Machine Learning for Risk and Resilience Assessment in Structural Engineering: Progress and Future Trends
Publication: Journal of Structural Engineering
Volume 148, Issue 8
Abstract
Population growth, economic development, and rapid urbanization in many areas have led to increased exposure and vulnerability of structural and infrastructure systems to hazards. Thus, developing risk-based assessment and management tools is crucial for stakeholders and the general public to make informed decisions on prehazard planning and posthazard recovery. To this end, structural risk and resilience assessment has been an ongoing research topic in the past 20 years. Recently, machine learning (ML) techniques have been shown as promising tools for advancing the risk and resilience assessment of structure and infrastructure systems. To date, however, there is a lack of a holistic review on ML progress across various branches of structural engineering; an in-depth analysis of literature that can provide a timely evaluation of risk and resilience assessment methods of the built environment, where different types of structural and infrastructure facilities are interconnected. For this reason, this study conducted a comprehensive review on ML for risk and resilience assessment in four main branches of structural engineering (buildings, bridges, pipelines, and electric power systems). To cover the crucial modules in the prevailing risk and resilience assessment frameworks, existing literature is thoroughly examined and characterized in terms of six attributes of ML, including method, task type, data source, analysis scale, event type, and topic area. Moreover, limitations and challenges are identified, and future research needs are highlighted to move forward the frontiers of ML for structural risk and resilience assessment.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, and codes that support the findings of the study are available from the corresponding author upon reasonable request.
Acknowledgments
This study was part of a Special Project of ASCE Structural Engineering Institutes (SEI). The first author also gratefully acknowledges the support of the National Natural Science Foundation of China (No. 52008155). However, the authors take sole responsibility for the opinions expressed in this paper, which may not represent the position of the funding institutions.
References
Abas, K., K. Obraczka, and L. Miller. 2018. “Solar-powered, wireless smart camera network: An IoT solution for outdoor video monitoring.” Comput. Commun. 118 (Mar): 217–233. https://doi.org/10.1016/j.comcom.2018.01.007.
Akiyama, M., D. M. Frangopol, and H. Ishibashi. 2020. “Toward life-cycle reliability-, risk- and resilience-based design and assessment of bridges and bridge networks under independent and interacting hazards: Emphasis on earthquake, tsunami and corrosion.” Struct. Infrastruct. Eng. 16 (1): 26–50. https://doi.org/10.1080/15732479.2019.1604770.
Alimoradi, A., and J. L. Beck. 2015. “Machine-learning methods for earthquake ground motion analysis and simulation.” J. Eng. Mech. 141 (4): 04014147. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000869.
Amari, S. I. 1993. “Backpropagation and stochastic gradient descent method.” Neurocomputing 5 (4–5): 185–196. https://doi.org/10.1016/0925-2312(93)90006-O.
Ancheta, T. D., et al. 2014. “NGA-West2 database.” Earthquake Spectra 30 (3): 989–1005. https://doi.org/10.1193/070913EQS197M.
Andriotis, C. P., and K. G. Papakonstantinou. 2019. “Managing engineering systems with large state and action spaces through deep reinforcement learning.” Reliab. Eng. Syst. Saf. 191: 106483. https://doi.org/10.1016/j.ress.2019.04.036.
Ansari, S., M. Phillips, and S. D. Greco. 2009. “NEXRAD severe weather signatures in the NOAA severe weather data inventory.” In Proc., 34th Conf. on Radar Meteorology, edited by G. M. Heymsfield and A. Tokay. Williamsburg, VA: American Meteorological Society.
Apley, D. W., and J. Zhu. 2020. “Visualizing the effects of predictor variables in black box supervised learning models.” J. R. Stat. Soc. B 82 (4): 1059–1086. https://doi.org/10.1111/rssb.12377.
Argyroudis, S. A., S. Mitoulis, M. G. Winter, and A. M. Kaynia. 2019. “Fragility of transport assets exposed to multiple hazards: State-of-the-art review toward infrastructural resilience.” Reliab. Eng. Syst. Saf. 191 (Nov): 106567. https://doi.org/10.1016/j.ress.2019.106567.
Arslan, M., A.-M. Roxin, C. Cruz, and D. Ginhac. 2017. “A review on applications of big data for disaster management.” In Proc., 13th Int. Conf. on Signal-Image Technology & Internet-Based Systems (SITIS), 370–375. New York: IEEE.
ASME. 2009. ASME B31G-2009—Manual for determining the remaining strength of corroded pipe: A supplement to ASME B31 code for pressure piping. ANSI/ASME B31G-2009. New York: ASME.
Assis, F. A., A. J. C. Coelho, L. D. Rezende, A. M. Leite da Silva, and L. C. Resende. 2021. “Unsupervised machine learning techniques applied to composite reliability assessment of power systems.” Int. Trans. Electr. Energy Syst. 31 (11): e13109. https://doi.org/10.1002/2050-7038.13109.
Astroza, R., J. P. Conte, J. I. Restrepo, H. Ebrahimian, and T. Hutchinson. 2021. “Seismic response analysis and modal identification of a full-scale five-story base-isolated building tested on the NEES@UCSD shake table.” Eng. Struct. 238 (Jul): 112087. https://doi.org/10.1016/j.engstruct.2021.112087.
Ataei, N., and J. E. Padgett. 2015. “Fragility surrogate models for coastal bridges in hurricane prone zones.” Eng. Struct. 103 (Nov): 203–213. https://doi.org/10.1016/j.engstruct.2015.07.002.
Atef, A., and O. Moselhi. 2013. “Understanding the effect of interdependency and vulnerability on the performance of civil infrastructure.” In Proc., Int. Symp. on Automation and Robotics in Construction. Montréal: IAARC Publications.
Attary, N., V. U. Unnikrishnan, J. W. van de Lindt, D. T. Cox, and A. R. Barbosa. 2017. “Performance-based tsunami engineering methodology for risk assessment of structures.” Eng. Struct. 141 (Jun): 676–686. https://doi.org/10.1016/j.engstruct.2017.03.071.
Ayyub, B. M. 2015. “Practical resilience metrics for planning, design, and decision making.” ASCE-ASME J. Risk Uncertainty Eng. Syst. Part A: Civ. Eng. 1 (3): 04015008. https://doi.org/10.1061/AJRUA6.0000826.
Bagriacik, A., R. A. Davidson, M. W. Hughes, B. A. Bradley, and M. Cubrinovski. 2018. “Comparison of statistical and machine learning approaches to modeling earthquake damage to water pipelines.” Soil Dyn. Earthquake Eng. 112 (Sep): 76–88. https://doi.org/10.1016/j.soildyn.2018.05.010.
Balomenos, G. P., S. Kameshwar, and J. E. Padgett. 2020. “Parameterized fragility models for multi-bridge classes subjected to hurricane loads.” Eng. Struct. 208 (Apr): 110213. https://doi.org/10.1016/j.engstruct.2020.110213.
Banerjee, S., B. S. Vishwanath, and D. K. Devendiran. 2019. “Multihazard resilience of highway bridges and bridge networks: A review.” Struct. Infrastruct. Eng. 15 (12): 1694–1714. https://doi.org/10.1080/15732479.2019.1648526.
Barbato, M., F. Petrini, V. U. Unnikrishnan, and M. Ciampoli. 2013. “Performance-based hurricane engineering (PBHE) framework.” Struct. Saf. 45 (Nov): 24–35. https://doi.org/10.1016/j.strusafe.2013.07.002.
Bendito, A., and E. Barrios. 2016. “Convergent agency: Encouraging transdisciplinary approaches for effective climate change adaptation and disaster risk reduction.” Int. J. Disaster Risk Sci. 7 (4): 430–435. https://doi.org/10.1007/s13753-016-0102-9.
Berry, M., M. Parrish, and M. Eberhard. 2004. PEER structural performance database user ’s manual (version 1.0). Berkeley, CA: Pacific Earthquake Engineering Research Center.
Biringe, B., E. Vugrin, and D. Warren. 2013. Critical infrastructure system security and resiliency. Boca Raton, FL: CRC Press.
Box, G. E. P., and J. S. Hunter. 1957. “Multi-factor experimental designs for exploring response surfaces.” Ann. Math. Stat. 28 (1): 195–241. https://doi.org/10.1214/aoms/1177707047.
Bramer, M. 2020. “Avoiding overfitting of decision trees.” In Principles of data mining, 121–136. London: Springer.
Briaud, J.-L., P. Gardoni, and C. Yao. 2014. “Statistical, risk, and reliability analyses of bridge scour.” J. Geotech. Geoenviron. Eng. 140 (2): 04013011. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000989.
Burton, H. V., and M. Mieler. 2021. “Machine learning applications: Hope, hype, or hindrance for structural engineering.” Struct. Mag. 6: 16–20.
Cardoni, A., A. Zamani Noori, R. Greco, and G. P. Cimellaro. 2021. “Resilience assessment at the regional level using census data.” Int. J. Disaster Risk Reduct. 55 (Mar): 102059. https://doi.org/10.1016/j.ijdrr.2021.102059.
