Application of machine learning to construction injury prediction

By Antoine Jean-Pierre Tixier, Matthew R. Hallowell, Balaji Rajagopalan, Dean Bowman. 2016

311 citations recorded by the source corpus.

Abstract The needs to ground construction safety-related decisions under uncertainty on knowledge extracted from objective, empirical data are pressing. Although construction research has considered machine learning (ML) for more than two decades, it had yet to be applied to safety concerns. We applied two state-of-the-art ML models, Random Forest (RF) and Stochastic Gradient Tree Boosting (SGTB), 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 (NLP) tool developed by past research. The models can predict injury type, energy type, and body part with high skill (0.236

Subfields: Civil and Structural Engineering, Radiological and Ultrasound Technology, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/3f624144a3c7d99d2e9a1063ba861fd56646bc1a

Citations in the corpus

• AISA: AI Safety Assistant Framework for Continuous Improvement of Highway Construction (2026, unknown): “Researchers have centered on predictive analytics, in which algorithms analyze historical accident reports alongside project variables such as weather, time of day, and site activity to forecast hazards and allocate safety resources [5].”