Machine Learning-based Optimum Reinforced Concrete Design for Progressive Collapse

MohammadJavad  Esfandiari; Homa Haghighi; Girum  Urgessa

doi:10.56748/ejse.233642

Authors

MohammadJavad Esfandiari PhD student at George Mason University
Homa Haghighi PhD student at George Mason University https://orcid.org/0000-0003-4622-2541
Girum Urgessa Associate Professor at George Mason University https://orcid.org/0000-0001-8051-7197

DOI:

https://doi.org/10.56748/ejse.233642

Keywords:

Machine Learning, Structural Optimization, Progressive Collapse, Reinforced Concrete, Artificial Intelligence

Abstract

This paper investigated progressive collapse analysis of three-dimensional (3D) reinforced concrete (RC) frames that are optimized for carrying structural loads by introducing a unique simultaneous multi-column removal load path using Machine Learning. The investigation includes formulating an integrated computational framework that incorporates a self-training machine learning algorithm. This algorithm is used to train the largest machine learning models of 3D RC frames containing more than 600 optimized structures to predict the posterior based on the trained priors. The efficiency of the computational framework was shown by conducting a comprehensive study on the optimization and behavior of structures considering seismic loading, alternative load path due to progressive collapse, and second order (P–delta) effects. The results show that the proposed framework ensures that system solutions meet both structural integrity and constructability requirements of the ACI and the Unified Facilities Criteria.

Downloads

Download data is not yet available.

References

Alam MN, Das B and Pant V. (2015) A comparative study of metaheuristic optimization approaches for directional overcurrent relays coordination. Electric Power Systems Research 128: 39-52. DOI: https://doi.org/10.1016/j.epsr.2015.06.018

Bao Y, Kunnath SK, El-Tawil S, et al. (2008) Macromodel-Based Simulation of Progressive Collapse: RC Frame Structures. Journal of Structural Engineering 134: 1079-1091. DOI: https://doi.org/10.1061/(ASCE)0733-9445(2008)134:7(1079)

Bui D-K, Nguyen T, Chou J-S, et al. (2018) A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete. Construction and Building Materials 180: 320-333. DOI: https://doi.org/10.1016/j.conbuildmat.2018.05.201

Buscemi N and Marjanishvili S. (2005) SDOF Model for Progressive Collapse Analysis. Structures Congress 2005. 1-12. DOI: https://doi.org/10.1061/40753(171)221

Byfield M, Mudalige W, Morison C, et al. (2014) A review of progressive collapse research and regulations. Proceedings of the Institution of Civil Engineers - Structures and Buildings 167: 447-456. DOI: https://doi.org/10.1680/stbu.12.00023

Defense Do. (2005) Unified Facilities Criteria: Design of Buildings to Resist Progressive Collapse (UFC 4-023-03). Department of Defense Washington, DC, USA.

Duan K, Keerthi SS, Chu W, et al. (2003) Multi-category Classification by Soft-Max Combination of Binary Classifiers, Berlin, Heidelberg: Springer Berlin Heidelberg. DOI: https://doi.org/10.1007/3-540-44938-8_13

Eberhart RC and Shi Y. (2000) Comparing inertia weights and constriction factors in particle swarm optimization. Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512). 84-88 DOI: https://doi.org/10.1109/CEC.2000.870279

Esfandiari M and Urgessa G. (2018) A Pull-down Dynamic Analysis of Two-Span Steel Frames Subjected to Progressive Collapse. DOI: https://doi.org/10.31224/osf.io/75muh

Esfandiari MJ, Urgessa GS, Sheikholarefin S, et al. (2018a) Optimization of reinforced concrete frames subjected to historical time-history loadings using DMPSO algorithm. Structural and Multidisciplinary Optimization 58: 2119-2134. DOI: https://doi.org/10.1007/s00158-018-2027-y

Esfandiari MJ, Urgessa GS, Sheikholarefin S, et al. (2018b) Optimum design of 3D reinforced concrete frames using DMPSO algorithm. Advances in Engineering Software 115: 149-160. DOI: https://doi.org/10.1016/j.advengsoft.2017.09.007

Gsa U. (2003) Progressive collapse analysis and design guidelines for new federal office buildings and major modernization projects. Washington, DC.

