Applications of Artificial Intelligence in Structural Engineering: A Review
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Abstract
Artificial intelligence (AI) is a computational approach that aims to mimic human-like thinking/cognitive abilities to tackle complicated engineering issues. AI is appropriate for engineering contexts with a large set of inputs. AI is a feasible alternative to traditional modelling and statistical techniques. Experimentation is a herculean task in the domain of structural engineering, so AI-based techniques are viable alternatives for the prediction of various engineering design parameters, such as structural response, compressive strength, etc. The goal of this research is to outline numerous applications of artificial intelligence in structural engineering that have emerged in recent years. Initially, a broad introduction to AI is provided, followed by a discussion of the relevance of AI in the field of structural engineering. Thereafter, a review of recent applications of AI techniques such as deep learning (DL), pattern recognition (PR), and machine learning (ML) in structural engineering is presented, and the ability of such techniques to meet the constraints of conventional models is explored. Furthermore, the benefits of adopting such algorithmic approaches are thoroughly addressed. Finally, future research areas and latest innovations by using deep learning, pattern recognition, and machine learning are given, along with their shortcomings.
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M. Z. Naser, V. Kodur, H. Thai, R. Hawileh, J. Abdalla, V. V. Degtyarev “Benchmarking databases and machine learning algorithms in structural and fire engineering domains”, Journal of Building Engineering, Volume 44 (2021) 102977. https://doi.org/10.1016/j.jobe.2021.102977
Y. An, H. Li, T. Su & Y. Wang, “Determining Uncertainties in AI Applications in AEC Sector and their Corresponding Mitigation Strategies”, Automation in Construction, Volume 131 (2021) 103883. https://doi.org/10.1016/j.autcon.2021.103883
M. Z. Naser, “AI–based cognitive framework for evaluating response of concrete structures in extreme conditions”, Engineering Applications of Artificial Intelligence, Volume 81 (2019), Pages 437- 449. https://doi.org/10.1016/j.engappai.2019.03.004
P. Geyer & S. Singaravel, "Component-based machine learning for performance prediction in building design," Applied Energy Elsevier, vol. 228 (2018), pages 1439-1453. https://doi.org/10.1016/j.apenergy.2018.07.011
A. Pham, N. Ngo, and T. Nguyen, “Machine learning for predicting long-term deflections in reinforced concrete flexural structures”, Journal of Computational Design and Engineering, (2020) vol. 7(1) pages 95–106. http://dx.doi.org/10.1093/jcde/qwaa010
D. Thaler, M. Stoffel, B. Markert, F. Bamer, “Machine-learning-enhanced tail end prediction of structural response statistics in earthquake engineering”, Wiley Earthquake Engg. & Structural Dynamics (2021) pages 1–17. https://doi.org/10.1002/eqe.3432
M. K. Almustafa and M. L. Nehdi, “Machine learning model for predicting structural response of RC slabs exposed to blast loading”, Elsevier Engineering Structures, 221 (2020) 111109. https://doi.org/10.1016/j.engstruct.2020.111109
J. Won and J. Shin, “Machine Learning-Based Approach for Seismic Damage Prediction Method of Building Structures Considering Soil-Structure Interaction”, Sustainability Multidisciplinary Digital Publishing Institute (MDPI), (2021) vol. 13 4334. https://doi.org/10.3390/su13084334
M. K. Almustafa and M.L. Nehdi, “Machine learning prediction of structural response for FRP retrofitted RC slabs subjected to blast loading”, Elsevier Engineering Structures, 244 (2020) 112752. https://doi.org/10.1016/j.engstruct.2021.112752
D. Birky, J. Ladd, I. Guardiola and A. Young, “Predicting the dynamic response of a structure using an artificial neural network”, Journal of Low Frequency Noise, Vibration and Active Control (2021), Vol. 0(0) pages 1–14. http://dx.doi.org/10.1177/14613484211038408
D. Feng et.al, “Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach”, Construction and Building Materials, (2020) Volume 230 117000. https://doi.org/10.1016/j.conbuildmat.2019.117000
C. J. Lin and N. J. Wu, “An ANN Model for Predicting the Compressive Strength of Concrete”, Applied Sciences (2021), vol. 11, 3798. https://doi.org/10.3390/app11093798
