Print Email Facebook Twitter Machine learning Title Machine learning: the role of machines for resilient communities Author Kammouh, O. (TU Delft System Engineering) Cimellaro, Gian Paolo (Politecnico di Torino) Date 2022 Abstract This chapter introduces the role of machine learning (ML) in resilience engineering and discusses actual cases of emergencies in which ML contributed positively. To identify its benefits within the resilience-relevant aspects (social, economic, infrastructural, institutional, environmental, and communitywise), the role of ML in various disaster management applications is analyzed, including model identification, emergency detection, and solution generation. The problem of data scarcity in model identification is presented. The application of ML in different fields of emergency detection (e.g., physical, virtual) is highlighted. Finally, the effectiveness of ML in solution generation to support human decision making is evaluated. Real examples are included in which machines exceed humans in providing solutions. To reference this document use: http://resolver.tudelft.nl/uuid:2e721312-0cd5-486a-ac21-b17efee88cac DOI https://doi.org/10.1061/9780784415894.ch5 Publisher American Society of Civil Engineers (ASCE) Embargo date 2022-10-08 ISBN 9780784415894 Source Objective Resilience: Objective Processes Bibliographical note Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type book chapter Rights © 2022 O. Kammouh, Gian Paolo Cimellaro Files PDF Book_5124_C005.pdf 761.06 KB Close viewer /islandora/object/uuid:2e721312-0cd5-486a-ac21-b17efee88cac/datastream/OBJ/view