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Optimization of Steel Moment Frame Structures: A Review</title> <meta name="description" content="Artificial Intelligent in Optimization of Steel Moment Frame Structures: A Review"> <meta name="keywords" content="Artificial Intelligent, optimization process, steel moment frame, structural engineering."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="Artificial Intelligent in Optimization of Steel Moment Frame Structures: A Review"> <meta name="citation_author" content="Mohsen Soori"> <meta name="citation_author" content="Fooad Karimi Ghaleh Jough"> <meta name="citation_publication_date" content="2024/03/29"> <meta name="citation_journal_title" content="International Journal of Structural and Construction Engineering"> <meta name="citation_volume" content="18"> <meta name="citation_issue" content="3"> <meta name="citation_firstpage" content="141"> <meta 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<input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 33093</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Artificial Intelligent in Optimization of Steel Moment Frame Structures: A Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mohsen%20Soori">Mohsen Soori</a>, <a href="https://publications.waset.org/search?q=Fooad%20Karimi%20Ghaleh%20Jough"> Fooad Karimi Ghaleh Jough</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The integration of Artificial Intelligence (AI) techniques in the optimization of steel moment frame structures represents a transformative approach to enhance the design, analysis, and performance of these critical engineering systems. The review encompasses a wide spectrum of AI methods, including machine learning algorithms, evolutionary algorithms, neural networks, and optimization techniques, applied to address various challenges in the field. The synthesis of research findings highlights the interdisciplinary nature of AI applications in structural engineering, emphasizing the synergy between domain expertise and advanced computational methodologies. This synthesis aims to serve as a valuable resource for researchers, practitioners, and policymakers seeking a comprehensive understanding of the state-of-the-art in AI-driven optimization for steel moment frame structures. The paper commences with an overview of the fundamental principles governing steel moment frame structures and identifies the key optimization objectives, such as efficiency of structures. Subsequently, it delves into the application of AI in the conceptual design phase, where algorithms aid in generating innovative structural configurations and optimizing material utilization. The review also explores the use of AI for real-time structural health monitoring and predictive maintenance, contributing to the long-term sustainability and reliability of steel moment frame structures. Furthermore, the paper investigates how AI-driven algorithms facilitate the calibration of structural models, enabling accurate prediction of dynamic responses and seismic performance. Thus, by reviewing and analyzing the recent achievements in applications artificial intelligent in optimization of steel moment frame structures, the process of designing, analysis, and performance of the structures can be analyzed and modified.</p> <iframe src="https://publications.waset.org/10013579.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Artificial%20Intelligent" title="Artificial Intelligent">Artificial Intelligent</a>, <a href="https://publications.waset.org/search?q=optimization%20process" title=" optimization process"> optimization process</a>, <a href="https://publications.waset.org/search?q=steel%20moment%20frame" title=" steel moment frame"> steel moment frame</a>, <a href="https://publications.waset.org/search?q=structural%20engineering." title=" structural engineering."> structural engineering.</a> </p> <a href="https://publications.waset.org/10013579/artificial-intelligent-in-optimization-of-steel-moment-frame-structures-a-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013579/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013579/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013579/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013579/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013579/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013579/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013579/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013579/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013579/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013579/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013579.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">249</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] H.D. Nguyen, J.M. LaFave, Y.-J. Lee, and M. Shin, “Rapid seismic damage-state assessment of steel moment frames using machine learning”, Engineering Structures, 2022, 252, pp. 113737. <br>[2] H.