Title page for ETD etd-06042012-145429


Type of Document Master's Thesis
Author Rogers, Jennifer Kathleen
Author's Email Address jerogers@vt.edu
URN etd-06042012-145429
Title Safety Benchmarking of Industrial Construction Projects Based on Zero Accidents Techniques
Degree Master of Science
Department Vecillio Construction Engineering and Management
Advisory Committee
Advisor Name Title
de la Garza, Jesus M. Committee Chair
Fallah-Fini, Saeideh Committee Member
Mulva, Stephen Committee Member
Ozbek, Mehmet Committee Member
Van Aken, Eileen M. Committee Member
Keywords
  • Construction safety
  • data envelopment analysis
  • zero inflated poisson regression
  • zero accidents techniques
  • construction industry institute
Date of Defense 2012-05-30
Availability restricted
Abstract
Safety is a continually significant issue in the construction industry. The Occupation Safety and Health Administration as well as individual construction companies are constantly working on verifying that their selected safety plans have a positive effect on reduction of workplace injuries. Worker safety is a large concern for both the workers and employers in construction and the government also attempts to impose effective regulations concerning minimum safety requirements.

There are many different methods for creating and implementing a safety plan, most notably the Construction Industry Institute’s (CII) Zero Accidents Techniques (ZAT). This study will attempt to identify a relationship between the level of ZAT implementation and safety performance on industrial construction projects. This research also proposes that focusing efforts on certain ZAT elements over others will show different safety performance results.

There are three findings in this study that can be used to assist safety professionals in designing efficient construction safety plans. The first is a significant log-log relationship that is identified between the DEA efficiency scores and Recordable Incident Rate (RIR). There is also a significant difference in safety performance found between the Light Industrial and Heavy Industrial sectors. Lastly, regression is used to show that the pre-construction and worker selection ZAT components can predict a better safety performance.

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