volume 10, Issue 1, 117 PB - MDPI ER - TY - JOUR A1 - Harirchian, Ehsan A1 - Isik, Ercan T1 - A Comparative Probabilistic Seismic Hazard Analysis for Eastern Turkey (Bitlis) Based on Updated Hazard Map and Its Effect on Regular RC Structures JF - Buildings N2 - Determining the earthquake hazard of any settlement is one of the primary studies for reducing earthquake damage. Therefore, earthquake hazard maps used for this purpose must be renewed over time. Turkey Earthquake Hazard Map has been used instead of Turkey Earthquake Zones Map since 2019. A probabilistic seismic hazard was performed by using these last two maps and different attenuation relationships for Bitlis Province (Eastern Turkey) were located in the Lake Van Basin, which has a high seismic risk. The earthquake parameters were determined by considering all districts and neighborhoods in the province. Probabilistic seismic hazard analyses were carried out for these settlements using seismic sources and four different attenuation relationships. The obtained values are compared with the design spectrum stated in the last two earthquake maps. Significant differences exist between the design spectrum obtained according to the different exceedance probabilities. In this study, adaptive pushover analyses of sample-reinforced concrete buildings were performed using the design ground motion level. Structural analyses were carried out using three different design spectra, as given in the last two seismic design codes and the mean spectrum obtained from attenuation relationships. Different design spectra significantly change the target displacements predicted for the performance levels of the buildings. KW - Erbeben KW - Schwellenwert KW - Seismic risk KW - Adaptive Pushover KW - Design Spectra KW - OA-Publikationsfonds2022 Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20221028-47283 UR - https://www.mdpi.com/2075-5309/12/10/1573 VL - 2022 IS - Volume 12, issue 10, article 1573 SP - 1 EP - 19 PB - MDPI CY - Basel ER - TY - JOUR A1 - Harirchian, Ehsan A1 - Jadhav, Kirti A1 - Mohammad, Kifaytullah A1 - Aghakouchaki Hosseini, Seyed Ehsan A1 - Lahmer, Tom T1 - A Comparative Study of MCDM Methods Integrated with Rapid Visual Seismic Vulnerability Assessment of Existing RC Structures JF - Applied Sciences N2 - Recently, the demand for residence and usage of urban infrastructure has been increased, thereby resulting in the elevation of risk levels of human lives over natural calamities. The occupancy demand has rapidly increased the construction rate, whereas the inadequate design of structures prone to more vulnerability. Buildings constructed before the development of seismic codes have an additional susceptibility to earthquake vibrations. The structural collapse causes an economic loss as well as setbacks for human lives. An application of different theoretical methods to analyze the structural behavior is expensive and time-consuming. Therefore, introducing a rapid vulnerability assessment method to check structural performances is necessary for future developments. The process, as mentioned earlier, is known as Rapid Visual Screening (RVS). This technique has been generated to identify, inventory, and screen structures that are potentially hazardous. Sometimes, poor construction quality does not provide some of the required parameters; in this case, the RVS process turns into a tedious scenario. Hence, to tackle such a situation, multiple-criteria decision-making (MCDM) methods for the seismic vulnerability assessment opens a new gateway. The different parameters required by RVS can be taken in MCDM. MCDM evaluates multiple conflicting criteria in decision making in several fields. This paper has aimed to bridge the gap between RVS and MCDM. Furthermore, to define the correlation between these techniques, implementation of the methodologies from Indian, Turkish, and Federal Emergency Management Agency (FEMA) codes has been done. The effects of seismic vulnerability of structures have been observed and compared. KW - Erdbebensicherheit KW - damaged buildings KW - earthquake safety assessment KW - soft computing techniques KW - rapid visual screening KW - seismic risk estimation KW - Multi-criteria decision making KW - vulnerability assessment KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200918-42360 UR - https://www.mdpi.com/2076-3417/10/18/6411/htm VL - 2020 IS - Volume 10, issue 18, article 6411 PB - MDPI CY - Basel ER - TY - JOUR A1 - Zhuang, Xiaoying A1 - Huang, Runqiu A1 - Liang, Chao A1 - Rabczuk, Timon T1 - A coupled thermo-hydro-mechanical model of jointed hard rock for compressed air energy storage JF - Mathematical Problems in Engineering N2 - Renewable energy resources such as wind and solar are intermittent, which causes instability when being connected to utility grid of electricity. Compressed air energy storage (CAES) provides an economic and technical viable solution to this problem by utilizing subsurface rock cavern to store the electricity generated by renewable energy in the form of compressed air. Though CAES has been used for over three decades, it is only restricted to salt rock or aquifers for air tightness reason. In this paper, the technical feasibility of utilizing hard rock for CAES is investigated by using a coupled thermo-hydro-mechanical (THM) modelling of nonisothermal gas flow. Governing equations are derived from the rules of energy balance, mass balance, and static equilibrium. Cyclic volumetric mass source and heat source models are applied to simulate the gas injection and production. Evaluation is carried out for intact rock and rock with discrete crack, respectively. In both cases, the heat and pressure losses using air mass control and supplementary air injection are compared. KW - Energiespeicherung KW - Druckluft KW - Kaverne KW - Modellierung Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170428-31726 ER - TY - JOUR A1 - Alalade, Muyiwa A1 - Reichert, Ina A1 - Köhn, Daniel A1 - Wuttke, Frank A1 - Lahmer, Tom ED - Qu, Chunxu ED - Gao, Chunxu ED - Zhang, Rui ED - Jia, Ziguang ED - Li, Jiaxiang T1 - A Cyclic Multi-Stage Implementation of the Full-Waveform Inversion for the Identification of Anomalies in Dams JF - Infrastructures N2 - For the safe and efficient operation of dams, frequent monitoring and maintenance are required. These are usually expensive, time consuming, and cumbersome. To alleviate these issues, we propose applying a wave-based scheme for the location and quantification of damages in dams. To obtain high-resolution “interpretable” images of the damaged regions, we drew inspiration from non-linear full-multigrid methods for inverse problems and applied a new cyclic multi-stage full-waveform inversion (FWI) scheme. Our approach is less susceptible to the stability issues faced by the standard FWI scheme when dealing with ill-posed problems. In this paper, we first selected an optimal acquisition setup and then applied synthetic data to demonstrate the capability of our approach in identifying a series of anomalies in dams by a mixture of reflection and transmission tomography. The results had sufficient robustness, showing the prospects of application in the field of non-destructive testing of dams. KW - Damm KW - Defekt KW - inverse analysis KW - damage identification KW - full-waveform inversion KW - dams KW - wave propagation KW - OA-Publikationsfonds2022 Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20221201-48396 UR - https://www.mdpi.com/2412-3811/7/12/161 VL - 2022 IS - Volume 7, issue 12, article 161 PB - MDPI CY - Basel ER - TY - JOUR A1 - Harirchian, Ehsan A1 - Kumari, Vandana A1 - Jadhav, Kirti A1 - Raj Das, Rohan A1 - Rasulzade, Shahla A1 - Lahmer, Tom T1 - A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings JF - Applied Sciences N2 - Although averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service. These buildings were also designed before establishing national seismic codes or without the introduction of construction regulations. In that case, risk reduction is significant for developing alternatives and designing suitable models to enhance the existing structure’s performance. Such models will be able to classify risks and casualties related to possible earthquakes through emergency preparation. Thus, it is crucial to recognize structures that are susceptible to earthquake vibrations and need to be prioritized for retrofitting. However, each building’s behavior under seismic actions cannot be studied through performing structural analysis, as it might be unrealistic because of the rigorous computations, long period, and substantial expenditure. Therefore, it calls for a simple, reliable, and accurate process known as Rapid Visual Screening (RVS), which serves as a primary screening platform, including an optimum number of seismic parameters and predetermined performance damage conditions for structures. In this study, the damage classification technique was studied, and the efficacy of the Machine Learning (ML) method in damage prediction via a Support Vector Machine (SVM) model was explored. The ML model is trained and tested separately on damage data from four different earthquakes, namely Ecuador, Haiti, Nepal, and South Korea. Each dataset consists of varying numbers of input data and eight performance modifiers. Based on the study and the results, the ML model using SVM classifies the given input data into the belonging classes and accomplishes the performance on hazard safety evaluation of buildings. KW - Erdbeben KW - Vulnerability KW - Earthquake KW - damaged buildings KW - earthquake safety assessment KW - soft computing techniques KW - rapid visual screening KW - Machine Learning KW - vulnerability assessment KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20201022-42744 UR - https://www.mdpi.com/2076-3417/10/20/7153 VL - 2020 IS - Volume 10, issue 20, article 7153 PB - MDPI CY - Basel ER - TY - JOUR A1 - Karimimoshaver, Mehrdad A1 - Hajivaliei, Hatameh A1 - Shokri, Manouchehr A1 - Khalesro, Shakila A1 - Aram, Farshid A1 - Shamshirband, Shahaboddin T1 - A Model for Locating Tall Buildings through a Visual Analysis Approach JF - Applied Sciences N2 - Tall buildings have become an integral part of cities despite all their pros and cons. Some current tall buildings have several problems because of their unsuitable location; the problems include increasing density, imposing traffic on urban thoroughfares, blocking view corridors, etc. Some of these buildings have destroyed desirable views of the city. In this