Application of Data Mining and Machine Learning on Occupational Health and Safety Struck-by Incidents on South African Construction Sites - A CRISP-DM approach

Aug 5, 2026

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Occupational Health and Safety in the South African construction industry faces performance challenges that contribute to potentially avoidable struck-by incidents. The study proposes data mining and classification machine learning models to enhance data understanding, support knowledge and information extraction, and improve prediction through classification. A mixed-methods design combines interviews to assess current OHS data states and management with exploratory data analysis and multiple classification models. Data from Federated Employers Mutual Assurance and external macroeconomic sources enables safety, incident, and outcome-focused modeling, yielding variable-specific predictive performance and practical implementation recommendations.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages208

Reading Time8 h 44 min

AccessFree to download

Abstract

Research Aim and Approach

Data Sources and Exploratory Data Analysis

Machine Learning Models and Outputs

Results, Limitations, and Recommendations

Application of Data Mining and Machine Learning on Occupational Health and Safety Struck-by Incidents on South African Construction Sites: A CRISP-DM approach by Logan Charl Adams Thesis presented in fulfilment of the requirements for the degree of Master of Engineering in Civil Engineering in the Faculty of Engineering at Stellenbosch University Supervisor: Prof J.A Wium March 2023 Declaration of Authorship By submitting this thesis electronically, I declare that the entirety of the work contained therein is my own, original work, that I am the sole author thereof (save to the extent explicitly otherwise stated), that reproduction and publication thereof by Stellenbosch University will not infringe any third party rights and that I have not previously in its entirety or in part submitted it for obtaining any qualification March 2023 Copyright © 2023 Stellenbosch University All rights reserved Abstract Occupational Health and Safety in the South African construction industry face many performance challenges that result in potentially avoidable incident occurrences. The study aims to propose the utilisation of data mining and classification machine learning models to improve data understanding, promote knowledge and information extraction, and encourage prediction capabilities through classification methods. A mixed research approach was applied in the study to enable a holistic usage of data and its applications. Interviews (qualitative research component) allowed the identification of the current state of OHS data and data management in the South African construction industry while identifying data considerations for the quantitative research component (Exploratory Data Analysis and classification machine learning models) . Data sourced from Federated Employers Mutual Assurance Company (an insurance database), and additional databases (sourced from the Federal Reserve Bank of St. Louis and Organisation for Economic Cooperation and Development), enabled a quantitative Exploratory Data Analysis and the development of multiple classification machine learning models. The Exploratory Data Analysis provided insights into data understanding and the potential of using it to enable datadriven safety decision-making. The classification models provided insights into the possibility of an industry-wide classification prediction model based on existing data while also providing valuable insights into the fundamental concerns and limitations. The qualitative and quantitative components of the study highlighted several concerns regarding data, data management, and data innovations across OHS in the South African construction industry. At the core was the lack of understanding regarding the possibilities of data and the misaligned value proposition witnessed. Furthermore, the notable limitations in the quality of data and the mechanisms that influence its quality were highlighted, including the effects of ineffective incident investigations for fact-finding and prominent underreporting experienced in the construction industry. Data mining and machine learning offered the ability to extract deeper insights from incidents and enable improvements in OHS performance through data-driven safety decision-making. Three output variables were evaluated across several machine learning algorithms in terms of the model's ability to successfully predict and classify the state of an incident namely (1) Injury Location (the physical injury location on the affected individual's body) , (2) Nature of Injury (the type of injury the affected individual experienced), and (3) Days off (number of days required off from work for recovery) . The results obtained from the machine learning models demonstrate the capability to predict the Days off variable to high accuracy levels (average of 81.8%), moderate accuracy levels for the Nature of Injury (average of 37.4%), and low accuracy levels for Injury Location (average of 17.8%) . The p

FAQs

What is the main objective of the study?

To apply data mining and classification machine learning models to improve understanding of occupational health and safety (OHS) struck-by incidents and strengthen prediction capabilities for incident outcomes.

How was the research conducted?

A mixed research approach was used. Interviews supported qualitative assessment of current OHS data and data management, while exploratory data analysis and classification machine learning models addressed the quantitative component.

Which incident-related variables were predicted by the machine learning models?

Three outputs were evaluated: Injury Location, Nature of Injury, and Days off. The models achieved high accuracy for Days off, moderate accuracy for Nature of Injury, and lower accuracy for Injury Location.

Aug 5, 2026

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