Cha, Y.-J., W. Choi, and O. Büyüköztürk. 2017. “Deep learning-based crack damage detection using convolutional neural networks.” Comput.-Aided Civ. Infrastruct. Eng. 32 (5): 361–378. https://doi.org/10.1111/mice.12263.
Chen, S., and D. Feng. 2022. “Multifidelity approach for data-driven prediction models of structural behaviors with limited data.” Comput.-Aided Civ. Infrastruct. Eng. 1–16. https://doi.org/10.1111/mice.12817.
Chen, S., S. Zhang, W. Han, and G. Wu. 2021. “Ensemble learning based approach for FRP-concrete bond strength prediction.” Constr. Build. Mater. 302 (Oct): 124230. https://doi.org/10.1016/j.conbuildmat.2021.124230.
Chen, T., and C. Guestrin. 2016. “XGBoost: A scalable tree boosting system.” In Proc., 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 785–794. New York: ACM.
Cheng, L., and T. Yu. 2019. “A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems.” Int. J. Energy Res. 43 (6): 1928–1973. https://doi.org/10.1002/er.4333.
Chin, H.-C., and Y.-S. Su. 2005. “Application of the ant-based network for power system restoration.” In Proc., EEE/PES Transmission & Distribution Conf. & Exposition: Asia and Pacific, 1–5. New York: IEEE.
Ciampoli, M., F. Petrini, and G. Augusti. 2011. “Performance-based wind engineering: Towards a general procedure.” Struct. Saf. 33 (6): 367–378. https://doi.org/10.1016/j.strusafe.2011.07.001.
Cimellaro, G. P., A. M. Reinhorn, and M. Bruneau. 2010a. “Framework for analytical quantification of disaster resilience.” Eng. Struct. 32 (11): 3639–3649. https://doi.org/10.1016/j.engstruct.2010.08.008.
Cimellaro, G. P., A. M. Reinhorn, and M. Bruneau. 2010b. “Seismic resilience of a hospital system.” Struct. Infrastruct. Eng. 6 (1–2): 127–144. https://doi.org/10.1080/15732470802663847.
Codjo, E. L., B. Bakhshideh Zad, J.-F. Toubeau, B. François, and F. Vallée. 2021. “Machine learning-based classification of electrical low voltage cable degradation.” Energies 14 (10): 2852. https://doi.org/10.3390/en14102852.
Cortes, C., and V. Vapnik. 1995. “Support-vector networks.” Mach. Learn. 20 (3): 273–297. https://doi.org/10.1007/BF00994018.
Costa, R., T. Haukaas, and S. E. Chang. 2020. “Agent-based model for post-earthquake housing recovery.” Earthquake Spectra 37 (1): 46–72. https://doi.org/10.1177/8755293020944175.
Cui, Y., H. Ma, and T. K. Saha. 2013. “Power transformer condition assessment using support vector machine with heuristic optimization.” In Proc., Australasian Universities Power Engineering Conf. (AUPEC), 1–6. New York: IEEE.
da Silva, S., M. Dias Júnior, V. Lopes Junior, and M. J. Brennan. 2008. “Structural damage detection by fuzzy clustering.” Mech. Syst. Sig. Process. 22 (7): 1636–1649. https://doi.org/10.1016/j.ymssp.2008.01.004.
Degtyarev, V. V., and M. Z. Naser. 2021. “Boosting machines for predicting shear strength of CFS channels with staggered web perforations.” Structures 34: 3391–3403. https://doi.org/10.1016/j.istruc.2021.09.060.
Dehghani, N. L., A. B. Jeddi, and A. Shafieezadeh. 2021a. “Intelligent hurricane resilience enhancement of power distribution systems via deep reinforcement learning.” Appl. Energy 285 (Mar): 116355. https://doi.org/10.1016/j.apenergy.2020.116355.
Dehghani, N. L., S. Zamanian, and A. Shafieezadeh. 2021b. “Adaptive network reliability analysis: Methodology and applications to power grid.” Reliab. Eng. Syst. Saf. 216 (Dec): 107973. https://doi.org/10.1016/j.ress.2021.107973.
Dehghanian, P., B. Zhang, T. Dokic, and M. Kezunovic. 2019. “Predictive risk analytics for weather-resilient operation of electric power systems.” IEEE Trans. Sustainable Energy 10 (1): 3–15. https://doi.org/10.1109/TSTE.2018.2825780.
Deierlein, G. G., H. Krawinkler, and C. A. Cornell. 2003. “A framework for performance-based earthquake engineering.” In Proc., Pacific Conf. on Earthquake Engineering, 1–8. Tokyo: International Association of Earthquake Engineering.
De Iuliis, M., O. Kammouh, G. P. Cimellaro, and S. Tesfamariam. 2019. “Downtime estimation of building structures using fuzzy logic.” Int. J. Disaster Risk Reduct. 34 (Mar): 196–208. https://doi.org/10.1016/j.ijdrr.2018.11.017.
DeRousseau, M. A., E. Laftchiev, J. R. Kasprzyk, B. Rajagopalan, and W. V. Srubar. 2019. “A comparison of machine learning methods for predicting the compressive strength of field-placed concrete.” Constr. Build. Mater. 228 (Dec): 116661. https://doi.org/10.1016/j.conbuildmat.2019.08.042.
Dhulipala, S. L. N., H. V. Burton, and H. Baroud. 2021. “A Markov framework for generalized post-event systems recovery modeling: From single to multihazards.” Struct. Saf. 91 (Jul): 102091. https://doi.org/10.1016/j.strusafe.2021.102091.
Dick, K., L. Russell, Y. Souley Dosso, F. Kwamena, and J. R. Green. 2019. “Deep learning for critical infrastructure resilience.” J. Infrastruct. Syst. 25 (2): 05019003. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000477.
Diego, F.-J., B. Esteban, and P. Merello. 2015. “Design of a hybrid (wired/wireless) acquisition data system for monitoring of cultural heritage physical parameters in smart cities.” Sensors 15 (4): 7246–7266. https://doi.org/10.3390/s150407246.
Diez-Olivan, A., J. Del Ser, D. Galar, and B. Sierra. 2019. “Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0.” Inf. Fusion 50 (Oct): 92–111. https://doi.org/10.1016/j.inffus.2018.10.005.
Din, I. U., M. Guizani, J. J. P. C. Rodrigues, S. Hassan, and V. V. Korotaev. 2019. “Machine learning in the Internet of Things: Designed techniques for smart cities.” Future Gener. Comput. Syst. 100 (Nov): 826–843. https://doi.org/10.1016/j.future.2019.04.017.
DNV (Det Norske Veritas). 2004. RP-F101, corroded pipelines: DNV recommended practice. Oslo, Norway: Det Norske Veritas.
Du, A., and J. E. Padgett. 2020. “Investigation of multivariate seismic surrogate demand modeling for multi-response structural systems.” Eng. Struct. 207: 110210. https://doi.org/10.1016/j.engstruct.2020.110210.
Duchesne, L., E. Karangelos, and L. Wehenkel. 2017. “Machine learning of real-time power systems reliability management response.” In Proc., IEEE Manchester PowerTech, 1–6. New York: IEEE.
Dueñas-Osorio, L., J. I. Craig, B. J. Goodno, and A. Bostrom. 2007. “Interdependent response of networked systems.” J. Infrastruct. Syst. 13 (3): 185–194. https://doi.org/10.1061/(ASCE)1076-0342(2007)13:3(185).
El-Abbasy, M. S., A. Senouci, T. Zayed, F. Mirahadi, and L. Parvizsedghy. 2014. “Artificial neural network models for predicting condition of offshore oil and gas pipelines.” Autom. Constr. 45 (Sep): 50–65. https://doi.org/10.1016/j.autcon.2014.05.003.
El-Werfelli, M., R. Dunn, and P. Iravani. 2009. “Backbone-network reconfiguration for power system restoration using genetic algorithm and expert system.” In Proc., Int. Conf. on Sustainable Power Generation and Supply, 1–6. New York: IEEE.
Ezzeldin, M., and W. El-Dakhakhni. 2020. “Metaresearching structural engineering using text mining: Trend identifications and knowledge gap discoveries.” J. Struct. Eng. 146 (5): 04020061. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002523.
Fan, W., Y. Chen, J. Li, Y. Sun, J. Feng, H. Hassanin, and P. Sareh. 2021. “Machine learning applied to the design and inspection of reinforced concrete bridges: Resilient methods and emerging applications.” Structures 33: 3954–3963. https://doi.org/10.1016/j.istruc.2021.06.110.
Fan, X., X. Wang, X. Zhang, and X. B. Yu. 2022. “Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors.” Reliab. Eng. Syst. Saf. 219 (Mar): 108185. https://doi.org/10.1016/j.ress.2021.108185.
Fan, X., and X. Yu. 2021. “An innovative machine learning based framework for water distribution network leakage detection and localization.” Struct. Health Monit. 147592172110402. https://doi.org/10.1177/14759217211040269.
