Rapid seismic damage prediction of prestressed concrete bridge columns using validated machine learning models

Abstract

This study presents a machine learning–based framework for predicting seismic damage states in partially prestressed reinforced concrete bridge piers. A database of 2163 numerically simulated bridge columns was developed using Latin Hypercube Sampling, covering wide variations in geometry, material strengths, axial load ratios, and prestressing levels representing a diverse range of demand–capacity conditions. Ten supervised learning algorithms including support vector machines, neural networks, random forests, gradient boosting methods, and CatBoost were trained and evaluated through multiple statistical metrics. Model predictions were validated against experimental results for selected column specimens, demonstrating strong agreement between simulations and physical behavior. Among the evaluated models, the Deep Neural Network (DNN) exhibited the highest overall predictive accuracy, achieving Pearson correlation coefficients of 0.982, 0.998, 0.9996, 0.988, and 0.9989 for residual displacement, residual force, force at collapse drift, hysteretic energy, and maximum base-shear, respectively. The corresponding RMSE values were 0.0275, 0.0023, 0.0036, 0.0281, and 0.0293. Although, CatBoost and Artificial Neural Network showed slightly better performance than the DNN in predicting hysteretic energy, the DNN remained the most reliable model across the structural responses examined. Overall, it provides a robust and consistent tool for damage estimation and performance assessment of prestressed Reinforced Concrete bridge piers subjected to seismic loading.

Graphical abstract

Keywords

prestressed reinforced concrete columns / machine learning / seismic response / damage limit

References

[1] Yang D Y , Frangopol D M . Life-cycle management of deteriorating bridge networks with network-level risk bounds and system reliability analysis. Structural Safety, 2020, 83: 101911

[2] Kennedy-Kuiper R C S , Wakjira T G , Alam M S . Repair and retrofit of RC bridge piers with steel-reinforced grout jackets: An experimental investigation. Journal of Bridge Engineering, 2022, 27(8): 04022067

[3] Mahboubi SShiravand M R. A proposed input energy-based damage index for RC bridge piers. Journal of Bridge Engineering, 2019a, 24(1): 04018103

[4] Mahboubi SShiravand M R. Failure assessment of skew RC bridges with FRP piers based on damage indices. Engineering Failure Analysis, 2019b, 99: 153–168

[5] Mahboubi SShiravand M R. Seismic evaluation of bridge bearings based on damage index. Bulletin of Earthquake Engineering, 2019c, 17(7): 4269–4297

[6] Mahboubi S , Kioumarsi M . Damage assessment of RC bridges considering joint impact of corrosion and seismic loads: A systematic literature review. Construction and Building Materials, 2021, 295: 123662

[7] Wakjira T G , Nehdi M L , Ebead U . Fractional factorial design model for seismic performance of RC bridge piers retrofitted with steel-reinforced polymer composites. Engineering Structures, 2020, 221: 111100

[8] Poorahad Anzabi PShiravand M RMahboubi S. Machine learning-aided prediction of seismic response of RC bridge piers exposed to chloride-induced corrosion. In: The International Conference on Net-Zero Civil Infrastructures: Innovations in Materials, Structures, and Management Practices (NTZR). Cham: Springer, 2024, 1409–1421

[9] Rassoulpour S , Shiravand M R , Safi M . Proposed seismic-resistant dual system for continuous-span concrete bridges using self-centering cores. Engineering Structures, 2023, 274: 115181

[10] Poorahad Anzabi P , Shiravand M R . Segments arrangement effect on improvement of self-centering precast post-tensioned segmental piers seismic performance. Structural Concrete, 2023, 25(1): 185–206

[11] Poorahad P , Shiravand M R . Data-driven multi-criteria framework for the seismic and post-earthquake performance assessment of corroded RC bridge piers. Structures, 2025, 82: 110535

[12] Poorahad P , Shiravand M R . Seismic reliability analysis of self-centering post-tensioned piers under influence of prestress loss. Engineering Structures, 2024, 314: 118315

[13] Shiramama H , Yamaguchi T , Ikeda S . Seismic response behavior of concrete piers prestressed in axial direction. Proceedings of the Japan Concrete Institute, 1998, 20(3): 745–750

