Date of Award
Master of Science (MS)
Civil, Environmental & Architectural Engineering
Matthew R. Hallowell
Construction fatalities continue to plague the industry. In order to prevent fatalities, new methods of evaluating work conditions and making predictions are needed. Assorted industries and organizations, such as nuclear energy, NASA, chemical manufacturing, and commercial airlines have used precursor analysis to predict and prevent catastrophic events. Through a series of three papers, the presented thesis aims to adapt the fundamental processes of precursor analysis to the construction arena in an effort to predict and prevent future fatality and disabling injury.
First, a comprehensive catalog of 43 potential precursors was established by triangulating results from a literature review; deterministic event analysis of 21 fatalities; and brainstorming sessions with construction safety, law, regulation, and psychology experts. The 43 potential precursors were then translated into a precursor data collection protocol. The protocol involved questions and field observations to assess the presence or absence of each precursor before an event occurs. The protocol was applied to collect data for 19 new cases, which included (1) events where high-energy work was successfully completed without incident; (2) near misses where high-energy was released but no one was harmed; and (3) fatal or disabling injury events. Using these cases, a controlled experiment was conducted where a group of 12 experts were asked to predict each case outcome using only the leading information collected via the protocol and their judgment. Later, the same experiment was conducted with moderately experienced professionals and students for validation and to test generalizability. A permutation test of the predictions indicate that people of all levels are able to distinguish between success and failure far better than random using only leading information.
Next, the following hypothesis was tested: the probability of fatal and disabling events can be predicted by a small number of precursors that can be identified prior to an incident. Testing this hypothesis involved obtaining case data using the precursor analysis protocol, performing principal component analysis to reduce the dimensions of the dataset, building a mathematical predictive model using generalized linear modeling, and testing the predictive validity of the model with independent validation cases. The results indicated that there are 16 principal precursors that, when organized into a generalized linear model, are able to predict the outcome of new cases far better than random (p < 0.001). With further validation and testing, this new methodology can serve as the foundation for the first objective and valid precursor analysis program for construction.
Lastly, there was a need to determine when the created precursor analysis process should be implemented. Precursor analysis requires dedicated time and resource and therefore should only be used on those work situations that have potential to cause fatal and disabling injury. To do this, the hypothesis that the quantity and intensity of energy observable prior to an incident predicts variability in the severity of the incident was tested. The hypothesis is built upon the theory that energy is translated to an injury through uncontrolled release of the energy, transfer of the energy to the human body, and the vulnerability of the body and associated protective equipment. To test the hypothesis, a multi-phase experiment was conducted. First, over 500 injury reports were gathered from national databases and private companies for fall and struck-by injuries involving either potential or kinetic energy. For each report, the leading information describing the work operations and environment and the lagging information describing the injury were extracted, separated, and isolated. Second, the magnitude of the energy was estimated by a group of engineers who were only given leading information. Once energy magnitude was quantified, th
Alexander, Dillon Charles, "Using Precursor Analysis to Predict and Prevent Fatal and Disabling Injury in Construction" (2016). Civil Engineering Graduate Theses & Dissertations. 55.