research, different criteria have been chosen, such as environment, access, social-economic, land-use, and physical context. These criteria and sub-criteria are prioritized and weighted by the analytic network process (ANP) based on experts’ opinions, using Super Decisions V2.8 software. On the other hand, layers corresponding to sub-criteria were made in ArcGIS 10.3 simultaneously, then via a weighted overlay (map algebra), a locating plan was created. In the next step seven hypothetical tall buildings (20 stories), in the best part of the locating plan, were considered to evaluate how much of theses hypothetical buildings would be visible (fuzzy visibility) from the street and open spaces throughout the city. These processes have been modeled by MATLAB software, and the final fuzzy visibility plan was created by ArcGIS. Fuzzy visibility results can help city managers and planners to choose which location is suitable for a tall building and how much visibility may be appropriate. The proposed model can locate tall buildings based on technical and visual criteria in the future development of the city and it can be widely used in any city as long as the criteria and weights are localized. KW - Gebäude KW - Energieeffizienz KW - Sustainability KW - Infrastructures KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210122-43350 UR - https://www.mdpi.com/2076-3417/10/17/6072 VL - 2020 IS - Volume 10, issue 17, article 6072 SP - 1 EP - 25 PB - MDPI CY - Basel ER - TY - JOUR A1 - Saadatfar, Hamid A1 - Khosravi, Samiyeh A1 - Hassannataj Joloudari, Javad A1 - Mosavi, Amir A1 - Shamshirband, Shahaboddin T1 - A New K-Nearest Neighbors Classifier for Big Data Based on Efficient Data Pruning JF - Mathematics N2 - The K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric classification method. However, like other traditional data mining methods, applying it on big data comes with computational challenges. Indeed, KNN determines the class of a new sample based on the class of its nearest neighbors; however, identifying the neighbors in a large amount of data imposes a large computational cost so that it is no longer applicable by a single computing machine. One of the proposed techniques to make classification methods applicable on large datasets is pruning. LC-KNN is an improved KNN method which first clusters the data into some smaller partitions using the K-means clustering method; and then applies the KNN for each new sample on the partition which its center is the nearest one. However, because the clusters have different shapes and densities, selection of the appropriate cluster is a challenge. In this paper, an approach has been proposed to improve the pruning phase of the LC-KNN method by taking into account these factors. The proposed approach helps to choose a more appropriate cluster of data for looking for the neighbors, thus, increasing the classification accuracy. The performance of the proposed approach is evaluated on different real datasets. The experimental results show the effectiveness of the proposed approach and its higher classification accuracy and lower time cost in comparison to other recent relevant methods. KW - Maschinelles Lernen KW - Machine learning KW - K-nearest neighbors KW - KNN KW - classifier KW - big data KW - clustering KW - cluster shape KW - cluster density KW - classification KW - reinforcement learning KW - data science KW - computation KW - artificial intelligence KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200225-40996 UR - https://www.mdpi.com/2227-7390/8/2/286 VL - 2020 IS - volume 8, issue 2, article 286 PB - MDPI ER - TY - JOUR A1 - Vu-Bac, N. A1 - Nguyen-Xuan, Hung A1 - Chen, Lei A1 - Lee, C.K. A1 - Zi, Goangseup A1 - Zhuang, Xiaoying A1 - Liu, G.R. A1 - Rabczuk, Timon T1 - A phantom-node method with edge-based strain smoothing for linear elastic fracture mechanics JF - Journal of Applied Mathematics N2 - This paper presents a novel numerical procedure based on the combination of an edge-based smoothed finite element (ES-FEM) with a phantom-node method for 2D linear elastic fracture mechanics. In the standard phantom-node method, the cracks are formulated by adding phantom nodes, and the cracked element is replaced by two new superimposed elements. This approach is quite simple to implement into existing explicit finite element programs. The shape functions associated with discontinuous elements are similar to those of the standard finite elements, which leads to certain simplification with implementing in the existing codes. The phantom-node method allows modeling discontinuities at an arbitrary location in the mesh. The ES-FEM model owns a close-to-exact stiffness that is much softer than lower-order finite element methods (FEM). Taking advantage of both the ES-FEM and the phantom-node method, we introduce an edge-based strain smoothing technique for the phantom-node method. Numerical results show that the proposed method achieves high accuracy compared with the extended finite element method (XFEM) and other reference solutions. KW - Finite-Elemente-Methode KW - Steifigkeit KW - Bruchmechanik KW - Riss Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170426-31676 ER - TY - JOUR A1 - Alkam, Feras A1 - Lahmer, Tom T1 - A robust method of the status monitoring of catenary poles installed along high-speed electrified train tracks JF - Results in Engineering N2 - Electric trains are considered one of the most eco-friendly and safest means of transportation. Catenary poles are used worldwide to support overhead power lines for electric trains. The performance of the catenary poles has an extensive influence on the integrity of the train systems and, consequently, the connected human services. It became a must nowadays to develop SHM systems that provide the instantaneous status of catenary poles in- service, making the decision-making processes to keep or repair the damaged poles more feasible. This study develops a data-driven, model-free approach for status monitoring of cantilever structures, focusing on pre-stressed, spun-cast ultrahigh-strength concrete catenary poles installed along high-speed train tracks. The pro-posed approach evaluates multiple damage features in an unfied damage index, which leads to straightforward interpretation and comparison of the output. Besides, it distinguishes between multiple damage scenarios of the poles, either the ones caused by material degradation of the concrete or by the cracks that can be propagated during the life span of the given structure. Moreover, using a logistic function to classify the integrity of structure avoids the expensive learning step in the existing damage detection approaches, namely, using the modern machine and deep learning methods. The findings of this study look very promising when applied to other types of cantilever structures, such as the poles that support the power transmission lines, antenna masts, chimneys, and wind turbines. KW - Fahrleitung KW - Catenary poles KW - SHM KW - Model-free status monitoring KW - Sigmoid function KW - High-speed electric train KW - Schaden KW - OA-Publikationsfonds2021 Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20211011-45212 UR - https://www.sciencedirect.com/science/article/pii/S2590123021000906?via%3Dihub VL - 2021 IS - volume 12, article 100289 SP - 1 EP - 8 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Talebi, Hossein A1 - Zi, Goangseup A1 - Silani, Mohammad A1 - Samaniego, Esteban A1 - Rabczuk, Timon T1 - A simple circular cell method for multilevel finite element analysis JF - Journal of Applied Mathematics N2 - A simple multiscale analysis framework for heterogeneous solids based on a computational homogenization technique is presented. The macroscopic strain is linked kinematically to the boundary displacement of a circular or spherical representative volume which contains the microscopic information of the material. The macroscopic stress is obtained from the energy principle between the macroscopic scale and the microscopic scale. This new method is applied to several standard examples to show its accuracy and consistency of the method proposed. KW - Finite-Elemente-Methode KW - Feststoff Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170426-31639 ER - TY - JOUR A1 - Harirchian, Ehsan A1 - Kumari, Vandana A1 - Jadhav, Kirti A1 - Rasulzade, Shahla A1 - Lahmer, Tom A1 - Raj Das, Rohan T1 - A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings JF - Applied Sciences N2 - A vast number of existing buildings were constructed before the development and enforcement of seismic design codes, which run into the risk of being severely damaged under the action of seismic excitations. This poses not only a threat to the life of people but also affects the socio-economic stability in the affected area. Therefore, it is necessary to assess such buildings’ present vulnerability to make an educated decision regarding risk mitigation by seismic strengthening techniques such as retrofitting. However, it is economically and timely manner not feasible to inspect, repair, and augment every old building on an urban scale. As a result, a reliable rapid screening methods, namely Rapid Visual Screening (RVS), have garnered increasing interest among researchers and decision-makers alike. In this study, the effectiveness of five different Machine Learning (ML) techniques in vulnerability prediction applications have been investigated. The damage data of four different earthquakes from Ecuador, Haiti, Nepal, and South Korea, have been utilized to train and test the developed models. Eight performance modifiers have been implemented as variables with a supervised ML. The investigations on this paper illustrate that the assessed vulnerability classes by ML techniques were very close to the actual damage levels observed in the buildings. KW - Maschinelles Lernen KW - Neuronales Netz KW - Machine learning KW - Building safety assessment KW - artificial neural networks KW - supervised learning KW - damaged buildings KW - rapid classification KW - OA-Publikationsfonds2021 Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210818-44853 UR - https://www.mdpi.com/2076-3417/11/16/7540 VL - 2021 IS - Volume 11, issue 16, article 7540 SP - 1 EP - 33 PB - MDPI CY - Basel ER - TY - JOUR A1 - Rafiee, Roham A1 - Rabczuk, Timon A1 - Milani, Abbas S. A1 - Tserpes, Konstantinos I. T1 - Advances in Characterization and Modeling of Nanoreinforced Composites JF - JOURNAL OF NANOMATERIALS N2 - This special issue deals with a range of recently developed