{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T22:04:00Z","timestamp":1779746640661,"version":"3.53.1"},"reference-count":51,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Reliability Engineering &amp; System Safety"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.ress.2026.112900","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T23:21:21Z","timestamp":1779232881000},"page":"112900","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Probabilistic deep learning regression framework for post-hazard hurricane wind damage assessment of buildings"],"prefix":"10.1016","volume":"276","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8011-3551","authenticated-orcid":false,"given":"Ahmed A.","family":"Ewis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4206-1904","authenticated-orcid":false,"given":"Omar","family":"Nofal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ress.2026.112900_bib0001","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109579","article-title":"Probabilistic risk assessment of hurricane-induced debris impacts on coastal transportation infrastructure","volume":"240","author":"Amini","year":"2023","journal-title":"Reliab Eng Syst Saf"},{"issue":"2","key":"10.1016\/j.ress.2026.112900_bib0002","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.rcns.2023.07.003","article-title":"Multi-hazard socio-physical resilience assessment of hurricane-induced hazards on coastal communities","volume":"2","author":"Nofal","year":"2023","journal-title":"Resil Cities Struct"},{"issue":"1","key":"10.1016\/j.ress.2026.112900_bib0003","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1061\/(ASCE)NH.1527-6996.0000058","article-title":"Loss analysis for combined wind and surge in hurricanes","volume":"13","author":"Li","year":"2012","journal-title":"Nat Haz Rev"},{"issue":"1","key":"10.1016\/j.ress.2026.112900_bib0004","doi-asserted-by":"crossref","DOI":"10.1061\/(ASCE)ST.1943-541X.0002241","article-title":"Combined wind-wave-surge hurricane-induced damage prediction for buildings","volume":"145","author":"Masoomi","year":"2019","journal-title":"J Struct Eng"},{"key":"10.1016\/j.ress.2026.112900_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2020.106971","article-title":"Multi-variate and single-variable flood fragility and loss approaches for buildings","volume":"202","author":"Nofal","year":"2020","journal-title":"Reliab Eng Syst Saf"},{"issue":"11","key":"10.1016\/j.ress.2026.112900_bib0006","doi-asserted-by":"crossref","DOI":"10.1061\/(ASCE)ST.1943-541X.0003144","article-title":"Methodology for regional multihazard hurricane damage and risk assessment","volume":"147","author":"Nofal","year":"2021","journal-title":"J Struct Eng"},{"key":"10.1016\/j.ress.2026.112900_bib0007","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.jweia.2012.03.023","article-title":"Assessment of hurricane-induced internal damage to low-rise buildings in the Florida Public Hurricane Loss Model","volume":"104","author":"Pita","year":"2012","journal-title":"J Wind Eng Ind Aerodyn"},{"issue":"10","key":"10.1016\/j.ress.2026.112900_bib0008","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1175\/1520-0442(1991)004<1035:EPOHWS>2.0.CO;2","article-title":"Estimating probabilities of hurricane wind speeds using a large-scale empirical model","volume":"4","author":"Darling","year":"1991","journal-title":"J Clim"},{"issue":"5","key":"10.1016\/j.ress.2026.112900_bib0009","doi-asserted-by":"crossref","first-page":"495","DOI":"10.5194\/nhess-7-495-2007","article-title":"Towards an empirical vulnerability function for use in debris flow risk assessment","volume":"7","author":"Fuchs","year":"2007","journal-title":"Nat Haz Earth Syst Sci"},{"issue":"25","key":"10.1016\/j.ress.2026.112900_bib0010","doi-asserted-by":"crossref","first-page":"4132","DOI":"10.1111\/mice.70052","article-title":"Leveraging national datasets for systematic socio-physical coastal flood risk assessment at the community level","volume":"40","author":"Ewis","year":"2025","journal-title":"Comput-Aided Civil Infra Eng"},{"key":"10.1016\/j.ress.2026.112900_bib51","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijdrr.2026.106193","article-title":"National-level