Hybrid machine learning-enabled multivariate bridge-specific seismic vulnerability and resilience assessment of UHPC bridges

Abstract

Efficient seismic vulnerability and resilience assessment is essential for ultra-high-performance concrete (UHPC) bridges, given their distinctive mechanical and structural properties. However, existing single-parameter-based probabilistic seismic demand (PSD) models overlook critical bridge‐specific characteristics and uncertainties. Besides, studies on seismic vulnerability and resilience assessment of UHPC bridges are scarce. Thus, this study proposes a hybrid machine learning (ML)-enabled multivariate bridge-specific seismic vulnerability and resilience assessment framework for UHPC bridges. Key design parameters and associated uncertainties are identified, and a Latin Hypercube Sampling (LHS) technique is employed to establish a representative UHPC bridge database, which is used to develop a hybrid ML model-based multivariate PSD model. A comparative analysis with the conventional PSD model, as well as widely used ML algorithms, demonstrated that the proposed PSD model achieves the highest predictive performance, characterized by the highest coefficient of determination and lowest prediction errors. Additionally, SHapley Additive exPlanation (SHAP) analysis is used to investigate the effect of different parameters on the PSD of UHPC bridges. The results of SHAP show the peak ground acceleration (PGA) as the most important factor, followed by bridge span and column diameter. The hybrid ML-enabled multi-variate bridge-specific fragility analysis results are used to investigate the functionality recovery and resilience of the bridge, which demonstrate the reduction in the residual functionality and overall bridge resilience with the increase in the ground motion intensity.

Keywords

Ultra-high-performance concrete (UHPC) / Fragility analysis / Resilience / Functionality / Machine Learning

Relevance to resilience Content

The paper presents a hybrid machine learning-enabled framework for multivariate bridge-specific seismic vulnerability and resilience assessment of ultra-highperformance concrete (UHPC) bridges. It considers bridge-specific characteristics and uncertainties, which enables more accurate seismic demand modeling and fragility analysis. Thus, the study enhances proactive planning, informed decision-making, and adaptive responses to ensure the continued functionality and safety of UHPC bridges.

CRediT authorship contribution statement

Tadesse G. Wakjira: Writing - review & editing, Writing - original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. M. Shahria Alam: Writing - review & editing, Validation, Supervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

The financial contribution of Kon Kast Concrete Products Inc and Mitacs through the Accelerate grant is gratefully acknowledged.

References

[1] Padgett JE, Desroches R, Nilsson E. Regional seismic risk assessment of bridge network in Charleston, South Carolina. J Earthq Eng 2010;14:918-33. doi: 10.1080/13632460903447766.

[2] Padgett JE, DesRoches R. Retrofitted bridge fragility analysis for typical classes of multispan bridges. Earthq Spectra 2009;25:117-41. doi: 10.1193/1.3049405.

[3] Choi E, DesRoches R, Nielson B. Seismic fragility of typical bridges in moderate seismic zones. Eng Struct 2004;26:187-99. doi: 10.1016/j.engstruct.2003.09.006.

[4] Nielson BG, Desroches R. Seismic fragility methodology for highway bridges using a component level approach. Earthq Eng Struct Dyn 2007;36:823-39. doi: 10.1002/eqe.

[5] Salkhordeh M, Govahi E, Mirtaheri M. Seismic fragility evaluation of various mitigation strategies proposed for bridge piers. Structures 2021;33:1892-905. doi: 10.1016/j.istruc.2021.05.041.

[6] M S, MQ F, J L, T N. Statistical analysis of fragility curves. J Eng Mech 2000;126:1224-31.

[7] P G, A DK, KM M. Probabilistic capacity models and fragility estimates for reinforced concrete columns based on experimental observations. J Eng Mech 2002;128:1024-38.

[8] Rossetto T, Elnashai A. Derivation of vulnerability functions for Europeantype RC structures based on observational data. Eng Struct 2003;25:1241-63. doi: 10.1016/S0141-0296(03)00060-9.

[9] Billah AHMM, Alam MS. Seismic fragility assessment of highway bridges: a state-of-the-art review. Struct Infrastruct Eng 2015;11:804-32.