[14] Ito T , Yamaguchi T , Ikeda S . Seismic performance of reinforced concrete piers prestressed in axial direction. Proceedings of the Japan Concrete Institute, 1997, 19(2): 1197–1202

[15] Sakai JMahin S A. Analytical Investigations of New Methods for Reducing Residual Displacements of Reinforced Concrete Bridge Columns. PEER Report 2004-02, 2004

[16] Sakai JMahin S AEspinoza A. Earthquake Simulator Tests on Reducing Residual Displacements of Reinforced Concrete Bridge Columns. PEER Report 2005-17, 2005

[17] Iemura HTakahashi YSogabe N. Development of unbonded bar reinforced concrete structures. In: Proceedings of the 13th World Conference on Earthquake Engineering. Vancouver: Canadian Association for Earthquake Engineering, 2004

[18] Lee W KBillington S L. Simulation and Performance-Based Earthquake Engineering Assessment of Self-centering Prestressed Concrete Bridge Systems. PEER Report 2009-109, 2009

[19] Kwan W P , Billington S L . Unbonded posttensioned concrete bridge piers. I: Monotonic and cyclic analyses. Journal of Bridge Engineering, 2003, 8(2): 92–101

[20] Kwan W P , Billington S L . Unbonded posttensioned concrete bridge piers. II: Seismic analyses. Journal of Bridge Engineering, 2003, 8(2): 102–111

[21] Sun Z , Wang D , Bi K , Si B . Experimental and numerical investigations on the seismic behavior of bridge piers with vertical unbonded prestressing strands. Bulletin of Earthquake Engineering, 2016, 14(2): 501–527

[22] Luo X , Cheng J , Xiang P , Long H . Seismic behavior of corroded reinforced concrete column joints under low-cyclic repeated loading. Archives of Civil and Mechanical Engineering, 2020, 20(2): 40

[23] Feng D C , Liu Z T , Wang X D , Jiang Z M , Liang S X . Failure mode classification and bearing capacity prediction for reinforced concrete columns based on ensemble machine learning algorithm. Advanced Engineering Informatics, 2020, 45: 101126

[24] Wang C , Song L , Fan J . End-to-end structural analysis in civil engineering based on deep learning. Automation in Construction, 2022, 138: 104255

[25] Soleimani F , Liu X . Artificial neural network application in predicting probabilistic seismic demands of bridge components. Earthquake Engineering & Structural Dynamics, 2021, 51(3): 612–629

[26] Wakjira T G , Alam M S , Ebead U . Plastic hinge length of rectangular RC columns using ensemble machine learning model. Engineering Structures, 2021, 244: 112808

[27] Zhang W , Gu X , Tang L , Yin Y , Liu D , Zhang Y . Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: comprehensive review and future challenge. Gondwana Research, 2022, 109: 1–17

[28] Wakjira T G , Ibrahim M , Ebead U , Alam M S . Explainable machine learning model and reliability analysis for flexural capacity prediction of RC beams strengthened in flexure with FRCM. Engineering Structures, 2022, 255: 113903

[29] Wakjira T G , Shahria Alam M . Performance-based seismic design of Ultra-High-Performance Concrete (UHPC) bridge columns with design example—Powered by explainable machine learning mode. Engineering Structures, 2024, 314: 118346

[30] Wakjira T G , Abushanab A , Alam M S . Hybrid machine learning model and predictive equations for compressive stress−strain constitutive modelling of confined ultra-high-performance concrete (UHPC) with normal-strength steel and high-strength steel spirals. Engineering Structures, 2024, 304: 117633

[31] Wakjira T G , Shahria Alam M . Peak and ultimate stress-strain model of confined ultra-high-performance concrete (UHPC) using hybrid machine learning model with conditional tabular generative adversarial network. Applied Soft Computing, 2024, 154: 111353

[32] Wakjira T G , Alam M S . Hybrid machine learning-enabled multivariate bridge-specific seismic vulnerability and resilience assessment of UHPC bridges. Resilient Cities and Structures, 2025, 4(2): 92–102