Fang, C., H. Tang, and Y. Li. 2020. “Stochastic response assessment of cross-sea bridges under correlated wind and waves via machine learning.” J. Bridge Eng. 25 (6): 04020025. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001554.
Farrar, C., and K. Worden. 2012. Structural health monitoring: A machine learning perspective. Hoboken, NJ: Wiley.
Feng, D., S. Chen, M. R. Azadi Kakavand, and E. Taciroglu. 2021a. “Probabilistic model based on Bayesian model averaging for predicting the plastic hinge lengths of reinforced concrete columns.” J. Eng. Mech. 147 (10): 04021066. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001976.
Feng, D., and B. Fu. 2020. “Shear strength of internal reinforced concrete beam-column joints: Intelligent modeling approach and sensitivity analysis.” Adv. Civ. Eng 2020: 8850417. https://doi.org/10.1155/2020/8850417.
Feng, D., W. Wang, S. Mangalathu, G. Hu, and T. Wu. 2021b. “Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements.” Eng. Struct. 235 (May): 111979. https://doi.org/10.1016/j.engstruct.2021.111979.
Feng, D., W. Wang, S. Mangalathu, and E. Taciroglu. 2021c. “Interpretable XGBoost-SHAP machine-learning model for shear strength prediction of squat RC walls.” J. Struct. Eng. 147 (11): 04021173. https://doi.org/10.1061/(ASCE)ST.1943-541X.0003115.
Feng, D. C., Z. T. Liu, X. D. Wang, Z. M. Jiang, and S. X. Liang. 2020. “Failure mode classification and bearing capacity prediction for reinforced concrete columns based on ensemble machine learning algorithm.” Adv. Eng. Inf. 45 (Aug): 101126. https://doi.org/10.1016/j.aei.2020.101126.
Fereshtehnejad, E., and A. Shafieezadeh. 2017. “A randomized point-based value iteration POMDP enhanced with a counting process technique for optimal management of multi-state multi-element systems.” Struct. Saf. 65 (Mar): 113–125. https://doi.org/10.1016/j.strusafe.2017.01.003.
FHWA (Federal Highway Administration). 1995. Recording and coding guide for the structure inventory and appraisal of the nation’s bridges. Washington, DC: USDOT.
Flah, M., I. Nunez, W. Ben Chaabene, and M. L. Nehdi. 2021. “Machine learning algorithms in civil structural health monitoring: A systematic review.” Arch. Comput. Methods Eng. 28 (4): 2621–2643. https://doi.org/10.1007/s11831-020-09471-9.
Flood, I. 2008. “Towards the next generation of artificial neural networks for civil engineering.” Adv. Eng. Inf. 22 (1): 4–14. https://doi.org/10.1016/j.aei.2007.07.001.
Friedman, J. 2001. “Greedy function approximation: A gradient boosting machine.” Ann. Stat. 29 (5): 1189–1232. https://doi.org/10.1214/aos/1013203451.
Fu, B., and D.-C. Feng. 2021. “A machine learning-based time-dependent shear strength model for corroded reinforced concrete beams.” J. Build. Eng. 36 (Apr): 102118. https://doi.org/10.1016/j.jobe.2020.102118.
Ghosn, M., et al. 2016. “Performance indicators for structural systems and infrastructure networks.” J. Struct. Eng. 142 (9): F4016003. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001542.
Ghosn, M., G. Fiorillo, M. Liu, and B. R. Ellingwood. 2019. Risk-based structural evaluation methods: Best practices and development of standards. Reston, VA: ASCE.
Gidaris, I., J. E. Padgett, A. R. Barbosa, S. Chen, D. Cox, B. Webb, and A. Cerato. 2017. “Multiple-hazard fragility and restoration models of highway bridges for regional risk and resilience assessment in the United States: State-of-the-art review.” J. Struct. Eng. 143 (3): 04016188. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001672.
Gonzalez, D., D. Rueda-Plata, A. B. Acevedo, J. C. Duque, R. Ramos-Pollán, A. Betancourt, and S. García. 2020. “Automatic detection of building typology using deep learning methods on street level images.” Build. Environ. 177 (Jun): 106805. https://doi.org/10.1016/j.buildenv.2020.106805.
González-Dueñas, C., and J. E. Padgett. 2021. “Performance-based coastal engineering framework.” Front. Built Environ. 7: 690715. https://doi.org/10.3389/fbuil.2021.690715.
Guan, X., H. Burton, and M. Shokrabadi. 2021. “A database of seismic designs, nonlinear models, and seismic responses for steel moment-resisting frame buildings.” Earthquake Spectra 37 (2): 1199–1222. https://doi.org/10.1177/8755293020971209.
Guikema, S. D., R. A. Davidson, and H. Liu. 2006. “Statistical models of the effects of tree trimming on power system outages.” IEEE Trans. Power Deliv. 21 (3): 1549–1557. https://doi.org/10.1109/TPWRD.2005.860238.
Guikema, S. D., and J. P. Goffelt. 2008. “A flexible count data regression model for risk analysis.” Risk Anal. Int. J. 28 (1): 213–223. https://doi.org/10.1111/j.1539-6924.2008.01014.x.
Guikema, S. D., R. Nateghi, S. M. Quiring, A. Staid, A. C. Reilly, and M. Gao. 2014. “Predicting hurricane power outages to support storm response planning.” IEEE Access 2 (Nov): 1364–1373. https://doi.org/10.1109/ACCESS.2014.2365716.
Halkos, G., and A. Zisiadou. 2020. “An overview of the technological environmental hazards over the last century.” Econ. Disasters Clim. Change 4 (2): 411–428. https://doi.org/10.1007/s41885-019-00053-z.
Han, S., S. D. Guikema, and S. M. Quiring. 2009. “Improving the predictive accuracy of hurricane power outage forecasts using generalized additive models.” Risk Anal Int. J. 29 (10): 1443–1453. https://doi.org/10.1111/j.1539-6924.2009.01280.x.
Hao, N., and Z. Dong. 2011. “Condition assessment of current transformer based on multi-classification support vector machine.” In Proc., Int. Conf. on Transportation, Mechanical, and Electrical Engineering (TMEE), 2402–2405. New York: IEEE.
Hassan, S. I., L. M. Dang, I. Mehmood, S. Im, C. Choi, J. Kang, Y.-S. Park, and H. Moon. 2019. “Underground sewer pipe condition assessment based on convolutional neural networks.” Autom. Constr. 106 (Oct): 102849. https://doi.org/10.1016/j.autcon.2019.102849.
Hastie, T., R. Tibshirani, and J. Friedman. 2009. The elements of statistical learning. Springer series in statistics. New York: Springer.
Haykin, S. 2008. Neural networks and learning machines, 936. Englewood Cliff, NJ: Prentice Hall.
Hegde, J., and B. Rokseth. 2020. “Applications of machine learning methods for engineering risk assessment—A review.” Saf. Sci. 122 (Feb): 104492. https://doi.org/10.1016/j.ssci.2019.09.015.
Heresi, P., and E. Miranda. 2021. “Fragility curves and methodology for estimating postearthquake occupancy of wood-frame single-family houses on a regional scale.” J. Struct. Eng. 147 (5): 04021039. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002989.
Hernandez-Fajardo, I., and L. Dueñas-Osorio. 2013. “Probabilistic study of cascading failures in complex interdependent lifeline systems.” Reliab. Eng. Syst. Saf. 111 (Mar): 260–272. https://doi.org/10.1016/j.ress.2012.10.012.
Hink, R. C. B., J. M. Beaver, M. A. Buckner, T. Morris, U. Adhikari, and S. Pan. 2014. “Machine learning for power system disturbance and cyber-attack discrimination.” In Proc., 7th Int. Symp. on Resilient Control Systems (ISRCS), 1–8. New York: IEEE.
Ho, T. K. 1995. “Random decision forests.” In Proc., Int. Conf. on Document Analysis and Recognition, ICDAR. New York: IEEE.
Hoerl, A. E., and R. W. Kennard. 1970. “Ridge regression: Biased estimation for nonorthogonal problems.” Technometrics 12 (1): 55–67. https://doi.org/10.1080/00401706.1970.10488634.
Huang, C., Y. Li, Q. Gu, and J. Liu. 2022. “Machine learning–based hysteretic lateral force-displacement models of reinforced concrete columns.” J. Struct. Eng. 148 (3): 04021291. https://doi.org/10.1061/(ASCE)ST.1943-541X.0003257.
Huang, H., and H. V. Burton. 2020. “A database of test results from steel and reinforced concrete infilled frame experiments.” Earthquake Spectra 36 (3): 1525–1548. https://doi.org/10.1177/8755293019899950.
Hwang, S.-H., S. Mangalathu, J. Shin, and J.-S. Jeon. 2021. “Machine learning-based approaches for seismic demand and collapse of ductile reinforced concrete building frames.” J. Build. Eng. 34 (Feb): 101905. https://doi.org/10.1016/j.jobe.2020.101905.
