Soft computing for nonlinear risk assessment of complex socio-technical systems

•A novel soft computing application for risk assessment in socio-technical systems is proposed.•Outputs of the FRAM method were computed using a fuzzy inference system.•The framework was applied to a recycling of construction waste process.•The risk priority for the key activities of the model is de...

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Bibliographic Details
Published in:Expert systems with applications Vol. 206; p. 117828
Main Authors: de Souza, Ivenio Teixeira, Carolina Rosa, Ana, Patriarca, Riccardo, Haddad, Assed
Format: Journal Article
Language:English
Published: Elsevier Ltd 15.11.2022
Subjects:
Functional Resonance Analysis Method (FRAM)
Fuzzy sets
Nonlinear method
Risk assessment
Soft computing
Fuzzy sets
Functional Resonance Analysis Method (FRAM)
Soft computing
Nonlinear method
Risk assessment
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:•A novel soft computing application for risk assessment in socio-technical systems is proposed.•Outputs of the FRAM method were computed using a fuzzy inference system.•The framework was applied to a recycling of construction waste process.•The risk priority for the key activities of the model is determined. Work in socio-technical systems (STS) exhibits dynamic and complex behaviors, becoming difficult to model, evaluate and predict. This study develops an integrated soft computing approach for nonlinear risk assessment in STS: the functional resonance analysis method (FRAM) has been integrated with fuzzy sets. While FRAM is helpful to model performance variability in qualitative terms, the assessments are usually subjected to a high degree of uncertainty. This novel approach is meant to overcome the subjectivity associated with the qualitative analyses performed by experts’ judgments required by FRAM. For demonstration purposes, the approach has been applied to model a waste recycling process for construction materials. The results show how the approach allows assessing and ranking critical activities in STS operations.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2022.117828