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 - 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 - Zhang, Yongzheng T1 - Nonlocal dynamic Kirchhoff plate formulation based on nonlocal operator method JF - Engineering with Computers N2 - In this study, we propose a nonlocal operator method (NOM) for the dynamic analysis of (thin) Kirchhoff plates. The nonlocal Hessian operator is derived based on a second-order Taylor series expansion. The NOM does not require any shape functions and associated derivatives as ’classical’ approaches such as FEM, drastically facilitating the implementation. Furthermore, NOM is higher order continuous, which is exploited for thin plate analysis that requires C1 continuity. The nonlocal dynamic governing formulation and operator energy functional for Kirchhoff plates are derived from a variational principle. The Verlet-velocity algorithm is used for the time discretization. After confirming the accuracy of the nonlocal Hessian operator, several numerical examples are simulated by the nonlocal dynamic Kirchhoff plate formulation. KW - Angewandte Mathematik KW - nonlocal operator method KW - nonlocal Hessian operator KW - operator energy functional KW - dual-support KW - variational principle Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220209-45849 UR - https://link.springer.com/article/10.1007/s00366-021-01587-1 VL - 2022 SP - 1 EP - 35 PB - Springer CY - London ER - TY - JOUR A1 - Zhang, Chao A1 - Hao, Xiao-Li A1 - Wang, Cuixia A1 - Wei, Ning A1 - Rabczuk, Timon T1 - Thermal conductivity of graphene nanoribbons under shear deformation: A molecular dynamics simulation JF - Scientific Reports N2 - Tensile strain and compress strain can greatly affect the thermal conductivity of graphene nanoribbons (GNRs). However, the effect of GNRs under shear strain, which is also one of the main strain effect, has not been studied systematically yet. In this work, we employ reverse nonequilibrium molecular dynamics (RNEMD) to the systematical study of the thermal conductivity of GNRs (with model size of 4 nm × 15 nm) under the shear strain. Our studies show that the thermal conductivity of GNRs is not sensitive to the shear strain, and the thermal conductivity decreases only 12–16% before the pristine structure is broken. Furthermore, the phonon frequency and the change of the micro-structure of GNRs, such as band angel and bond length, are analyzed to explore the tendency of thermal conductivity. The results show that the main influence of shear strain is on the in-plane phonon density of states (PDOS), whose G band (higher frequency peaks) moved to the low frequency, thus the thermal conductivity is decreased. The unique thermal properties of GNRs under shear strains suggest their great potentials for graphene nanodevices and great potentials in the thermal managements and thermoelectric applications. KW - Wärmeleitfähigkeit KW - Graphen KW - Schubspannung Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170428-31718 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 - 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 - Shirazi, A. H. N. A1 - Mohebbi, Farzad A1 - Azadi Kakavand, M. R. A1 - He, B. A1 - Rabczuk, Timon T1 - Paraffin Nanocomposites for Heat Management of Lithium-Ion Batteries: A Computational Investigation JF - JOURNAL OF NANOMATERIALS N2 - Lithium-ion (Li-ion) batteries are currently considered as vital components for advances in mobile technologies such as those in communications and transport. Nonetheless, Li-ion batteries suffer from temperature rises which sometimes lead to operational damages or may even cause fire. An appropriate solution to control the temperature changes during the operation of Li-ion batteries is to embed batteries inside a paraffin matrix to absorb and dissipate heat. In the present work, we aimed to investigate the possibility of making paraffin nanocomposites for better heat management of a Li-ion battery pack. To fulfill this aim, heat generation during a battery charging/discharging cycles was simulated using Newman’s well established electrochemical pseudo-2D model. We couple this model to a 3D heat transfer model to predict the temperature evolution during the battery operation. In the later model, we considered different paraffin nanocomposites structures made by the addition of graphene, carbon nanotubes, and fullerene by assuming the same thermal conductivity for all fillers. This way, our results mainly correlate with the geometry of the fillers. Our results assess the degree of enhancement in heat dissipation of Li-ion batteries through the use of paraffin nanocomposites. Our results may be used as a guide for experimental set-ups to improve the heat management of Li-ion batteries. KW - Batterie KW - Wärmeleitfähigkeit Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170411-31141 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 - Shamshirband, Shahaboddin A1 - Babanezhad, Meisam A1 - Mosavi, Amir A1 - Nabipour, Narjes A1 - Hajnal, Eva A1 - Nadai, Laszlo A1 - Chau, Kwok-Wing T1 - Prediction of flow