characterization and modeling techniques employed to better understand and predict the response of nanoreinforced composites at different scales. KW - Physikalische Eigenschaft KW - Werkstoff KW - nanoreinforced composites Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170411-31134 ER - TY - JOUR A1 - Homaei, Mohammad Hossein A1 - Soleimani, Faezeh A1 - Shamshirband, Shahaboddin A1 - Mosavi, Amir A1 - Nabipour, Narjes A1 - Varkonyi-Koczy, Annamaria R. T1 - An Enhanced Distributed Congestion Control Method for Classical 6LowPAN Protocols Using Fuzzy Decision System JF - IEEE Access N2 - The classical Internet of things routing and wireless sensor networks can provide more precise monitoring of the covered area due to the higher number of utilized nodes. Because of the limitations in shared transfer media, many nodes in the network are prone to the collision in simultaneous transmissions. Medium access control protocols are usually more practical in networks with low traffic, which are not subjected to external noise from adjacent frequencies. There are preventive, detection and control solutions to congestion management in the network which are all the focus of this study. In the congestion prevention phase, the proposed method chooses the next step of the path using the Fuzzy decision-making system to distribute network traffic via optimal paths. In the congestion detection phase, a dynamic approach to queue management was designed to detect congestion in the least amount of time and prevent the collision. In the congestion control phase, the back-pressure method was used based on the quality of the queue to decrease the probability of linking in the pathway from the pre-congested node. The main goals of this study are to balance energy consumption in network nodes, reducing the rate of lost packets and increasing quality of service in routing. Simulation results proved the proposed Congestion Control Fuzzy Decision Making (CCFDM) method was more capable in improving routing parameters as compared to recent algorithms. KW - Internet der dinge KW - IOT KW - Internet of things KW - wireless sensor network KW - congestion control KW - fuzzy decision making KW - back-pressure Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200213-40805 UR - https://ieeexplore.ieee.org/document/8967114 IS - volume 8 SP - 20628 EP - 20645 PB - IEEE ER - TY - JOUR A1 - Mosavi, Amir A1 - Najafi, Bahman A1 - Faizollahzadeh Ardabili, Sina A1 - Shamshirband, Shahaboddin A1 - Rabczuk, Timon T1 - An Intelligent Artificial Neural Network-Response Surface Methodology Method for Accessing the Optimum Biodiesel and Diesel Fuel Blending Conditions in a Diesel Engine from the Viewpoint of Exergy and Energy Analysis JF - Energies N2 - Biodiesel, as the main alternative fuel to diesel fuel which is produced from renewable and available resources, improves the engine emissions during combustion in diesel engines. In this study, the biodiesel is produced initially from waste cooking oil (WCO). The fuel samples are applied in a diesel engine and the engine performance has been considered from the viewpoint of exergy and energy approaches. Engine tests are performed at a constant 1500 rpm speed with various loads and fuel samples. The obtained experimental data are also applied to develop an artificial neural network (ANN) model. Response surface methodology (RSM) is employed to optimize the exergy and energy efficiencies. Based on the results of the energy analysis, optimal engine performance is obtained at 80% of full load in presence of B10 and B20 fuels. However, based on the exergy analysis results, optimal engine performance is obtained at 80% of full load in presence of B90 and B100 fuels. The optimum values of exergy and energy efficiencies are in the range of 25–30% of full load, which is the same as the calculated range obtained from mathematical modeling. KW - Biodiesel KW - ANN modeling KW - biodiesel KW - Artificial Intelligence KW - diesel engines KW - energy, exergy KW - mathematical modeling KW - OA-Publikationsfonds2018 Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20180507-37467 UR - http://www.mdpi.com/1996-1073/11/4/860 VL - 2018 IS - 11, 4 PB - MDPI CY - Basel ER - TY - JOUR A1 - Guo, Hongwei A1 - Zhuang, Xiaoying A1 - Chen, Pengwan A1 - Alajlan, Naif A1 - Rabczuk, Timon T1 - Analysis of three-dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and sensitivity analysis JF - Engineering with Computers N2 - In this work, we present a deep collocation method (DCM) for three-dimensional potential problems in non-homogeneous media. This approach utilizes a physics-informed neural network with material transfer learning reducing the solution of the non-homogeneous partial differential equations to an optimization problem. We tested different configurations of the physics-informed neural network including smooth activation functions, sampling methods for collocation points generation and combined optimizers. A material transfer learning technique is utilized for non-homogeneous media with different material gradations and parameters, which enhance the generality and robustness of the proposed method. In order to identify the most influential parameters of the network configuration, we carried out a global sensitivity analysis. Finally, we provide a convergence proof of our DCM. The approach is validated through several benchmark problems, also testing different material variations. KW - Deep learning KW - Kollokationsmethode KW - Collocation method KW - Potential problem KW - Activation function KW - Transfer learning Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220811-46764 UR - https://link.springer.com/article/10.1007/s00366-022-01633-6 VL - 2022 SP - 1 EP - 22 ER - TY - JOUR A1 - Lizarazu, Jorge A1 - Harirchian, Ehsan A1 - Shaik, Umar Arif A1 - Shareef, Mohammed A1 - Antoni-Zdziobek, Annie A1 - Lahmer, Tom T1 - Application of machine learning-based algorithms to predict the stress-strain curves of additively manufactured mild steel out of its microstructural characteristics JF - Results in Engineering N2 - The study presents a Machine Learning (ML)-based framework designed to forecast the stress-strain relationship of arc-direct energy deposited mild steel. Based on microstructural characteristics previously extracted using microscopy and X-ray diffraction, approximately 1000 new parameter sets are generated by applying the Latin Hypercube Sampling Method (LHSM). For each parameter set, a Representative Volume Element (RVE) is synthetically created via Voronoi Tessellation. Input raw data for ML-based algorithms comprises these parameter sets or RVE-images, while output raw data includes their corresponding stress-strain relationships calculated after a Finite Element (FE) procedure. Input data undergoes preprocessing involving standardization, feature selection, and image resizing. Similarly, the stress-strain curves, initially unsuitable for training traditional ML algorithms, are preprocessed using cubic splines and occasionally Principal Component Analysis (PCA). The later part of the study focuses on employing multiple ML algorithms, utilizing two main models. The first model predicts stress-strain curves based on microstructural parameters, while the second model does so solely from RVE images. The most accurate prediction yields a Root Mean Squared Error of around 5 MPa, approximately 1% of the yield stress. This outcome suggests that ML models offer precise and efficient methods for characterizing dual-phase steels, establishing a framework for accurate results in material analysis. KW - Maschinelles Lernen KW - Baustahl KW - Spannungs-Dehnungs-Beziehung KW - Arc-direct energy deposition KW - Mild steel KW - Dual phase steel KW - Stress-strain curve KW - OA-Publikationsfonds2023 Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20231207-65028 UR - https://www.sciencedirect.com/science/article/pii/S2590123023007144 VL - 2023 IS - Volume 20 (2023) SP - 1 EP - 12 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Harirchian, Ehsan A1 - Lahmer, Tom A1 - Kumari, Vandana A1 - Jadhav, Kirti T1 - Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings JF - Energies N2 - The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 Düzce Earthquake in Turkey, where the building’s data consists of 22 performance modifiers that have been implemented with supervised machine learning. KW - Erdbeben KW - Maschinelles Lernen KW - earthquake vulnerability assessment KW - rapid visual screening KW - machine learning KW - support vector machine KW - buildings KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200707-41915 UR - https://www.mdpi.com/1996-1073/13/13/3340 VL - 2020 IS - volume 13, issue 13, 3340 PB - MDPI CY - Basel ER - TY - JOUR A1 - Lashkar-Ara, Babak A1 - Kalantari, Niloofar A1 - Sheikh Khozani, Zohreh A1 - Mosavi, Amir T1 - Assessing Machine Learning versus a Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel JF - Mathematics N2 - One of the most important subjects of hydraulic engineering is the reliable estimation of the transverse distribution in the rectangular channel of bed and wall shear stresses. This study makes use of the Tsallis entropy, genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) methods to assess the shear stress distribution (SSD) in the rectangular channel. To evaluate the results of the Tsallis entropy, GP and ANFIS models, laboratory observations were used in which shear stress was measured using an optimized Preston tube. This is then used to measure the SSD in various aspect ratios in the rectangular channel. To investigate the shear stress percentage, 10 data series with a total of 112 different data for were used. The results of the sensitivity analysis show that the most influential parameter for the SSD in smooth rectangular channel is the dimensionless parameter B/H, Where the transverse coordinate is B, and the flow depth is H. With the parameters (b/B), (B/H) for the bed and (z/H), (B/H) for the wall as inputs, the modeling of the GP was better than the other one. Based on the analysis, it can be concluded that the use of GP and ANFIS algorithms is more effective in estimating shear stress in smooth rectangular channels than the Tsallis entropy-based equations. KW - Maschinelles Lernen KW - smooth rectangular channel KW - Tsallis entropy KW - genetic programming KW - artificial intelligence KW - machine learning KW - big data KW - computational hydraulics Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210504-44197 UR - https://www.mdpi.com/2227-7390/9/6/596 VL - 2021 IS - Volume 9, Issue 6, Article 596 PB - MDPI CY - Basel ER - TY - JOUR A1 - Faridmehr, Iman A1 - Tahir, Mamood Md. A1 - Lahmer, Tom T1 - Classification System for Semi-Rigid Beam-to-Column Connections JF - LATIN AMERICAN JOURNAL OF SOLIDS AND STRUCTURES 11 N2 - The current study attempts to recognise an adequate classification for a semi-rigid beam-to-column connection by investigating strength, stiffness and ductility. For this purpose, an experimental test was carried out to investigate the moment-rotation (M-theta) features of flush end-plate (FEP) connections including variable parameters like size and number of bolts, thickness of end-plate, and finally, size of beams and columns. The initial elastic stiffness and ultimate moment capacity of connections were determined by an extensive analytical procedure from the proposed method prescribed by ANSI/AISC 360-10, and Eurocode 3 Part 1-8 specifications. The behaviour of beams with partially restrained or semi-rigid connections were also studied by incorporating classical analysis methods. The results confirmed that thickness of the column flange and end-plate substantially govern over the initial rotational stiffness of of flush end-plate connections. The results also clearly showed that EC3 provided a more reliable classification index for flush end-plate (FEP) connections. The findings from this study make significant contributions to the current literature as the actual response characteristics of such connections are non-linear. Therefore, such semirigid behaviour should be used to for an analysis and design method. KW - Tragfähigkeit KW - Stütze KW - Träger KW - Beam-to-column connection; semi-rigid; flush end-plate connection; moment-rotation curve Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170401-30988 SP - 2152 EP - 2175 ER - TY - JOUR A1 - Band, Shahab S. A1 - Janizadeh, Saeid A1 - Chandra Pal, Subodh A1 - Chowdhuri, Indrajit A1 - Siabi, Zhaleh A1 - Norouzi, Akbar A1 - Melesse, Assefa M. A1 - Shokri, Manouchehr A1 - Mosavi, Amir Hosein T1 - Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration JF - Sensors N2 - Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer. KW - Grundwasser KW - Nitratbelastung KW - Künstliche Intelligenz KW - ground water contamination KW - machine learning KW - big data KW - hydrological model KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210122-43364 UR - https://www.mdpi.com/1424-8220/20/20/5763 VL - 2020 IS - Volume 20, issue 20, article 5763 SP - 1 EP - 23 PB - MDPI CY - Basel ER - TY - JOUR A1 - Mosavi, Amir A1 - Shamshirband, Shahaboddin A1 - Esmaeilbeiki, Fatemeh A1 - Zarehaghi, Davoud A1 - Neyshabouri, Mohammadreza A1 - Samadianfard, Saeed A1 - Ghorbani, Mohammad Ali A1 - Nabipour, Narjes A1 - Chau, Kwok-Wing T1 - Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths JF - Engineering Applications of Computational Fluid Mechanics N2 - This research aims to model soil temperature (ST) using machine learning models of multilayer perceptron (MLP) algorithm and support vector machine (SVM) in hybrid form with the Firefly optimization algorithm, i.e. MLP-FFA and SVM-FFA. In the current study, measured ST and meteorological parameters of Tabriz and Ahar weather stations in a period of 2013–2015 are used for training and testing of the studied models with one and two days as a delay. To ascertain conclusive results for validation of the proposed hybrid models, the error metrics are benchmarked in an independent testing period. Moreover, Taylor diagrams utilized for that purpose. Obtained results showed that, in a case of one day delay, except in predicting ST at 5 cm below the soil surface (ST5cm) at Tabriz station, MLP-FFA produced superior results compared with MLP, SVM, and SVM-FFA models. However, for two days delay, MLP-FFA indicated increased accuracy in predicting ST5cm and ST 20cm of Tabriz station and ST10cm of Ahar station in comparison with SVM-FFA. Additionally, for all of the prescribed models, the performance of the MLP-FFA and SVM-FFA hybrid models in the testing phase was found to be meaningfully superior to the classical MLP and SVM models. KW - Bodentemperatur KW - Algorithmus KW - Maschinelles Lernen KW - Neuronales Netz KW - firefly optimization algorithm KW - soil temperature KW - artificial neural networks KW - hybrid machine learning KW - OA-Publikationsfonds2019 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200911-42347 UR - https://www.tandfonline.com/doi/full/10.1080/19942060.2020.1788644 VL - 2020 IS - Volume 14, Issue 1 SP - 939 EP - 953 ER - TY - JOUR A1 - Faroughi, Maryam A1 - Karimimoshaver, Mehrdad A1 - Aram, Farshid A1 - Solgi, Ebrahim A1 - Mosavi, Amir A1 - Nabipour, Narjes A1 - Chau, Kwok-Wing T1 - Computational modeling of land surface temperature using remote sensing data to investigate the spatial arrangement of buildings and energy consumption relationship JF - Engineering Applications of Computational Fluid Mechanics N2 - The effect of urban form on energy consumption has been the subject of various studies around the world. Having examined the effect of buildings on energy consumption, these studies indicate that the physical form of a city has a notable impact on the amount of energy consumed in its spaces. The present study identified the variables that affected energy consumption in residential buildings and analyzed their effects on energy consumption in four neighborhoods in Tehran: Apadana, Bimeh, Ekbatan-phase I, and Ekbatan-phase II. After extracting the variables, their effects are estimated with statistical methods, and the results are compared with the land surface temperature (LST) remote sensing data derived from Landsat 8 satellite images taken in the winter of 2019. The results showed that physical variables, such as the size of buildings, population density, vegetation cover, texture concentration, and surface color, have the greatest impacts on energy usage. For the Apadana neighborhood, the factors with the most potent effect on energy consumption were found to be the size of buildings and the population density. However, for other neighborhoods, in addition to these two factors, a third factor was also recognized to have a significant effect on energy consumption. This third factor for the Bimeh, Ekbatan-I, and Ekbatan-II neighborhoods was the type of buildings, texture concentration, and orientation of buildings, respectively. KW - Fernerkung KW - Intelligente Stadt KW - Oberflächentemperatur KW - remote sensing KW - smart cities KW - Land surface temperature KW - energy consumption KW - residential buildings KW - urban morphology KW - urban sustainability Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200110-40585 UR - https://www.tandfonline.com/doi/full/10.1080/19942060.2019.1707711 VL - 2020 IS - Volume 14, No. 1 SP - 254 EP - 270 PB - Taylor & Francis ER - TY - JOUR A1 - Hanna, John T1 - Computational Modelling for the Effects of Capsular Clustering on Fracture of Encapsulation-Based Self-Healing Concrete Using XFEM and Cohesive Surface Technique JF - Applied Sciences N2 - The fracture of microcapsules is an important issue to release the healing agent for healing the cracks in encapsulation-based self-healing concrete. The capsular clustering generated from the concrete mixing process is considered one of the critical factors in the fracture mechanism. Since there is a lack of studies in the literature regarding this issue, the design of self-healing concrete cannot be made without an appropriate modelling strategy. In this paper, the effects of microcapsule size and clustering on the fractured microcapsules are studied computationally. A simple 2D computational modelling approach is developed based on the eXtended Finite Element Method (XFEM) and cohesive surface technique. The proposed model shows that the microcapsule size and clustering have significant roles in governing the load-carrying capacity and the crack propagation pattern and determines whether the microcapsule will be fractured or debonded from the concrete matrix. The higher the microcapsule circumferential contact length, the higher the load-carrying capacity. When it is lower than 25% of the microcapsule circumference, it will result in a greater possibility for the debonding of the microcapsule from the concrete. The greater the core/shell ratio (smaller shell thickness), the greater the likelihood of microcapsules being fractured. KW - Beton KW - Mikrokapsel KW - Rissausbreitung KW - Tragfähigkeit KW - self-healing concrete KW - microcapsule KW - capsular clustering KW - circumferential contact length KW - OA-Publikationsfonds2022 Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220721-46717 UR - https://www.mdpi.com/2076-3417/12/10/5112 VL - 2022 IS - Volume 12, issue 10, article 5112 SP - 1 EP - 17 PB - MDPI CY - Basel ER - TY - JOUR A1 - Jilte, Ravindra A1 - Ahmadi, Mohammad Hossein A1 - Kumar, Ravinder A1 - Kalamkar, Vilas A1 - Mosavi, Amir T1 - Cooling Performance of a Novel Circulatory Flow Concentric Multi-Channel Heat Sink with Nanofluids JF - Nanomaterials N2 - Heat rejection from electronic devices such as processors necessitates a high heat removal rate. The present study focuses on liquid-cooled novel heat sink geometry made from four channels (width 4 mm and depth 3.5 mm) configured in a concentric shape with alternate flow passages (slot of 3 mm gap). In this study, the cooling performance of the heat sink was tested under simulated controlled conditions.The lower bottom surface of the heat sink was heated at a constant heat flux condition based on dissipated power of 50 W and 70 W. The computations were carried out for different volume fractions of nanoparticles, namely 0.5% to 5%, and water as base fluid at a flow rate of 30 to 180 mL/min. The results showed a higher rate of heat rejection from the nanofluid cooled heat sink compared with water. The enhancement in performance was analyzed with the help of a temperature difference of nanofluid outlet temperature and water outlet temperature under similar operating conditions. The enhancement was ~2% for 0.5% volume fraction nanofluids and ~17% for a 5% volume fraction. KW - Nanostrukturiertes Material KW - Kühlkörper KW - Nasskühlung KW - nanofluid KW - Nanomaterials KW - Machine learning KW - heat sink Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200401-41241 UR - https://www.mdpi.com/2079-4991/10/4/647 VL - 2020 IS - Volume 10, Issue 4, 647 PB - MDPI CY - Basel ER - TY - JOUR A1 - Hassannataj Joloudari, Javad A1 - Hassannataj