An Analysis on Safety Risk Judgment Patterns Towards Computer Vision Based Construction Safety Management

Abstract

Due to dynamic and constantly changing nature of construction projects, the highest accident and fatality rate makes the industry infamous in mitigating hazardous safety risks and protecting workers at jobsites. Despite of enormous efforts and serious attention by government agencies and professional bodies, current safety management still relies on traditional manual approach by auditing and supervising safety rule compliance which are infrequent, inefficient and prone to error. With the advent of new emerging technologies such as BIM, VR/AR, AI, computer vision, and big data analytics, various tech-based solutions to help manage and reduce site risks has been introduced during the last decade. Computer vision technology, in particular, has been most attractive to site safety monitoring by academics and construction startups around the globe. However, literature review has revealed that the vision-based researches are limited to object detection such as workers' PPEs and machines to help subsidize the manual approach prototypically. The purpose of this study is to propose a wide-range applicability of computer vision technologies by investigating safety risk patterns. In doing so, entire safety rules and clauses described in the Korea Occupational Safety and Health Agency (KOSHA) regulations of construction sector is reviewed and analyzed with safety experts. Four main safety risk judgment patterns were found and grouped for various vision technology applications. The remaining clauses was classified into two different types. It is expected that the findings of this study would provide an insight to researchers and developers in construction safety domain. Economics 2020.