Bayesian Optimized LightGBM model for predicting the fundamental vibrational period of masonry infilled RC frames

Abstract

The precise prediction of the fundamental vibrational period for reinforced concrete (RC) buildings with infilled walls is essential for structural design, especially earthquake-resistant design. Machine learning models from previous studies, while boasting commendable accuracy in predicting the fundamental period, exhibit vulnerabilities due to lengthy training times and inherent dependence on pre-trained models, especially when engaging with continually evolving data sets. This predicament emphasizes the necessity for a model that adeptly balances predictive accuracy with robust adaptability and swift data training. The latter should include consistent re-training ability as demanded by real-time, continuously updated data sets. This research implements an optimized Light Gradient Boosting Machine (LightGBM) model, highlighting its augmented predictive capabilities, realized through the astute use of Bayesian Optimization for hyperparameter tuning on the FP4026 research data set, and illuminating its adaptability and efficiency in predictive modeling. The results show that the R² score of LightGBM model is 0.9995 and RMSE is 0.0178, while training speed is 23.2 times faster than that offered by XGBoost and 45.5 times faster than for Gradient Boosting. Furthermore, this study introduces a practical application through a streamlit-powered, web-based dashboard, enabling engineers to effortlessly utilize and augment the model, contributing data and ensuring precise fundamental period predictions, effectively bridging scholarly research and practical applications.

Graphical abstract

Keywords

masonry-infilled RC frame / fundamental period / LightGBM / FP4026 research dataset / machine learning / data-driven approach / Bayesian Optimization

References

[1] Chiou Y J, Tzeng J C, Liou Y W. Experimental and analytical study of masonry infilled frames. Journal of Structural Engineering, 1999, 125(10): 1109–1117

[2] Colangelo F. Pseudo-dynamic seismic response of reinforced concrete frames infilled with non-structural brick masonry. Earthquake Engineering & Structural Dynamics, 2005, 34(10): 1219–1241

[3] De Angelis A, Pecce M R. The structural identification of the infill walls contribution in the dynamic response of framed buildings. Structural Control and Health Monitoring, 2019, 26(9): e2405

[4] Fardis M N, Panagiotakos T B. Seismic design and response of bare and masonry-infilled reinforced concrete buildings. Part II: Infilled structures. Journal of Earthquake Engineering, 1997, 1(3): 475–503

[5] Gago A S, Alfaiate J, Lamas A. The effect of the infill in arched structures: Analytical and numerical modelling. Engineering Structures, 2011, 33(5): 1450–1458

[6] Singh H, Paul D K, Sastry V V. Inelastic dynamic response of reinforced concrete infilled frames. Computers & Structures, 1998, 69(6): 685–693

[7] Wang F, Zhao K, Zhang J, Yan K. Influence of different types of infill walls on the hysteretic performance of reinforced concrete frames. Buildings, 2021, 11(7): 310–328

[8] Asteris P G, Tsaris A K, Cavaleri L, Repapis C C, Papalou A, Di Trapani F, Karypidis D F. Prediction of the fundamental period of infilled RC frame structures using artificial neural networks. Computational Intelligence and Neuroscience, 2016, 2016: 1–12

[9] Asteris P G, Repapis C C, Repapi E V, Cavaleri L. Fundamental period of infilled reinforced concrete frame structures. Structure and Infrastructure Engineering, 2017, 13(7): 929–941

[10] Asteris P G, Repapis C C, Cavaleri L, Sarhosis V, Athanasopoulou A. On the fundamental period of infilled RC frame buildings. Structural Engineering and Mechanics, 2015, 54(6): 1175–1200

[11] Asteris P G, Repapis C C, Tsaris A K, Di Trapani F, Cavaleri L. Parameters affecting the fundamental period of infilled RC frame structures. Earthquakes and Structures, 2015, 9(5): 999–1028

[12] Chethan K, Babu R, Venkataramana K, Sharma A. Influence of masonry infill on fundamental natural frequency of 2D RC frames. Journal of Structural Engineering, 2010, 37(2): 135–141

[13] Jiang R, Jiang L, Hu Y, Ye J, Zhou L. A simplified method for estimating the fundamental period of masonry infilled reinforced concrete frames. Structural Engineering and Mechanics, 2020, 74(6): 821–832

[14] Koçak A, Kalyoncuoğlu A, Zengin B. Effect of infill wall and wall openings on the fundamental period of RC buildings. Earthquake Resistant Engineering Structures IX, 2013, 132: 121–131

