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While Monte Carlo simulation remains the dominant approach for uncertainty propagation, its high computational cost limits its scalability, particularly in metropolitan-scale networks. This study proposes an EQResNet framework for accelerated post-earthquake resilience assessment of HTNs. The model integrates network topology, interregional traffic demand, and roadway characteristics into a streamlined deep neural network architecture. A comprehensive surrogate modeling strategy is developed to replace conventional traffic simulation modules, including highway status realization, shortest path computation, and traffic flow assignment. Combined with seismic fragility models and recovery functions for regional bridges, the framework captures the dynamic evolution of HTN functionality following seismic events. A multi-dimensional resilience evaluation system is also established to quantify network performance from emergency response and recovery perspectives. A case study on the Sioux Falls network under probabilistic earthquake scenarios demonstrates the effectiveness of the proposed method, achieving 95% prediction accuracy while reducing computational time by 90% compared to traditional numerical simulations. The results highlight the framework\u2019s potential as a scalable, efficient, and reliable tool for large-scale post-disaster transportation system analysis.<\/jats:p>","DOI":"10.3390\/computation13080188","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T13:25:11Z","timestamp":1754486711000},"page":"188","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["EQResNet: Real-Time Simulation and Resilience Assessment of Post-Earthquake Emergency Highway Transportation Networks"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6123-8978","authenticated-orcid":false,"given":"Zhenliang","family":"Liu","sequence":"first","affiliation":[{"name":"School of Safety Engineering and Emergency Management, Shijiazhuang Tiedao University, Shijiazhuang 050043, China"}]},{"given":"Chuxuan","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Hong Kong 999077, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108918","DOI":"10.1016\/j.ress.2022.108918","article-title":"Resilience modeling and pre-hazard mitigation planning of transportation network to support post-earthquake emergency medical response","volume":"230","author":"Wu","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1080\/15732479.2019.1604770","article-title":"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","volume":"16","author":"Akiyama","year":"2020","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1080\/15732479.2018.1547766","article-title":"Seismic performance assessment of electric power systems subjected to spatially correlated earthquake excitations","volume":"15","author":"Wang","year":"2018","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"114581","DOI":"10.1016\/j.engstruct.2022.114581","article-title":"Multi-level time-variant vulnerability assessment of deteriorating bridge networks with structural condition records","volume":"266","author":"Lei","year":"2022","journal-title":"Eng. Struct."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"107800","DOI":"10.1016\/j.ress.2021.107800","article-title":"Resilience-based Recovery Scheduling of Transportation Network in Mixed Traffic Environment: A Deep-Ensemble-Assisted Active Learning Approach","volume":"215","author":"Zou","year":"2021","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1404","DOI":"10.1080\/15732479.2016.1271813","article-title":"Resilience-based post-disaster recovery strategies for road-bridge networks","volume":"13","author":"Zhang","year":"2017","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.strusafe.2017.05.001","article-title":"Bridge network maintenance prioritization under budget constraint","volume":"67","author":"Zhang","year":"2017","journal-title":"Struct. Saf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1193\/120812EQS347M","article-title":"Seismic Damage Accumulation in Highway Bridges in Earthquake-Prone Regions","volume":"31","author":"Ghosh","year":"2015","journal-title":"Earthq. Spectra"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/s11803-009-8162-0","article-title":"Seismic damage of highway bridges during the 2008 Wenchuan earthquake","volume":"8","author":"Han","year":"2009","journal-title":"Earthq. Eng. Eng. Vib."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1193\/1.1586027","article-title":"Statistical Analysis of Bridge Damage Data from the 1994 Northridge, CA, Earthquake","volume":"15","author":"Kiremidjian","year":"1999","journal-title":"Earthq. Spectra"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3526","DOI":"10.1002\/eqe.3734","article-title":"Direct seismic loss estimation of predominantly plan-symmetrical frame buildings using simplified nonlinear models","volume":"51","year":"2022","journal-title":"Earthq. Eng. Struct. Dyn."