[10] Mangalathu S, Heo G, Jeon JS. Artificial neural network based multi-dimensional fragility development of skewed concrete bridge classes. Eng Struct 2018;162:166-76. doi: 10.1016/j.engstruct.2018.01.053.

[11] Ang A-S, De Leon D. Modeling and analysis of uncertainties for risk-informed decisions in infrastructures engineering. Struct Infrastruct Eng 2005;1:19-31. doi: 10.1080/15732470412331289350.

[12] Soleimani F, Hajializadeh D. State-of-the-art review on probabilistic seismic demand models of bridges: machine-learning application. Infrastructures (Basel) 2022;7. doi: 10.3390/infrastructures7050064.

[13] Soleimani F. Probabilistic seismic analysis of bridges through machine learning approaches. Structures 2022;38:157-67. doi: 10.1016/j.istruc.2022.02.006.

[14] Kiani J, Camp C, Pezeshk S. On the application of machine learning techniques to derive seismic fragility curves. Comput Struct 2019;218:108-22. doi: 10.1016/j.compstruc.2019.03.004.

[15] Mitropoulou CC, Papadrakakis M. Developing fragility curves based on neural network IDA predictions. Eng Struct 2011;33:3409-21. doi: 10.1016/j.engstruct.2011.07.005.

[16] Kameshwar S, Padgett JE. Response and fragility assessment of bridge columns subjected to barge-bridge collision and scour. Eng Struct 2018;168:308-19. doi: 10.1016/j.engstruct.2018.04.082.

[17] Pang Y, Dang X, Yuan W. An artificial neural network based method for seismic fragility analysis of highway bridges. Adv Struct Eng 2014;17:413-28. doi: 10.1260/1369-4332.17.3.413.

[18] Wang M, Zhang H, Dai H, Shen L. A deep learning-aided seismic fragility analysis method for bridges. Structures 2022;40:1056-64. doi: 10.1016/j.istruc.2022.04.058.

[19] MO R, Chen L, Xing Z, Ye X, Xiong C, Liu C, et al. Bridge seismic fragility model based on support vector machine and relevance vector machine. Structures 2023;52:768-78. doi: 10.1016/j.istruc.2023.03.179.

[20] Xie Y, Ebad Sichani M, Padgett JE, DesRoches R. The promise of implementing machine learning in earthquake engineering: a state-of-the-art review. Earthq Spectra 2020;36:1769-801. doi: 10.1177/8755293020919419.

[21] Huang C, Huang S. Predicting capacity model and seismic fragility estimation for RC bridge based on artificial neural network. Structures 2020;27:1930-9. doi: 10.1016/j.istruc.2020.07.063.

[22] Lagaros ND, Fragiadakis M. Fragility assessment of steel frames using neural networks. Earthq Spectra 2007;23:735-52. doi: 10.1193/1.2798241.

[23] Lundberg S.M., Lee S.A. Unified Approach to Interpreting Model Predictions. 31st Conf. neural Inf. Process. Syst. (NIPS 2017), Long Beach, CA, USA: 2017, p. 1-10.

[24] Wakjira TG, Alam MS. Peak and ultimate stress-strain model of confined ultrahigh- performance concrete (UHPC) using hybrid machine learning model with conditional tabular generative adversarial network. Appl Soft Comput 2024; 154. doi: 10.1016/j.asoc.2024.111353.

[25] Tavares DH, Padgett JE, Paultre P. Fragility curves of typical asbuilt highway bridges in eastern Canada. Eng Struct 2012;40:107-18. doi: 10.1016/j.engstruct.2012.02.019.

[26] Cornell CA, Jalayer F, Hamburger RO, Foutch DA. Probabilistic basis for 2000 SAC federal emergency management agency steel moment frame guidelines. J Struct Eng 2002;128:526-33.

[27] Padgett JE, Nielson BG, Desroches R. Selection of optimal intensity measures in probabilistic seismic demand models of highway bridge portfolios. Earthq Eng Struct Dyn 2008;37:711-25. doi: 10.1002/eqe.

[28] Padgett JE, Desroches R. Methodology for the development of analytical fragility curves for retrofitted bridges. Earthq Eng Struct Dyn 2008;37:1157-74. doi: 10.1002/eqe.