-B. Ly, L.M. Le, H.T. Duong, T.C. Nguyen, T.A. Pham, T.-T. Le, V.M. Le, L. Nguyen-Ngoc, and B.T. Pham, “Hybrid artificial intelligence approaches for predicting critical buckling load of structural members under compression considering the influence of initial geometric imperfections”, Applied Sciences, 2019, 9 (11), pp. 2258. <br>[3] H.Q. Nguyen, H.-B. Ly, V.Q. Tran, T.-A. Nguyen, T.-T. Le, and B.T. Pham, “Optimization of artificial intelligence system by evolutionary algorithm for prediction of axial capacity of rectangular concrete filled steel tubes under compression”, Materials, 2020, 13 (5), pp. 1205. <br>[4] F.K.G. Jough, “Prediction of Seismic Collapse Risk in Steel Moment Framed Structures by Metaheuristic Algorithm”, 2016, pp. <br>[5] M.P. Saka, and Z.W. Geem, “Mathematical and metaheuristic applications in design optimization of steel frame structures: an extensive review”, Mathematical problems in engineering, 2013, 2013, pp. <br>[6] W. Shan, J. Liu, and J. Zhou, “Integrated method for intelligent structural design of steel frames based on optimization and machine learning algorithm”, Engineering Structures, 2023, 284, pp. 115980. <br>[7] Z. Aksöz, and C. Preisinger, An interactive structural optimization of space frame structures using machine learning, in: Impact: Design With All Senses: Proceedings of the Design Modelling Symposium, Berlin 2019, Springer, 2020, pp. 18-31. <br>[8] F.K.G. Jough, and S. Şensoy, “Prediction of seismic collapse risk of steel moment frame mid-rise structures by meta-heuristic algorithms”, Earthquake Engineering and Engineering Vibration, 2016, 15, pp. 743-757. <br>[9] F. Karimi Ghaleh Jough, and S. Şensoy, “Steel moment-resisting frame reliability via the interval analysis by FCM-PSO approach considering various uncertainties”, Journal of Earthquake Engineering, 2020, 24 (1), pp. 109-128. <br>[10] F. Karimi Ghaleh Jough, and M. Golhashem, “Assessment of out-of-plane behavior of non-structural masonry walls using FE simulations”, Bulletin of Earthquake Engineering, 2020, 18 (14), pp. 6405-6427. <br>[11] F. Karimi Ghaleh Jough, and S. Beheshti Aval, “Uncertainty analysis through development of seismic fragility curve for an SMRF structure using an adaptive neuro-fuzzy inference system based on fuzzy C-means algorithm”, Scientia Iranica, 2018, 25 (6), pp. 2938-2953. <br>[12] B. Ghasemzadeh, T. Celik, F. Karimi Ghaleh Jough, and J. C Matthews, “Road map to BIM use for infrastructure domains: Identifying and contextualizing variables of infrastructure projects”, Scientia Iranica, 2022, 29 (6), pp. 2803-2824. <br>[13] F. Karimi Ghaleh Jough, M. Veghar, and S.B. Beheshti-Aval, “Epistemic Uncertainty Treatment Using Group Method of Data Handling Algorithm in Seismic Collapse Fragility”, Latin American Journal of Solids and Structures, 2021, 18, pp. e355. <br>[14] F. Karimi Ghaleh Jough, and B. Ghasemzadeh, “Uncertainty Interval Analysis of Steel Moment Frame by Development of 3D-Fragility Curves Towards Optimized Fuzzy Method”, Arabian Journal for Science and Engineering, 2023, pp. 1-18. <br>[15] <br>[15] F. Karimi Ghaleh Jough, “The contribution of steel wallposts to out-of-plane behavior of non-structural masonry walls”, Earthquake Engineering and Engineering Vibration, 2023, pp. 1-20. <br>[16] M. Soori, B. Arezoo, and M. Habibi, “Accuracy analysis of tool deflection error modelling in prediction of milled surfaces by a virtual machining system”, International Journal of Computer Applications in Technology, 2017, 55 (4), pp. 308-321. <br>[17] M. Soori, B. Arezoo, and M. Habibi, “Virtual machining considering dimensional, geometrical and tool deflection errors in three-axis CNC milling machines”, Journal of Manufacturing Systems, 2014, 33 (4), pp. 498-507. <br>[18] M. Soori, B. Arezoo, and M. Habibi, “Dimensional and geometrical errors of three-axis CNC milling machines in a virtual machining system”, Computer-Aided Design, 2013, 45 (11), pp. 1306-1313. <br>[19] M. Soori, B. Arezoo, and M. Habibi, “Tool deflection error of three-axis computer numerical control milling machines, monitoring and minimizing by a virtual machining system”, Journal of Manufacturing Science and Engineering, 2016, 138 (8), pp. 081005. <br>[20] M. Soori, B. Arezoo, and R. Dastres, “Machine Learning and Artificial Intelligence in CNC Machine Tools, A Review”, Sustainable Manufacturing and Service Economics, 2023, pp. 100009. <br>[21] M. Soori, and B. Arezoo, “A Review in Machining-Induced Residual Stress”, Journal of New Technology and Materials, 2022, 12 (1), pp. 64-83. <br>[22] M. Soori, and B. Arezoo, “Minimization of Surface Roughness and Residual Stress in Grinding Operations of Inconel 718”, Journal of Materials Engineering and Performance, 2022, pp. 1-10. <br>[23] R. Dastres, and M. Soori, “Advances in web-based decision support systems”, International Journal of Engineering and Future Technology, 2021, 19 (1), pp. 1-15. <br>[24] R. Dastres, and