characteristics in the bubble column reactor by the artificial pheromone-based communication of biological ants JF - Engineering Applications of Computational Fluid Mechanics N2 - A novel combination of the ant colony optimization algorithm (ACO)and computational fluid dynamics (CFD) data is proposed for modeling the multiphase chemical reactors. The proposed intelligent model presents a probabilistic computational strategy for predicting various levels of three-dimensional bubble column reactor (BCR) flow. The results prove an enhanced communication between ant colony prediction and CFD data in different sections of the BCR. KW - Maschinelles Lernen KW - Machine learning KW - Bubble column reactor KW - ant colony optimization algorithm (ACO) KW - flow pattern KW - computational fluid dynamics (CFD) KW - big data KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200227-41013 UR - https://www.tandfonline.com/doi/full/10.1080/19942060.2020.1715842 VL - 2020 IS - volume 14, issue 1 SP - 367 EP - 378 PB - Taylor & Francis 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 - 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 - 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 - Sadeghzadeh, Milad A1 - Maddah, Heydar A1 - Ahmadi, Mohammad Hossein A1 - Khadang, Amirhosein A1 - Ghazvini, Mahyar A1 - Mosavi, Amir Hosein A1 - Nabipour, Narjes T1 - Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network JF - Nanomaterials N2 - In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid. TiO2-Al2O3/water in the role of an innovative type of nanofluid was synthesized by the sol–gel method. The results indicated that 1.5 vol.% of nanofluids enhanced the thermal conductivity by up to 25%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable. View Full-Text KW - Wärmeleitfähigkeit KW - Fluid KW - Neuronales Netz KW - Thermal conductivity KW - Nanofluid KW - Artificial neural network Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200421-41308 UR - https://www.mdpi.com/2079-4991/10/4/697 VL - 2020 IS - Volume 10, Issue 4, 697 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 - Ren, Huilong A1 - Zhuang, Xiaoying A1 - Oterkus, Erkan A1 - Zhu, Hehua A1 - Rabczuk, Timon T1 - Nonlocal strong forms of thin plate, gradient elasticity, magneto-electro-elasticity and phase-field fracture by nonlocal operator method JF - Engineering with Computers N2 - The derivation of nonlocal strong forms for many physical problems remains cumbersome in traditional methods. In this paper, we apply the variational principle/weighted residual method based on nonlocal operator method for the derivation of nonlocal forms for elasticity, thin plate, gradient elasticity, electro-magneto-elasticity and phase-field fracture method. The nonlocal governing equations are expressed as an integral form on support and dual-support. The first example shows that the nonlocal elasticity has the same form as dual-horizon non-ordinary state-based peridynamics. The derivation is simple and general and it can convert efficiently many local physical models into their corresponding nonlocal forms. In addition, a criterion based on the instability of the nonlocal gradient is proposed for the fracture modelling in linear elasticity. Several numerical examples are presented to validate nonlocal elasticity and the nonlocal thin plate. KW - Bruchmechanik KW - Elastizität KW - Peridynamik KW - energy form KW - weak form KW - peridynamics KW - variational principle KW - explicit time integration Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20211207-45388 UR - https://link.springer.com/article/10.1007/s00366-021-01502-8 VL - 2021 SP - 1 EP - 22 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 - Rabczuk, Timon A1 - Guo, Hongwei A1 - Zhuang, Xiaoying A1 - Chen, Pengwan A1 - Alajlan, Naif T1 - Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media JF - Engineering with Computers N2 - We present a stochastic deep collocation method (DCM) based on neural architecture search (NAS) and transfer learning for heterogeneous porous media. We first carry out a sensitivity analysis to determine the key hyper-parameters of the network to reduce the search space and subsequently employ hyper-parameter optimization to finally obtain the parameter values. The presented NAS based DCM also saves the weights and biases of the most favorable architectures, which is then used in the fine-tuning process. We also employ transfer learning techniques to drastically reduce the computational cost. The presented DCM is then applied to the stochastic analysis of heterogeneous porous material. Therefore, a three dimensional stochastic flow model is built providing a benchmark to the simulation of groundwater flow in highly heterogeneous aquifers. The performance of the presented NAS based DCM is verified in different dimensions using the method of manufactured solutions. We show that it significantly outperforms finite difference methods in both accuracy and computational cost. KW - Maschinelles Lernen KW - Neuronales Lernen KW - Fehlerabschätzung KW - deep