Requisite Empirical Risk Data for Integration of Safety with Advanced Technologies and Intelligent Systems

Desvignes, Matthieu

Graduate Thesis Or Dissertation

Requisite Empirical Risk Data for Integration of Safety with Advanced Technologies and Intelligent Systems Public Deposited

Analytics

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Citeable URL: https://scholar.colorado.edu/concern/graduate_thesis_or_dissertations/rn301196k

Abstract

The Construction field is known to account for a disproportionate number of disabling injuries and fatalities. Unfortunately, the industry has reached saturation with respect to the traditional safety strategies (Esmaeili and Hallowell 2012), and the emerging risk-based methods have shown to not be robust enough to adapt the transient, dynamic, and variable nature of construction work. To tackle these issues, professionals have tried to adapt emerging technologies and intelligent systems to improve construction safety. Also, empirically driven attribute-based risk data have been introduced but have been limited in application to struck-by injuries (Esmaeili 2012). Despite these advancements, there are still major limitations with significant opportunities for improvement. In this study, the authors review the actual safety applications of ten technologies to highlight the quasi- systematic lack of robust safety data as sources. They then present an attribute-based risk analysis method as a mean to improve the quality and versatility with which safety data can be integrated with technologies in both design and construction. Our team vastly improves the quality and quantity of available data by considering all injury types and leveraging 7,033 detailed injury reports provided by a total of 243 independent contractors. In total, 79 safety attributes were identified following a strict manual content analysis procedure and an attribute-based risk analysis was conducted based on these robust and viable safety data. The findings indicate that `No/Improper PPE' (69.49), `Pontoon' (21.75), and `Lifting/Pulling' (20.54) attributes presented the highest risks on construction sites. New safety applications and insights are detailed as primary uses of the attribute risk data with the technologies. The authors also discuss the combination of several technologies to create an intelligent system that aims at reducing the number of injuries on worksites. The combination of attribute risk data and technologies is believed to have the potential to change safety managers' approach to construction risks, lay the foundations for innovative technological safety applications and make construction sites safer.

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