M. Soori, “Artificial Neural Network Systems”, International Journal of Imaging and Robotics (IJIR), 2021, 21 (2), pp. 13-25. <br>[25] R. Dastres, and M. Soori, “The Role of Information and Communication Technology (ICT) in Environmental Protection”, International Journal of Tomography and Simulation, 2021, 35 (1), pp. 24-37. <br>[26] M. Soori, and B. Arezoo, “Dimensional, geometrical, thermal and tool deflection errors compensation in 5-Axis CNC milling operations”, Australian Journal of Mechanical Engineering, 2023, pp. 1-15. <br>[27] M. Soori, B. Arezoo, and R. Dastres, “Artificial Intelligence, Machine Learning and Deep Learning in Advanced Robotics, A Review”, Cognitive Robotics, 2023, 3, pp. 54-70. <br>[28] M. Soori, and B. Arezoo, “Effect of cutting parameters on tool life and cutting temperature in milling of AISI 1038 carbon steel”, Journal of New Technology and Materials, 2023, pp. <br>[29] M. Soori, and B. Arezoo, “The effects of coolant on the cutting temperature, surface roughness and tool wear in turning operations of Ti6Al4V alloy”, Mechanics Based Design of Structures and Machines, 2023, pp. 1-23. <br>[30] M. Soori, B. Arezoo, and R. Dastres, “Internet of things for smart factories in industry 4.0, a review”, Internet of Things and Cyber-Physical Systems, 2023. <br>[31] M. Soori, and B. Arezoo, “Cutting tool wear minimization in drilling operations of titanium alloy Ti-6Al-4V”, Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 2023, pp. 13506501231158259. <br>[32] M. Soori, and B. Arezoo, “Minimization of surface roughness and residual stress in abrasive water jet cutting of titanium alloy Ti6Al4V”, Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 2023, pp. 09544089231157972. <br>[33] M. Soori, “Deformation error compensation in 5-Axis milling operations of turbine blades”, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2023, 45 (6), pp. 289. <br>[34] M. Soori, and B. Arezoo, “Modification of CNC Machine Tool Operations and Structures Using Finite Element Methods, A Review”, Jordan Journal of Mechanical and Industrial Engineering, 2023. <br>[35] M. Soori, B. Arezoo, and R. Dastres, “Optimization of Energy Consumption in Industrial Robots, A Review”, Cognitive Robotics, 2023. <br>[36] M. Soori, B. Arezoo, and R. Dastres, “Virtual manufacturing in industry 4.0: A review”, Data Science and Management, 2023, pp. <br>[37] M. Soori, B. Arezoo, and R. Dastres, “Artificial Neural Networks in Supply Chain Management, A Review”, Journal of Economy and Technology, 2023. <br>[38] M. Liu, S.A. Burns, and Y. Wen, “Multiobjective optimization for performance‐based seismic design of steel moment frame structures”, Earthquake Engineering & Structural Dynamics, 2005, 34 (3), pp. 289-306. <br>[39] Y. Dai, K. Roy, Z. Fang, B. Chen, G.M. Raftery, and J.B. Lim, “A novel machine learning model to predict the moment capacity of cold-formed steel channel beams with edge-stiffened and un-stiffened web holes”, Journal of Building Engineering, 2022, 53, pp. 104592. <br>[40] H.T. Saraskanroud, and M. Babaei, “Connection Topology Optimization of Steel Moment Frames Using Genetic Algorithm”, Int. J. Optim. Civil Eng, 2023, 13 (4), pp. 533-561. <br>[41] T.P. Ribeiro, L.F. Bernardo, and J.M. Andrade, “Topology optimisation in structural steel design for additive manufacturing”, Applied Sciences, 2021, 11 (5), pp. 2112. <br>[42] O. Ibhadode, Z. Zhang, J. Sixt, K.M. Nsiempba, J. Orakwe, A. Martinez-Marchese, O. Ero, S.I. Shahabad, A. Bonakdar, and E. Toyserkani, “Topology optimization for metal additive manufacturing: current trends, challenges, and future outlook”, Virtual and Physical Prototyping, 2023, 18 (1), pp. e2181192. <br>[43] Y. Pang, Y. Sun, and J. Zhong, “Resilience-based performance and design of SMA/sliding bearing isolation system for highway bridges”, Bulletin of Earthquake Engineering, 2021, 19, pp. 6187-6211. <br>[44] M. Kociecki, and H. Adeli, “Two-phase genetic algorithm for topology optimization of free-form steel space-frame roof structures with complex curvatures”, Engineering Applications of Artificial Intelligence, 2014, 32, pp. 218-227. <br>[45] J. Rade, A. Balu, E. Herron, J. Pathak, R. Ranade, S. Sarkar, and A. Krishnamurthy, “Algorithmically-consistent deep learning frameworks for structural topology optimization”, Engineering Applications of Artificial Intelligence, 2021, 106, pp. 104483. <br>[46] J. Chen, Y. Wang, and X. Zhan, “Topology optimization of steel structure for waste incineration steam generator based on DE and PSO”, International Journal of Steel Structures, 2021, 21, pp. 1210-1227. <br>[47] C. Wang, J. Zhao, and T.