learning KW - neural architecture search KW - randomized spectral representation Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220209-45835 UR - https://link.springer.com/article/10.1007/s00366-021-01586-2 VL - 2022 SP - 1 EP - 26 PB - Springer CY - London ER - TY - JOUR A1 - Ouaer, Hocine A1 - Hosseini, Amir Hossein A1 - Amar, Menad Nait A1 - Ben Seghier, Mohamed El Amine A1 - Ghriga, Mohammed Abdelfetah A1 - Nabipour, Narjes A1 - Andersen, Pål Østebø A1 - Mosavi, Amir A1 - Shamshirband, Shahaboddin T1 - Rigorous Connectionist Models to Predict Carbon Dioxide Solubility in Various Ionic Liquids JF - Applied Sciences N2 - Estimating the solubility of carbon dioxide in ionic liquids, using reliable models, is of paramount importance from both environmental and economic points of view. In this regard, the current research aims at evaluating the performance of two data-driven techniques, namely multilayer perceptron (MLP) and gene expression programming (GEP), for predicting the solubility of carbon dioxide (CO2) in ionic liquids (ILs) as the function of pressure, temperature, and four thermodynamical parameters of the ionic liquid. To develop the above techniques, 744 experimental data points derived from the literature including 13 ILs were used (80% of the points for training and 20% for validation). Two backpropagation-based methods, namely Levenberg–Marquardt (LM) and Bayesian Regularization (BR), were applied to optimize the MLP algorithm. Various statistical and graphical assessments were applied to check the credibility of the developed techniques. The results were then compared with those calculated using Peng–Robinson (PR) or Soave–Redlich–Kwong (SRK) equations of state (EoS). The highest coefficient of determination (R2 = 0.9965) and the lowest root mean square error (RMSE = 0.0116) were recorded for the MLP-LMA model on the full dataset (with a negligible difference to the MLP-BR model). The comparison of results from this model with the vastly applied thermodynamic equation of state models revealed slightly better performance, but the EoS approaches also performed well with R2 from 0.984 up to 0.996. Lastly, the newly established correlation based on the GEP model exhibited very satisfactory results with overall values of R2 = 0.9896 and RMSE = 0.0201. KW - Maschinelles Lernen KW - Machine learning KW - OA-Publikationsfonds2020 Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200107-40558 UR - https://www.mdpi.com/2076-3417/10/1/304 VL - 2020 IS - Volume 10, Issue 1, 304 PB - MDPI ER - TY - JOUR A1 - Noori, Hamidreza A1 - Mortazavi, Bohayra A1 - Keshtkari, Leila A1 - Zhuang, Xiaoying A1 - Rabczuk, Timon T1 - Nanopore creation in MoS2 and graphene monolayers by nanoparticles impact: a reactive molecular dynamics study JF - Applied Physics A N2 - In this work, extensive reactive molecular dynamics simulations are conducted to analyze the nanopore creation by nano-particles impact over single-layer molybdenum disulfide (MoS2) with 1T and 2H phases. We also compare the results with graphene monolayer. In our simulations, nanosheets are exposed to a spherical rigid carbon projectile with high initial velocities ranging from 2 to 23 km/s. Results for three different structures are compared to examine the most critical factors in the perforation and resistance force during the impact. To analyze the perforation and impact resistance, kinetic energy and displacement time history of the projectile as well as perforation resistance force of the projectile are investigated. Interestingly, although the elasticity module and tensile strength of the graphene are by almost five times higher than those of MoS2, the results demonstrate that 1T and 2H-MoS2 phases are more resistive to the impact loading and perforation than graphene. For the MoS2nanosheets, we realize that the 2H phase is more resistant to impact loading than the 1T counterpart. Our reactive molecular dynamics results highlight that in addition to the strength and toughness, atomic structure is another crucial factor that can contribute substantially to impact resistance of 2D materials. The obtained results can be useful to guide the experimental setups for the nanopore creation in MoS2or other 2D lattices. KW - Nanomechanik KW - Molekülstruktur KW - Nanoporöser Stoff KW - MoS2 KW - molecular dynamics KW - Nanopore KW - Graphene Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210804-44756 UR - https://link.springer.com/article/10.1007/s00339-021-04693-5 VL - 2021 IS - volume 127, article 541 SP - 1 EP - 13 PB - Springer CY - Heidelberg 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 - Nabipour, Narjes A1 - Dehghani, Majid A1 - Mosavi, Amir A1 - Shamshirband, Shahaboddin T1 - Short-Term Hydrological Drought Forecasting Based on Different Nature-Inspired Optimization Algorithms Hybridized With Artificial Neural Networks JF - IEEE Access N2 - Hydrological drought forecasting plays a substantial role in water resources management. Hydrological drought highly affects the water allocation and hydropower generation. In this