M
Matthew R. Hallowell
Researcher at University of Colorado Boulder
Publications - 128
Citations - 5860
Matthew R. Hallowell is an academic researcher from University of Colorado Boulder. The author has contributed to research in topics: Construction site safety & Construction management. The author has an hindex of 35, co-authored 119 publications receiving 4233 citations. Previous affiliations of Matthew R. Hallowell include Oregon State University & Bucknell University.
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Qualitative Research: Application of the Delphi Method to CEM Research
TL;DR: In this article, the Delphi technique has been used to identify and qualify potential expert panelists according to objective guidelines and select appropriate parameters of the study such as the number of panelists, number of rounds, type of feedback, and measure of consensus.
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Wearable technology for personalized construction safety monitoring and trending: Review of applicable devices
TL;DR: The review indicates that the existing wearable technologies applied in other industrial sectors can be used to monitor and measure a wide variety of safety performance metrics within the construction industry.
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Construction Safety Risk Mitigation
TL;DR: In this paper, the authors describe the results of a recent study designed to determine the relative effectiveness of safety program elements by quantifying their individual ability to mitigate construction safety and health risks.
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Automated content analysis for construction safety: A natural language processing system to extract precursors and outcomes from unstructured injury reports
TL;DR: The proposition that manual content analysis of injury reports can be eliminated using natural language processing (NLP) is tested and the results indicate that the NLP system is capable of quickly and automatically scanning unstructured injury reports for 101 attributes and outcomes with over 95% accuracy.
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Application of machine learning to construction injury prediction
TL;DR: Two state-of-the-art ML models, Random Forest and Stochastic Gradient Tree Boosting, were applied to a data set of carefully featured attributes and categorical safety outcomes, extracted from a large pool of textual construction injury reports via a highly accurate Natural Language Processing tool developed by past research.