G. Du, Bu L, Hou Q, Zhou J, Lu B, “Prediction of the compressive strength of high-performance self-compacting concrete by an ultrasonic-rebound method based on a GA-BP neural network”, PLoS ONE 16(5): e0250795. https://doi.org/10.1371/journal.pone.0250795
K. Jadhav, E. Harirchian, V. Kumari & T. Lahmer, “ML-EHSAPP: A prototype for machine learning- based earthquake hazard safety assessment of structures by using a smartphone app”, European Journal of Environmental and Civil Engineering, (2021). http://dx.doi.org/10.1080/19648189.2021.1892829
B. Sun, Y. Zhang and C. Huang, “Machine Learning-Based Seismic Fragility Analysis of Large-Scale Steel Buckling Restrained Brace Frames”, Computer Modelling in Engineering & Sciences Tech Sci. Press, (2020) vol.125. https://doi.org/10.32604/cmes.2020.09632
R. Segura, J. E. Padgett, and P. Paultre, “Metamodel-Based Seismic Fragility Analysis of Concrete Gravity Dams”, J. Struct. Engg. (ASCE) 2020, 146(7): 04020121. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002629
C. Long & Nguyen-Xuan H. (2020). “Deep learning for computational structural optimisation.” ISA Transactions, 103. https://doi.org/10.1016/j.isatra.2020.03.033
Melchiorre J., Bertetto A. and Carlo Marano G. (2021), “Application of a Machine Learning Algorithm for the Structural Optimisation of Circular Arches with Different Cross-Sections”, Journal of Applied Mathematics and Physics, 9, 1159-1170. http://dx.doi.org/10.4236/jamp.2021.95079
Y. Hooda, P. Kuhar, K. Sharma and N. K. Verma, “Emerging Applications of Artificial Intelligence in Structural Engineering and Construction Industry”, IOP Publishing, Journal of Physics: Conference Series, 1950 (2021) 012062. https://doi.org/10.1088/1742-6596/1950/1/012062
Y. Bao, Li H., “Machine learning paradigm for structural health monitoring. Structural Health Monitoring”, (2021) SAGE Publications, 20(4):1353-1372. https://doi.org/10.1177/1475921720972416
M. Mishra, “Machine learning techniques for structural health monitoring of heritage buildings: A state-of-the-art review and case studies”, Journal of Cultural Heritage (2021) Volume 47 Pages 227-245. https://doi.org/10.1016/j.culher.2020.09.005
P. Singh, U. F. Ahmad, and S. Yadav, “Structural Health Monitoring and Damage Detection through Machine Learning approaches, E3S Web of Conferences (2020), 220, 01096. http://dx.doi.org/10.1051/e3sconf/202022001096
S.R. Bhagat, G.A. Suryawanshi, Monali Mahajan and Lomesh S. Mahajan, “Artificial neural network techniques for evaluation of pollution”, IOP Conf. Ser..: Earth Environ. Sci. 796 012052. https://iopscience.iop.org/article/10.1088/1755-1315/796/1/012052/pdf
O. Avci, O. Abdeljaber, S. Kiranyaz, M. Hussein, M. Gabbouj and D. J. Inman, “A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications”, Elsevier Mechanical Systems and Signal Processing (2021), Volume 147,107077. https://doi.org/10.1016/j.ymssp.2020.107077
Y. Xie, M. E. Sichani, J. E. Padgett and Des Roches R., “The promise of implementing machine learning in earthquake engineering: A state-of-the-art review”, Earthquake Spectra (2020);36(4):1769-1801. http://dx.doi.org/10.1177/8755293020919419
R. Jena, B. Pradhan, S. P. Naik and A. M. Alamri, “Earthquake risk assessment in NE India using deep learning and geospatial analysis”, Geoscience Frontiers (2021) Volume 12, Issue 3, May, 101110. https://doi.org/10.1016/j.gsf.2020.11.007
L. Mei and Wang Q., “Structural Optimisation in Civil Engineering: A Literature Review”, Buildings MDPI (2021), 11, 66. https://doi.org/10.3390/buildings11020066
H. Zheng, V. Moosavi and M. Akbarzadeh, “Machine learning assisted evaluations in structural design and construction”, Automation in Construction, (2020) Volume 119, 103346. https://doi.org/10.1016/j.autcon.2020.103346
P. F. S. Silva, G. Moita and V. Arruda, “Machine learning techniques to predict the compressive strength of concrete”, Rev. int. métodos numér. cálc. diseño ing. (2020). Vol. 36, (4), 48. http://dx.doi.org/10.23967/j.rimni.2020.09.008
S. D. Latif, “Concrete compressive strength prediction modelling utilising deep learning long short-term memory algorithm for a sustainable environment”, Environ Sci Pollution Res 28, 30294–30302 (2021). https://doi.org/10.1007/s11356-021-12877-y