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cimellaro, G.P., Arcidiacono, V., Reinhorn, A.M., and Bruneau, M. (2013). Disaster Resilience of hospitals considering emergency ambulance services. Structures Congress 2013, ASCE.","DOI":"10.1061\/9780784412848.246"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"100729","DOI":"10.1016\/j.seps.2019.07.005","article-title":"Assessing hospital system resilience to disaster events involving physical damage and Demand Surge","volume":"70","author":"Shahverdi","year":"2020","journal-title":"Socio-Econ. Plan. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/j.ijdrr.2017.09.037","article-title":"Emergency decision making for natural disasters: An overview","volume":"27","author":"Zhou","year":"2018","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_15","first-page":"61","article-title":"Integrated Redundancy Assessment of Highway Bridge Network Systems Subjected to Emergencies","volume":"4","author":"Liu","year":"2025","journal-title":"Earthq. Eng. Resil."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3097","DOI":"10.1002\/eqe.3715","article-title":"Seismic reliability assessment of bridge networks considering travel time and connectivity reliabilities","volume":"51","author":"Chen","year":"2022","journal-title":"Earthq. Eng. Struct. Dyn."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"112212","DOI":"10.1016\/j.engstruct.2021.112212","article-title":"Bridge fragilities to network fragilities in seismic scenarios: An integrated approach","volume":"237","author":"Chen","year":"2021","journal-title":"Eng. Struct."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1193\/1.1623497","article-title":"A Framework to Quantitatively Assess and Enhance the Seismic Resilience of Communities","volume":"19","author":"Bruneau","year":"2003","journal-title":"Earthq. Spectra"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ma, L., Zhang, C., Liu, X., Fang, K., and Liu, Z. (2024). Rapid Emergency Response Resilience Assessment of Highway Bridge Networks under Moderate Earthquakes. Sustainability, 16.","DOI":"10.3390\/su16135491"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2333","DOI":"10.1111\/risa.14236","article-title":"Resilience patterns of urban road networks under the worst-case localized disruptions","volume":"44","author":"Du","year":"2024","journal-title":"Risk Anal."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.tra.2015.06.002","article-title":"Vulnerability and resilience of transport systems\u2014A discussion of recent research","volume":"81","author":"Mattsson","year":"2015","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"103496","DOI":"10.1016\/j.ijdrr.2022.103496","article-title":"Resource-based seismic resilience optimization of the blocked urban road network in emergency response phase considering uncertainties","volume":"85","author":"Hosseini","year":"2023","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_23","first-page":"129","article-title":"A Hypernetwork Based Model for Emergency Response System","volume":"31","author":"Wang","year":"2022","journal-title":"Chin. J. Electron."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1578","DOI":"10.1080\/15732479.2020.1713170","article-title":"Post-earthquake modelling of transportation networks using an agent-based model","volume":"16","author":"Feng","year":"2020","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1002\/eqe.3288","article-title":"Application of open tools and datasets for probabilistic modeling of road traffic disruptions due to earthquake damage","volume":"49","author":"Costa","year":"2020","journal-title":"Earthq. Eng. Struct. Dyn."},{"key":"ref_26","first-page":"431","article-title":"Decision tree based bridge restoration models for extreme event performance assessment of regional road networks","volume":"3","author":"Kameshwar","year":"2019","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1287\/trsc.2016.0725","article-title":"Combining Worst Case and Average Case Considerations in an Integrated Emergency Response Network Design Problem","volume":"52","author":"Dalal","year":"2018","journal-title":"Transp. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"04020046","DOI":"10.1061\/(ASCE)IS.1943-555X.0000592","article-title":"Modeling Interaction of Emergency Inspection Routing and Restoration Scheduling for Postdisaster Resilience of Highway-Bridge Networks","volume":"27","author":"Zhang","year":"2021","journal-title":"J. Infrastruct. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"114395","DOI":"10.1016\/j.engstruct.2022.114395","article-title":"Post-earthquake assessment model for highway bridge networks considering traffic congestion due to earthquake-induced bridge damage","volume":"262","author":"Liu","year":"2022","journal-title":"Eng. Struct."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"103687","DOI":"10.1016\/j.tra.2023.103687","article-title":"An integrated resilience assessment model of urban transportation network: A case study of 40 cities in China","volume":"173","author":"Yin","year":"2023","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.istruc.2022.02.003","article-title":"Machine learning for structural engineering: A state-of-the-art review","volume":"38","author":"Thai","year":"2022","journal-title":"Structures"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1016\/j.istruc.2022.05.063","article-title":"ANN-based rapid seismic fragility analysis for multi-span concrete bridges","volume":"41","author":"Liu","year":"2022","journal-title":"Structures"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1002\/eqe.3582","article-title":"Artificial neural network application in predicting probabilistic seismic demands of bridge components","volume":"51","author":"Soleimani","year":"2022","journal-title":"Earthq. Eng. Struct. Dyn."