-M. Chan, “Artificial intelligence (AI)-assisted simulation-driven earthquake-resistant design framework: Taking a strong back system as an example”, Engineering Structures, 2023, 297, pp. 116892. <br>[48] R.K. Tipu, V. Panchal, and K. Pandya, An ensemble approach to improve BPNN model precision for predicting compressive strength of high-performance concrete, in: Structures, Elsevier, 2022, pp. 500-508. <br>[49] J. Liu, J. Huang, Y. Zheng, S. Hou, S. Xu, Y. Ma, C. Huang, B. Zou, and L. Li, “Challenges in topology optimization for hybrid additive-subtractive manufacturing: A review”, Computer-Aided Design, 2023, pp. 103531. <br>[50] D. Lee, J. Lee, J. Kim, and U. Srarossek, “Investigation on material layouts of structural diagrid frames by using topology optimization”, KSCE Journal of Civil Engineering, 2014, 18, pp. 549-557. <br>[51] H. Afshari, W. Hare, and S. Tesfamariam, “Constrained multi-objective optimization algorithms: Review and comparison with application in reinforced concrete structures”, Applied Soft Computing, 2019, 83, pp. 105631. <br>[52] Y. Wang, W. Du, H. Wang, and Y. Zhao, “Intelligent generation method of innovative structures based on topology optimization and deep learning”, Materials, 2021, 14 (24), pp. 7680. <br>[53] A. Mikulikova, J. Mesicek, J. Karger, J. Hajnys, Q.-P. Ma, A. Sliva, J. Smiraus, D. Srnicek, S. Cienciala, and M. Pagac, “Topology Optimization of the Clutch Lever Manufactured by Additive Manufacturing”, Materials, 2023, 16 (9), pp. 3510. <br>[54] S. Shah, N.R. Sulong, and A. El-Shafie, “New approach for developing soft computational prediction models for moment and rotation of boltless steel connections”, Thin-Walled Structures, 2018, 133, pp. 206-215. <br>[55] N. Ezami, A. Özyüksel Çiftçioğlu, M. Mirrashid, and H. Naderpour, “Advancing Shear Capacity Estimation in Rectangular RC Beams: A Cutting-Edge Artificial Intelligence Approach for Assessing the Contribution of FRP”, Sustainability, 2023, 15 (22), pp. 16126. <br>[56] F. Ranalli, An Artificial Intelligence Framework for Multi-Disciplinary Design Optimization of Steel Buildings, Stanford University, 2021. <br>[57] B. Xue, and Z. Wu, “Key technologies of steel plate surface defect detection system based on artificial intelligence machine vision”, Wireless Communications and Mobile Computing, 2021, 2021, pp. 1-12. <br>[58] K. Ma, L. Xu, A.M. Abed, D.H. Elkamchouchi, M.A. Khadimallah, H.E. Ali, H. Algarni, and H. Assilzadeh, “An artificial intelligence approach study for assessing hydrogen energy materials for energy saving in building”, Sustainable Energy Technologies and Assessments, 2023, 56, pp. 103052. <br>[59] M. Azizi, and S. Talatahari, “Improved arithmetic optimization algorithm for design optimization of fuzzy controllers in steel building structures with nonlinear behavior considering near fault ground motion effects”, Artificial Intelligence Review, 2022, 55 (5), pp. 4041-4075. <br>[60] P.G. Asteris, and M. Nikoo, “Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures”, Neural Computing and Applications, 2019, 31 (9), pp. 4837-4847. <br>[61] M. Mangal, and J.C. Cheng, “Automated optimization of steel reinforcement in RC building frames using building information modeling and hybrid genetic algorithm”, Automation in Construction, 2018, 90, pp. 39-57. <br>[62] Y.F. Zhu, Y. Yao, Y. Huang, C.H. Chen, H.Y. Zhang, and Z. Huang, Machine learning applications for assessment of dynamic progressive collapse of steel moment frames, in: Structures, Elsevier, 2022, pp. 927-934. <br>[63] H.T. Nguyen, K.T. Nguyen, T.C. Le, and G. Zhang, “Review on the use of artificial intelligence to predict fire performance of construction materials and their flame retardancy”, molecules, 2021, 26 (4), pp. 1022. <br>[64] J.-R. Wu, and L. Di Sarno, “A machine-learning method for deriving state-dependent fragility curves of existing steel moment frames with masonry infills”, Engineering Structures, 2023, 276, pp. 115345. <br>[65] <br>[65] N. Asgarkhani, F. Kazemi, and R. Jankowski, “Machine learning-based prediction of residual drift and seismic risk assessment of steel moment-resisting frames considering soil-structure interaction”, Computers & Structures, 2023, 289, pp. 107181. <br>[66] B. Güler, Ö. Şengör, O. Yavuz, and F. Öztürk, “Prediction of Self-Loosening Mechanism and Behavior of Bolted Joints on Automotive Chassis Using Artificial Intelligence”, Machines, 2023, 11 (9), pp. 895. <br>[67] A.T.G. Tapeh, and M. Naser, “Artificial intelligence, machine learning, and deep learning in structural engineering: a scientometrics review of trends and best practices”, Archives of Computational Methods in Engineering, 2023, 30 (1), pp. 115-159. <br>[68] M. Jazbi, A. Aghazadeh, and S. Mirvalad, “A Comprehensive Review on the Artificial Intelligence (AI) Approaches Used for Examining the Mechanical Properties of Concrete Incorporating Various Materials”, Iran University of Science & Technology, 2023, 13 (1), pp. 93-110. <br>[69] B. Üstüner, and E. Doğan, “Structure Optimization with Metaheuristic Algorithms and Analysis by Finite Element Method”, KSCE Journal of Civil Engineering, 2023, pp. 1-14. <br>[70] J. Li, G. Yan, L.H. Abbud, T. Alkhalifah, F. Alturise, M.A. Khadimallah, and R. Marzouki, “Predicting the shear strength of concrete beam through ANFIS-GA–PSO hybrid