SITE MAP

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Plant projects, referred to as Engineering Procurement and Construction (EPC), generate

massive amounts of data throughout their life cycle, from the planning stages to the operation and

maintenance (OM) stages. Many EPC contractors struggle with their projects due to the complexity

of the decision-making processes, owing to the vast amount of project data generated during each

project stage. In line with the fourth industrial revolution, the demand for engineering project

management solutions to apply artificial intelligence (AI) in big data technology is increasing. The

purpose of this study was to predict the risk of contractor and support decision-making at each project

stage using machine-learning (ML) technology based on data generated in the bidding, engineering,

construction, andOMstages of EPC projects. As a result of this study, the Engineering Machine-learning

Automation Platform (EMAP), a cloud-based integrated analysis tool applied with big data and AI/ML

technology, was developed. EMAP is an intelligent decision support system that consists of five

modules: Invitation to Bid (ITB) Analysis, Design Cost Estimation, Design Error Checking, Change

Order Forecasting, and Equipment Predictive Maintenance, using advanced AI/ML algorithms. In

addition, each module was validated through case studies to assure the performance and accuracy of

the module. This study contributes to the strengthening of the risk response for each stage of the EPC

project, especially preventing errors by the project managers, and improving their work accuracy.

Project risk management using AI/ML breaks away from the existing risk management practices

centered on statistical analysis, and further expands the research scalability of related works.

Keywords: digitalized AI tool; engineering big data; EPC contract risk extraction; NLP; machine

learning; design cost estimation; design error check; change order forecast; predictive maintenance;

sustainable project management