resilience-informed risk scoring approach: Generalized socio-physical flood risk factor for buildings","volume":"141","author":"Ewis","year":"2026","journal-title":"Int J Disaster Risk Reduct"},{"key":"10.1016\/j.ress.2026.112900_bib0011","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2020.103474","article-title":"Machine learning-based regional scale intelligent modeling of building information for natural hazard risk management","volume":"122","author":"Wang","year":"2021","journal-title":"Autom Const"},{"key":"10.1016\/j.ress.2026.112900_bib0012","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2021.107530","article-title":"Machine learning for reliability engineering and safety applications: Review of current status and future opportunities","volume":"211","author":"Xu","year":"2021","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112900_bib0013","article-title":"Modeling Expert Risk Assessments in Utility Tunnels with Deep Learning","author":"Xue","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112900_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109754","article-title":"Multi-agent deep reinforcement learning based decision support model for resilient community post-hazard recovery","volume":"242","author":"Yang","year":"2024","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112900_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.111284","article-title":"Enhancing power grid resilience during tropical cyclones: Deep learning-based real-time wind forecast corrections for dynamic risk prediction","author":"Wu","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112900_bib0016","doi-asserted-by":"crossref","unstructured":"Trekin, A., Novikov, G., Potapov, G., Ignatiev, V., & Burnaev, E. (2018). Satellite imagery analysis for operational damage assessment in Emergency situations. ArXiv Preprint ArXiv:1803.00397.","DOI":"10.1007\/978-3-319-93931-5_25"},{"key":"10.1016\/j.ress.2026.112900_bib0017","unstructured":"Gupta, R., Hosfelt, R., Sajeev, S., Patel, N., Goodman, B., Doshi, J., Heim, E., Choset, H., & Gaston, M. (2019). xbd: A dataset for assessing building damage from satellite imagery. ArXiv Preprint ArXiv:1911.09296."},{"issue":"15","key":"10.1016\/j.ress.2026.112900_bib0018","doi-asserted-by":"crossref","first-page":"2389","DOI":"10.1111\/mice.13197","article-title":"Automated building damage assessment and large-scale mapping by integrating satellite imagery, GIS, and deep learning","volume":"39","author":"Braik","year":"2024","journal-title":"Comput-Aided Civil Infra Eng"},{"key":"10.1016\/j.ress.2026.112900_bib0019","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2021.103831","article-title":"Real-time multiple damage mapping using autonomous UAV and deep faster region-based neural networks for GPS-denied structures","volume":"130","author":"Ali","year":"2021","journal-title":"Autom Const"},{"key":"10.1016\/j.ress.2026.112900_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2019.102994","article-title":"Automated regional seismic damage assessment of buildings using an unmanned aerial vehicle and a convolutional neural network","volume":"109","author":"Xiong","year":"2020","journal-title":"Autom Const"},{"key":"10.1016\/j.ress.2026.112900_bib0021","unstructured":"Kamilaris, A., & Prenafeta-Bold\u00fa, F. X. (2018). Disaster monitoring using unmanned aerial vehicles and deep learning. ArXiv Preprint ArXiv:1807.11805."},{"issue":"3","key":"10.1016\/j.ress.2026.112900_bib0022","first-page":"565","article-title":"Investigation of the usability of G\u00f6kt\u00fcrk-2 data and UAV data for pond construction project","volume":"27","author":"Karatas","year":"2024","journal-title":"Egyptian Jf Remote Sens Space Sci"},{"issue":"5","key":"10.1016\/j.ress.2026.112900_bib0023","doi-asserted-by":"crossref","first-page":"2689","DOI":"10.1007\/s11042-024-20300-0","article-title":"Enhanced satellite imagery analysis for post-disaster building damage assessment using integrated ResNet-U-Net model","volume":"84","author":"Bhardwaj","year":"2025","journal-title":"Multimedia