Joloudari, Edris A1 - Saadatfar, Hamid A1 - GhasemiGol, Mohammad A1 - Razavi, Seyyed Mohammad A1 - Mosavi, Amir A1 - Nabipour, Narjes A1 - Shamshirband, Shahaboddin A1 - Nadai, Laszlo T1 - Coronary Artery Disease Diagnosis: Ranking the Significant Features Using a Random Trees Model JF - International Journal of Environmental Research and Public Health, IJERPH N2 - Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), and decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models. KW - Maschinelles Lernen KW - Machine learning KW - Deep learning KW - coronary artery disease KW - heart disease diagnosis KW - health informatics KW - data science KW - big data KW - predictive model KW - ensemble model KW - random forest KW - industry 4.0 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200213-40819 UR - https://www.mdpi.com/1660-4601/17/3/731 VL - 2020 IS - Volume 17, Issue 3, 731 PB - MDPI ER - TY - JOUR A1 - Al-Yasiri, Zainab Riyadh Shaker A1 - Mutashar, Hayder Majid A1 - Gürlebeck, Klaus A1 - Lahmer, Tom ED - Shafiullah, GM T1 - Damage Sensitive Signals for the Assessment of the Conditions of Wind Turbine Rotor Blades Using Electromagnetic Waves JF - Infrastructures N2 - One of the most important renewable energy technologies used nowadays are wind power turbines. In this paper, we are interested in identifying the operating status of wind turbines, especially rotor blades, by means of multiphysical models. It is a state-of-the-art technology to test mechanical structures with ultrasonic-based methods. However, due to the density and the required high resolution, the testing is performed with high-frequency waves, which cannot penetrate the structure in depth. Therefore, there is a need to adopt techniques in the fields of multiphysical model-based inversion schemes or data-driven structural health monitoring. Before investing effort in the development of such approaches, further insights and approaches are necessary to make the techniques applicable to structures such as wind power plants (blades). Among the expected developments, further accelerations of the so-called “forward codes” for a more efficient implementation of the wave equation could be envisaged. Here, we employ electromagnetic waves for the early detection of cracks. Because in many practical situations, it is not possible to apply techniques from tomography (characterized by multiple sources and sensor pairs), we focus here on the question of whether the existence of cracks can be determined by using only one source for the sent waves. KW - Windkraftwerk KW - Rotorblatt KW - Elektrostatische Welle KW - MATLAB KW - wind turbine rotor blades KW - electromagnetic waves KW - crack detection KW - Empire XPU 8.01 KW - Matlab KW - OA-Publikationsfonds2022 Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220831-47093 UR - https://www.mdpi.com/2412-3811/7/8/104 VL - 2022 IS - Volume 7, Issue 8 (August 2022), article 104 PB - MDPI CY - Basel ER - TY - JOUR A1 - Meng, Yinghui A1 - Noman Qasem, Sultan A1 - Shokri, Manouchehr A1 - Shamshirband, Shahaboddin T1 - Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis JF - Mathematics N2 - In this research, an attempt was made to reduce the dimension of wavelet-ANFIS/ANN (artificial neural network/adaptive neuro-fuzzy inference system) models toward reliable forecasts as well as to decrease computational cost. In this regard, the principal component analysis was performed on the input time series decomposed by a discrete wavelet transform to feed the ANN/ANFIS models. The models were applied for dissolved oxygen (DO) forecasting in rivers which is an important variable affecting aquatic life and water quality. The current values of DO, water surface temperature, salinity, and turbidity have been considered as the input variable to forecast DO in a three-time step further. The results of the study revealed that PCA can be employed as a powerful tool for dimension reduction of input variables and also to detect inter-correlation of input variables. Results of the PCA-wavelet-ANN models are compared with those obtained from wavelet-ANN models while the earlier one has the advantage of less computational time than the later models. Dealing with ANFIS models, PCA is more beneficial to avoid wavelet-ANFIS models creating too many rules which deteriorate the efficiency of the ANFIS models. Moreover, manipulating the wavelet-ANFIS models utilizing PCA leads to a significant decreasing in computational time. Finally, it was found that the PCA-wavelet-ANN/ANFIS models can provide reliable forecasts of dissolved oxygen as an important water quality indicator in rivers. KW - Maschinelles Lernen KW - machine learning KW - dimensionality reduction KW - wavelet transform KW - water quality KW - principal component analysis KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200811-42125 UR - https://www.mdpi.com/2227-7390/8/8/1233 VL - 2020 IS - volume 8, issue 8, article 1233 PB - MDPI CY - Basel ER - TY - JOUR A1 - Chakraborty, Ayan A1 - Anitescu, Cosmin A1 - Zhuang, Xiaoying A1 - Rabczuk, Timon T1 - Domain adaptation based transfer learning approach for solving PDEs on complex geometries JF - Engineering with Computers N2 - In machine learning, if the training data is independently and identically distributed as the test data then a trained model can make an accurate predictions for new samples of data. Conventional machine learning has a strong dependence on massive amounts of training data which are domain specific to understand their latent patterns. In contrast, Domain adaptation and Transfer learning methods are sub-fields within machine learning that are concerned with solving the inescapable problem of insufficient training data by relaxing the domain dependence hypothesis. In this contribution, this issue has been addressed and by making a novel combination of both the methods we develop a computationally efficient and practical algorithm to solve boundary value problems based on nonlinear partial differential equations. We adopt a meshfree analysis framework to integrate the prevailing geometric modelling techniques based on NURBS and present an enhanced deep collocation approach that also plays an important role in the accuracy of solutions. We start with a brief introduction on how these methods expand upon this framework. We observe an excellent agreement between these methods and have shown that how fine-tuning a pre-trained network to a specialized domain may lead to an outstanding performance compare to the existing ones. As proof of concept, we illustrate the performance of our proposed model on several benchmark problems. KW - Maschinelles Lernen KW - NURBS KW - Transfer learning KW - Domain Adaptation KW - NURBS geometry KW - Navier–Stokes equations Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220811-46776 UR - https://link.springer.com/article/10.1007/s00366-022-01661-2 VL - 2022 SP - 1 EP - 20 ER - TY - JOUR A1 - Harirchian, Ehsan A1 - Lahmer, Tom A1 - Rasulzade, Shahla T1 - Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network JF - Energies N2 - The latest earthquakes have proven that several existing buildings, particularly in developing countries, are not secured from damages of earthquake. A variety of statistical and machine-learning approaches have been proposed to identify vulnerable buildings for the prioritization of retrofitting. The present work aims to investigate earthquake susceptibility through the combination of six building performance variables that can be used to obtain an optimal prediction of the damage state of reinforced concrete buildings using artificial neural network (ANN). In this regard, a multi-layer perceptron network is trained and optimized using a database of 484 damaged buildings from the Düzce earthquake in Turkey. The results demonstrate the feasibility and effectiveness of the selected ANN approach to classify concrete structural damage that can be used as a preliminary assessment technique to identify vulnerable buildings in disaster risk-management programs. KW - Erdbeben KW - Maschinelles Lernen KW - earthquake damage KW - seismic vulnerability KW - artificial neural network KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200504-41575 UR - https://www.mdpi.com/1996-1073/13/8/2060/htm VL - 2020 IS - Volume 13, Issue 8, 2060 PB - MDPI CY - Basel ER - TY - JOUR A1 - Harirchian, Ehsan A1 - Lahmer, Tom A1 - Buddhiraju, Sreekanth A1 - Mohammad, Kifaytullah A1 - Mosavi, Amir T1 - Earthquake Safety Assessment of Buildings through Rapid Visual Screening JF - Buildings N2 - Earthquake is among the most devastating natural disasters causing severe economical, environmental, and social destruction. Earthquake safety assessment and building hazard monitoring can highly contribute to urban sustainability through identification and insight into optimum materials and structures. While the vulnerability of structures mainly depends on the structural resistance, the safety assessment of buildings can be highly challenging. In this paper, we consider the Rapid Visual Screening (RVS) method, which is a qualitative procedure for estimating structural scores for buildings suitable for medium- to high-seismic cases. This paper presents an overview of the common RVS methods, i.e., FEMA P-154, IITK-GGSDMA, and EMPI. To examine the accuracy and validation, a practical comparison is performed between their assessment and observed damage of reinforced concrete buildings from a street survey in the Bingöl region, Turkey, after the 1 May 2003 earthquake. The results demonstrate that the application of RVS methods for preliminary damage estimation is a vital tool. Furthermore, the comparative analysis showed that FEMA P-154 creates an assessment that overestimates damage states and is not economically viable, while EMPI and IITK-GGSDMA provide more accurate and practical estimation, respectively. KW - Maschinelles Lernen KW - Machine learning KW - Erdbeben KW - buildings KW - earthquake safety assessment KW - earthquake KW - extreme events KW - seismic assessment KW - natural hazard KW - mitigation KW - rapid visual screening Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200331-41153 UR - https://www.mdpi.com/2075-5309/10/3/51 VL - 2020 IS - Volume 10, Issue 3 PB - MDPI ER - TY - JOUR A1 - Schmidt, Albrecht A1 - Lahmer, Tom T1 - Efficient domain decomposition based reliability analysis for polymorphic uncertain material parameters JF - Proceedings in Applied Mathematics & Mechanics N2 - Realistic uncertainty description incorporating