@article{AbbaspourGilandehMolaeeSabzietal., author = {Abbaspour-Gilandeh, Yousef and Molaee, Amir and Sabzi, Sajad and Nabipour, Narjes and Shamshirband, Shahaboddin and Mosavi, Amir}, title = {A Combined Method of Image Processing and Artificial Neural Network for the Identification of 13 Iranian Rice Cultivars}, series = {agronomy}, volume = {2020}, journal = {agronomy}, number = {Volume 10, Issue 1, 117}, publisher = {MDPI}, doi = {10.3390/agronomy10010117}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200123-40695}, pages = {21}, abstract = {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.}, subject = {Maschinelles Lernen}, language = {en} } @article{HarirchianIsik, author = {Harirchian, Ehsan and Isik, Ercan}, title = {A Comparative Probabilistic Seismic Hazard Analysis for Eastern Turkey (Bitlis) Based on Updated Hazard Map and Its Effect on Regular RC Structures}, series = {Buildings}, volume = {2022}, journal = {Buildings}, number = {Volume 12, issue 10, article 1573}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/buildings12101573}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20221028-47283}, pages = {1 -- 19}, abstract = {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.}, subject = {Erbeben}, language = {en} } @article{HarirchianJadhavMohammadetal., author = {Harirchian, Ehsan and Jadhav, Kirti and Mohammad, Kifaytullah and Aghakouchaki Hosseini, Seyed Ehsan and Lahmer, Tom}, title = {A Comparative Study of MCDM Methods Integrated with Rapid Visual Seismic Vulnerability Assessment of Existing RC Structures}, series = {Applied Sciences}, volume = {2020}, journal = {Applied Sciences}, number = {Volume 10, issue 18, article 6411}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/app10186411}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200918-42360}, pages = {24}, abstract = {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.}, subject = {Erdbebensicherheit}, language = {en} } @article{ZhuangHuangLiangetal., author = {Zhuang, Xiaoying and Huang, Runqiu and Liang, Chao and Rabczuk, Timon}, title = {A coupled thermo-hydro-mechanical model of jointed hard rock for compressed air energy storage}, series = {Mathematical Problems in Engineering}, journal = {Mathematical Problems in Engineering}, doi = {10.1155/2014/179169}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170428-31726}, abstract = {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.}, subject = {Energiespeicherung}, language = {en} } @article{AlaladeReichertKoehnetal., author = {Alalade, Muyiwa and Reichert, Ina and K{\"o}hn, Daniel and Wuttke, Frank and Lahmer, Tom}, title = {A Cyclic Multi-Stage Implementation of the Full-Waveform Inversion for the Identification of Anomalies in Dams}, series = {Infrastructures}, volume = {2022}, journal = {Infrastructures}, number = {Volume 7, issue 12, article 161}, editor = {Qu, Chunxu and Gao, Chunxu and Zhang, Rui and Jia, Ziguang and Li, Jiaxiang}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/infrastructures7120161}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20221201-48396}, pages = {19}, abstract = {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.}, subject = {Damm}, language = {en} } @article{HarirchianKumariJadhavetal., author = {Harirchian, Ehsan and Kumari, Vandana and Jadhav, Kirti and Raj Das, Rohan and Rasulzade, Shahla and Lahmer, Tom}, title = {A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings}, series = {Applied Sciences}, volume = {2020}, journal = {Applied Sciences}, number = {Volume 10, issue 20, article 7153}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/app10207153}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20201022-42744}, pages = {18}, abstract = {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.}, subject = {Erdbeben}, language = {en} } @article{KarimimoshaverHajivalieiShokrietal., author = {Karimimoshaver, Mehrdad and Hajivaliei, Hatameh and Shokri, Manouchehr and Khalesro, Shakila and Aram, Farshid and Shamshirband, Shahaboddin}, title = {A Model for Locating Tall Buildings through a Visual Analysis Approach}, series = {Applied Sciences}, volume = {2020}, journal = {Applied Sciences}, number = {Volume 10, issue 17, article 6072}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/app10176072}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210122-43350}, pages = {1 -- 25}, abstract = {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.}, subject = {Geb{\"a}ude}, language = {en} } @article{SaadatfarKhosraviHassannatajJoloudarietal., author = {Saadatfar, Hamid and Khosravi, Samiyeh and Hassannataj Joloudari, Javad and Mosavi, Amir and Shamshirband, Shahaboddin}, title = {A New K-Nearest Neighbors Classifier for Big Data Based on Efficient Data Pruning}, series = {Mathematics}, volume = {2020}, journal = {Mathematics}, number = {volume 8, issue 2, article 286}, publisher = {MDPI}, doi = {10.3390/math8020286}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200225-40996}, pages = {12}, abstract = {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.}, subject = {Maschinelles Lernen}, language = {en} } @article{VuBacNguyenXuanChenetal., author = {Vu-Bac, N. and Nguyen-Xuan, Hung and Chen, Lei and Lee, C.K. and Zi, Goangseup and Zhuang, Xiaoying and Liu, G.R. and Rabczuk, Timon}, title = {A phantom-node method with edge-based strain smoothing for linear elastic fracture mechanics}, series = {Journal of Applied Mathematics}, journal = {Journal of Applied Mathematics}, doi = {10.1155/2013/978026}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170426-31676}, abstract = {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.}, subject = {Finite-Elemente-Methode}, language = {en} } @article{AlkamLahmer, author = {Alkam, Feras and Lahmer, Tom}, title = {A robust method of the status monitoring of catenary poles installed along high-speed electrified train tracks}, series = {Results in Engineering}, volume = {2021}, journal = {Results in Engineering}, number = {volume 12, article 100289}, publisher = {Elsevier}, address = {Amsterdam}, doi = {10.1016/j.rineng.2021.100289}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20211011-45212}, pages = {1 -- 8}, abstract = {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.}, subject = {Fahrleitung}, language = {en} }