Date of Award

Spring 1-1-2017

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

First Advisor

Matthew R. Hallowell

Second Advisor

Ray L. Littlejohn

Third Advisor

Paul M. Goodrum

Fourth Advisor

Sathyanarayanan Rajendran

Fifth Advisor

Eric Marks

Abstract

The construction industry presents a fatality rate three times greater than the average considering all industries. Electrical transmission and distribution work presents one of the highest fatality rates inside the construction industry. Such accidents come at a great economic and social cost. Decades of accident causation research demonstrate that organizational and human factors, rather than technical failures, are the principal causes of accidents. Fatigue showed to be a significant trigger to human error, accident causation, and a bundle of other safety risks. This dissertation represents the first research effort to meta-analyze the causes and consequences of occupational fatigue and address the way in which fatigue can be identified, predicted and managed for electrical transmission and distribution (TD) workers. Over the course of a year and a half, a total of 343 TD workers, distributed across the US, participated in interviews, surveys, and tests to accomplish the purpose of this dissertation. Additionally, a group of 52 general construction (GC) workers also took part in the data collection process. The data was coded and analyzed applying several statistical methods such as: Meta-analysis, Chi-square test, Proportion test, Correspondence analysis, and Multiple Linear Regression. The results identified 9 of the principal causes and 5 of the most relevant consequences of occupational fatigue together with their relative impact. Furthermore, extreme temperatures and long shifts where identified as the major causes for TD workers´ fatigue and loss of attention and slowing down were recognized as significant consequences of fatigue among TD workers. Additionally, current fatigue identification and management techniques where documented. Lastly, fatigue predictive models for TD workers and GC workers were created based on empirical data collected in the field. The level of predictability of these models was low to medium, indicating that additional predictors need to be identified. Fatigue predictors, as measured by two of the most reliable and valid tools to objectively and subjectively assess fatigue, showed to vary between TD and GC workers. However, sleep deprivation showed to be a common predictor. Future research should engage in the strengthening of these models as well as the study of fatigue among other trades.

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