Tools Appl"},{"issue":"1","key":"10.1016\/j.ress.2026.112900_bib0024","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1038\/s41597-023-02799-4","article-title":"RescueNet: A high resolution UAV semantic segmentation dataset for natural disaster damage assessment","volume":"10","author":"Rahnemoonfar","year":"2023","journal-title":"Sci Data"},{"issue":"4","key":"10.1016\/j.ress.2026.112900_bib0025","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1111\/mice.12890","article-title":"Post-disaster damage classification based on deep multi-view image fusion","volume":"38","author":"Khajwal","year":"2023","journal-title":"Comput-Aided Civil Infra Eng"},{"key":"10.1016\/j.ress.2026.112900_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.apgeog.2020.102252","article-title":"Damage assessment using google street view: evidence from hurricane Michael in Mexico beach, Florida","volume":"123","author":"Zhai","year":"2020","journal-title":"Appl Geogr"},{"key":"10.1016\/j.ress.2026.112900_bib0027","doi-asserted-by":"crossref","DOI":"10.1111\/mice.70033","article-title":"Debris segmentation using post-hurricane aerial imagery","author":"Amini","year":"2025","journal-title":"Comput-Aided Civil Infra Eng"},{"issue":"1","key":"10.1016\/j.ress.2026.112900_bib0028","doi-asserted-by":"crossref","DOI":"10.1061\/NHREFO.NHENG-2278","article-title":"Computer vision-enabled roof subassembly damage detection from hurricanes using aerial reconnaissance imagery","volume":"26","author":"Hamburger","year":"2025","journal-title":"Nat Haz Rev"},{"key":"10.1016\/j.ress.2026.112900_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110589","article-title":"Maximum entropy-based modeling of community-level hazard responses for civil infrastructures","volume":"254","author":"Chu","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112900_bib0030","first-page":"1050","article-title":"Dropout as a bayesian approximation: Representing model uncertainty in deep learning","author":"Gal","year":"2016","journal-title":"Int Conf Mach Learn"},{"key":"10.1016\/j.ress.2026.112900_bib0031","article-title":"What uncertainties do we need in bayesian deep learning for computer vision?","volume":"30","author":"Kendall","year":"2017","journal-title":"Adv Neural Inform Proces Syst"},{"issue":"3\u20134","key":"10.1016\/j.ress.2026.112900_bib0032","first-page":"93","article-title":"Developing measurement science for community resilience assessment: preface to the special issue of sustainable and resilient infrastructure on the centerville testbed","volume":"1","author":"Ellingwood","year":"2016","journal-title":"Sustain Resil Infra"},{"key":"10.1016\/j.ress.2026.112900_bib0033","article-title":"Calibration and uncertainty quantification for deep learning-based drought detection","volume":"140","author":"Zhang","year":"2025","journal-title":"Int J Appl Earth Observ Geoinform"},{"key":"10.1016\/j.ress.2026.112900_bib0034","series-title":"Computing in Civil Engineering 2021","first-page":"156","article-title":"Bayesian inference for uncertainty-aware post-disaster damage assessment using artificial intelligence","author":"Cheng","year":"2022"},{"issue":"7","key":"10.1016\/j.ress.2026.112900_bib0035","doi-asserted-by":"crossref","first-page":"2339","DOI":"10.1109\/TUFFC.2022.3176926","article-title":"Uncertainty quantification for deep learning in ultrasonic crack characterization","volume":"69","author":"Pyle","year":"2022","journal-title":"IEEE Trans Ultrasonics, Ferro, Freq Control"},{"key":"10.1016\/j.ress.2026.112900_bib0036","series-title":"HAZUS: FEMA\u2019s Flood Mapping and Risk Assessment Tool","year":"2003"},{"issue":"1","key":"10.1016\/j.ress.2026.112900_bib0037","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0245230","article-title":"Multi-view classification with convolutional neural networks","volume":"16","author":"Seeland","year":"2021","journal-title":"Plos One"},{"key":"10.1016\/j.ress.2026.112900_bib0038","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.neucom.2022.06.111","article-title":"Activation functions in deep learning: A comprehensive survey and