Ibrahim, M. S., W. Dong, and Q. Yang. 2020. “Machine learning driven smart electric power systems: Current trends and new perspectives.” Appl. Energy 272 (Aug): 115237. https://doi.org/10.1016/j.apenergy.2020.115237.
Jain, P., S. C. P. Coogan, S. G. Subramanian, M. Crowley, S. Taylor, and M. D. Flannigan. 2020. “A review of machine learning applications in wildfire science and management.” Environ. Rev. 28 (4): 478–505. https://doi.org/10.1139/er-2020-0019.
Jeon, J.-S., A. Shafieezadeh, and R. DesRoches. 2014. “Statistical models for shear strength of RC beam-column joints using machine-learning techniques.” Earthquake Eng. Struct. Dyn. 43 (14): 2075–2095. https://doi.org/10.1002/eqe.2437.
Ji, D., C. Li, C. Zhai, Y. Dong, E. I. Katsanos, and W. Wang. 2021. “Prediction of ground-motion parameters for the NGA-West2 database using refined second-order deep neural networks.” Bull. Seismol. Soc. Am. 111 (6): 3278–3296. https://doi.org/10.1785/0120200388.
Jiang, K., Q. Han, X. Du, and P. Ni. 2021. “A decentralized unsupervised structural condition diagnosis approach using deep auto-encoders.” Comput.-Aided Civ. Infrastruct. Eng. 36 (6): 711–732. https://doi.org/10.1111/mice.12641.
Jootoo, A., and D. Lattanzi. 2017. “Bridge type classification: Supervised learning on a modified NBI data set.” J. Comput. Civ. Eng. 31 (6): 04017063. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000712.
Jufri, F. H., S. Oh, J. Jung, and M.-H. Choi. 2019. “A method to forecast storm-caused distribution grid damages using cost-sensitive regression algorithm.” In Proc., IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), 3986–3990. New York: IEEE.
Kabir, E., S. D. Guikema, and S. M. Quiring. 2019. “Predicting thunderstorm-induced power outages to support utility restoration.” IEEE Trans. Power Syst. 34 (6): 4370–4381. https://doi.org/10.1109/TPWRS.2019.2914214.
Kafali, C., and M. Grigoriu. 2005. “Rehabilitation decision analysis.” In Proc., 9th Int. Conf. on Structural Safety and Reliability (ICOSSAR05), edited by G. Augusti, G. I. Schuëller, and M. Ciampoli. Rotterdam, Netherlands: MillPress.
Kalakonas, P., and V. Silva. 2021. “Seismic vulnerability modelling of building portfolios using artificial neural networks.” Earthquake Eng. Struct. Dyn. 51 (2): 310–327. https://doi.org/10.1002/eqe.3567.
Kammouh, O., G. P. Cimellaro, and S. A. Mahin. 2018. “Downtime estimation and analysis of lifelines after an earthquake.” Eng. Struct. 173 (Oct): 393–403. https://doi.org/10.1016/j.engstruct.2018.06.093.
Kang, H., H. V. Burton, and H. Miao. 2018. “Replicating the recovery following the 2014 south Napa Earthquake using stochastic process models.” Earthquake Spectra 34 (3): 1247–1266. https://doi.org/10.1193/012917EQS020M.
Kankanala, P., S. Das, and A. Pahwa. 2013. “AdaBoost+: An ensemble learning approach for estimating weather-related outages in distribution systems.” IEEE Trans. Power Syst. 29 (1): 359–367. https://doi.org/10.1109/TPWRS.2013.2281137.
Karamlou, A., and P. Bocchini. 2017. “From component damage to system-level probabilistic restoration functions for a damaged bridge.” J. Infrastruct. Syst. 23 (3): 04016042. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000342.
Karanci, E., and R. Betti. 2018. “Modeling corrosion in suspension bridge main cables. I: Annual corrosion rate.” J. Bridge Eng. 23 (6): 04018025. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001233.
Kawashima, K., Y. Takahashi, H. Ge, Z. Wu, and J. Zhang. 2009. “Reconnaissance report on damage of bridges in 2008 Wenchuan, China, earthquake.” J. Earthquake Eng. 13 (7): 965–996. https://doi.org/10.1080/13632460902859169.
Kiani, J., C. Camp, and S. Pezeshk. 2019. “On the application of machine learning techniques to derive seismic fragility curves.” Comput. Struct. 218 (Jul): 108–122. https://doi.org/10.1016/j.compstruc.2019.03.004.
Kim, H.-S. 2020. “Development of seismic response simulation model for building structures with semi-active control devices using recurrent neural network.” Appl. Sci. 10 (11): 3915. https://doi.org/10.3390/app10113915.
Koliou, M., J. W. van de Lindt, T. P. McAllister, B. R. Ellingwood, M. Dillard, and H. Cutler. 2020. “State of the research in community resilience: Progress and challenges.” Sustainable Resilient Infrastruct. 5 (3): 131–151. https://doi.org/10.1080/23789689.2017.1418547.
Kong, Q., D. T. Trugman, Z. E. Ross, M. J. Bianco, B. J. Meade, and P. Gerstoft. 2019. “Machine learning in seismology: Turning data into insights.” Seismol. Res. Lett. 90 (1): 3–14. https://doi.org/10.1785/0220180259.
Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2012. “ImageNet classification with deep convolutional neural networks.” In Advances in neural information processing systems, 1097–1105. Red Hook, NY: Curran Associates.
Kumar, S. S., D. M. Abraham, M. R. Jahanshahi, T. Iseley, and J. Starr. 2018. “Automated defect classification in sewer closed circuit television inspections using deep convolutional neural networks.” Autom. Constr. 91 (Jul): 273–283. https://doi.org/10.1016/j.autcon.2018.03.028.
Leauprasert, K., T. Suwanasri, C. Suwanasri, and N. Poonnoy. 2020. “Intelligent machine learning techniques for condition assessment of power transformers.” In Proc., Int. Conf. on Power, Energy and Innovations (ICPEI), 65–68. New York: IEEE.
Lee, J.-W., J. L. Irish, M. T. Bensi, and D. C. Marcy. 2021. “Rapid prediction of peak storm surge from tropical cyclone track time series using machine learning.” Coastal Eng. 170: 104024. https://doi.org/10.1016/j.coastaleng.2021.104024.
Lee, S., J. Ha, M. Zokhirova, H. Moon, and J. Lee. 2018. “Background information of deep learning for structural engineering.” Arch. Comput. Methods Eng. 25 (1): 121–129. https://doi.org/10.1007/s11831-017-9237-0.
Li, C., H. Li, and X. Chen. 2021. “A framework for fast estimation of structural seismic responses using ensemble machine learning model.” Smart Struct. Syst. 28 (3): 425–441. https://doi.org/10.12989/sss.2021.28.3.425.
Li, F., W. Wang, J. Xu, J. Yi, and Q. Wang. 2019. “Comparative study on vulnerability assessment for urban buried gas pipeline network based on SVM and ANN methods.” Process Saf. Environ. Prot. 122 (Feb): 23–32. https://doi.org/10.1016/j.psep.2018.11.014.
Li, M., and G. Jia. 2020. “Multifidelity Gaussian process model integrating low- and high-fidelity data considering censoring.” J. Struct. Eng. 146 (3): 04019215. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002531.
Li, S., S. Laima, and H. Li. 2018. “Data-driven modeling of vortex-induced vibration of a long-span suspension bridge using decision tree learning and support vector regression.” J. Wind Eng. Ind. Aerodyn. 172 (Jan): 196–211. https://doi.org/10.1016/j.jweia.2017.10.022.
Li, Y. 2012. “Assessment of damage risks to residential buildings and cost–benefit of mitigation strategies considering hurricane and earthquake hazards.” J. Perform. Constr. Facil. 26 (1): 7–16. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000204.
Li, Y., A. Ahuja, and J. E. Padgett. 2012. “Review of methods to assess, design for, and mitigate multiple hazards.” J. Perform. Constr. Facil. 26 (1): 104–117. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000279.
Li, Y., S. Yeddanapudi, J. D. McCalley, A. A. Chowdhury, and M. Moorehead. 2005. “Degradation-path model for wood pole asset management.” In Proc., 37th Annual North American Power Symp., 275–280. New York: IEEE.
Lim, S., and S. Chi. 2019. “XGBoost application on bridge management systems for proactive damage estimation.” Adv. Eng. Inf. 41 (Aug): 100922. https://doi.org/10.1016/j.aei.2019.100922.
Lin, C.-Y., and E. J. Cha. 2020. “Hurricane risk assessment for residential buildings in the southeastern US coastal region in changing climate conditions using artificial neural networks.” Nat. Hazard. Rev. 21 (3): 4020024. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000390.
Lin, P., and N. Wang. 2017. “Stochastic post-disaster functionality recovery of community building portfolios II: Application.” Struct. Saf. 69 (Nov): 106–117. https://doi.org/10.1016/j.strusafe.2017.05.004.
