Development of Automatic-Extraction Model of Poisonous Clauses in International Construction Contracts Using Rule-Based NLP

Lee, Jeehee; Yi, June Seong; Son, JeongWook

doi:10.1061/(asce)cp.1943-5487.0000807

Cited by 95 publications

(38 citation statements)

References 33 publications

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“…Zhang and El-Gohary [11] proposed a semantic rule-based natural language processing (NLP) approach for automated information extraction (IE) from construction regulatory documents. Singh [12] introduced various techniques and the latest research on IE and NLP techniques, and Lee et al [13] proposed an automatic contract risk extraction model for construction projects by applying NLP and IE techniques. Their study, which was analyzed using the Fédération Internationale Des Ingénieurs-Conseils (FIDIC) Redbook, showed a remarkably low rate (1.2%) of risk sentence extraction from the total number of sentences.…”

Section: Machine Learning's Application To Plant Projectsmentioning

confidence: 99%

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The Engineering Machine-Learning Automation Platform (EMAP): A Big-Data-Driven AI Tool for Contractors’ Sustainable Management Solutions for Plant Projects

Choi

Kim²

2021

Sustainability

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Plant projects, referred to as Engineering Procurement and Construction (EPC), generate massive amounts of data throughout their life cycle, from the planning stages to the operation and maintenance (OM) stages. Many EPC contractors struggle with their projects due to the complexity of the decision-making processes, owing to the vast amount of project data generated during each project stage. In line with the fourth industrial revolution, the demand for engineering project management solutions to apply artificial intelligence (AI) in big data technology is increasing. The purpose of this study was to predict the risk of contractor and support decision-making at each project stage using machine-learning (ML) technology based on data generated in the bidding, engineering, construction, and OM stages of EPC projects. As a result of this study, the Engineering Machine-learning Automation Platform (EMAP), a cloud-based integrated analysis tool applied with big data and AI/ML technology, was developed. EMAP is an intelligent decision support system that consists of five modules: Invitation to Bid (ITB) Analysis, Design Cost Estimation, Design Error Checking, Change Order Forecasting, and Equipment Predictive Maintenance, using advanced AI/ML algorithms. In addition, each module was validated through case studies to assure the performance and accuracy of the module. This study contributes to the strengthening of the risk response for each stage of the EPC project, especially preventing errors by the project managers, and improving their work accuracy. Project risk management using AI/ML breaks away from the existing risk management practices centered on statistical analysis, and further expands the research scalability of related works.

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Section: Machine Learning's Application To Plant Projectsmentioning

confidence: 99%

“…The method of extracting information in a document can be divided into a rule-based approach and an ML approach [13]. The ITB Analysis module of this study applies both the rule-based approach and the ML approach.…”

Section: The Itb Analysis Module (M1)mentioning

confidence: 99%

The Engineering Machine-Learning Automation Platform (EMAP): A Big-Data-Driven AI Tool for Contractors’ Sustainable Management Solutions for Plant Projects

Choi

Kim²

2021

Sustainability

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“…To overcome the limitations of the previous studies, some researchers developed information extraction models using machine learning approaches (Kim and Chi 2019;Moon et al 2019). Although the manually built rules have the advantage of being comprehensive and accurate for the analyzed data, they require significant labor in development and the rules cannot be applied to other data sets (Beach et al 2015;Lee et al 2019). However, the machine learning models can automatically extract rules by training the input and output and determining useful features from the data.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Construction Specification Review with Named Entity Recognition Using Natural Language Processing

Moon

Lee

Chi

et al. 2021

J. Constr. Eng. Manage.

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“…Automatic model of contract risk evaluation in international construction projects using rulebased NLP (Lee et al 2019). Apply and test NLP system in the industrial settings of a railway signaling manufacturer.…”

Section: Procurement Managementmentioning

confidence: 99%

Natural Language Processing and Bim in Aeco Sector: A State of the Art

Giuda¹,

Locatelli²,

Seghezzi³

2020

Proceedings of International Structural Engineering and Construction

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The research provides state-of-the-art theories, methods, and applications of Natural Language Processing in a BIM approach to define advantages, weaknesses, and potential developments. Traditionally, the design and construction process requirements are expressed through verbal qualitative evaluations instead of using numerical and structured data. Information modeling and management methods can hardly manage requirements expressed by means of qualitative and unstructured expressions. This paper aims to define the state of the art on Natural Language Processing applications for the numerical translation of basic design requirements in the AECO sector. The major advantages of this research would be the optimization and automation of numerical definition and validation of requirements in a structured data form. Structured data can, in fact, be easily managed in a BIM approach. NLP supporting information modeling methods allows the application of requirements engineering and management techniques to handle construction processes from a data-driven perspective. An investigation of the setting of a decision support system, based on NLP, for the definition and validation of requirements in the early stages of the construction process, is also provided as a potential further development.

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Development of Automatic-Extraction Model of Poisonous Clauses in International Construction Contracts Using Rule-Based NLP

Cited by 95 publications

References 33 publications

The Engineering Machine-Learning Automation Platform (EMAP): A Big-Data-Driven AI Tool for Contractors’ Sustainable Management Solutions for Plant Projects

The Engineering Machine-Learning Automation Platform (EMAP): A Big-Data-Driven AI Tool for Contractors’ Sustainable Management Solutions for Plant Projects

Automated Construction Specification Review with Named Entity Recognition Using Natural Language Processing

Natural Language Processing and Bim in Aeco Sector: A State of the Art

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