research, short term hydrological drought forecasted based on the hybridized of novel nature-inspired optimization algorithms and Artificial Neural Networks (ANN). For this purpose, the Standardized Hydrological Drought Index (SHDI) and the Standardized Precipitation Index (SPI) were calculated in one, three, and six aggregated months. Then, three states where proposed for SHDI forecasting, and 36 input-output combinations were extracted based on the cross-correlation analysis. In the next step, newly proposed optimization algorithms, including Grasshopper Optimization Algorithm (GOA), Salp Swarm algorithm (SSA), Biogeography-based optimization (BBO), and Particle Swarm Optimization (PSO) hybridized with the ANN were utilized for SHDI forecasting and the results compared to the conventional ANN. Results indicated that the hybridized model outperformed compared to the conventional ANN. PSO performed better than the other optimization algorithms. The best models forecasted SHDI1 with R2 = 0.68 and RMSE = 0.58, SHDI3 with R 2 = 0.81 and RMSE = 0.45 and SHDI6 with R 2 = 0.82 and RMSE = 0.40. KW - Maschinelles Lernen KW - Machine learning KW - Deep learning KW - Hydrological drought KW - precipitation KW - hydrology Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200213-40796 UR - https://ieeexplore.ieee.org/document/8951168 VL - 2020 IS - volume 8 SP - 15210 EP - 15222 PB - IEEE ER - TY - JOUR A1 - Mousavi, Seyed Nasrollah A1 - Steinke Júnior, Renato A1 - Teixeira, Eder Daniel A1 - Bocchiola, Daniele A1 - Nabipour, Narjes A1 - Mosavi, Amir A1 - Shamshirband, Shahaboddin T1 - Predictive Modeling the Free Hydraulic Jumps Pressure through Advanced Statistical Methods JF - Mathematics N2 - Pressure fluctuations beneath hydraulic jumps potentially endanger the stability of stilling basins. This paper deals with the mathematical modeling of the results of laboratory-scale experiments to estimate the extreme pressures. Experiments were carried out on a smooth stilling basin underneath free hydraulic jumps downstream of an Ogee spillway. From the probability distribution of measured instantaneous pressures, pressures with different probabilities could be determined. It was verified that maximum pressure fluctuations, and the negative pressures, are located at the positions near the spillway toe. Also, minimum pressure fluctuations are located at the downstream of hydraulic jumps. It was possible to assess the cumulative curves of pressure data related to the characteristic points along the basin, and different Froude numbers. To benchmark the results, the dimensionless forms of statistical parameters include mean pressures (P*m), the standard deviations of pressure fluctuations (σ*X), pressures with different non-exceedance probabilities (P*k%), and the statistical coefficient of the probability distribution (Nk%) were assessed. It was found that an existing method can be used to interpret the present data, and pressure distribution in similar conditions, by using a new second-order fractional relationships for σ*X, and Nk%. The values of the Nk% coefficient indicated a single mean value for each probability. KW - Maschinelles Lernen KW - Machine learning KW - mathematical modeling KW - extreme pressure KW - hydraulic jump KW - stilling basin KW - standard deviation of pressure fluctuations KW - statistical coeffcient of the probability distribution Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200402-41140 UR - https://www.mdpi.com/2227-7390/8/3/323 VL - 2020 IS - Volume 8, Issue 3, 323 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 - 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 - 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 - 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 - Mortazavi, Bohayra A1 - Pereira, Luiz Felipe C. A1 - Jiang, Jin-Wu A1 - Rabczuk, Timon T1 - Modelling heat conduction in polycrystalline hexagonal boron-nitride films JF - Scientific Reports N2 - We conducted extensive molecular dynamics simulations to investigate the thermal conductivity of polycrystalline hexagonal boron-nitride (h-BN) films. To this aim, we constructed large atomistic models of polycrystalline h-BN sheets with random and uniform grain configuration. By performing equilibrium molecular dynamics (EMD) simulations, we investigated the influence of the average grain size on the thermal conductivity of polycrystalline h-BN films at various temperatures. Using the EMD results, we constructed finite element models of polycrystalline h-BN sheets to probe the thermal conductivity of samples with larger grain sizes. Our multiscale investigations not only provide a general viewpoint regarding the heat conduction in h-BN films but also propose that polycrystalline h-BN sheets present high thermal conductivity comparable to monocrystalline sheets. KW - Wärmeleitfähigkeit KW - Bornitrid KW - Finite-Elemente-Methode Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170425-31534 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - Jiang, Jin-Wu A1 - Zhuang, Xiaoying A1 - Rabczuk, Timon T1 - Orientation