Liu, F., F. Xu, and S. Yang. 2017. “A flood forecasting model based on deep learning algorithm via integrating stacked autoencoders with BP neural network.” In Proc., IEEE 3rd Int. Conf. on Multimedia Big Data (BigMM), 58–61. New York: IEEE.
Liu, H., R. A. Davidson, D. V. Rosowsky, and J. R. Stedinger. 2005. “Negative binomial regression of electric power outages in hurricanes.” J. Infrastruct. Syst. 11 (4): 258–267. https://doi.org/10.1061/(ASCE)1076-0342(2005)11:4(258).
Liu, S., S. You, H. Yin, Z. Lin, Y. Liu, W. Yao, and L. Sundaresh. 2020. “Model-free data authentication for cyber security in power systems.” IEEE Trans. Smart Grid 11 (5): 4565–4568. https://doi.org/10.1109/TSG.2020.2986704.
Liu, W., and Z. Song. 2020. “Review of studies on the resilience of urban critical infrastructure networks.” Reliab. Eng. Syst. Saf. 193 (Jan): 106617. https://doi.org/10.1016/j.ress.2019.106617.
Liu, Y., and X. Gu. 2007. “Skeleton-network reconfiguration based on topological characteristics of scale-free networks and discrete particle swarm optimization.” IEEE Trans. Power Syst. 22 (3): 1267–1274. https://doi.org/10.1109/TPWRS.2007.901486.
Liu, Y., X. Ma, Y. Li, Y. Tie, Y. Zhang, and J. Gao. 2019. “Water pipeline leakage detection based on machine learning and wireless sensor networks.” Sensors 19 (23): 5086. https://doi.org/10.3390/s19235086.
Loggins, R. A., and W. A. Wallace. 2015. “Rapid assessment of hurricane damage and disruption to interdependent civil infrastructure systems.” J. Infrastruct. Syst. 21 (4): 04015005. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000249.
Loridan, T., R. P. Crompton, and E. Dubossarsky. 2017. “A machine learning approach to modeling tropical cyclone wind field uncertainty.” Mon. Weather Rev. 145 (8): 3203–3221. https://doi.org/10.1175/MWR-D-16-0429.1.
Lu, H., Z.-D. Xu, T. Iseley, and J. C. Matthews. 2021. “Novel data-driven framework for predicting residual strength of corroded pipelines.” J. Pipeline Syst. Eng. Pract. 12 (4): 04021045. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000587.
Lu, Q., and W. Zhang. 2021. “Integrating dynamic Bayesian network and physics-based modeling for risk analysis of a time-dependent power distribution system during hurricanes.” Reliab. Eng. Syst. Saf. 220 (Jan): 108290. https://doi.org/10.1016/j.ress.2021.108290.
Lu, Z. H., H. Li, W. Li, Y. G. Zhao, and W. Dong. 2018. “An empirical model for the shear strength of corroded reinforced concrete beam.” Constr. Build. Mater. 188 (Nov): 1234–1248. https://doi.org/10.1016/j.conbuildmat.2018.08.123.
Lundberg, S. M., and S.-I. Lee. 2017. “A unified approach to interpreting model predictions.” In Proc., 31st Conf. on Neural Information Processing Systems (NIPS 2017). Red Hook, NY: Curran Associates.
Luo, H., and S. G. Paal. 2018. “Machine learning-based backbone curve model of reinforced concrete columns subjected to cyclic loading reversals.” J. Comput. Civ. Eng. 32 (5): 04018042. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000787.
Luo, H., and S. G. Paal. 2021. “Data-driven seismic response prediction of structural components.” Earthquake Spectra 875529302110533. https://doi.org/10.1177/87552930211053345.
Ma, H., T. K. Saha, and C. Ekanayake. 2012. “Statistical learning techniques and their applications for condition assessment of power transformer.” IEEE Trans. Dielectr. Electr. Insul. 19 (2): 481–489. https://doi.org/10.1109/TDEI.2012.6180241.
Makkeasorn, A., N.-B. Chang, and X. Zhou. 2008. “Short-term streamflow forecasting with global climate change implications—A comparative study between genetic programming and neural network models.” J. Hydrol. 352 (3–4): 336–354. https://doi.org/10.1016/j.jhydrol.2008.01.023.
Mangalathu, S., S. H. Hwang, and J. S. Jeon. 2020a. “Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach.” Eng. Struct. 219 (Sep): 110927. https://doi.org/10.1016/j.engstruct.2020.110927.
Mangalathu, S., H. Jang, S. H. Hwang, and J. S. Jeon. 2020b. “Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls.” Eng. Struct. 208 (Apr): 110331. https://doi.org/10.1016/j.engstruct.2020.110331.
Mangalathu, S., and J.-S. Jeon. 2019a. “Machine learning–based failure mode recognition of circular reinforced concrete bridge columns: Comparative study.” J. Struct. Eng. 145 (10): 04019104. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002402.
Mangalathu, S., and J.-S. Jeon. 2019b. “Stripe-based fragility analysis of multispan concrete bridge classes using machine learning techniques.” Earthquake Eng. Struct. Dyn. 48 (11): 1238–1255. https://doi.org/10.1002/eqe.3183.
Mangalathu, S., and J.-S. Jeon. 2020. “Regional seismic risk assessment of infrastructure systems through machine learning: Active learning approach.” J. Struct. Eng. 146 (12): 04020269. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002831.
Mangalathu, S., J.-S. Jeon, and R. DesRoches. 2018. “Critical uncertainty parameters influencing seismic performance of bridges using LASSO regression.” Earthquake Eng. Struct. Dyn. 47 (3): 784–801. https://doi.org/10.1002/eqe.2991.
Mangalathu, S., K. Karthikeyan, D.-C. Feng, and J.-S. Jeon. 2022. “Machine-learning interpretability techniques for seismic performance assessment of infrastructure systems.” Eng. Struct. 250 (Jan): 112883. https://doi.org/10.1016/j.engstruct.2021.112883.
Mangalathu, S., H. Shin, E. Choi, and J.-S. Jeon. 2021. “Explainable machine learning models for punching shear strength estimation of flat slabs without transverse reinforcement.” J. Build. Eng. 39 (Jul): 102300. https://doi.org/10.1016/j.jobe.2021.102300.
Mangalathu, S., H. Sun, C. C. Nweke, Z. Yi, and H. V. Burton. 2020c. “Classifying earthquake damage to buildings using machine learning.” Earthquake Spectra 36 (1): 183–208. https://doi.org/10.1177/8755293019878137.
Mazumder, R. K., A. M. Salman, and Y. Li. 2021a. “Failure risk analysis of pipelines using data-driven machine learning algorithms.” Struct. Saf. 89 (Mar): 102047. https://doi.org/10.1016/j.strusafe.2020.102047.
Mazumder, R. K., A. M. Salman, and Y. Li. 2021b. “Post-disaster sequential recovery planning for water distribution systems using topological and hydraulic metrics.” Struct. Infrastruct. Eng. 18 (5): 728–743. https://doi.org/10.1080/15732479.2020.1864415.
Mazumder, R. K., A. M. Salman, Y. Li, and X. Yu. 2018. “Performance evaluation of water distribution systems and asset management.” J. Infrastruct. Syst. 24 (3): 03118001. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000426.
Miao, P., H. Yokota, and Y. Zhang. 2021. “Deterioration prediction of existing concrete bridges using a LSTM recurrent neural network.” Struct. Infrastruct. Eng. 1–15. https://doi.org/10.1080/15732479.2021.1951778.
Misra, S., J. E. Padgett, A. R. Barbosa, and B. M. Webb. 2020. “An expert opinion survey on post-hazard restoration of roadways and bridges: Data and key insights.” Earthquake Spectra 36 (2): 983–1004. https://doi.org/10.1177/8755293019891722.
Mitoulis, S. A., and S. A. Argyroudis. 2021. “Restoration models of flood resilient bridges: Survey data.” Data Brief 36 (Apr): 107088. https://doi.org/10.1016/j.dib.2021.107088.
Mitoulis, S. A., S. A. Argyroudis, M. Loli, and B. Imam. 2021. “Restoration models for quantifying flood resilience of bridges.” Eng. Struct. 238 (Jul): 112180. https://doi.org/10.1016/j.engstruct.2021.112180.
Morfidis, K., and K. Kostinakis. 2017. “Seismic parameters’ combinations for the optimum prediction of the damage state of R/C buildings using neural networks.” Adv. Eng. Software 106 (Apr): 1–16. https://doi.org/10.1016/j.advengsoft.2017.01.001.
Morgenthal, G., and N. Hallermann. 2014. “Quality assessment of unmanned aerial vehicle (UAV) based visual inspection of structures.” Adv. Struct. Eng. 17 (3): 289–302. https://doi.org/10.1260/1369-4332.17.3.289.
Mottahedi, A., F. Sereshki, M. Ataei, A. Nouri Qarahasanlou, and A. Barabadi. 2021. “The resilience of critical infrastructure systems: A systematic literature review.” Energies 14 (6): 1571. https://doi.org/10.3390/en14061571.