modeling”, Advances in Engineering Software, 2023, 181, pp. 103475. <br>[71] S.A.M. Hejazi, A. Feyzpour, A. Eslami, M. Fouladgar, S.A. Eftekhari, and D. Toghraie, “Numerical investigation of rigidity and flexibility parameters effect on superstructure foundation behavior using three-dimensional finite element method”, Case Studies in Construction Materials, 2023, 18, pp. e01867. <br>[72] T. Ali, M.N. Eldin, and W. Haider, “The Effect of Soil-Structure Interaction on the Seismic Response of Structures Using Machine Learning, Finite Element Modeling and ASCE 7-16 Methods”, Sensors, 2023, 23 (4), pp. 2047. <br>[73] R. Falcone, A. Ciaramella, F. Carrabs, N. Strisciuglio, and E. Martinelli, Artificial neural network for technical feasibility prediction of seismic retrofitting in existing RC structures, in: Structures, Elsevier, 2022, pp. 1220-1234. <br>[74] S.V. Subramanian, and N. Umamaheswari, “Finite element analysis of cold-formed steel stud wall subjected to blast load and validated using artificial neural network combined with response surface method”, Asian Journal of Civil Engineering, 2023, pp. 1-20. <br>[75] C.Y.G. Satriawan, A.R. Prabowo, T. Muttaqie, R. Ridwan, N. Muhayat, H. Carvalho, and F. Imaduddin, “Assessment of the beam configuration effects on designed beam–column connection structures using FE methodology based on experimental benchmarking”, Journal of the Mechanical Behavior of Materials, 2023, 32 (1), pp. 20220284. <br>[76] S. Lee, T. Kim, Q.X. Lieu, T.P. Vo, and J. Lee, “A novel data-driven analysis for sequentially formulated plastic hinges of steel frames”, Computers & Structures, 2023, 281, pp. 107031. <br>[77] M. Naresh, S. Sikdar, and J. Pal, “Vibration data‐driven machine learning architecture for structural health monitoring of steel frame structures”, Strain, 2023, pp. e12439. <br>[78] X. Guan, Performance-Based Analytics-Driven Seismic Design of Steel Moment Frame Buildings, University of California, Los Angeles, 2021. <br>[79] T.T. Truong, J. Lee, and T. Nguyen-Thoi, “A data-driven based method for damage detection of combining joints and elements of frame structures using noisy incomplete data”, Engineering Applications of Artificial Intelligence, 2023, 126, pp. 107160. <br>[80] A. Malá, Z. Padovec, T. Mareš, and N. Chakraborti, “A method for designing filament-wound composite frame structures using a data-driven evolutionary optimisation algorithm EvoDN2”, Philosophical Magazine Letters, 2023, 103 (1), pp. 2272975. <br>[81] F. Di Trapani, A.P. Sberna, and G.C. Marano, “A new genetic algorithm-based framework for optimized design of steel-jacketing retrofitting in shear-critical and ductility-critical RC frame structures”, Engineering Structures, 2021, 243, pp. 112684. <br>[82] S.K. Singh, A.T. Sose, F. Wang, K.K. Bejagam, and S.A. Deshmukh, “Data Driven Discovery of MOFs for Hydrogen Gas Adsorption”, Journal of Chemical Theory and Computation, 2023, 19 (19), pp. 6686-6703. <br>[83] M.S. Barkhordari, and M. Tehranizadeh, “Data-driven Dynamic-classifiers-based Seismic Failure Mode Detection of Deep Steel W-shape Columns”, Periodica Polytechnica Civil Engineering, 2023, 67 (3), pp. 936-944. <br>[84] H. Sun, H.V. Burton, and H. Huang, “Machine learning applications for building structural design and performance assessment: State-of-the-art review”, Journal of Building Engineering, 2021, 33, pp. 101816. <br>[85] L. Regenwetter, Data-driven bicycle design using performance-aware deep generative models, in, Massachusetts Institute of Technology, 2022. <br>[86] I. Omar, M. Khan, and A. Starr, “Compatibility and challenges in machine learning approach for structural crack assessment”, Structural Health Monitoring, 2022, 21 (5), pp. 2481-2502. <br>[87] L. Rampini, and F.R. Cecconi, “Artificial intelligence in construction asset management: A review of present status, challenges and future opportunities”, Journal of Information Technology in Construction, 2022, 27, pp. 884-913. <br>[88] X. Wang, R.K. Mazumder, B. Salarieh, A.M. Salman, A. Shafieezadeh, and Y. Li, “Machine learning for risk and resilience assessment in structural engineering: Progress and future trends”, Journal of Structural Engineering, 2022, 148 (8), pp. 03122003. <br>[89] S.M. Harle, “Advancements and challenges in the application of artificial intelligence in civil engineering: a comprehensive review”, Asian Journal of Civil Engineering, 2023, pp. 1-18. <br>[90] H. Liang, K. Roy, Z. Fang, and J.B. Lim, “A critical review on optimization of cold-formed steel members for better structural and thermal performances”, Buildings, 2022, 12 (1), pp. 34. <br>[91] G.S. Gawande, and L.M. Gupta, “Rotation Capacity Prediction of Open Web Steel Beams Using Artificial Neural Networks”, International Journal of Steel Structures, 2023, pp. 1-14. <br>[92] M.G. Azandariani, A.G. Azandariani, A.M. Rousta, and H.M. Nia, “Seismic fragility assessment of reinforced concrete moment frames retrofitted with strongback braced