A Machine Learning-Based Safety Assessment Framework for Roadway Construction Projects in Flood-Prone Regions

Document Type

Conference Proceeding

Publication Date

1-1-2024

Abstract

Transportation construction sites generally encompass unsecured materials, temporary facilities, various equipment, and structures that are incomplete which result in an unsafe area, especially during or after the occurrence of environmental disaster events such as floods or hurricanes. Improper and insufficient safety pre-assessment and measures planned before a project begins can cause not only physical and financial losses but also additional costs and delays, especially for a construction project in a disaster-prone area. To address the existing challenge, this study aims to predict potential safety hazards according to a jobsite condition and expected severe weather in roadway construction projects. The proposed framework adopts machine learning for safety assessment that is conducted based on a project schedule, occupational safety database, and flood zone areas. With the scope of flood disasters and roadway construction, the framework is expected to evaluate the impact of severe weather on roadway construction safety, identify the possible sources of work events, and predict the class of events for a project team to promote safe and resilient work environment against flood disasters.

Publication Source (Journal or Book title)

Computing in Civil Engineering 2023: Resilience, Safety, and Sustainability - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023

First Page

426

Last Page

433

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