dependent thermal conductance in single-layer MoS 2 JF - Scientific Reports N2 - We investigate the thermal conductivity in the armchair and zigzag MoS2 nanoribbons, by combining the non-equilibrium Green's function approach and the first-principles method. A strong orientation dependence is observed in the thermal conductivity. Particularly, the thermal conductivity for the armchair MoS2 nanoribbon is about 673.6 Wm−1 K−1 in the armchair nanoribbon, and 841.1 Wm−1 K−1 in the zigzag nanoribbon at room temperature. By calculating the Caroli transmission, we disclose the underlying mechanism for this strong orientation dependence to be the fewer phonon transport channels in the armchair MoS2 nanoribbon in the frequency range of [150, 200] cm−1. Through the scaling of the phonon dispersion, we further illustrate that the thermal conductivity calculated for the MoS2 nanoribbon is esentially in consistent with the superior thermal conductivity found for graphene. KW - Mechanische Eigenschaft KW - Wärmeleitfähigkeit KW - Nanoribbons, thermal conductivity Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170418-31417 ER - TY - JOUR A1 - Işık, Ercan A1 - Büyüksaraç, Aydın A1 - Levent Ekinci, Yunus A1 - Aydın, Mehmet Cihan A1 - Harirchian, Ehsan T1 - The Effect of Site-Specific Design Spectrum on Earthquake-Building Parameters: A Case Study from the Marmara Region (NW Turkey) JF - Applied Sciences N2 - The Marmara Region (NW Turkey) has experienced significant earthquakes (M > 7.0) to date. A destructive earthquake is also expected in the region. To determine the effect of the specific design spectrum, eleven provinces located in the region were chosen according to the Turkey Earthquake Building Code updated in 2019. Additionally, the differences between the previous and updated regulations of the country were investigated. Peak Ground Acceleration (PGA) and Peak Ground Velocity (PGV) were obtained for each province by using earthquake ground motion levels with 2%, 10%, 50%, and 68% probability of exceedance in 50-year periods. The PGA values in the region range from 0.16 to 0.7 g for earthquakes with a return period of 475 years. For each province, a sample of a reinforced-concrete building having two different numbers of stories with the same ground and structural characteristics was chosen. Static adaptive pushover analyses were performed for the sample reinforced-concrete building using each province’s design spectrum. The variations in the earthquake and structural parameters were investigated according to different geographical locations. It was determined that the site-specific design spectrum significantly influences target displacements for performance-based assessments of buildings due to seismicity characteristics of the studied geographic location. KW - Erdbeben KW - earthquake KW - site-specific spectrum KW - Marmara Region KW - seismic hazard analysis KW - adaptive pushover KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20201022-42758 UR - https://www.mdpi.com/2076-3417/10/20/7247 VL - 2020 IS - Volume 10, issue 20, article 7247 PB - MDPI CY - Basel ER - TY - JOUR A1 - Ilyani Akmar, A.B. A1 - Kramer, O. A1 - Rabczuk, Timon T1 - Multi-objective evolutionary optimization of sandwich structures: An evaluation by elitist non-dominated sorting evolution strategy JF - American Journal of Engineering and Applied Sciences N2 - In this study, an application of evolutionary multi-objective optimization algorithms on the optimization of sandwich structures is presented. The solution strategy is known as Elitist Non-Dominated Sorting Evolution Strategy (ENSES) wherein Evolution Strategies (ES) as Evolutionary Algorithm (EA) in the elitist Non-dominated Sorting Genetic algorithm (NSGA-II) procedure. Evolutionary algorithm seems a compatible approach to resolve multi-objective optimization problems because it is inspired by natural evolution, which closely linked to Artificial Intelligence (AI) techniques and elitism has shown an important factor for improving evolutionary multi-objective search. In order to evaluate the notion of performance by ENSES, the well-known study case of sandwich structures are reconsidered. For Case 1, the goals of the multi-objective optimization are minimization of the deflection and the weight of the sandwich structures. The length, the core and skin thicknesses are the design variables of Case 1. For Case 2, the objective functions are the fabrication cost, the beam weight and the end deflection of the sandwich structures. There are four design variables i.e., the weld height, the weld length, the beam depth and the beam width in Case 2. Numerical results are presented in terms of Paretooptimal solutions for both evaluated cases. KW - Optimierung KW - Stahlbau KW - Multi-objective Evolutionary Optimization, Elitist Non- Dominated Sorting Evolution Strategy (ENSES), Sandwich Structure, Pareto-Optimal Solutions, Evolutionary Algorithm Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170418-31402 SP - 185 EP - 201 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 -