[29] Mackie KR, Stojadinovi ć B. Fragility basis for california highway overpass bridge seismic decision making pacific earthquake engineering research center. Berkeley, USA: College of Engineering, University of California; 2005.

[30] Wakjira TG, Alam MS. Performance-based seismic design of Ultra-High-Performance Concrete (UHPC) bridge columns with design example - Powered by explainable machine learning model. Eng Struct 2024; 314. doi: 10.1016/j.engstruct.2024.118346.

[31] Decò A, Bocchini P, Frangopol DM. A probabilistic approach for the prediction of seismic resilience of bridges. Earthq Eng Struct Dyn 2013;42:1469-87. doi: 10.1002/eqe.2282.

[32] Pang Y, Wang X. Enhanced endurance-time-method (EETM) for efficient seismic fragility, risk and resilience assessment of structures. Soil Dyn Earthq Eng 2021; 147. doi: 10.1016/j.soildyn.2021.106731.

[33] Siddiquee K, Shahria Alam M. Highway bridge infrastructure in the province of British Columbia (BC), Canada. Infrastructures (Basel) 2017;2. doi: 10.3390/infrastructures2020007.

[34] Wakjira TG, Kutty AA, Alam MS. A novel framework for developing environmentally sustainable and cost-effective ultra-high-performance concrete (UHPC) using advanced machine learning and multi-objective optimization techniques. Constr Build Mater 2024; 416. doi: 10.1016/j.conbuildmat.2024.135114.

[35] Nielson B.G. Analytical fragility curves for highway bridges in moderate seismic zones. 2005. https://doi.org/10.1016/j.engstruct.2017.03.041.

[36] Zhu L, Elwood KJ, Haukaas T. Classification and seismic safety evaluation of existing reinforced concrete columns. J Struct Eng 2007;133:1316-30. doi: 10.1061/(ASCE)0733-9445(2007)133:9(1316).

[37] Mckay MD, Beckman RJ, Conover WJ. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 1979;21:239-45.

[38] Olsson A, Sandberg G, Dahlblom O. On Latin hypercube sampling for structural reliability analysis. Struct Saf 2003;25:47-68.

[39] Wei B, Zheng X, Jiang L, Lai Z, Zhang R, Chen J, et al. Seismic response prediction and fragility assessment of high-speed railway bridges using machine learning technology. Structures 2024; 66. doi: 10.1016/j.istruc.2024.106845.

[40] Zaker Esteghamati M, Flint MM. Developing data-driven surrogate models for holistic performance-based assessment of mid-rise RC frame buildings at early design. Eng Struct 2021; 245. doi: 10.1016/j.engstruct.2021.112971.

[41] Ding JY, Feng DC, Brunesi E, Parisi F, Wu G. Efficient seismic fragility analysis method utilizing ground motion clustering and probabilistic machine learning. Eng Struct 2023; 294. doi: 10.1016/j.engstruct.2023.116739.

[42] PEER Pacific earthquake engineering research center, strong motion database Available from http://ngawest2.berkeley.edu/.

[43] CSA. Csa S6-14Canadian highway bridge design code. Mississauga: Can Stand Assoc (CSA); 2014.

[44] Mckenna F. OpenSees: a framework for earthquake engineering simulation. Comput Sci Eng 2011;13:58-66.

[45] Wakjira TG, Abushanab A, Alam MS. Hybrid machine learning model and predictive equations for compressive stress-strain constitutive modelling of confined ultra-highperformance concrete (UHPC) with normal-strength steel and high-strength steel spirals. Eng Struct 2024; 304. doi: 10.1016/j.engstruct.2024.117633.

[46] Muthukumar S, DesRoches R. A Hertz contact model with nonlinear damping for pounding simulation. Earthq Eng Struct Dyn 2006;35:811-28. doi: 10.1002/eqe.557.

[47] Akiba T, Sano S, Yanase T, Ohta T, Koyama M. Optuna: a next-generation hyperparameter optimization framework. Proc ACM SIGKDD Int Conf Knowl Discov Data Min 2019:2623-31. doi: 10.1145/3292500.3330701.