커뮤니티

연구성과

PML [PML]Prof. Eul-Bum Lee:AI and Text-Mining Applications for Analyzing Contractor’s Risk in Invitation…

페이지 정보

profile_image

작성자 최고관리자

댓글 0건 조회 20회 작성일 2022-04-01 09:33

본문

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.

댓글목록

등록된 댓글이 없습니다.