Engineering, Procurement, and Construction (EPC) projects span the entire cycle of industrial plants, from bidding to engineering, construction, and start-up operation and maintenance. Most EPC contractors do not have systematic decision-making tools when bidding for the project; therefore, they rely on manual analysis and experience in evaluating the bidding contract documents, including technical specifications. Oftentimes, they miss or underestimate the presence of technical risk clauses or risk severity, potentially create with a low bid price and tight construction schedule, and eventually experience severe cost overrun or/and completion delays. Through this study, two digital modules, Technical Risk Extraction and Design Parameter Extraction, were developed to extract and analyze risks in the project’s technical specifications based on machine learning and AI algorithms. In the Technical Risk Extraction module, technical risk keywords in the bidding technical specifications are collected, lexiconized, and then extracted through phrase matcher technology, a machine learning natural language processing technique. The Design Parameter Extraction module compares the collected engineering standards’ so-called standard design parameters and the plant owner’s technical requirements on the bid so that a contractor’s engineers can detect the difference between them and negotiate them. As described above, through the two modules, the risk clauses of the technical specifications of the project are extracted, and the risks are detected and reconsidered in the bidding or execution of the project, thereby minimizing project risk and providing a theoretical foundation and system for contractors. As a result of the pilot test performed to verify the performance and validity of the two modules, the design risk extraction accuracy of the system module has a relative advantage of 50 percent or more, compared to the risk extraction accuracy of manual evaluation by engineers. In addition, the speed of the automatic extraction and analysis of the system modules are 80 times faster than the engineer’s manual analysis time, thereby minimizing project loss due to errors or omissions due to design risk analysis during the project bidding period with a set deadline.
Differential Evolution Based Manifold Gaussian Process Machine Learning for Microwave Filter’s Parameter Extraction