system”, Results in Engineering, 2023, 20, pp. 101504. <br>[93] M. Esfandiari, H. Haghighi, and G. Urgessa, “Machine learning-based optimum reinforced concrete design for progressive collapse”, Electronic Journal of Structural Engineering, 2023, 23 (2), pp. 1-8. <br>[94] S. Hu, and X. Lei, “Machine learning and genetic algorithm-based framework for the life-cycle cost-based optimal design of self-centering building structures”, Journal of Building Engineering, 2023, 78, pp. 107671. <br>[95] F. Freddi, C. Galasso, G. Cremen, A. Dall’Asta, L. Di Sarno, A. Giaralis, F. Gutiérrez-Urzúa, C. Málaga-Chuquitaype, S.A. Mitoulis, and C. Petrone, “Innovations in earthquake risk reduction for resilience: Recent advances and challenges”, International Journal of Disaster Risk Reduction, 2021, 60, pp. 102267. <br>[96] S.K. Rahman, and R. Al-Ameri, “Structural assessment of Basalt FRP reinforced self-compacting geopolymer concrete using artificial neural network (ANN) modelling”, Construction and Building Materials, 2023, 397, pp. 132464. <br>[97] G. Cere, Y. Rezgui, W. Zhao, and I. Petri, “Shear walls optimization in a reinforced concrete framed building for seismic risk reduction”, Journal of Building Engineering, 2022, 54, pp. 104620. <br>[98] S.-Y. Lee, S.-Y. Noh, and D. Lee, “Evaluation of progressive collapse resistance of steel moment frames designed with different connection details using energy-based approximate analysis”, Sustainability, 2018, 10 (10), pp. 3797. <br>[99] K. Cao, Y. Cui, L. Li, J. Zhou, and S. Hu, “CPU-GPU cooperative QoS optimization of personalized digital healthcare using machine learning and swarm intelligence”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022, pp. <br>[100] A. Mortazavi, “Bayesian interactive search algorithm: a new probabilistic swarm intelligence tested on mathematical and structural optimization problems”, Advances in Engineering Software, 2021, 155, pp. 102994. <br>[101] M. Noureldin, A. Ali, S. Memon, and J. Kim, “Fragility-based framework for optimal damper placement in low-rise moment-frame buildings using machine learning and genetic algorithm”, Journal of Building Engineering, 2022, 54, pp. 104641. <br>[102] V. Toğan, “Design of planar steel frames using teaching–learning based optimization”, Engineering Structures, 2012, 34, pp. 225-232. <br>[103] C.-J. Liang, T.-H. Le, Y. Ham, B.R. Mantha, M.H. Cheng, and J.J. Lin, “Ethics of Artificial Intelligence and Robotics in the Architecture, Engineering, and Construction Industry”, arXiv preprint arXiv:2310.05414, 2023, pp. <br>[104] C. Li, Q. Chen, H. Huang, and Q. Zeng, Building Structure Optimization Based on Computer Big Data, in: International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Springer, 2022, pp. 629-636. <br>[105] L. Sun, Z. Shang, Y. Xia, S. Bhowmick, and S. Nagarajaiah, “Review of bridge structural health monitoring aided by big data and artificial intelligence: From condition assessment to damage detection”, Journal of Structural Engineering, 2020, 146 (5), pp. 04020073. <br>[106] T.J. Ma, Artificial Intelligence technology in a portal frame structure measuring, in, ResearchSpace@ Auckland, 2022. <br>[107] <br>[107] T.R. van Woudenberg, and F.P. van Der Meer, “A grouping method for optimization of steel skeletal structures by applying a combinatorial search algorithm based on a fully stressed design”, Engineering Structures, 2021, 249, pp. 113299. <br>[108] J.M. Górriz, J. Ramírez, A. Ortíz, F.J. Martinez-Murcia, F. Segovia, J. Suckling, M. Leming, Y.-D. Zhang, J.R. Álvarez-Sánchez, and G. Bologna, “Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications”, Neurocomputing, 2020, 410, pp. 237-270. <br>[109] J. Pal, S. Sikdar, S. Banerjee, and P. Banerji, “A Combined Machine Learning and Model Updating Method for Autonomous Monitoring of Bolted Connections in Steel Frame Structures Using Vibration Data”, Applied Sciences, 2022, 12 (21), pp. 11107. <br>[110] P. Limin Sun, A. ASCE, Z. Shang, P. Ye Xia, S. Bhowmick, S. Nagarajaiah, and F. ASCE, “Review of bridge structural health monitoring aided by big data and artificial intelligence: from condition assessment to damage detection”, J. Struct. Eng, 2020, 146 (5), pp. 04020073. <br>[111] A.A.A. Ahmed, A. Mahalakshmi, K. ArulRajan, J. Alanya-Beltran, and M. Naved, “Integrated artificial intelligence effect on crisis management and lean production: structural equation modelling frame work”, International Journal of System Assurance Engineering and Management, 2023, 14 (1), pp. 220-227. <br>[112] A. Ibrahim, A. Eltawil, Y. Na, and S. El-Tawil, “A machine learning approach for structural health monitoring using noisy data sets”, IEEE Transactions on Automation Science and Engineering, 2019, 17 (2), pp. 900-908. <br>[113] P. Jiao, “Emerging artificial intelligence in piezoelectric and triboelectric nanogenerators”, Nano Energy, 2021, 88, pp. 106227. <br>[114] Y. Zhang, S. Balochian, P. Agarwal, V. Bhatnagar, and O.J. Housheya, Artificial