aleatoric and epistemic uncertainties can be described within the framework of polymorphic uncertainty, which is computationally demanding. Utilizing a domain decomposition approach for random field based uncertainty models the proposed level-based sampling method can reduce these computational costs significantly and shows good agreement with a standard sampling technique. While 2-level configurations tend to get unstable with decreasing sampling density 3-level setups show encouraging results for the investigated reliability analysis of a structural unit square. KW - Polymorphie KW - Stoffeigenschaft KW - Stochastik KW - polymorphe Unschärfemodellierung KW - Materialverhalten KW - hybride Werkstoffe Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220112-45563 UR - https://onlinelibrary.wiley.com/doi/full/10.1002/pamm.202100014 VL - 2021 IS - Volume 21, issue 1 SP - 1 EP - 4 PB - Wiley-VHC CY - Weinheim ER - TY - JOUR A1 - Alkam, Feras A1 - Lahmer, Tom T1 - Eigenfrequency-Based Bayesian Approach for Damage Identification in Catenary Poles JF - Infrastructures N2 - This study proposes an efficient Bayesian, frequency-based damage identification approach to identify damages in cantilever structures with an acceptable error rate, even at high noise levels. The catenary poles of electric high-speed train systems were selected as a realistic case study to cover the objectives of this study. Compared to other frequency-based damage detection approaches described in the literature, the proposed approach is efficiently able to detect damages in cantilever structures to higher levels of damage detection, namely identifying both the damage location and severity using a low-cost structural health monitoring (SHM) system with a limited number of sensors; for example, accelerometers. The integration of Bayesian inference, as a stochastic framework, in the proposed approach, makes it possible to utilize the benefit of data fusion in merging the informative data from multiple damage features, which increases the quality and accuracy of the results. The findings provide the decision-maker with the information required to manage the maintenance, repair, or replacement procedures. KW - Fahrleitung KW - Schaden KW - Fahrleitungsmast KW - Schadenserkennung KW - vibration-based damage identification KW - Bayesian inference Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210510-44256 UR - https://www.mdpi.com/2412-3811/6/4/57 VL - 2021 IS - Volume 6, issue 4, article 57 SP - 1 EP - 19 PB - MDPI CY - Basel ER - TY - JOUR A1 - Amirinasab, Mehdi A1 - Shamshirband, Shahaboddin A1 - Chronopoulos, Anthony Theodore A1 - Mosavi, Amir A1 - Nabipour, Narjes T1 - Energy‐Efficient Method for Wireless Sensor Networks Low‐Power Radio Operation in Internet of Things JF - electronics N2 - The radio operation in wireless sensor networks (WSN) in Internet of Things (IoT)applications is the most common source for power consumption. Consequently, recognizing and controlling the factors affecting radio operation can be valuable for managing the node power consumption. Among essential factors affecting radio operation, the time spent for checking the radio is of utmost importance for monitoring power consumption. It can lead to false WakeUp or idle listening in radio duty cycles and ContikiMAC. ContikiMAC is a low‐power radio duty‐cycle protocol in Contiki OS used in WakeUp mode, as a clear channel assessment (CCA) for checking radio status periodically. This paper presents a detailed analysis of radio WakeUp time factors of ContikiMAC. Furthermore, we propose a lightweight CCA (LW‐CCA) as an extension to ContikiMAC to reduce the Radio Duty‐Cycles in false WakeUps and idle listening though using dynamic received signal strength indicator (RSSI) status check time. The simulation results in the Cooja simulator show that LW‐CCA reduces about 8% energy consumption in nodes while maintaining up to 99% of the packet delivery rate (PDR). KW - Internet der Dinge KW - Internet of things KW - wireless sensor networks KW - ContikiMAC KW - energy efficiency KW - duty-cycles KW - clear channel assessments KW - fog computing KW - smart sensors KW - signal processing KW - received signal strength indicator KW - OA-Publikationsfonds2020 KW - RSSI Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200213-40954 UR - https://www.mdpi.com/2079-9292/9/2/320 VL - 2020 IS - volume 9, issue 2, 320 PB - MDPI ER - TY - JOUR A1 - Amani, Jafar A1 - Saboor Bagherzadeh, Amir A1 - Rabczuk, Timon T1 - Error estimate and adaptive refinement in mixed discrete least squares meshless method JF - Mathematical Problems in Engineering N2 - The node moving and multistage node enrichment adaptive refinement procedures are extended in mixed discrete least squares meshless (MDLSM) method for efficient analysis of elasticity problems. In the formulation of MDLSM method, mixed formulation is accepted to avoid second-order differentiation of shape functions and to obtain displacements and stresses simultaneously. In the refinement procedures, a robust error estimator based on the value of the least square residuals functional of the governing differential equations and its boundaries at nodal points is used which is inherently available from the MDLSM formulation and can efficiently identify the zones with higher numerical errors. The results are compared with the refinement procedures in the irreducible formulation of discrete least squares meshless (DLSM) method and show the accuracy and efficiency of the proposed procedures. Also, the comparison of the error norms and convergence rate show the fidelity of the proposed adaptive refinement procedures in the MDLSM method. KW - Elastizität KW - Fehlerabschätzung KW - MDLSM method Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170413-31181 ER - TY - JOUR A1 - Kargar, Katayoun A1 - Samadianfard, Saeed A1 - Parsa, Javad A1 - Nabipour, Narjes A1 - Shamshirband, Shahaboddin A1 - Mosavi, Amir A1 - Chau, Kwok-Wing T1 - Estimating longitudinal dispersion coefficient in natural streams using empirical models and machine learning algorithms JF - Engineering Applications of Computational Fluid Mechanics N2 - The longitudinal dispersion coefficient (LDC) plays an important role in modeling the transport of pollutants and sediment in natural rivers. As a result of transportation processes, the concentration of pollutants changes along the river. Various studies have been conducted to provide simple equations for estimating LDC. In this study, machine learning methods, namely support vector regression, Gaussian process regression, M5 model tree (M5P) and random forest, and multiple linear regression were examined in predicting the LDC in natural streams. Data sets from 60 rivers around the world with different hydraulic and geometric features were gathered to develop models for LDC estimation. Statistical criteria, including correlation coefficient (CC), root mean squared error (RMSE) and mean absolute error (MAE), were used to scrutinize the models. The LDC values estimated by these models were compared with the corresponding results of common empirical models. The Taylor chart was used to evaluate the models and the results showed that among the machine learning models, M5P had superior performance, with CC of 0.823, RMSE of 454.9 and MAE of 380.9. The model of Sahay and Dutta, with CC of 0.795, RMSE of 460.7 and MAE of 306.1, gave more precise results than the other empirical models. The main advantage of M5P models is their ability to provide practical formulae. In conclusion, the results proved that the developed M5P model with simple formulations was superior to other machine learning models and empirical models; therefore, it can be used as a proper tool for estimating the LDC in rivers. KW - Maschinelles Lernen KW - Gaussian process regression KW - longitudinal dispersion coefficient KW - M5 model tree KW - random forest KW - support vector regression KW - rivers Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200128-40775 UR - https://www.tandfonline.com/doi/full/10.1080/19942060.2020.1712260 VL - 2020 IS - Volume 14, No. 1 SP - 311 EP - 322 PB - Taylor & Francis ER - TY - JOUR A1 - Band, Shahab S. A1 - Janizadeh, Saeid A1 - Saha, Sunil A1 - Mukherjee, Kaustuv A1 - Khosrobeigi Bozchaloei, Saeid A1 - Cerdà, Artemi A1 - Shokri, Manouchehr A1 - Mosavi, Amir Hosein T1 - Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data JF - Land N2 - Piping erosion is one form of water erosion that leads to significant changes in the landscape and environmental degradation. In the present study, we evaluated piping erosion modeling in the Zarandieh watershed of Markazi province in Iran based on random forest (RF), support vector machine (SVM), and Bayesian generalized linear models (Bayesian GLM) machine learning algorithms. For this goal, due to the importance of various geo-environmental and soil properties in the evolution and creation of piping erosion, 18 variables were considered for modeling the piping erosion susceptibility in the Zarandieh watershed. A total of 152 points of piping erosion were recognized in the study area that were divided into training (70%) and validation (30%) for modeling. The area under curve (AUC) was used to assess the effeciency of the RF, SVM, and Bayesian GLM. Piping erosion susceptibility results indicated that all three RF, SVM, and Bayesian GLM models had high efficiency in the testing step, such as the AUC shown with values of 0.9 for RF, 0.88 for SVM, and 0.87 for Bayesian GLM. Altitude, pH, and bulk density were the variables that had the greatest influence on the piping erosion susceptibility in the Zarandieh watershed. This result indicates that geo-environmental and soil chemical variables are accountable for the expansion of piping erosion in the Zarandieh watershed. KW - Maschinelles Lernen KW - Bayes-Verfahren KW - Naturkatastrophe KW - random forest KW - support vector machine KW - geoinformatics KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210122-43424 UR - https://www.mdpi.com/2073-445X/9/10/346 VL - 2020 IS - volume 9, issue 10, article 346 SP - 1 EP - 22 PB - MDPI CY - Basel ER - TY - JOUR A1 - Ahmadi, Mohammad Hossein A1 - Baghban, Alireza A1 - Sadeghzadeh, Milad A1 - Zamen, Mohammad A1 - Mosavi, Amir A1 - Shamshirband, Shahaboddin A1 - Kumar, Ravinder A1 - Mohammadi-Khanaposhtani, Mohammad T1 - Evaluation of electrical efficiency of photovoltaic thermal solar collector JF - Engineering Applications of Computational Fluid Mechanics N2 - In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the input variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced models and evaluate their performances. The proposed LSSVM model outperformed the ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations. KW - Fotovoltaik KW - Erneuerbare Energien KW - Solar KW - Deep learning KW - Machine learning KW - Renewable energy KW - neural networks (NNs) KW - adaptive neuro-fuzzy inference system (ANFIS) KW - least square support vector machine (LSSVM) KW - photovoltaic-thermal (PV/T) KW - hybrid machine learning model KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200304-41049 UR - https://www.tandfonline.com/doi/full/10.1080/19942060.2020.1734094 VL - 2020 IS - volume 14, issue 1 SP - 545 EP - 565 PB - Taylor & Francis ER - TY - JOUR A1 - Kumari, Vandana A1 - Harirchian, Ehsan A1 - Lahmer, Tom A1 - Rasulzade, Shahla T1 - Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings JF - Buildings N2 - The seismic vulnerability assessment of existing reinforced concrete (RC) buildings is a significant source of disaster mitigation plans and rescue services. Different countries evolved various Rapid Visual Screening (RVS) techniques and methodologies to deal with the devastating consequences of earthquakes on the structural characteristics of buildings and human casualties. Artificial intelligence (AI) methods, such as machine learning (ML) algorithm-based methods, are increasingly used in various scientific and technical applications. The investigation toward using these techniques in civil engineering applications has shown encouraging results and reduced human intervention, including uncertainties and biased judgment. In this study, several known non-parametric algorithms are investigated toward RVS using a dataset employing different earthquakes. Moreover, the methodology encourages the possibility of examining the buildings’ vulnerability based on the factors related to the buildings’ importance and exposure. In addition, a web-based application built on Django is introduced. The interface is designed with the idea to ease the seismic vulnerability investigation in real-time. The concept was validated using two case studies, and the achieved results showed the proposed approach’s potential efficiency KW - Maschinelles Lernen KW - rapid assessment KW - Machine learning KW - Vulnerability assessment KW - OA-Publikationsfonds2022 Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220509-46387 UR - https://www.mdpi.com/2075-5309/12/5/578 VL - 2022 IS - Volume 12, issue 5, article 578 SP - 1 EP - 23 PB - MDPI CY - Basel ER - TY - JOUR A1 - Nabipour, Narjes A1 - Mosavi, Amir A1 - Baghban, Alireza A1 - Shamshirband, Shahaboddin A1 - Felde, Imre T1 - Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions JF - Processes N2 - Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering. In this work, a novel solubility estimation tool has been proposed for hydrocarbon gases—including methane, ethane, propane, and butane—in aqueous electrolyte solutions based on extreme learning machine (ELM) algorithm. Comparing the ELM outputs with a comprehensive real databank which has 1175 solubility points yielded R-squared values of 0.985 and 0.987 for training and testing phases respectively. Furthermore, the visual comparison of estimated and actual hydrocarbon solubility led to confirm the ability of proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of model to identify their impacts on hydrocarbon solubility. Such a comprehensive and reliable study can help engineers and scientists to successfully determine the important thermodynamic properties, which are key factors in optimizing and designing different industrial units such as refineries and petrochemical plants. KW - Maschinelles Lernen KW - Machine learning KW - Deep learning Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200113-40624 UR - https://www.mdpi.com/2227-9717/8/1/92 VL - 2020 IS - Volume 8, Issue 1, 92 PB - MDPI ER - TY - JOUR A1 - Shamshirband, Shahaboddin A1 - Joloudari, Javad Hassannataj A1 - GhasemiGol, Mohammad A1 - Saadatfar, Hamid A1 - Mosavi, Amir A1 - Nabipour, Narjes T1 - FCS-MBFLEACH: Designing an Energy-Aware Fault Detection System for Mobile Wireless Sensor Networks JF - Mathematics N2 - Wireless sensor networks (WSNs) include large-scale sensor nodes that are densely distributed over a geographical region that is completely randomized for monitoring, identifying, and analyzing physical events. The crucial challenge in wireless sensor networks is the very high dependence of the sensor nodes on limited battery power to exchange information wirelessly as well as the non-rechargeable battery of the wireless sensor nodes, which makes the management and monitoring of these nodes in terms of abnormal changes very difficult. These anomalies appear under faults, including hardware, software, anomalies, and attacks by raiders, all of which affect the comprehensiveness of the data collected by wireless sensor networks. Hence, a crucial contraption should be taken to detect the early faults in the network, despite the limitations of the sensor nodes. Machine learning methods include solutions that can be used to detect the sensor node faults in the network. The purpose of this study is to use several classification methods to compute the fault detection accuracy with different densities under two scenarios in regions of interest such as MB-FLEACH, one-class support vector machine (SVM), fuzzy one-class, or a combination of SVM and FCS-MBFLEACH methods. It should be noted that in the study so far, no super cluster head (SCH) selection has been performed to detect node faults in the network. The simulation outcomes demonstrate that the FCS-MBFLEACH method has the best performance in terms of the accuracy of fault detection, false-positive rate (FPR), average remaining energy, and network lifetime compared to other classification methods. KW - Vernetzung KW - wireless sensor networks KW - machine learning KW - Funktechnik KW - Sensor KW - Maschinelles Lernen KW - Internet of Things KW - OA-Publikationsfonds2019 Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200107-40541 UR - https://www.mdpi.com/2227-7390/8/1/28 VL - 2020 IS - Volume 8, Issue 1, article 28 PB - MDPI ER - TY - JOUR A1 - Mosavi, Amir Hosein A1 - Qasem, Sultan Noman A1 - Shokri, Manouchehr A1 - Band, Shahab S. A1 - Mohammadzadeh, Ardashir T1 - Fractional-Order Fuzzy Control Approach for Photovoltaic/Battery Systems under Unknown Dynamics, Variable Irradiation and Temperature JF - Electronics N2 - For this paper, the problem of energy/voltage management in photovoltaic (PV)/battery systems was studied, and a new fractional-order control system on basis of type-3 (T3) fuzzy logic systems (FLSs) was developed. New fractional-order learning rules are derived for tuning of T3-FLSs such that the stability is ensured. In addition, using fractional-order calculus, the robustness was studied versus dynamic uncertainties, perturbation of irradiation, and temperature and abruptly faults in output loads, and, subsequently, new compensators were proposed. In several examinations under difficult operation conditions, such as random temperature, variable irradiation, and abrupt changes in output load, the capability of the schemed controller was verified. In addition, in comparison with other methods, such as proportional-derivative-integral (PID), sliding mode controller (SMC), passivity-based control systems (PBC), and linear quadratic regulator (LQR), the superiority of the suggested method was demonstrated. KW - Fuzzy-Logik KW - Fotovoltaik KW - type-3 fuzzy systems KW - fractional-order control KW - battery KW - photovoltaic KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210122-43381 UR - https://www.mdpi.com/2079-9292/9/9/1455 VL - 2020 IS - Volume 9, issue 9, article 1455 SP - 1 EP - 19 PB - MDPI CY - Basel ER - TY - JOUR A1 - Saqlai, Syed Muhammad A1 - Ghani, Anwar A1 - Khan, Imran A1 - Ahmed Khan Ghayyur, Shahbaz A1 - Shamshirband, Shahaboddin A1 - Nabipour, Narjes A1 - Shokri, Manouchehr T1 - Image Analysis Using Human Body Geometry and Size Proportion Science for Action Classification JF - Applied Sciences N2 - Gestures are one of the basic modes of human communication and are usually used to represent different actions. Automatic recognition of these actions forms the basis for solving more complex problems like human behavior analysis, video surveillance, event detection, and sign language recognition, etc. Action recognition from images is a challenging task as the key information like temporal data, object trajectory, and optical flow are not available in still images. While measuring the size of different regions of the human body i.e., step size, arms span, length of the arm, forearm, and hand, etc., provides valuable clues for identification of the human actions. In this article, a framework for classification of the human actions is presented where humans are detected and localized through faster region-convolutional neural networks followed by morphological image processing techniques. Furthermore, geometric features from human blob are extracted and incorporated into the classification rules for the six human actions i.e., standing, walking, single-hand side wave, single-hand top wave, both hands side wave, and both hands top wave. The performance of the proposed technique has been evaluated using precision, recall, omission error, and commission error. The proposed technique has been comparatively analyzed in terms of overall accuracy with existing approaches showing that it performs well in contrast to its counterparts. KW - Bildanalyse KW - Mensch KW - Größenverhältnis KW - Geometrie KW - Körper KW - action recognition KW - rule based classification KW - human body proportions KW - human blob KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200904-42322 UR - https://www.mdpi.com/2076-3417/10/16/5453 VL - 2020 IS - volume 10, issue 16, article 5453 PB - MDPI CY - Basel ER - TY - JOUR A1 - Zhang, Yongzheng A1 - Ren, Huilong T1 - Implicit implementation of the nonlocal operator method: an open source code JF - Engineering with computers N2 - In this paper, we present an open-source code for the first-order and higher-order nonlocal operator method (NOM) including a detailed description of the implementation. The NOM is based on so-called support, dual-support, nonlocal operators, and an operate energy functional ensuring stability. The nonlocal operator is a generalization of the conventional differential operators. Combined