Predicting maintenance cost overruns in public school buildings using a rough topological approach

Cost overruns in maintenance projects should be monitored and effectively managed by construction professionals using proactive systems. To establish more effective proactive systems for addressing cost overruns in maintenance projects, this paper presents a topological approach for machine learning-based prediction, integrated into various machine learning models to enhance the feature selection process. Project data from 1807 public schools renovated between 2016 and 2022 was collected to test the proposed mathematical method . The results indicate that the proposed method demonstrates superior performance in 6 out of 7 machine learning algorithms and hybrid models, achieving higher accuracy. This method will enable construction professionals to establish and achieve more efficient proactive systems for managing cost problems in maintenance projects. In addition, this paper will open new doors for developing effective machine-learning models without using optimization methods for other construction issues such as time, quality, or safety. • Introducing an ML algorithm using a topological approach • A different feature selection method is applied to identify the most important attributes for the classification of dataset • Identifying the most crucial attributes and their importance ranks that have an impact on cost overrun • Determining which attributes are more important in which season by using the topological dimension reduction algorithm • Recommending new strategies for construction experts for ML-based proactive systems.