Jeon J-S, Shafieezadeh A and DesRoches R. (2014) Statistical models for shear strength of RC beam-column joints using machine-learning techniques. Earthquake Engineering & Structural Dynamics 43: 2075-2095. DOI: https://doi.org/10.1002/eqe.2437

Kennedy J and Eberhart R. (1995) Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks. Perth, WA, Australia: IEEE, 1942-1948 DOI: https://doi.org/10.1109/ICNN.1995.488968

Marchand KA and Stevens DJ. (2015) Progressive Collapse Criteria and Design Approaches Improvement. Journal of Performance of Constructed Facilities 29: B4015004. DOI: https://doi.org/10.1061/(ASCE)CF.1943-5509.0000706

Mosavi A and Varkonyi-Koczy AR. (2017) Integration of Machine Learning and Optimization for Robot Learning. Advances in Intelligent Systems and Computing Recent Global Research and Education: Technological Challenges, 519: 349-355. DOI: https://doi.org/10.1007/978-3-319-46490-9_47

Ni H-G and Wang J-Z. (2000) Prediction of compressive strength of concrete by neural networks. Cement and Concrete Research 30: 1245-1250. DOI: https://doi.org/10.1016/S0008-8846(00)00345-8

Nick W, Asamene K, Bullock G, et al. (2015) A study of machine learning techniques for detecting and classifying structural damage. International Journal of Machine Learning and Computing 5: 313. DOI: https://doi.org/10.7763/IJMLC.2015.V5.526

Paya I, Yepes V, González-Vidosa F, et al. (2008) Multiobjective Optimization of Concrete Frames by Simulated Annealing. Computer-Aided Civil and Infrastructure Engineering 23: 596-610. DOI: https://doi.org/10.1111/j.1467-8667.2008.00561.x

Qian K and Li B. (2015) Research Advances in Design of Structures to Resist Progressive Collapse. Journal of Performance of Constructed Facilities 29: B4014007. DOI: https://doi.org/10.1061/(ASCE)CF.1943-5509.0000698

Randall W. Poston and Basile G. Rabbat. (2011) Building Code Requirements for Structural Concrete (ACI 318-11): American Concrete Institute.

Safavian SR and Landgrebe D. (1991) A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics 21: 660-674. DOI: https://doi.org/10.1109/21.97458

Sra S, Nowozin S and Wright SJ. (2012) Optimization for machine learning, Cambridge, Massachusetts, London, England: Mit Press. DOI: https://doi.org/10.7551/mitpress/8996.001.0001

Tahmouresi, A., Robati, A. et al. (2021) A Combined Genetic Algorithm-Artificial Neural Network Optimization Method for Mix Design of Self Consolidating Concrete, International Journal of Structural and Civil Engineering Research, Vol. 10, No. 3, doi: 10.18178/ijscer.10.3.106-112. DOI: https://doi.org/10.18178/ijscer.10.3.106-112

Talaat M and Mosalam KM. (2007) Towards Modeling Progressive Collapse in Reinforced Concrete Buildings. Research Frontiers at Structures Congress Long Beach, California, United States: American Society of Civil Engineers, 1-16. DOI: https://doi.org/10.1061/40944(249)14

Wang H, Zhang A, Li Y, et al. (2014) A review on progressive collapse of building structures. The Open Civil Engineering Journal 8: 183-192. DOI: https://doi.org/10.2174/1874149501408010183

Zhu J-Y, Park T, Isola P, et al. (2017) Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. (Accessed March 01, 2017). DOI: https://doi.org/10.1109/ICCV.2017.244