Gerami Seresht, Nima (2022) Enhancing Resilience in Construction Against Infectious Diseases Using Stochastic Multi-Agent Approach. Automation in Construction, 140. p. 104315. ISSN 0926-5805
|
Text
1-s2.0-S0926580522001881-main.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (2MB) | Preview |
|
|
Text
Automation_Resiliency_of_Construction_Industry_Against_Infectious_Diseases_1_.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (5MB) | Preview |
Abstract
To recover from the adverse impacts of COVID-19 on construction and to avoid further losses to the industry in future pandemics, the resilience of construction industry needs to be enhanced against infectious diseases. Currently, there is a gap for modeling frameworks to simulate the spread of infectious diseases in construction projects at micro-level and to test interventions’ effectiveness for data-informed decision-making. Here, this gap is addressed by developing a simulation framework using stochastic agent-based modeling, which enables construction researchers and practitioners to simulate and limit the spread of infectious diseases in construction projects. This is specifically important, since the results of a building project case-study reveals that, in comparison to the general population, infectious diseases may spread faster among construction workers and fatalities can be significantly higher. The proposed framework motivates future research on micro-level modeling of infectious diseases and efforts for intervening the spread of diseases in construction projects.
Item Type: | Article |
---|---|
Uncontrolled Keywords: | Infectious diseases, COVID 19, Resilience, Risk Management, Agent-Based Modelling, Monte Carlo Simulation |
Subjects: | H300 Mechanical Engineering K900 Others in Architecture, Building and Planning |
Department: | Faculties > Engineering and Environment > Mechanical and Construction Engineering |
Depositing User: | Elena Carlaw |
Date Deposited: | 06 May 2022 11:57 |
Last Modified: | 11 May 2023 08:00 |
URI: | https://nrl.northumbria.ac.uk/id/eprint/49060 |
Downloads
Downloads per month over past year