Mousavi, M. J., and K. L. Butler-Purry. 2009. “A novel condition assessment system for underground distribution applications.” IEEE Trans. Power Syst. 24 (3): 1115–1125. https://doi.org/10.1109/TPWRS.2009.2022977.
Muhammad, R., M. M. Boukar, S. Adeshina, and N. M. Ibrahim. 2021. “Ensemble learning models for predicting failure of oil pipelines.” SSRN Electr. J. https://doi.org/10.2139/ssrn.3882379.
Nagata, T., H. Sasaki, and M. Kitagawa. 1995. “Power system restoration by joint usage of expert system and mathematical programming approach.” IEEJ Trans. Power Energy 115 (5): 459–465. https://doi.org/10.1541/ieejpes1990.115.5_459.
Nateghi, R. 2018. “Multi-dimensional infrastructure resilience modeling: An application to hurricane-prone electric power distribution systems.” IEEE Access 6 (Jan): 13478–13489. https://doi.org/10.1109/ACCESS.2018.2792680.
Nateghi, R., S. Guikema, and S. M. Quiring. 2014. “Power outage estimation for tropical cyclones: Improved accuracy with simpler models.” Risk Anal. Int. J. 34 (6): 1069–1078. https://doi.org/10.1111/risa.12131.
Nateghi, R., S. D. Guikema, and S. M. Quiring. 2011. “Comparison and validation of statistical methods for predicting power outage durations in the event of hurricanes.” Risk Anal. Int. J. 31 (12): 1897–1906. https://doi.org/10.1111/j.1539-6924.2011.01618.x.
National Research Council. 2012. Disaster resilience: A national imperative. Washington, DC: National Academies Press.
Nguyen-Sy, T., J. Wakim, Q. D. To, M. N. Vu, T. D. Nguyen, and T. T. Nguyen. 2020. “Predicting the compressive strength of concrete from its compositions and age using the extreme gradient boosting method.” Constr. Build. Mater. 260 (Nov): 119757. https://doi.org/10.1016/j.conbuildmat.2020.119757.
Ni, P., S. Mangalathu, and K. Liu. 2020. “Enhanced fragility analysis of buried pipelines through Lasso regression.” Acta Geotech. 15 (2): 471–487. https://doi.org/10.1007/s11440-018-0719-5.
Ning, C.-L., and B. Li. 2016. “Probabilistic approach for estimating plastic hinge length of reinforced concrete columns.” J. Struct. Eng. 142 (3): 04015164. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001436.
Oh, S., K. Heo, F. H. Jufri, M. Choi, and J. Jung. 2021. “Storm-induced power grid damage forecasting method for solving low probability event data.” IEEE Access 9: 20521–20530. https://doi.org/10.1109/ACCESS.2021.3055146.
Ospina, C., G. Birkle, W. Y. Widianto, S. Fernando, S. Fernando, A. Catlin, and S. Pujol. 2015. “NEES: ACI 445 punching shear collected databank.” Accessed September 30, 2021. https://datacenterhub.org/resources/256.
Ouadah, A. 2018. “Pipeline defects risk assessment using machine learning and analytical hierarchy process.” In Proc., Int. Conf. on Applied Smart Systems (ICASS), 1–6. New York: IEEE.
Ouyang, M. 2014. “Review on modeling and simulation of interdependent critical infrastructure systems.” Reliab. Eng. Syst. Saf. 121 (Jan): 43–60. https://doi.org/10.1016/j.ress.2013.06.040.
Pan, S., T. Morris, and U. Adhikari. 2015. “Classification of disturbances and cyber-attacks in power systems using heterogeneous time-synchronized data.” IEEE Trans. Ind. Inf. 11 (3): 650–662. https://doi.org/10.1109/TII.2015.2420951.
Pang, Y., X. Dang, and W. Yuan. 2014. “An artificial neural network based method for seismic fragility analysis of highway bridges.” Adv. Struct. Eng. 17 (3): 413–428. https://doi.org/10.1260/1369-4332.17.3.413.
Pang, Y., and X. Wang. 2021a. “Cloud-IDA-MSA conversion of fragility curves for efficient and high-fidelity resilience assessment.” J. Struct. Eng. 147 (5): 04021049. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002998.
Pang, Y., and X. Wang. 2021b. “Enhanced endurance-time-method (EETM) for efficient seismic fragility, risk and resilience assessment of structures.” Soil Dyn. Earthquake Eng. 147 (Aug): 106731. https://doi.org/10.1016/j.soildyn.2021.106731.
Pang, Y., X. Zhou, W. He, J. Zhong, and O. Hui. 2021. “Uniform design–based Gaussian process regression for data-driven rapid fragility assessment of bridges.” J. Struct. Eng. 147 (4): 04021008. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002953.
Parrado-Hernández, E., G. Robles, J. A. Ardila-Rey, and J. M. Martínez-Tarifa. 2018. “Robust condition assessment of electrical equipment with one class support vector machines based on the measurement of partial discharges.” Energies 11 (3): 486. https://doi.org/10.3390/en11030486.
Priyanka, E. B., S. Thangavel, X.-Z. Gao, and N. S. Sivakumar. 2021. “Digital twin for oil pipeline risk estimation using prognostic and machine learning techniques.” J. Ind. Inf. Integr. 26 (Mar): 100272. https://doi.org/10.1016/j.jii.2021.100272.
Pyayt, A. L., I. I. Mokhov, B. Lang, V. V. Krzhizhanovskaya, and R. J. Meijer. 2011. “Machine learning methods for environmental monitoring and flood protection.” Int. J. Comput. Inf. Eng. 5 (6): 549–554. https://doi.org/10.5281/zenodo.1075060.
Rachman, A., T. Zhang, and R. M. C. Ratnayake. 2021. “Applications of machine learning in pipeline integrity management: A state-of-the-art review.” Int. J. Press. Vessels Pip. 193 (Oct): 104471. https://doi.org/10.1016/j.ijpvp.2021.104471.
Rashid, S., U. Akram, and S. A. Khan. 2015. “WML: Wireless sensor network based machine learning for leakage detection and size estimation.” Procedia Comput. Sci. 63 (Jan): 171–176. https://doi.org/10.1016/j.procs.2015.08.329.
Rathje, E. M., et al. 2017. “DesignSafe: New cyberinfrastructure for natural hazards engineering.” Nat. Hazard. Rev. 18 (3): 06017001. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000246.
Reich, Y. 1997. “Machine learning techniques for civil engineering problems.” Comput.-Aided Civ. Infrastruct. Eng. 12 (4): 295–310. https://doi.org/10.1111/0885-9507.00065.
Reuland, Y., P. Lestuzzi, and I. F. C. Smith. 2019. “Measurement-based support for post-earthquake assessment of buildings.” Struct. Infrastruct. Eng. 15 (5): 647–662. https://doi.org/10.1080/15732479.2019.1569071.
Ribeiro, M. T., S. Singh, and C. Guestrin. 2016. “‘Why should I trust you?’ Explaining the predictions of any classifier.” In Proc., 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 1135–1144. New York: ACM.
Rinaldi, S. M., J. P. Peerenboom, and T. K. Kelly. 2001. “Identifying, understanding, and analyzing critical infrastructure interdependencies.” IEEE Control Syst. Mag. 21 (6): 11–25. https://doi.org/10.1109/37.969131.
Salehi, H., and R. Burgueño. 2018. “Emerging artificial intelligence methods in structural engineering.” Eng. Struct. 171 (Sep): 170–189. https://doi.org/10.1016/j.engstruct.2018.05.084.
Sangeetha, S., and D. Jayakumar. 2018. “Flash flood forecasting using different artificial intelligence method.” Int. J. Eng. Trends Technol. 59 (3): 140–144. https://doi.org/10.14445/22315381/IJETT-V59P225.
Saravi, S., R. Kalawsky, D. Joannou, M. Rivas Casado, G. Fu, and F. Meng. 2019. “Use of artificial intelligence to improve resilience and preparedness against adverse flood events.” Water 11 (5): 973. https://doi.org/10.3390/w11050973.
Sarzaeim, P., O. Bozorg-Haddad, A. Bozorgi, and H. A. Loáiciga. 2017. “Runoff projection under climate change conditions with data-mining methods.” J. Irrig. Drain. Eng. 143 (8): 4017026. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001205.
Sayghe, A., Y. Hu, I. Zografopoulos, X. Liu, R. G. Dutta, Y. Jin, and C. Konstantinou. 2020. “Survey of machine learning methods for detecting false data injection attacks in power systems.” IET Smart Grid 3 (5): 581–595. https://doi.org/10.1049/iet-stg.2020.0015.
Schmidhuber, J. 2015. “Deep learning in neural networks: An overview.” Neural Netw. 61 (Jan): 85–117. https://doi.org/10.1016/j.neunet.2014.09.003.
Senouci, A., M. Elabbasy, E. Elwakil, B. Abdrabou, and T. Zayed. 2014. “A model for predicting failure of oil pipelines.” Struct. Infrastruct. Eng. 10 (3): 375–387. https://doi.org/10.1080/15732479.2012.756918.
