A Data-Driven Approach for Deploying Safety Policies for Schedule Planning in Industrial Construction Projects: A Case Study
Publication: Journal of Construction Engineering and Management
Volume 149, Issue 12
Abstract
Construction, by the nature of the work, is more accident-prone than other industries despite advancements in improving safety performance. Proactive mitigation and assessment of the safety performance of construction projects remain challenging due to the difficulty of acquiring, storing, and using data to produce accurate predictive models. This research focused on devising methods that allow decision makers to leverage existing data in the planning phase to streamline the development of predictive models. A data-driven approach to predict the probability of a safety incident occurring in a given construction project and within a novel discipline-level schedule is presented. By implementing the proposed model, decision makers can evaluate and mitigate the risk of a given project incident occurring by deploying discipline-level safety policies in the planning phase and modifying the schedule accordingly. A predictive model was developed based on selected safety-related metrics extracted from a data set comprising daily payroll data and incident reports, which represent 28 million working hours within eight different industrial construction projects in Canada. The model was implemented in a case study based on an industrial project to demonstrate the framework’s functionality and practical utility during the project planning phase. The results show that the revised safe plan can be achieved by incorporating safety considerations in the planning phase.
Practical Applications
This research provides a practical solution for enhancing safety in the planning phase of construction projects using a data-driven model. By leveraging existing historical data, decision makers can predict potential safety incidents within specific disciplines without the need for detailed quantitative planning information. This approach also enables effective adjustments to be made to the schedule in order to mitigate risks. Furthermore, the discipline-level approach facilitates proactive safety planning by implementing discipline-specific safety policies that align with the unique characteristics of each discipline. Using a case study based on an industrial project, the proposed framework demonstrated its functionality and practical utility by identifying suitable safety-related metrics that construction enterprises typically record. These sources can include safety-related data, such as incident reports, as well as data recorded for other purposes, such as payroll data. The results highlight that incorporating safety considerations in the planning phase enables the development of a revised safety plan. In conclusion, by considering safety-related metrics and utilizing human resources (HR) data available in all companies, organizations can proactively assess and improve safety performance.
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Data Availability Statement
Data used in this study were provided by a third party. Direct requests for these materials may be made to the provider indicated in the Acknowledgments.
Acknowledgments
This project was supported by a Collaborative Research and Development Grant (CRDPJ 492657) from the Natural Sciences and Engineering Council of Canada. The authors thank PCL Industrial Management for their continued support, collaboration, in-depth knowledge, and provision of historical project data.
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Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 149 • Issue 12 • December 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Feb 21, 2023
Accepted: Aug 18, 2023
Published online: Oct 12, 2023
Published in print: Dec 1, 2023
Discussion open until: Mar 12, 2024
ASCE Technical Topics:
- Business management
- Case studies
- Construction engineering
- Construction industry
- Construction management
- Engineering fundamentals
- Management methods
- Methodology (by type)
- Mitigation and remediation
- Occupational safety
- Practice and Profession
- Project management
- Public administration
- Public health and safety
- Research methods (by type)
- Safety
- Scheduling
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