intelligence and its applications, in, Hindawi, 2014. <br>[115] O. Bezsonov, O. Ilyunin, B. Kaldybaeva, O. Selyakov, O. Perevertaylenko, A. Khusanov, O. Rudenko, S. Udovenko, A. Shamraev, and V. Zorenko, “Resource and energy saving neural network-based control approach for continuous carbon steel pickling process”, Journal of Sustainable Development of Energy, Water and Environment Systems, 2019, 7 (2), pp. 275-292. <br>[116] R. Hercik, and R. Svoboda, “Collecting and Pre-Processing Data for Industry 4.0 Implementation Using Hydraulic Press”, Data, 2023, 8 (4), pp. 72. <br>[117] A. Sivasuriyan, D.S. Vijayan, W. Górski, Ł. Wodzyński, M.D. Vaverková, and E. Koda, “Practical implementation of structural health monitoring in multi-story buildings”, Buildings, 2021, 11 (6), pp. 263. <br>[118] J. De Anda, S.E. Ruiz, E. Bojórquez, and I. Inzunza-Aragon, “Towards optimal reliability-based design of wind turbines towers using artificial intelligence”, Engineering Structures, 2023, 294, pp. 116778. <br>[119] J. Kang, W. Dong, and Y. Huang, “A Bim-Based Automatic Design Optimization Method for Modular Steel Structures: Rectangular Modules as an Example”, Buildings, 2023, 13 (6), pp. 1410. <br>[120] F. Noori, and H. Varaee, “Nonlinear Seismic Response Approximation of Steel Moment Frames Using Artificial Neural Networks”, Jordan Journal of Civil Engineering, 2022, 16 (1), pp. <br>[121] Z. Wang, A.F. Leong, A. Dragone, A.E. Gleason, R. Ballabriga, C. Campbell, M. Campbell, S.J. Clark, C. Da Vià, and D.M. Dattelbaum, “Ultrafast radiographic imaging and tracking: An overview of instruments, methods, data, and applications”, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2023, pp. 168690. <br>[122] D. Merayo, A. Rodríguez-Prieto, and A.M. Camacho, “Topological optimization of artificial neural networks to estimate mechanical properties in metal forming using machine learning”, Metals, 2021, 11 (8), pp. 1289. <br>[123] T. Zhou, K. Sun, Z. Chen, Z. Yang, and H. Liu, “Automated Optimum Design of Light Steel Frame Structures in Chinese Rural Areas Using Building Information Modeling and Simulated Annealing Algorithm”, Sustainability, 2023, 15 (11), pp. 9000. <br>[124] M. Noureldin, T. Ali, and J. Kim, “Machine learning-based seismic assessment of framed structures with soil-structure interaction”, Frontiers of Structural and Civil Engineering, 2023, pp. 1-19. <br>[125] S. Himmetoğlu, Y. Delice, and E.K. Aydoğan, “PSACONN mining algorithm for multi-factor thermal energy-efficient public building design”, Journal of Building Engineering, 2021, 34, pp. 102020. <br>[126] A. Sadeghi, H. Kazemi, and M. Samadi, “Reliability and reliability-based sensitivity analyses of steel moment-resisting frame structure subjected to extreme actions”, Frattura ed Integrità Strutturale, 2021, 15 (57), pp. 138-159. <br>[127] C. Anton, F. Leon, M. Gavrilescu, E.-N. Drăgoi, S.-A. Floria, S. Curteanu, and C. Lisa, “Obtaining Bricks Using Silicon-Based Materials: Experiments, Modeling and Optimization with Artificial Intelligence Tools”, Mathematics, 2022, 10 (11), pp. 1891. <br>[128] Z. Shao, Y. Li, P. Huang, A.M. Abed, E. Ali, D.H. Elkamchouchi, M. Abbas, and G. Zhang, “Analysis of the opportunities and costs of energy saving in lightning system of library buildings with the aid of building information modelling and Internet of things”, Fuel, 2023, 352, pp. 128918. <br>[129] R. Binali, A.D. Patange, M. Kuntoğlu, T. Mikolajczyk, and E. Salur, “Energy Saving by Parametric Optimization and Advanced Lubri-Cooling Techniques in the Machining of Composites and Superalloys: A Systematic Review”, Energies, 2022, 15 (21), pp. 8313. <br>[130] I. Inzunza-Aragón, S.E. Ruiz, and L. Cruz-Reyes, “Use of artificial neural networks and response surface methodology for evaluating the reliability index of steel wind towers”, Advances in Civil Engineering, 2022, 2022, pp. <br>[131] S. Ronghui, and N. Liangrong, “An intelligent fuzzy-based hybrid metaheuristic algorithm for analysis the strength, energy and cost optimization of building material in construction management”, Engineering with Computers, 2022, 38 (Suppl 4), pp. 2663-2680. <br>[132] S. Steffen, A. Zeller, M. Böhm, O. Sawodny, L. Blandini, and W. Sobek, “Actuation concepts for adaptive high-rise structures subjected to static wind loading”, Engineering Structures, 2022, 267, pp. 114670. <br>[133] T. Ahmad, D. Zhang, C. Huang, H. Zhang, N. Dai, Y. Song, and H. Chen, “Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities”, Journal of Cleaner Production, 2021, 289, pp. 125834. <br>[134] L.M. Le, H.-B. Ly, B.T. Pham, V.M. Le, T.A. Pham, D.-H. Nguyen, X.-T. Tran, and T.-T. Le, “Hybrid artificial intelligence approaches for predicting buckling damage of steel columns under axial compression”, Materials, 2019, 12 (10), pp. 1670. <br>[135] R. Assaad, and I.H. El-adaway, “Bridge infrastructure asset management system: Comparative computational machine learning approach for evaluating and predicting deck deterioration conditions”, Journal of Infrastructure Systems, 2020, 26 (3), pp. 04020032. <br>[136] H. Salehi, and R. Burgueño, “Emerging artificial intelligence methods in structural engineering”, Engineering structures, 2018, 171, pp. 170-189. <br>[137] A. Gaikwad, B. Giera, G.M. Guss, J.