[33] Tijani I A , Wakjira T G , Haroglu H , Alam M S . Explainable machine learning and application-oriented tool for predicting effective hoop strain of fiber-reinforced polymer-confined concrete. Frontiers of Structural and Civil Engineering, 2025, 19(10): 1621–1636

[34] Tijani I AWakjira T GAlam M SUddin N. Digital Image Correlation (DIC) for structural health monitoring of bridge systems: A state-of-the-art review with future research directions. Archives of Computational Methods in Engineering, 2026, 33: 4325–4341

[35] Todorov B , Muntasir Billah A . Machine learning driven seismic performance limit state identification for performance-based seismic design of bridge piers. Engineering Structures, 2022, 255: 113919

[36] Kim S , Hwang H , Oh K , Shin J . A machine-learning-based failure mode classification model for reinforced concrete columns using simple structural information. Applied Sciences, 2024, 14(3): 1243

[37] Megalooikonomou K G , Beligiannis G N . Application of supervised neural networks to classify failure modes in reinforced concrete columns using basic structural data. Applied Sciences, 2025, 15(18): 10175

[38] Mazzoni SMcKenna FScott M HFenves G L. OpenSees Command Language Manual. PEER Report, 2007

[39] Kashani M M , Lowes L N , Crewe A J , Alexander N A . Nonlinear fiber element modeling of RC bridge piers considering inelastic buckling of reinforcement. Engineering Structures, 2016, 116: 163–177

[40] Su J , Wu D , Wang X . Influence of ground motion duration on seismic behavior of RC bridge piers: The role of low-cycle fatigue damage of reinforcing bars. Engineering Structures, 2023, 279: 115587

[41] Caltrans . Caltrans Seismic Design Criteria Version 1.6. Sacramento: California Department of Transportation, 2010

[42] Chang G AMander J B. Seismic Energy-Based Fatigue Damage Analysis of Bridge Columns. NCEER Report 94-0006, 1994

[43] Priestley M J NSeible FCalvi G M. Seismic Design and Retrofit of Bridges. Hoboken: Wiley, 1996

[44] Moshref A , Tehranizadeh M , Khanmohammadi M . Investigation of the reliability of nonlinear modeling approaches to capture the residual displacements of RC columns under seismic loading. Bulletin of Earthquake Engineering, 2015, 13(8): 2327–2345

[45] Ameli M J , Parks J E , Brown D N , Pantelides C P . Seismic evaluation of grouted splice sleeve connections for reinforced precast concrete column-to-cap beam joints in accelerated bridge construction. PCI Journal, 2015, 60(2): 80–103

[46] AASHTO . LRFD Bridge Design Specifications. 9th ed. Washington, D.C.: American Association of State Highway and Transportation Officials, 2020

[47] Li Y , Li J , Shen Y . Quasi-static and nonlinear time-history analyses of post-tensioned bridge rocking piers with internal ED bars. Structures, 2021, 32: 1455–1468

[48] Filippou F CPopov E PBertero V V. Effects of Bond Deterioration on Hysteretic Behavior of RC Joints. EERC Report 83-19, 1983

[49] Fix EHodges J L. Discriminatory analysis: Nonparametric discrimination: Consistency Properties. Texas: USAF School of Aviation Medicine, 1951

[50] Cover T , Hart P . Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 1967, 13(1): 21–27

[51] Qamar R , Zardari B A . Artificial neural networks: An overview. Mesopotamian Journal of Computer Science, 2023, 2023: 130–139

[52] Cortes C , Vapnik V . Support-vector networks. Machine Learning, 1995, 20(3): 273–297

[53] Vapnik V N. The Nature of Statistical Learning Theory. New York: Springer, 1995

[54] Yu HKim S. SVM Tutorial—Classification, Regression and Ranking. In: Handbook of Natural Computing. Berlin: Springer, 2012, 479–506

[55] Chang C , Lin C . LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 1–27

[56] Oluleye B I , Chan D W M , Antwi-Afari P . Adopting artificial Intelligence for enhancing the implementation of systemic circularity in the construction industry: A critical review. Sustainable Production and Consumption, 2023, 35: 509–524

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