Sfahani, M. G., H. Guan, and Y. C. Loo. 2015. “Seismic reliability and risk assessment of structures based on fragility analysis—A review.” Adv. Struct. Eng. 18 (10): 1653–1669. https://doi.org/10.1260/1369-4332.18.10.1653.
Shinozuka, M., Y. Murachi, X. Dong, Y. Zhou, and M. J. Orlikowski. 2003. “Effect of seismic retrofit of bridges on transportation networks.” Earthquake Eng. Eng. Vibr. 2 (2): 169–179. https://doi.org/10.1007/s11803-003-0001-0.
Snaiki, R., T. Wu, A. S. Whittaker, and J. F. Atkinson. 2020. “Hurricane wind and storm surge effects on coastal bridges under a changing climate.” Transp. Res. Rec. 2674 (6): 23–32. https://doi.org/10.1177/0361198120917671.
Soleimani, F., and D. Hajializadeh. 2022. “Bridge seismic hazard resilience assessment with ensemble machine learning.” Structures 38: 719–732. https://doi.org/10.1016/j.istruc.2022.02.013.
Somala, S. N., S. Chanda, K. Karthikeyan, and S. Mangalathu. 2021. “Explainable machine learning on New Zealand strong motion for PGV and PGA.” Structures 34: 4977–4985. https://doi.org/10.1016/j.istruc.2021.10.085.
Sony, S., K. Dunphy, A. Sadhu, and M. Capretz. 2021. “A systematic review of convolutional neural network-based structural condition assessment techniques.” Eng. Struct. 226 (Jan): 111347. https://doi.org/10.1016/j.engstruct.2020.111347.
Steinberg, L. J., and A. M. Cruz. 2004. “When natural and technological disasters collide: Lessons from the Turkey earthquake of August 17, 1999.” Nat. Hazard. Rev. 5 (3): 121–130. https://doi.org/10.1061/(ASCE)1527-6988(2004)5:3(121).
Stephens, D. R., and B. N. Leis. 2000. “Development of an alternative criterion for residual strength of corrosion defects in moderate- to high-toughness pipe.” In Vol. 2 of Proc., 3rd Int. Pipeline Conf.: Integrity and Corrosion; Offshore Issues; Pipeline Automation and Measurement; Rotating Equipment. New York: ASME. https://doi.org/10.1115/IPC2000-192.
Stojadinović, Z., M. Kovačević, D. Marinković, and B. Stojadinović. 2022. “Rapid earthquake loss assessment based on machine learning and representative sampling.” Earthquake Spectra 38 (1): 152–177. https://doi.org/10.1177/87552930211042393.
Su, Y., J. Li, B. Yu, Y. Zhao, and J. Yao. 2021. “Fast and accurate prediction of failure pressure of oil and gas defective pipelines using the deep learning model.” Reliab. Eng. Syst. Saf. 216 (Dec): 108016. https://doi.org/10.1016/j.ress.2021.108016.
Sun, H., H. Burton, and J. Wallace. 2019. “Reconstructing seismic response demands across multiple tall buildings using kernel-based machine learning methods.” Struct. Control Health Monit. 26 (7): 1–26. https://doi.org/10.1002/stc.2359.
Sun, H., H. V. Burton, and H. Huang. 2021. “Machine learning applications for building structural design and performance assessment: State-of-the-art review.” J. Build. Eng. 33 (Jan): 101816. https://doi.org/10.1016/j.jobe.2020.101816.
Sun, H., Z. Wang, J. Wang, Z. Huang, N. Carrington, and J. Liao. 2016. “Data-driven power outage detection by social sensors.” IEEE Trans. Smart Grid 7 (5): 2516–2524. https://doi.org/10.1109/TSG.2016.2546181.
Sun, J., and Z. Zhang. 2020. “A post-disaster resource allocation framework for improving resilience of interdependent infrastructure networks.” Transp. Res. Part D Transp. Environ. 85 (Aug): 102455. https://doi.org/10.1016/j.trd.2020.102455.
Sun, L., Z. Shang, Y. Xia, S. Bhowmick, and S. Nagarajaiah. 2020. “Review of bridge structural health monitoring aided by big data and artificial intelligence: From condition assessment to damage detection.” J. Struct. Eng. 146 (5): 04020073. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002535.
Sweet, W. W. V., R. Kopp, C. P. Weaver, J. T. B. Obeysekera, R. M. Horton, E. R. Thieler, and C. E. Zervas. 2017. “Global and regional sea level rise scenarios for the United States. Silver Spring, MD: National Oceanic and Atmospheric Administration.
Szczyrba, L., Y. Zhang, D. Pamukcu, D. I. Eroglu, and R. Weiss. 2021. “Quantifying the role of vulnerability in hurricane damage via a machine learning case study.” Nat. Hazard. Rev. 22 (3): 04021028. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000460.
Tang, Q., J. Dang, Y. Cui, X. Wang, and J. Jia. 2021. “Machine learning-based fast seismic risk assessment of building structures.” J. Earthquake Eng. 1–22. https://doi.org/10.1080/13632469.2021.1987354.
Tao, W., P. Lin, and N. Wang. 2021. “Optimum life-cycle maintenance strategies of deteriorating highway bridges subject to seismic hazard by a hybrid Markov decision process model.” Struct. Saf. 89 (Mar): 102042. https://doi.org/10.1016/j.strusafe.2020.102042.
Tao, W., N. Wang, B. Ellingwood, and C. Nicholson. 2020. “Enhancing bridge performance following earthquakes using Markov decision process.” Struct. Infrastruct. Eng. 17 (1): 62–73. https://doi.org/10.1080/15732479.2020.1730410.
Tibshirani, R. 1996. “Regression shrinkage and selection via the Lasso.” J. R. Stat. Soc. B 58 (1): 267–288.
Tomar, A., and H. V. Burton. 2021. “Active learning method for risk assessment of distributed infrastructure systems.” Comput.-Aided Civ. Infrastruct. Eng. 36 (4): 438–452. https://doi.org/10.1111/mice.12665.
Tu, H., Y. Xia, J. Wu, and X. Zhou. 2019. “Robustness assessment of cyber–physical systems with weak interdependency.” Phys. A 522 (May): 9–17. https://doi.org/10.1016/j.physa.2019.01.137.
Vu, Q., V. Truong, and H. Thai. 2021. “Machine learning-based prediction of CFST columns using gradient tree boosting algorithm.” Compos. Struct. 259 (Mar): 113505. https://doi.org/10.1016/j.compstruct.2020.113505.
Wang, C., Q. Yu, K. H. Law, F. McKenna, S. X. Yu, E. Taciroglu, A. Zsarnóczay, W. Elhaddad, and B. Cetiner. 2021a. “Machine learning-based regional scale intelligent modeling of building information for natural hazard risk management.” Autom. Constr. 122 (Feb): 103474. https://doi.org/10.1016/j.autcon.2020.103474.
Wang, H., and T. Wu. 2020. “Knowledge-enhanced deep learning for wind-induced nonlinear structural dynamic analysis.” J. Struct. Eng. 146 (11): 04020235. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002802.
Wang, S., X. Gu, S. Luan, and M. Zhao. 2021b. “Resilience analysis of interdependent critical infrastructure systems considering deep learning and network theory.” Int. J. Crit. Infrastruct. Prot. 35 (Dec): 100459. https://doi.org/10.1016/j.ijcip.2021.100459.
Wang, X., J. Fang, L. Zhou, and A. Ye. 2019a. “Transverse seismic failure mechanism and ductility of reinforced concrete pylon for long span cable-stayed bridges: Model test and numerical analysis.” Eng. Struct. 189 (Jun): 206–221. https://doi.org/10.1016/j.engstruct.2019.03.045.
Wang, X., Z. Li, and A. Shafieezadeh. 2021c. “Seismic response prediction and variable importance analysis of extended pile-shaft-supported bridges against lateral spreading: Exploring optimized machine learning models.” Eng. Struct. 236 (Jun): 112142. https://doi.org/10.1016/j.engstruct.2021.112142.
Wang, X., E. Lo, L. De Vivo, T. C. Hutchinson, and F. Kuester. 2021d. “Monitoring the earthquake response of full-scale structures using UAV vision-based techniques.” Struct. Control Health Monit. 29 (1): e2862. https://doi.org/10.1002/stc.2862.
Wang, X., A. Shafieezadeh, and A. Ye. 2019b. “Optimal EDPs for post-earthquake damage assessment of extended pile-shaft–supported bridges subjected to transverse spreading.” Earthquake Spectra 35 (3): 1367–1396. https://doi.org/10.1193/090417EQS171M.
Wang, X., A. Ye, Z. He, and Y. Shang. 2016. “Quasi-static cyclic testing of elevated RC pile-cap foundation for bridge structures.” J. Bridge Eng. 21 (2): 04015042. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000797.
