Machine learning: the role of machines for resilient communities

Omar Kammouh, Gian Paolo Cimellaro

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review

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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.
Original languageEnglish
Title of host publicationObjective Resilience
Subtitle of host publicationObjective Processes
PublisherAmerican Society of Civil Engineers (ASCE)
Number of pages21
ISBN (Electronic)9780784483756
ISBN (Print)9780784415894
Publication statusPublished - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project 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.


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