[15] Kose M M. Parameters affecting the fundamental period of RC buildings with infill walls. Engineering Structures, 2009, 31(1): 93–102

[16] Masi A, Vona M. Experimental and numerical evaluation of the fundamental period of undamaged and damaged RC framed buildings. Bulletin of Earthquake Engineering, 2010, 8(3): 643–656

[17] Ricci P, Verderame G M, Manfredi G. Analytical investigation of elastic period of infilled RC MRF buildings. Engineering Structures, 2011, 33(2): 308–319

[18] Dimiduk D M, Holm E A, Niezgoda S R. Perspectives on the impact of machine learning, deep learning, and artificial intelligence on materials, processes, and structures engineering. Integrating Materials and Manufacturing Innovation, 2018, 7(3): 157–172

[19] Jasmine P H, Arun S. Machine learning applications in structural engineering—A review. IOP Conference Series: Materials Science and Engineering, 2021, 1114(1): 012012

[20] Lee S, Ha J, Zokhirova M, Moon H, Lee J. Background information of deep learning for structural engineering. Archives of Computational Methods in Engineering, 2018, 25(1): 121–129

[21] Salehi H, Burgueño R. Emerging artificial intelligence methods in structural engineering. Engineering Structures, 2018, 171: 170–189

[22] Sun H, Burton H V, Huang H. Machine learning applications for building structural design and performance assessment: State-of-the-art review. Journal of Building Engineering, 2021, 33: 101816

[23] Hamdia K M, Zhuang X, Rabczuk T. An efficient optimization approach for designing machine learning models based on genetic algorithm. Neural Computing & Applications, 2021, 33(6): 1923–1933

[24] Nariman N A, Hamdia K, Ramadan A M, Sadaghian H. Optimum design of flexural strength and stiffness for reinforced concrete beams using machine learning. Applied Sciences, 2021, 11(18): 8762–8777

[25] Guo H, Zhuang X, Alajlan N, Rabczuk T. Physics-informed deep learning for melting heat transfer analysis with model-based transfer learning. Computers & Mathematics with Applications, 2023, 143: 303–317

[26] Guo H, Zhuang X, Chen P, Alajlan N, Rabczuk T. Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media. Engineering with Computers, 2022, 38(6): 5173–5198

[27] Guo H, Zhuang X, Fu X, Zhu Y, Rabczuk T. Physics-informed deep learning for three-dimensional transient heat transfer analysis of functionally graded materials. Computational Mechanics, 2023, 72(3): 513–524

[28] Guo H, Zhuang X, Rabczuk T. A deep collocation method for the bending analysis of Kirchhoff plate. Computers, Materials & Continua, 2019, 59(2): 433–456

[29] Samaniego E, Anitescu C, Goswami S, Nguyen-Thanh V M, Guo H, Hamdia K, Zhuang X, Rabczuk T. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications. Computer Methods in Applied Mechanics and Engineering, 2020, 362: 112790

[30] Zhuang X, Guo H, Alajlan N, Zhu H, Rabczuk T. Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning. European Journal of Mechanics. A, Solids, 2021, 87: 104225

[31] Sang-To T, Le-Minh H, Abdel Wahab M, Thanh C L. A new metaheuristic algorithm: Shrimp and Goby association search algorithm and its application for damage identification in large-scale and complex structures. Advances in Engineering Software, 2023, 176: 103363

[32] Minh H L, Khatir S, Rao R V, Abdel Wahab M, Cuong-Le T. A variable velocity strategy particle swarm optimization algorithm (VVS-PSO) for damage assessment in structures. Engineering with Computers, 2023, 39(2): 1055–1084

[33] Ho L V, Trinh T T, De Roeck G, Bui-Tien T, Nguyen-Ngoc L, Abdel Wahab M. An efficient stochastic-based coupled model for damage identification in plate structures. Engineering Failure Analysis, 2022, 131: 105866

[34] Nghia-Nguyen T, Kikumoto M, Nguyen-Xuan H, Khatir S, Abdel Wahab M, Cuong-Le T. Optimization of artificial neutral networks architecture for predicting compression parameters using piezocone penetration test. Expert Systems with Applications, 2023, 223: 119832

[35] Tran V T, Nguyen T K, Nguyen-Xuan H, Abdel Wahab M. Vibration and buckling optimization of functionally graded porous microplates using BCMO-ANN algorithm. Thin-walled Structures, 2023, 182: 110267

[36] Asteris P G, Nikoo M. Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures. Neural Computing & Applications, 2019, 31(9): 4837–4847

