CIB Conferences
Abstract
Global estimates indicate that the construction industry has one of the highest rates of occupational accidents. Given this scenario, the sector has adopted emergent technologies to improve safety conditions and decision-making. Thus, machine learning (ML) emerges as a promising tool to streamline data analysis besides being used to predict events based on datasets. In construction, the use of the ML model to predict site accidents remains emerging without studies using historical data from emerging South and Latin American countries. Therefore, this paper aims to develop a prediction model using machine learning for fatal accidents using historical data from construction. The dataset contains 2,305 accidents, including fatal and non-fatal, from 2018 to 2023, distributed in 17 binary and categorical variables. After the pre-processing, seven predictive models were generated with different classifiers to determine which models fit the dataset better. Among the classifiers, the gradient-boosting model exhibited the best performance, achieving an accuracy of 0.885, 0.88 precision, 0.833 recall, and 0.881 F1-Score. As a contribution, this study brings insights regarding the use of predictive models in construction safety management, calling attention to the attributes that have the most influence on a fatal accident. In addition, the model can assist managers in identifying scenarios that are more prone to accidents and propose effective preventive measures.
The paper will be presented:
In-person
Primary U.N. Sustainable Development Goals (SDG)
Industry, Innovation and Infrastructure - - Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
Secondary U.N. Sustainable Development Goals (SDG)
Decent Work and Economic Growth - - Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all
Primary CIB Task Group OR Working commission
W099 – Safety Health & Wellbeing in Construction
Secondary CIB Task Group OR Working commission
W107 – Construction in Developing Countries
Recommended Citation
Freitas, Filipe dos Santos; Santos, Mirian Caroline Farias; Melo, Roseneia Rodrigues Santos; Ferreira, Paulo Henrique; and Costa, Dayana Bastos
(2025)
"Machine Learning Techniques to Forecast Fatal Accidents on Construction Sites in Brazil,"
CIB Conferences: Vol. 1
Article 192.
DOI: https://doi.org/10.7771/3067-4883.1863