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AI and Text-Mining Applications for Analyzing Contractor’s Risk in Invitation to Bid (ITB) and Contracts for Engineering Procurement and Construction (EPC) Projects

Author

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  • Su Jin Choi

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

  • So Won Choi

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

  • Jong Hyun Kim

    (WISEiTECH, Seoul 13486, Korea)

  • Eul-Bum Lee

    (Graduate Institute of Ferrous & 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

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.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4632-:d:605336
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    References listed on IDEAS

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    1. Byung-Yun Son & Eul-Bum Lee, 2019. "Using Text Mining to Estimate Schedule Delay Risk of 13 Offshore Oil and Gas EPC Case Studies During the Bidding Process," Energies, MDPI, vol. 12(10), pages 1-25, May.
    2. 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.
    3. Sung-O Kang & Eul-Bum Lee & Hum-Kyung Baek, 2019. "A Digitization and Conversion Tool for Imaged Drawings to Intelligent Piping and Instrumentation Diagrams (P&ID)," Energies, MDPI, vol. 12(13), pages 1-26, July.
    4. Myung-Hun Kim & Eul-Bum Lee & Han-Suk Choi, 2018. "Detail Engineering Completion Rating Index System (DECRIS) for Optimal Initiation of Construction Works to Improve Contractors’ Schedule-Cost Performance for Offshore Oil and Gas EPC Projects," Sustainability, MDPI, vol. 10(7), pages 1-31, July.
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    Cited by:

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    2. Na Xu & Xueqing Zhou & Chaoran Guo & Bai Xiao & Fei Wei & Yuting Hu, 2022. "Text Mining Applications in the Construction Industry: Current Status, Research Gaps, and Prospects," Sustainability, MDPI, vol. 14(24), pages 1-29, December.
    3. U. M. Fernandes Dimlo & V. Rupesh & Yeligeti Raju, 2024. "The dynamics of natural language processing and text mining under emerging artificial intelligence techniques," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(9), pages 4512-4526, September.
    4. Anna Pamula & Zbigniew Gontar & Beata Gontar & Tetiana Fesenko, 2023. "Latent Dirichlet Allocation in Public Procurement Documents Analysis for Determining Energy Efficiency Issues in Construction Works at Polish Universities," Energies, MDPI, vol. 16(12), pages 1-23, June.
    5. 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.
    6. 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.
    7. So-Won Choi & Eul-Bum Lee & Jong-Hyun Kim, 2021. "The Engineering Machine-Learning Automation Platform ( EMAP ): A Big-Data-Driven AI Tool for Contractors’ Sustainable Management Solutions for Plant Projects," Sustainability, MDPI, vol. 13(18), pages 1-33, September.

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