[37] Mirrashid M, Naderpour H. Computational intelligence-based models for estimating the fundamental period of infilled reinforced concrete frames. Journal of Building Engineering, 2022, 46: 103456

[38] Latif I, Banerjee A, Surana M. Explainable machine learning aided optimization of masonry infilled reinforced concrete frames. Structures, 2022, 44: 1751–1766

[39] Somala S N, Karthikeyan K, Mangalathu S. Time period estimation of masonry infilled RC frames using machine learning techniques. Structures, 2021, 34: 1560–1566

[40] Charalampakis A E, Tsiatas G C, Kotsiantis S B. Machine learning and nonlinear models for the estimation of fundamental period of vibration of masonry infilled RC frame structures. Engineering Structures, 2020, 216: 110765

[41] BioudNLaidIBenbourasM A. Estimating the fundamental period of infilled RC frame structures via deep learning. Urbanism. Architecture. Constructions, 2023,14:1–22

[42] Cakiroglu C, Bekdaş G, Kim S, Geem Z W. Explainable ensemble learning models for the rheological properties of self-compacting concrete. Sustainability, 2022, 14(21): 14640

[43] Chakraborty D, Elhegazy H, Elzarka H, Gutierrez L. A novel construction cost prediction model using hybrid natural and light gradient boosting. Advanced Engineering Informatics, 2020, 46: 101201

[44] Chun P, Izumi S, Yamane T. Automatic detection method of cracks from concrete surface imagery using two-step light gradient boosting machine. Computer-Aided Civil and Infrastructure Engineering, 2021, 36(1): 61–72

[45] Kookalani S, Cheng B, Torres J L C. Structural performance assessment of GFRP elastic gridshells by machine learning interpretability methods. Frontiers of Structural and Civil Engineering, 2022, 16(10): 1249–1266

[46] Mangalathu S, Jang H, Hwang S H, Jeon J S. Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls. Engineering Structures, 2020, 208: 110331

[47] Naser M Z, Kodur V, Thai H T, Hawileh R, Abdalla J, Degtyarev V V. StructuresNet and FireNet: Benchmarking databases and machine learning algorithms in structural and fire engineering domains. Journal of Building Engineering, 2021, 44: 102977

[48] Ding Z, Zhang W, Zhu D. Neural-network based wind pressure prediction for low-rise buildings with genetic algorithm and Bayesian optimization. Engineering Structures, 2022, 260: 114203

[49] Lookman T, Alexander F, Rajan K. Information Science for Materials Discovery and Design. Switzerland: Springer, 2016,

[50] Mathern A, Steinholtz O S, Sjöberg A, Önnheim M, Ek K, Rempling R, Gustavsson E, Jirstrand M. Multi-objective constrained Bayesian optimization for structural design. Structural and Multidisciplinary Optimization, 2021, 63(2): 689–701

[51] Sajedi S, Liang X. Deep generative Bayesian optimization for sensor placement in structural health monitoring. Computer-Aided Civil and Infrastructure Engineering, 2022, 37(9): 1109–1127

[52] Zhang W, Wu C, Zhong H, Li Y, Wang L. Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization. Geoscience Frontiers, 2021, 12(1): 469–477

[53] Asteris P G. The FP4026 Research Database on the fundamental period of RC infilled frame structures. Data in Brief, 2016, 9: 704–709

[54] KeGMengQFinleyTWangTChenWMaWYeQLiuT. LightGBM: A highly efficient gradient boosting decision tree. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. New York: Curran Associates Inc., 2017, 3149–3157

[55] Friedman J H. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 2001, 29(5): 1189–1232

[56] BrochuECoraV Mde FreitasN. A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. 2010, arXiv: 1012.2599

[57] FrazierP I. A Tutorial on Bayesian Optimization. 2018. arXiv: 1807.02811

[58] Shahriari B, Swersky K, Wang Z, Adams R P, de Freitas N. Taking the human out of the loop: A review of Bayesian Optimization. proceedings of the IEEE, 2016, 104(1): 148–175

[59] RasmussenC E. Gaussian Processes in Machine Learning. In: Bousquet O, von Luxburg U, Rätsch G, eds. Advanced Lectures on Machine Learning. Berlin: Springer, 2004, 63–71

[60] SnoekJLarochelleHAdamsR P. Practical Bayesian optimization of machine learning algorithms. In: Proceedings of the 25th International Conference on Neural Information Processing Systems—Volume 2. New York: Curran Associates Inc., 2012, 2951–2959

RIGHTS & PERMISSIONS

Higher Education Press