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Contractor’s Risk Analysis of Engineering Procurement and Construction (EPC) Contracts Using Ontological Semantic Model and Bi-Long Short-Term Memory (LSTM) Technology

Author

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  • So-Won Choi

    (Graduate Institute of Ferrous and Energy Materials Technology, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea)

  • Eul-Bum Lee

    (Graduate Institute of Ferrous and Energy Materials Technology, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
    Department of Industrial and Management Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea)

Abstract

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.

Suggested Citation

  • So-Won Choi & Eul-Bum Lee, 2022. "Contractor’s Risk Analysis of Engineering Procurement and Construction (EPC) Contracts Using Ontological Semantic Model and Bi-Long Short-Term Memory (LSTM) Technology," Sustainability, MDPI, vol. 14(11), pages 1-32, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6938-:d:832751
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    References listed on IDEAS

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    1. Farzad Firouzi Jahantigh & Behnam Malmir & Behzad Aslani Avilaq, 2017. "Economic risk assessment of EPC projects using fuzzy TOPSIS approach," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 27(2), pages 161-179.
    2. Aretz, Kevin & Bartram, Söhnke M. & Pope, Peter F., 2011. "Asymmetric loss functions and the rationality of expected stock returns," International Journal of Forecasting, Elsevier, vol. 27(2), pages 413-437.
    3. Murat Gunduz & Mohammed Almuajebh, 2020. "Critical Success Factors for Sustainable Construction Project Management," Sustainability, MDPI, vol. 12(5), pages 1-17, March.
    4. Su Jin Choi & So Won Choi & Jong Hyun Kim & Eul-Bum Lee, 2021. "AI and Text-Mining Applications for Analyzing Contractor’s Risk in Invitation to Bid (ITB) and Contracts for Engineering Procurement and Construction (EPC) Projects," Energies, MDPI, vol. 14(15), pages 1-28, July.
    5. Pooyan Amir-Ahmadi & Christian Matthes & Mu-Chun Wang, 2020. "Choosing Prior Hyperparameters: With Applications to Time-Varying Parameter Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 124-136, January.
    6. Ortwin Renn, 1998. "Three decades of risk research: accomplishments and new challenges," Journal of Risk Research, Taylor & Francis Journals, vol. 1(1), pages 49-71, January.
    7. Min-Ji Park & Eul-Bum Lee & Seung-Yeab Lee & Jong-Hyun Kim, 2021. "A Digitalized Design Risk Analysis Tool with Machine-Learning Algorithm for EPC Contractor’s Technical Specifications Assessment on Bidding," Energies, MDPI, vol. 14(18), pages 1-31, September.
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    Cited by:

    1. Kyu-Jeong Lee & So-Won Choi & Eul-Bum Lee, 2025. "Artificial Intelligence-Driven Approach to Optimizing Boiler Power Generation Efficiency: The Advanced Boiler Combustion Control Model," Energies, MDPI, vol. 18(4), pages 1-45, February.

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