-B. Forien, M.J. Matthews, and P. Rao, “Heterogeneous sensing and scientific machine learning for quality assurance in laser powder bed fusion–A single-track study”, Additive Manufacturing, 2020, 36, pp. 101659. <br>[138] L.T. Suleiman, K. Bala, and A.A. Abdullahi, “Applications of Artificial Intelligence Techniques in Metal Casting-A Review”, 2020, pp. <br>[139] A.R. Kashani, C.V. Camp, M. Rostamian, K. Azizi, and A.H. Gandomi, “Population-based optimization in structural engineering: a review”, Artificial Intelligence Review, 2022, pp. 1-108. <br>[140] A. Rahgozar, H.E. Estekanchi, and S.A. Mirfarhadi, “On optimal triple friction pendulum base-isolation design for steel moment-frame buildings employing value-based seismic design methodology”, Journal of Building Engineering, 2023, 63, pp. 105494. <br>[141] P. Kourehpaz, and C. Molina Hutt, “Machine learning for enhanced regional seismic risk assessments”, Journal of Structural Engineering, 2022, 148 (9), pp. 04022126. <br>[142] Q. Wang, Q. Li, D. Wu, Y. Yu, F. Tin-Loi, J. Ma, and W. Gao, “Machine learning aided static structural reliability analysis for functionally graded frame structures”, Applied Mathematical Modelling, 2020, 78, pp. 792-815. <br>[143] X.-Y. Cao, D. Shen, D.-C. Feng, C.-L. Wang, Z. Qu, and G. Wu, “Seismic retrofitting of existing frame buildings through externally attached sub-structures: State of the art review and future perspectives”, Journal of Building Engineering, 2022, 57, pp. 104904. <br>[144] Y. Zhang, and H. Mo, “Intelligent Building Construction Cost Prediction Based on BIM and Elman Neural Network”, 2023, pp. <br>[145] J. Ye, J. Becque, I. Hajirasouliha, S.M. Mojtabaei, and J.B. Lim, “Development of optimum cold-formed steel sections for maximum energy dissipation in uniaxial bending”, Engineering structures, 2018, 161, pp. 55-67. <br>[146] A.A. Chojaczyk, A.P. Teixeira, L.C. Neves, J.B. Cardoso, and C.G. Soares, “Review and application of Artificial Neural Networks models in reliability analysis of steel structures”, Structural safety, 2015, 52, pp. 78-89. <br>[147] M. Parvizi, K. Nasserasadi, and E. Tafakori, Development of fragility functions of low-rise steel moment frame by artificial neural networks and identifying effective parameters using SHAP theory, in: Structures, Elsevier, 2023, pp. 105315. <br>[148] M.H. Lavaei, E.M. Dehcheshmeh, P. Safari, V. Broujerdian, and A.H. Gandomi, “Reliability-based design optimization of post-tensioned self-centering rocking steel frame structures”, Journal of Building Engineering, 2023, pp. 106955. <br>[149] I. Negrin, M. Kripka, and V. Yepes, “Metamodel-assisted meta-heuristic design optimization of reinforced concrete frame structures considering soil-structure interaction”, Engineering Structures, 2023, 293, pp. 116657. <br>[150] L. Stefanini, L. Badini, G. Mochi, G. Predari, and A. Ferrante, “Neural networks for the rapid seismic assessment of existing moment-frame RC buildings”, International Journal of Disaster Risk Reduction, 2022, 67, pp. 102677. <br>[151] M. Naser, “An engineer's guide to eXplainable Artificial Intelligence and Interpretable Machine Learning: Navigating causality, forced goodness, and the false perception of inference”, Automation in Construction, 2021, 129, pp. 103821. <br>[152] Q. Tang, J. Dang, Y. Cui, X. Wang, and J. Jia, “Machine learning-based fast seismic risk assessment of building structures”, Journal of Earthquake Engineering, 2022, 26 (15), pp. 8041-8062. <br>[153] L.-C. Lien, and U. Dolgorsuren, BIM-based steel reinforcing bar detail construction design and picking optimization, in: Structures, Elsevier, 2023, pp. 520-536. <br>[154] C.M. Renedo, I.M. Díaz, J.H. García-Palacios, and C. Gallegos-Calderón, Structural Optimization of Lightweight Composite Floors with Integrated Constrained Layer Damping for Vibration Control, in: Actuators, MDPI, 2023, pp. 288. <br>[155] S.T. Nguyen, Design and Development of Climbing Robotic Systems for Automated Inspection of Steel Structures and Bridges, in, University of Nevada, Reno, 2023. <br>[156] T. Bakhshpoori, A.A. Abadi, A. Cheraghi, and M. Farhadmanesh, “Performance-Based Seismic Design Optimization of Steel MRFs Under System and Component Constraints Using the IWSA Algorithm”, Iranian Journal of Science and Technology, Transactions of Civil Engineering, 2023, 47 (2), pp. 987-1006. <br>[157] W. Kong, Y. Huang, Z. Guo, X. Zhang, and Y. Chen, “Experimental study on square hollow stainless steel tube trusses with three joint types and different brace widths under vertical loads”, Reviews on Advanced Materials Science, 2021, 60 (1), pp. 519-540. </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>