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1080\/15732479.2020.1815807","article-title":"Reliability-based life-cycle cost design of asphalt pavement using artificial neural networks","volume":"17","author":"Xin","year":"2020","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1080\/23789689.2018.1518026","article-title":"Resilience modeling of traffic network in post-earthquake emergency medical response considering interactions between infrastructures, people, and hazard","volume":"4","author":"Wu","year":"2019","journal-title":"Sustain. Resilient Infrastruct."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"9203","DOI":"10.1038\/s41467-024-53303-4","article-title":"Deep learning resilience inference for complex networked systems","volume":"15","author":"Liu","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"100459","DOI":"10.1016\/j.ijcip.2021.100459","article-title":"Resilience analysis of interdependent critical infrastructure systems considering deep learning and network theory","volume":"35","author":"Wang","year":"2021","journal-title":"Int. J. Crit. Infrastruct. Prot."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1111\/mice.12359","article-title":"Deep learning for accelerated seismic reliability analysis of transportation networks","volume":"33","author":"Nabian","year":"2018","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"05024004","DOI":"10.1061\/JITSE4.ISENG-2264","article-title":"Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systems","volume":"30","author":"Liu","year":"2024","journal-title":"J. Infrastruct. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"104695","DOI":"10.1016\/j.trc.2024.104695","article-title":"End-to-end heterogeneous graph neural networks for traffic assignment","volume":"165","author":"Liu","year":"2024","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"04016039","DOI":"10.1061\/(ASCE)IS.1943-555X.0000338","article-title":"Graph Model for Probabilistic Resilience and Recovery Planning of Multi-Infrastructure Systems","volume":"23","author":"Bristow","year":"2017","journal-title":"J. Infrastruct. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"124692","DOI":"10.1016\/j.jhydrol.2020.124692","article-title":"Comparison of the use of a physical-based model with data assimilation and machine learning methods for simulating soil water dynamics","volume":"584","author":"Li","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"107326","DOI":"10.1016\/j.soildyn.2022.107326","article-title":"Comprehensive functional resilience assessment methodology for bridge networks using data-driven fragility models","volume":"159","author":"Liu","year":"2022","journal-title":"Soil Dyn. Earthq. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1007\/s41109-019-0139-y","article-title":"Estimation of traffic flow changes using networks in networks approaches","volume":"4","author":"Hackl","year":"2019","journal-title":"Appl. Netw. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1080\/15732479.2020.1801764","article-title":"Optimal restoration schedules of transportation network considering resilience","volume":"17","author":"Liu","year":"2020","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1193\/1.2830434","article-title":"Ground-Motion Prediction Equations for the Average Horizontal Component of PGA, PGV, and 5%-Damped PSA at Spectral Periods between 0.01\u2009s and 10.0\u2009s","volume":"24","author":"Boore","year":"2008","journal-title":"Earthq. Spectra"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1002\/eqe.655","article-title":"Seismic fragility methodology for highway bridges using a component level approach","volume":"36","author":"Nielson","year":"2007","journal-title":"Earthq. Eng. Struct. Dyn."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1080\/15732479.2019.1653937","article-title":"Lifetime seismic resilience of aging bridges and road networks","volume":"16","author":"Capacci","year":"2020","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Titi, A., Biondini, F., and Frangopol, D.M. (2015). Seismic Resilience of Deteriorating Concrete Structures. Structures Congress 2015, ASCE.","DOI":"10.1061\/9780784479117.142"}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/13\/8\/188\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:24:38Z","timestamp":1760034278000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/13\/8\/188"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,6]]},"references-count":49,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["computation13080188"],"URL":"https:\/\/doi.org\/10.3390\/computation13080188","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,6]]}}}