Matches in SemOpenAlex for { <https://semopenalex.org/work/W4281933943> ?p ?o ?g. }
- W4281933943 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." @default.
- W4281933943 created "2022-06-13" @default.
- W4281933943 creator A5006001183 @default.
- W4281933943 creator A5031462299 @default.
- W4281933943 creator A5044367029 @default.
- W4281933943 creator A5071287025 @default.
- W4281933943 creator A5072008551 @default.
- W4281933943 creator A5089465468 @default.
- W4281933943 date "2022-08-01" @default.
- W4281933943 modified "2023-10-16" @default.
- W4281933943 title "Machine Learning for Risk and Resilience Assessment in Structural Engineering: Progress and Future Trends" @default.
- W4281933943 cites W1538422615 @default.
- W4281933943 cites W1605872479 @default.
- W4281933943 cites W1646167220 @default.
- W4281933943 cites W1678356000 @default.
- W4281933943 cites W1679647726 @default.
- W4281933943 cites W185275295 @default.
- W4281933943 cites W1901410642 @default.
- W4281933943 cites W1912727951 @default.
- W4281933943 cites W1965782358 @default.
- W4281933943 cites W1973995342 @default.
- W4281933943 cites W1975838897 @default.
- W4281933943 cites W1979450117 @default.
- W4281933943 cites W1992649778 @default.
- W4281933943 cites W1998775978 @default.
- W4281933943 cites W2009267533 @default.
- W4281933943 cites W2011705946 @default.
- W4281933943 cites W2013492156 @default.
- W4281933943 cites W2018066684 @default.
- W4281933943 cites W2019949051 @default.
- W4281933943 cites W2020524857 @default.
- W4281933943 cites W2021247994 @default.
- W4281933943 cites W2029135909 @default.
- W4281933943 cites W2030490025 @default.
- W4281933943 cites W2030885102 @default.
- W4281933943 cites W2034996255 @default.
- W4281933943 cites W2047755199 @default.
- W4281933943 cites W2048001927 @default.
- W4281933943 cites W2048370585 @default.
- W4281933943 cites W2052872829 @default.
- W4281933943 cites W2059880243 @default.
- W4281933943 cites W2061933243 @default.
- W4281933943 cites W2062959223 @default.
- W4281933943 cites W2063241545 @default.
- W4281933943 cites W2064462974 @default.
- W4281933943 cites W2069639155 @default.
- W4281933943 cites W2072012972 @default.
- W4281933943 cites W2072642080 @default.
- W4281933943 cites W2076063813 @default.
- W4281933943 cites W2081652640 @default.
- W4281933943 cites W2081673388 @default.
- W4281933943 cites W2083420849 @default.
- W4281933943 cites W2090607740 @default.
- W4281933943 cites W2095820499 @default.
- W4281933943 cites W2098400865 @default.
- W4281933943 cites W2109759398 @default.
- W4281933943 cites W2110893990 @default.
- W4281933943 cites W2113188272 @default.
- W4281933943 cites W2114933419 @default.
- W4281933943 cites W2121809062 @default.
- W4281933943 cites W2122000057 @default.
- W4281933943 cites W2122825543 @default.
- W4281933943 cites W2132304657 @default.
- W4281933943 cites W2138432773 @default.
- W4281933943 cites W2144674014 @default.
- W4281933943 cites W2148571133 @default.
- W4281933943 cites W2155514770 @default.
- W4281933943 cites W2161577037 @default.
- W4281933943 cites W2162089259 @default.
- W4281933943 cites W2282821441 @default.
- W4281933943 cites W2332655816 @default.
- W4281933943 cites W2414931963 @default.
- W4281933943 cites W2518153285 @default.
- W4281933943 cites W2520705848 @default.
- W4281933943 cites W2526755141 @default.
- W4281933943 cites W2532287008 @default.
- W4281933943 cites W2551679322 @default.
- W4281933943 cites W2560511995 @default.
- W4281933943 cites W2569557400 @default.
- W4281933943 cites W2570371931 @default.
- W4281933943 cites W2580230114 @default.
- W4281933943 cites W2589227921 @default.
- W4281933943 cites W2595578839 @default.
- W4281933943 cites W2598457882 @default.
- W4281933943 cites W2604292161 @default.
- W4281933943 cites W2616290533 @default.
- W4281933943 cites W2616526553 @default.
- W4281933943 cites W2617209071 @default.
- W4281933943 cites W2724686580 @default.
- W4281933943 cites W2729750142 @default.
- W4281933943 cites W2769518517 @default.
- W4281933943 cites W2769788551 @default.
- W4281933943 cites W2782879790 @default.
- W4281933943 cites W2783623901 @default.
- W4281933943 cites W2783669729 @default.
- W4281933943 cites W2792190413 @default.
- W4281933943 cites W2792741217 @default.
- W4281933943 cites W2794011704 @default.
- W4281933943 cites W2797039576 @default.
- W4281933943 cites W2800115481 @default.