Wang, X., A. Ye, Y. Shang, and L. Zhou. 2019c. “Shake-table investigation of scoured RC pile-group-supported bridges in liquefiable and nonliquefiable soils.” Earthquake Eng. Struct. Dyn. 48 (11): 1217–1237. https://doi.org/10.1002/eqe.3186.
Wu, J., and J. W. Baker. 2020. “Statistical learning techniques for the estimation of lifeline network performance and retrofit selection.” Reliab. Eng. Syst. Saf. 200 (Aug): 106921. https://doi.org/10.1016/j.ress.2020.106921.
Wu, J. Y., and M. K. Lindell. 2004. “Housing reconstruction after two major earthquakes: The 1994 Northridge earthquake in the United States and the 1999 Chi-Chi earthquake in Taiwan.” Disasters 28 (1): 63–81. https://doi.org/10.1111/j.0361-3666.2004.00243.x.
Wu, R.-T., and M. R. Jahanshahi. 2019. “Deep convolutional neural network for structural dynamic response estimation and system identification.” J. Eng. Mech. 145 (1): 04018125. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001556.
Wuebbles, D. J., D. W. Fahey, K. A. Hibbard, J. R. Arnold, B. DeAngelo, S. Doherty, D. R. Easterling, J. Edmonds, T. Edmonds, and T. Hall. 2017. Climate science special report: Fourth national climate assessment, Volume I. Edited by D. J. Wuebbles, D. W. Fahey, K. A. Hibbard, D. J. Dokken, B. C. Stewart, and T. K. Maycock. Washington, DC: US Global Change Research Program.
Xie, M., and Z. Tian. 2018. “A review on pipeline integrity management utilizing in-line inspection data.” Eng. Fail. Anal. 92 (Oct): 222–239. https://doi.org/10.1016/j.engfailanal.2018.05.010.
Xie, Y., M. Ebad Sichani, J. E. Padgett, and R. DesRoches. 2020. “The promise of implementing machine learning in earthquake engineering: A state-of-the-art review.” Earthquake Spectra 36 (4): 1769–1801. https://doi.org/10.1177/8755293020919419.
Xie, Y., Y. Huo, and J. Zhang. 2017. “Development and validation of p-y modeling approach for seismic response predictions of highway bridges.” Earthquake Eng. Struct. Dyn. 46 (4): 585–604. https://doi.org/10.1002/eqe.2804.
Xie, Y., C. Ning, and L. Sun. 2022. “The twenty-first century of structural engineering research: A topic modeling approach.” Structures 35: 577–590. https://doi.org/10.1016/j.istruc.2022.02.013.
Xu, H., and S. K. Sinha. 2021. “Modeling pipe break data using survival analysis with machine learning imputation methods.” J. Perform. Constr. Facil. 35 (5): 04021071. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001649.
Xu, J.-G., S.-Z. Chen, W.-J. Xu, and Z.-S. Shen. 2021. “Concrete-to-concrete interface shear strength prediction based on explainable extreme gradient boosting approach.” Constr. Build. Mater. 308 (Nov): 125088. https://doi.org/10.1016/j.conbuildmat.2021.125088.
Yagan, O., D. Qian, J. Zhang, and D. Cochran. 2012. “Optimal allocation of interconnecting links in cyber-physical systems: Interdependence, cascading failures, and robustness.” IEEE Trans. Parallel Distrib. Syst. 23 (9): 1708–1720. https://doi.org/10.1109/TPDS.2012.62.
Ye, X. W., T. Jin, and C. B. Yun. 2019. “A review on deep learning-based structural health monitoring of civil infrastructures.” Smart Struct. Syst. 24 (5): 567–586. https://doi.org/10.12989/sss.2019.24.5.567.
Yeh, I.-C. 1998. “Modeling of strength of high-performance concrete using artificial neural networks.” Cem. Concr. Res. 28 (12): 1797–1808. https://doi.org/10.1016/S0008-8846(98)00165-3.
Yeh, I.-C. 2008. “Modeling slump of concrete with fly ash and superplasticizer.” Comput. Concr. 5 (6): 559–572. https://doi.org/10.12989/cac.2008.5.6.559.
Yousefi, S., H. R. Pourghasemi, S. N. Emami, S. Pouyan, S. Eskandari, and J. P. Tiefenbacher. 2020. “A machine learning framework for multi-hazards modeling and mapping in a mountainous area.” Sci. Rep. 10 (1): 12144. https://doi.org/10.1038/s41598-020-69233-2.
Yu, Q., C. Wang, F. McKenna, S. X. Yu, E. Taciroglu, B. Cetiner, and K. H. Law. 2020. “Rapid visual screening of soft-story buildings from street view images using deep learning classification.” Earthquake Eng. Eng. Vibr. 19 (4): 827–838. https://doi.org/10.1007/s11803-020-0598-2.
Zhang, R., Y. Liu, and H. Sun. 2020. “Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling.” Eng. Struct. 215 (Jul): 110704. https://doi.org/10.1016/j.engstruct.2020.110704.
Zhang, W., N. Wang, and C. Nicholson. 2017. “Resilience-based post-disaster recovery strategies for road-bridge networks.” Struct. Infrastruct. Eng. 13 (11): 1404–1413. https://doi.org/10.1080/15732479.2016.1271813.
Zhang, Y., and W. G. Peacock. 2009. “Planning for housing recovery? Lessons learned from hurricane Andrew.” J. Am. Plann. Assoc. 76 (1): 5–24. https://doi.org/10.1080/01944360903294556.
Zimmerman, A. T., and J. P. Lynch. 2006. “Data driven model updating using wireless sensor networks.” In Proc., 3rd Annual ANCRiSST Workshop. Daejeon, Korea: Techno-Press.
Zou, H., and T. Hastie. 2005. “Regularization and variable selection via the elastic net.” J. R. Stat. Soc. B 67 (2): 301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x.
Information & Authors
Information
Published In
Journal of Structural Engineering
Volume 148 • Issue 8 • August 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Published online: Jun 9, 2022
Published in print: Aug 1, 2022
Discussion open until: Nov 9, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Bo Xu, Xiaowei Wang, Chuang-Sheng Walter Yang, Yue Li, Machine Learning–Aided Rapid Estimation of Multilevel Capacity of Flexure-Identified Circular Concrete Bridge Columns with Corroded Reinforcement, Journal of Structural Engineering, 10.1061/JSENDH.STENG-12718, 150, 3, (2024).
- Shangtao Hu, Dongliang Meng, Menggang Yang, Renkang Hu, Parametric optimization of the Eddy current dampers applied to a suspension bridge considering bi-directional earthquakes, Journal of Vibration and Control, 10.1177/10775463231161456, (107754632311614), (2023).
- Gia Toai Truong, Kyoung-Kyu Choi, Tri-Hai Nguyen, Chang-Soo Kim, Prediction of shear strength of RC deep beams using XGBoost regression with Bayesian optimization, European Journal of Environmental and Civil Engineering, 10.1080/19648189.2023.2169357, (1-21), (2023).
- Wanting “Lisa” Wang, Maria Watson, John W. van de Lindt, Yu Xiao, Commercial Building Recovery Methodology for Use in Community Resilience Modeling, Natural Hazards Review, 10.1061/NHREFO.NHENG-1728, 24, 4, (2023).
- De-Cheng Feng, Yue Li, Abdollah Shafieezadeh, Ertugrul Taciroglu, Advances in Data-Driven Risk-Based Performance Assessment of Structures and Infrastructure Systems, Journal of Structural Engineering, 10.1061/JSENDH.STENG-12434, 149, 5, (2023).
- Shi-Zhi Chen, Shu-Ying Zhang, De-Cheng Feng, Ertugrul Taciroglu, Embedding Prior Knowledge into Data-Driven Structural Performance Prediction to Extrapolate from Training Domains, Journal of Engineering Mechanics, 10.1061/JENMDT.EMENG-7062, 149, 12, (2023).
- Tao Yang, Yuanyuan Wei, Jian Zhong, Potential bias of conventional structural seismic fragility for bridge structures under pulse-like ground motions: Bias evaluation and strategy improvement, Soil Dynamics and Earthquake Engineering, 10.1016/j.soildyn.2023.107787, 166, (107787), (2023).
- Junjun Guo, Aijun Ye, Xiaowei Wang, Zhongguo Guan, OpenSeesPyView: Python programming-based visualization and post-processing tool for OpenSeesPy, SoftwareX, 10.1016/j.softx.2022.101278, 21, (101278), (2023).
- Ridwan Taiwo, Ibrahim Abdelfadeel Shaban, Tarek Zayed, Development of sustainable water infrastructure: A proper understanding of water pipe failure, Journal of Cleaner Production, 10.1016/j.jclepro.2023.136653, 398, (136653), (2023).
- Jishuai Wang, Yazhou Xie, Tong Guo, Zhenyu Du, Predicting seismic interaction effect between soil and structure group using convolutional neural network, Structures, 10.1016/j.istruc.2023.02.087, 50, (1011-1020), (2023).
- See more