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Al-Ghzawi, M and El-Rayes, K (2023) Optimizing the planning of airport airside expansion projects to minimize air traffic disruptions and construction cost. Journal of Construction Engineering and Management, 149(04).

Barros, B A F S and Sotelino, E D (2023) Constructability and sustainability studies in conceptual projects: A BIM-based approach. Journal of Construction Engineering and Management, 149(04).

Guo, H, Zhang, Z, Yu, R, Sun, Y and Li, H (2023) Action recognition based on 3D skeleton and LSTM for the monitoring of construction workers' safety harness usage. Journal of Construction Engineering and Management, 149(04).

Hassan, F U, Le, T and Le, C (2023) Automated approach for digitalizing scope of work requirements to support contract management. Journal of Construction Engineering and Management, 149(04).

  • Type: Journal Article
  • Keywords: contract requirements; information extraction; natural language processing; scope of work
  • ISBN/ISSN: 0733-9364
  • URL: http://doi.org/10.1061/JCEMD4.COENG-12528
  • Abstract:
    Scope of work requirements (SOWRs) specify the contractual obligations for the contractor to fulfill. Managing the information is crucial to the success of a construction project. This process is, however, often challenging because requirements are typically voluminous and written in an unstructured paper-based format. The current state-of-the-art models are mostly applicable to quantitative constraints for use in automated code compliance checking of design features. This study proposes a novel natural language processing (NLP) framework capable of digitalizing nonquantitative natural language SOWRs outlined in construction contracts. The model includes a comprehensive set of semantic and syntactic rules of linguistic features for extracting contractual work information including actors, actions, objects, constraints, tasks, and obligation. The framework was evaluated on a large textual corpus of provisions of design-build highway contracts. The model yields an impressive precision and recall of more than 93% and 87%, respectively. The proposed system is expected to help the project planner quickly develop an electronic database of contractual work requirements, enabling significantly improved efficiency in project planning and verification.

Jeon, J, Zhang, Y, Yang, L, Xu, X, Cai, H and Tran, D (2023) Risk breakdown matrix for risk-based inspection of transportation infrastructure projects. Journal of Construction Engineering and Management, 149(04).

Koc, K, Ekmekcioǧlu, Ö and Gurgun, A P (2023) Developing a national data-driven construction safety management framework with interpretable fatal accident prediction. Journal of Construction Engineering and Management, 149(04).

Li, Y, Ning, Y and Rowlinson, S (2023) Social control in outsourced architectural and engineering design consulting projects: Behavioral consequences and motivational mechanism. Journal of Construction Engineering and Management, 149(04).

Nigra, M and Bossink, B (2023) Cooperative learning in green building demonstration projects: Insights from 30 innovative and environmentally sustainable demonstrations around the world. Journal of Construction Engineering and Management, 149(04).

Pushpakumara, B H J, Gunasekara, M T and Gannile, Y M T D (2023) Variation of mechanical and chemical properties of old and new clay bricks. Journal of Construction Engineering and Management, 149(04).

Shiha, A and Dorra, E M (2023) Resilience index framework for the construction industry in developing countries. Journal of Construction Engineering and Management, 149(04).

Shirazi, D H and Toosi, H (2023) Deep multilayer perceptron neural network for the prediction of Iranian dam project delay risks. Journal of Construction Engineering and Management, 149(04).

Xia, N, Griffin, M A, Xie, Q and Hu, X (2023) Antecedents of workplace safety behavior: Meta-analysis in the construction industry. Journal of Construction Engineering and Management, 149(04).

Xu, W and Wang, T K (2023) Construction worker safety prediction and active warning based on computer vision and the gray absolute decision analysis method. Journal of Construction Engineering and Management, 149(04).