with the method of weighed residuals and variational principles, NOM establishes the residual and tangent stiffness matrix of operate energy functional through some simple matrix without the need of shape functions as in other classical computational methods such as FEM. NOM only requires the definition of the energy drastically simplifying its implementation. The implementation in this paper is focused on linear elastic solids for sake of conciseness through the NOM can handle more complex nonlinear problems. The NOM can be very flexible and efficient to solve partial differential equations (PDEs), it’s also quite easy for readers to use the NOM and extend it to solve other complicated physical phenomena described by one or a set of PDEs. Finally, we present some classical benchmark problems including the classical cantilever beam and plate-with-a-hole problem, and we also make an extension of this method to solve complicated problems including phase-field fracture modeling and gradient elasticity material. KW - Strukturmechanik KW - Nonlocal operator method KW - Operator energy functional KW - Implicit KW - Dual-support KW - Variational principle KW - Taylor series expansion KW - Stiffness matrix Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220216-45930 UR - https://link.springer.com/article/10.1007/s00366-021-01537-x VL - 2022 SP - 1 EP - 35 PB - Springer CY - London ER - TY - JOUR A1 - Harirchian, Ehsan A1 - Lahmer, Tom T1 - Improved Rapid Visual Earthquake Hazard Safety Evaluation of Existing Buildings Using a Type-2 Fuzzy Logic Model JF - Applied Sciences N2 - Rapid Visual Screening (RVS) is a procedure that estimates structural scores for buildings and prioritizes their retrofit and upgrade requirements. Despite the speed and simplicity of RVS, many of the collected parameters are non-commensurable and include subjectivity due to visual observations. This might cause uncertainties in the evaluation, which emphasizes the use of a fuzzy-based method. This study aims to propose a novel RVS methodology based on the interval type-2 fuzzy logic system (IT2FLS) to set the priority of vulnerable building to undergo detailed assessment while covering uncertainties and minimizing their effects during evaluation. The proposed method estimates the vulnerability of a building, in terms of Damage Index, considering the number of stories, age of building, plan irregularity, vertical irregularity, building quality, and peak ground velocity, as inputs with a single output variable. Applicability of the proposed method has been investigated using a post-earthquake damage database of reinforced concrete buildings from the Bingöl and Düzce earthquakes in Turkey. KW - Fuzzy-Logik KW - Erdbeben KW - Fuzzy Logic KW - Rapid Visual Screening KW - Vulnerability assessment KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200331-41161 UR - https://www.mdpi.com/2076-3417/10/7/2375 VL - 2020 IS - Volume 10, Issue 3, 2375 PB - MDPI CY - Basel ER - TY - JOUR A1 - Chowdhury, Sharmistha A1 - Zabel, Volkmar T1 - Influence of loading sequence on wind induced fatigue assessment of bolts in TV-tower connection block JF - Results in Engineering N2 - Bolted connections are widely employed in structures like transmission poles, wind turbines, and television (TV) towers. The behaviour of bolted connections is often complex and plays a significant role in the overall dynamic characteristics of the structure. The goal of this work is to conduct a fatigue lifecycle assessment of such a bolted connection block of a 193 m tall TV tower, for which 205 days of real measurement data have been obtained from the installed monitoring devices. Based on the recorded data, the best-fit stochastic wind distribution for 50 years, the decisive wind action, and the locations to carry out the fatigue analysis have been decided. A 3D beam model of the entire tower is developed to extract the nodal forces corresponding to the connection block location under various mean wind speeds, which is later coupled with a detailed complex finite element model of the connection block, with over three million degrees of freedom, for acquiring stress histories on some pre-selected bolts. The random stress histories are analysed using the rainflow counting algorithm (RCA) and the damage is estimated using Palmgren-Miner's damage accumulation law. A modification is proposed to integrate the loading sequence effect into the RCA, which otherwise is ignored, and the differences between the two RCAs are investigated in terms of the accumulated damage. KW - Schadensakkumulation KW - Lebenszyklus KW - Fatigue life KW - Damage accumulation KW - Wind load KW - Rainflow counting algorithm KW - Loading sequence KW - Windlast KW - OA-Publikationsfonds2022 Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20221028-47303 UR - https://www.sciencedirect.com/science/article/pii/S2590123022002730?via%3Dihub VL - 2022 IS - Volume 16, article 100603 SP - 1 EP - 18 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Mosavi, Amir Hosein A1 - Shokri, Manouchehr A1 - Mansor, Zulkefli A1 - Qasem, Sultan Noman A1 - Band, Shahab S. A1 - Mohammadzadeh, Ardashir T1 - Machine Learning for Modeling the Singular Multi-Pantograph Equations JF - Entropy N2 - In this study, a new approach to basis of intelligent systems and machine learning algorithms is introduced for solving singular multi-pantograph differential equations (SMDEs). For the first time, a type-2 fuzzy logic based approach is formulated to find an approximated solution. The rules of the suggested type-2 fuzzy logic system (T2-FLS) are optimized by the square root cubature Kalman filter (SCKF) such that the proposed fineness function to be minimized. Furthermore, the stability and boundedness of the estimation error is proved by novel approach on basis of Lyapunov theorem. The accuracy and robustness of the suggested algorithm is verified by several statistical examinations. It is shown that the suggested method results in an accurate solution with rapid convergence and a lower computational cost. KW - Fuzzy-Regelung KW - square root cubature calman filter KW - statistical analysis KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210122-43436 UR - https://www.mdpi.com/1099-4300/22/9/1041 VL - 2020 IS - volume 22, issue 9, article 1041 SP - 1 EP - 18 PB - MDPI CY - Basel ER - TY - JOUR A1 - Abdelnour, Mena A1 - Zabel, Volkmar T1 - Modal identification of structures with a dynamic behaviour characterised by global and local modes at close frequencies JF - Acta Mechanica N2 - Identification of modal parameters of a space frame structure is a complex assignment due to a large number of degrees of freedom, close natural frequencies, and different vibrating mechanisms. Research has been carried out on the modal identification of rather simple truss structures. So far, less attention has been given to complex three-dimensional truss structures. This work develops a vibration-based methodology for determining modal information of three-dimensional space truss structures. The method uses a relatively complex space truss structure for its verification. Numerical modelling of the system gives modal information about the expected vibration behaviour. The identification process involves closely spaced modes that are characterised by local and global vibration mechanisms. To distinguish between local and global vibrations of the system, modal strain energies are used as an indicator. The experimental validation, which incorporated a modal analysis employing the stochastic subspace identification method, has confirmed that considering relatively high model orders is required to identify specific mode shapes. Especially in the case of the determination of local deformation modes of space truss members, higher model orders have to be taken into account than in the modal identification of most other types of structures. KW - Fachwerkbau KW - Holzkonstruktion KW - Schwingung KW - three-dimensional truss structures KW - vibration-based methodology KW - numerical modelling Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20230525-63822 UR - https://link.springer.com/article/10.1007/s00707-023-03598-z VL - 2023 SP - 1 EP - 21 PB - Springer CY - Wien ER - TY - JOUR A1 - Shabani, Sevda A1 - Samadianfard, Saeed A1 - Sattari, Mohammad Taghi A1 - Mosavi, Amir A1 - Shamshirband, Shahaboddin A1 - Kmet, Tibor A1 - Várkonyi-Kóczy, Annamária R. T1 - Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis JF - Atmosphere N2 - Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters. KW - Maschinelles Lernen KW - Machine learning KW - Deep learning Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200110-40561 UR - https://www.mdpi.com/2073-4433/11/1/66 VL - 2020 IS - Volume 11, Issue 1, 66 ER - TY - JOUR A1 - Meiabadi, Mohammad Saleh A1 - Moradi, Mahmoud A1 - Karamimoghadam, Mojtaba A1 - Ardabili, Sina A1 - Bodaghi, Mahdi A1 - Shokri, Manouchehr A1 - Mosavi, Amir Hosein T1 - Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication JF - polymers N2 - Polylactic acid (PLA) is a highly applicable material that is used in 3D printers due to some significant features such as its deformation property and affordable cost. For improvement of the end-use quality, it is of significant importance to enhance the quality of fused filament fabrication (FFF)-printed objects in PLA. The purpose of this investigation was to boost toughness and to reduce the production cost of the FFF-printed tensile test samples with the desired part thickness. To remove the need for numerous and idle printing samples, the response surface method (RSM) was used. Statistical analysis was performed to deal with this concern by considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The artificial intelligence method of artificial neural network (ANN) and ANN-genetic algorithm (ANN-GA) were further developed to estimate the toughness, part thickness, and production-cost-dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could enhance the accuracy of modeling by about 7.5, 11.5, and 4.5% for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other hand, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects. KW - 3D-Druck KW - Polymere KW - Maschinelles Lernen KW - 3D printing KW - machine learning KW - fused filament fabrication KW - OA-Publikationsfonds2021 Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220110-45518 UR - https://www.mdpi.com/2073-4360/13/19/3219 VL - 2021 IS - Volume 13, issue 19, article 3219 SP - 1 EP - 21 PB - MDPI CY - Basel ER -