benchmark","volume":"503","author":"Dubey","year":"2022","journal-title":"Neurocomputing"},{"key":"10.1016\/j.ress.2026.112900_bib0039","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.ress.2026.112900_bib0040","first-page":"1","article-title":"Enhanced bridge damage assessment via data-model coupling driven approach with vibration signals","author":"Wang","year":"2025","journal-title":"Structure and Infrastructure Engineering"},{"issue":"9","key":"10.1016\/j.ress.2026.112900_bib0041","doi-asserted-by":"crossref","first-page":"6486","DOI":"10.1109\/TPAMI.2024.3382294","article-title":"Towards understanding convergence and generalization of AdamW","volume":"46","author":"Zhou","year":"2024","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.ress.2026.112900_bib0042","doi-asserted-by":"crossref","DOI":"10.3389\/fdgth.2025.1523953","article-title":"Using artificial intelligence to develop a measure of orthopaedic treatment success from clinical notes","volume":"7","author":"Floyd","year":"2025","journal-title":"Front Digital Health"},{"issue":"1","key":"10.1016\/j.ress.2026.112900_bib0043","article-title":"Deep ensemble learning with transformer models for enhanced Alzheimer\u2019s disease detection","volume":"15","author":"Latif","year":"2025","journal-title":"Sci Reports"},{"key":"10.1016\/j.ress.2026.112900_bib0044","doi-asserted-by":"crossref","DOI":"10.2196\/49023","article-title":"Practical considerations and applied examples of cross-validation for model development and evaluation in health care: tutorial","volume":"2","author":"Wilimitis","year":"2023","journal-title":"Jmir Ai"},{"issue":"10","key":"10.1016\/j.ress.2026.112900_bib0045","doi-asserted-by":"crossref","DOI":"10.1002\/stc.3019","article-title":"Uncertainty-aware convolutional neural network for explainable artificial intelligence-assisted disaster damage assessment","volume":"29","author":"Cheng","year":"2022","journal-title":"Struct Control Health Monit"},{"key":"10.1016\/j.ress.2026.112900_bib0046","article-title":"Empirical hurricane fragility assessment of elevated and slab-on-grade residential houses","volume":"110","author":"Ibrahim","year":"2024","journal-title":"Int J Dis Risk Red"},{"issue":"7","key":"10.1016\/j.ress.2026.112900_bib0047","doi-asserted-by":"crossref","DOI":"10.1061\/(ASCE)ST.1943-541X.0002047","article-title":"Minimal building fragility portfolio for damage assessment of communities subjected to tornadoes","volume":"144","author":"Memari","year":"2018","journal-title":"J Struct Eng"},{"key":"10.1016\/j.ress.2026.112900_bib0048","article-title":"MV-HarveyNET: A labelled image dataset from Hurricane Harvey for damage assessment of residential houses based on multi-view CNN","volume":"101","author":"Khajwal","year":"2022","journal-title":"Des"},{"key":"10.1016\/j.ress.2026.112900_bib0049","unstructured":"StEER. (2022). Structural extreme event reconnaissance. https:\/\/www.steer.network\/."},{"issue":"8","key":"10.1016\/j.ress.2026.112900_bib0050","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0271225","article-title":"A lightweight deep neural network with higher accuracy","volume":"17","author":"Zhao","year":"2022","journal-title":"Plos One"}],"container-title":["Reliability Engineering &amp; System Safety"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0951832026007106?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0951832026007106?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T21:37:21Z","timestamp":1779745041000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0951832026007106"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":51,"alternative-id":["S0951832026007106"],"URL":"https:\/\/doi.org\/10.1016\/j.ress.2026.112900","relation":{},"ISSN":["0951-8320"],"issn-type":[{"value":"0951-8320","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Probabilistic deep learning regression framework for post-hazard hurricane wind damage assessment of buildings","name":"articletitle","label":"Article Title"},{"value":"Reliability Engineering & System Safety","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ress.2026.112900","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"112900"}}