Original scientific paper
https://doi.org/10.2478/otmcj-2024-0014
Unsupervised machine learning for identifying key risk factors contributing to construction delays
Fuad Al-Bataineh
; Department of Civil Engineering, Faculty of Engineering, Al-al Bayt University, Mafraq, Jordan
Ahmed Ali Khatatbeh
; Department of Civil Engineering, Faculty of Engineering, Al-al Bayt University, Mafraq, Jordan
Yazan Alzubi
; Department of Civil Engineering, Faculty of Engineering Technology, Al-Balqa Applied University, Amman, Jordan
*
* Corresponding author.
Abstract
The present study uses unsupervised machine
learning capabilities with an emphasis on K-means clustering for addressing the problem of construction delays.
The primary objective is to investigate the critical risk
factors that contribute to such delays, thereby enabling
more efficient risk-management strategies. The study
employs a large dataset compiled from contracting firms
operating in developing regions. This information is a vital
resource for identifying crucial risk variables. These variables are analysed and categorised using the Likert scale
into five levels based on their potential influence. This
systematic approach permits the development of a comprehensive understanding of the relevant factors. These
risk factors are grouped to enhance comprehension of the
intricate risk landscape using K-means clustering. This
allows for a broader, more comprehensive understanding of the factors contributing to construction delays.
The application of K-means clustering demonstrates the
potential of machine learning techniques to improve conventional approaches to risk management. This empirical
investigation significantly expands the existing body of
construction risk- management knowledge. It offers invaluable insights into various project stakeholders, allowing
for more informed decision-making. Notably, the clustering analysis results provide a practical, user-friendly tool.
This tool can assist project managers in enhancing their
risk foresight, drafting more effective plans and developing robust mitigation strategies. Consequently, this
research offers the potential for substantial improvements
in project timeline adherence, thereby substantially reducing the impact of construction delays in developing
nations.
Keywords
Machine learning, Unsupervised learning, Construction management, Project management, Delays
Hrčak ID:
321156
URI
Publication date:
3.10.2024.
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