Title: A predictive model for project progress index in EPC projects using data mining techniques

Authors: Ali Mohamad Ahmad; Fayez Jrad; Samah Makia

Addresses: Engineering and Construction Management, Department Faculty of Civil Engineering, Tishreen University, GRF4+3WH, Latakia, Syria ' Engineering and Construction Management, Department Faculty of Civil Engineering, Tishreen University, GRF4+3WH, Latakia, Syria ' Engineering and Construction Management, Department Faculty of Civil Engineering, Tishreen University, GRF4+3WH, Latakia, Syria

Abstract: This study proposes a database that utilises online analytical processing (OLAP) to help project management in engineering, procurement, and construction (EPC) companies. It uses data mining techniques to create a model that predicts the project progress index (PPI) at any stage of the project based on historical data. The model is evaluated using statistical parameters such as root mean squared error (RMSE) and mean absolute error (MAE). The results show that the model can predict PPI values accurately throughout the project life cycle. The study suggests using the model to improve forecasting and tracking of EPC projects, which can enhance decision-making and economic performance. This research addresses the challenges faced by EPC construction companies when collecting and organising historical operational data to support project-tracking decisions.

Keywords: EPC projects; earned duration management; EDM; project progress index; PPI; online analytical processing; OLAP; data mining.

DOI: 10.1504/IJMRI.2025.144877

International Journal of Masonry Research and Innovation, 2025 Vol.10 No.2, pp.189 - 205

Received: 08 Sep 2023
Accepted: 17 Nov 2023
Published online: 06 Mar 2025 *

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