Search Results (2)

The development of intelligent information technology in the era of the fourth industrial revolution requires the EPC (engineering, procurement, and construction) industry to increase productivity through a digital transformation. This study aims to automatically analyze the critical risk clauses in the invitation to bid (ITB) at the bidding stage to strengthen their competitiveness for the EPC contractors. To this end, we developed an automated analysis technology that effectively analyzes a large amount of ITB documents in a short time by applying natural language processing (NLP) and bi-directional long short-term memory (bi-LSTM) algorithms. This study proposes two models. First, the semantic analysis (SA) model is a rule-based approach that applies NLP to extract key risk clauses. Second, the risk level ranking (RLR) model is a train-based approach that ranks the risk impact for each clause by applying bi-LSTM. After developing and training an artificial intelligent (AI)-based ITB analysis model, its performance was evaluated through the actual project data. As a result of validation, the SA model showed an F1 score of 86.4 percent, and the RLR model showed an accuracy of 46.8 percent. The RLR model displayed relatively low performance because the ITB used in the evaluation test included the contract clauses that did not exist in the training dataset. Therefore, this study illustrated that the rule-based approach performed superior to the training-based method. The authors suggest that EPC contractors should apply both the SA and RLR modes in the ITB analysis, as one supplements the other. The two models were embedded in the Engineering Machine-learning Automation Platform (EMAP), a cloud-based platform developed by the authors. Rapid analysis through applying both the rule-based and AI-based automatic ITB analysis technology can contribute to securing timeliness for risk response and supplement possible human mistakes in the bidding stage. Full article

Contractors responsible for the whole execution of engineering, procurement, and construction (EPC) projects are exposed to multiple risks due to various unbalanced contracting methods such as lump-sum turn-key and low-bid selection. Although systematic risk management approaches are required to prevent unexpected damage to the EPC contractors in practice, there were no comprehensive digital toolboxes for identifying and managing risk provisions for ITB and contract documents. This study describes two core modules, Critical Risk Check (CRC) and Term Frequency Analysis (TFA), developed as a digital EPC contract risk analysis tool for contractors, using artificial intelligence and text-mining techniques. The CRC module automatically extracts risk-involved clauses in the EPC ITB and contracts by the phrase-matcher technique. A machine learning model was built in the TFA module for contractual risk extraction by using the named-entity recognition (NER) method. The risk-involved clauses collected for model development were converted into a database in JavaScript Object Notation (JSON) format, and the final results were saved in pickle format through the digital modules. In addition, optimization and reliability validation of these modules were performed through Proof of Concept (PoC) as a case study, and the modules were further developed to a cloud-service platform for application. The pilot test results showed that risk clause extraction accuracy rates with the CRC module and the TFA module were about 92% and 88%, respectively, whereas the risk clause extraction accuracy rates manually by the engineers were about 70% and 86%, respectively. The time required for ITB analysis was significantly shorter with the digital modules than by the engineers. Full article