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• W4383505884 endingPage "102085" @default.
• W4383505884 startingPage "102085" @default.
• W4383505884 abstract "Environmental hazards such as floods significantly frustrate the functionality of built assets. In addressing flood-induced challenges, data usage has become important. Despite existing vast flood-related research, no research has presented a comprehensive insight into global studies on data-driven built environment flood resilience. Hence, this study conducted a comprehensive review of data-driven approaches to flood resilience. Scientometric analysis revealed emerging countries, authorships, keywords, and research hotspots. The critical review revealed data-centric approaches such as Machine Learning (ML), Artificial Intelligence (AI), Flood Simulations, Bayesian Modelling, Building Information Modelling (BIM) and Geographic Information Systems (GIS). However, they were mainly deployed in hydraulic flood simulations for prediction, monitoring, risk, and damage assessments. Further, the potentials of computational methods in tackling built environment resilience challenges were identified. Deploying the approaches in the future requires a better understanding of the status quo. These methods include hybrid data-driven approaches, ontology-based knowledge representation, multiscale modelling, knowledge graphs, blockchain technology, convolutional neural networks, automated approaches integrated with social media data, data assimilation, BIM models linked with sensors and satellite imagery and ML and AI-based digital twin models. Nevertheless, reference to data-informed built-asset resilience decisions and clear-cut implications on built-asset resilience improvement remain indistinct in many studies. This suggests that more opportunities exist to contextualise data for built environment flood resilience. This study concluded with a conceptual map of flood context, methodologies, data types engaged, and future computational methods with directions for future research." @default.
• W4383505884 created "2023-07-08" @default.
• W4383505884 creator A5014225192 @default.
• W4383505884 creator A5049750085 @default.
• W4383505884 creator A5067798544 @default.
• W4383505884 date "2023-08-01" @default.
• W4383505884 modified "2023-10-16" @default.
• W4383505884 title "Data-driven approaches to built environment flood resilience: A scientometric and critical review" @default.
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