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Candaş, A B and Tokdemir, O B (2022) Automated Identification of Vagueness in the FIDIC Silver Book Conditions of Contract. Journal of Construction Engineering and Management, 148(04).

  • Type: Journal Article
  • Keywords: Vagueness; Natural language processing (NLP); Machine learning (ML); Rule-based; Supervised learning; Conditions of contract; Classification; Contract administration; Contract preparation;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0002254
  • Abstract:
    Contract conditions are crucial as they outline an agreement between different parties. The semantic terms in contract conditions need to be precisely designated. Where these conditions contain vague meanings, the interpretation of the conditions will vary, especially since the parties of the contract will be differently motivated to pursue their different expectations from it. The vague terms in contract conditions may thus cause a dispute and conflict among the parties that can jeopardize the eventual success of a construction project. The conventional practice of identifying vagueness in construction contract conditions is done manually, which is prone to error, time-consuming, and requires expert involvement. This study develops a methodology to automate the identification of vague terms in construction contract conditions with the sequential application of natural language processing (NLP) and machine learning (ML) techniques. Morphological and lexical analysis procedures are used to evaluate the corpus data obtained from a widely used typical construction contract published by International Federation of Consulting Engineers (FIDIC). Classifications of contract conditions in the corpus data are searched using several supervised ML techniques to determine the best performing classifier. The results show that the developed methodology reduces time spent on contract review, is reliable with a high level of accuracy in predicting the presence of vagueness, and removes dependence on expert participation in the contract review processes.

Guevara, J, Herrera, L and Salazar, J (2022) Interorganizational Sponsor Networks in Road and Social Infrastructure PPP Equity Markets. Journal of Construction Engineering and Management, 148(04).

Han, S, Jiang, Y and Bai, Y (2022) Fast-PGMED: Fast and Dense Elevation Determination for Earthwork Using Drone and Deep Learning. Journal of Construction Engineering and Management, 148(04).

Hosseinian, S M, Arjomand, A, Li, C Q and Zhang, G (2022) Developing a Model for Assessing Project Completion Time Reliability during Construction Using Time-Dependent Reliability Theory. Journal of Construction Engineering and Management, 148(04).

Liu, Q, Ye, G, Yang, J, Xiang, Q and Liu, Q (2022) Construction Workers’ Representativeness Heuristic in Decision Making: The Impact of Demographic Factors. Journal of Construction Engineering and Management, 148(04).

Ma, L and Fu, H (2022) A Governance Framework for the Sustainable Delivery of Megaprojects: The Interaction of Megaproject Citizenship Behavior and Contracts. Journal of Construction Engineering and Management, 148(04).

Tiruneh, G G and Fayek, A R (2022) Hybrid GA-MANFIS Model for Organizational Competencies and Performance in Construction. Journal of Construction Engineering and Management, 148(04).

Wang, P, Wang, K, Huang, Y, Fenn, P and Stewart, I (2022) Auditing Construction Cost from an In-Process Perspective Based on a Bayesian Predictive Model. Journal of Construction Engineering and Management, 148(04).

Xie, H, Hong, Y and Brilakis, I (2022) Analysis of User Needs in Time-Related Risk Management for Holistic Project Understanding. Journal of Construction Engineering and Management, 148(04).

Zhang, X and Liu, J (2022) Incentive Mechanism and Value-Added in PPP Projects Considering Financial Institutions’ Early Intervention. Journal of Construction Engineering and Management, 148(04).

Zhu, H, Hwang, B, Ngo, J and Tan, J P S (2022) Applications of Smart Technologies in Construction Project Management. Journal of Construction Engineering and Management, 148(04).