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Afzali Borujeni, S H, Zare, M and Adelzade Saadabadi, L (2024) Comparison of factors affecting the acceptance of the trenchless technology and open-trench method using ANP and AHP: Case study in Iran. Journal of Construction Engineering and Management, 150(01).

Aghililotf, M, Ramezanianpour, A M, Arbabi, H and Maghrebi, M (2024) Identifying construction managers' challenges: A novel approach based on social network analysis. Journal of Construction Engineering and Management, 150(01).

Alikhani, H and Latifi, M (2024) Evaluation of the healing performance of hot-mix asphalt containing waste steel shavings under different microwave induction healing cycles. Journal of Construction Engineering and Management, 150(01).

Anwer, S, Li, H, Antwi-Afari, M F, Mirza, A M, Rahman, M A, Mehmood, I, Guo, R and Wong, A Y L (2024) Evaluation of data processing and artifact removal approaches used for physiological signals captured using wearable sensing devices during construction tasks. Journal of Construction Engineering and Management, 150(01).

Arowoiya, V A, Oke, A E, Ojo, L D and Adelusi, A O (2024) Driving factors for the adoption of digital twin technology implementation for construction project performance in Nigeria. Journal of Construction Engineering and Management, 150(01).

D'Orazio, M, Bernardini, G and Di Giuseppe, E (2024) Improving sustainable management of university buildings based on occupancy data. Journal of Construction Engineering and Management, 150(01).

Da Silva, W O P, Farias, B A, Monteiro, I B, Pegorini, V, Casanova, D and Bisconsini, D R (2024) Development of global quality index of unpaved roads. Journal of Construction Engineering and Management, 150(01).

Ding, S, Hu, H, Chai, Z and Wang, W (2024) Secure and formalized blockchain-IPFS information sharing in precast construction from the whole supply chain perspective. Journal of Construction Engineering and Management, 150(01).

Garcia-Lopez, N P and Fischer, M (2024) Managing on-site production using an activity and flow-based construction model. Journal of Construction Engineering and Management, 150(01).

Jalloul, H, Choi, J, Manheim, D, Yesiller, N and Derrible, S (2024) Incorporating disaster debris into sustainable construction research and practice. Journal of Construction Engineering and Management, 150(01).

Koc, K, Budayan, C, Ekmekcioǧlu, Ö and Tokdemir, O B (2024) Predicting cost impacts of nonconformances in construction projects using interpretable machine learning. Journal of Construction Engineering and Management, 150(01).

Leung, M Y, Wei, X and Ojo, L D (2024) Developing a value-risk management model for construction projects. Journal of Construction Engineering and Management, 150(01).

Liu, Y, Wang, X, Guo, S, Shi, X and Wang, D (2024) Analyzing the optimization of subsidies for PPP urban rail transit projects: A choice between passenger demand, vehicle kilometer, or an improved efficiency-oriented framework. Journal of Construction Engineering and Management, 150(01).

Miao, K, Lou, W, Schonfeld, P and Xiao, Z (2024) Optimal earthmoving-equipment combination considering carbon emissions with an indicator-based multiobjective optimizer. Journal of Construction Engineering and Management, 150(01).

Ning, X, Zhai, F, Xia, N and Hu, X (2024) Protecting the ego: Anticipated image risk as a psychological deterrent to construction workers' safety citizenship behavior. Journal of Construction Engineering and Management, 150(01).

Olayiwola, J, Yusuf, A, Akanmu, A, Gonsalves, N and Abraham, Y (2024) Efficacy of annotated video-based learning environment for drawing students' attention to construction practice concepts. Journal of Construction Engineering and Management, 150(01).

Salih, F, Eissa, R and El-Adaway, I H (2024) Data-driven analysis of progressive design build in water and wastewater infrastructure projects. Journal of Construction Engineering and Management, 150(01).

Zhong, B, Shen, L, Pan, X, Zhong, X and He, W (2024) Dispute classification and analysis: Deep learning-based text mining for construction contract management. Journal of Construction Engineering and Management, 150(01).

  • Type: Journal Article
  • Keywords: construction contract management; contract dispute; legal knowledge; multilabel text classification; statute outcomes; text mining
  • ISBN/ISSN: 0733-9364
  • URL: http://doi.org/10.1061/JCEMD4.COENG-14080
  • Abstract:
    Disputes routinely arise in construction projects and significantly affect costs and scheduling. Learning from previous disputes is pivotal for construction contract management. This research focuses on extracting valuable information from government-issued statute that is involved in construction contract dispute, which is underexplored but useful for better construction contract management. The research presented in this study explores and evaluates five typical shallow learning models and four deep learning models for the multilabel text classification task that provide the ability to analyze dispute cases with statute outcomes automatically. Furthermore, model optimizations in some control variables (i.e., model grid search) are conducted to provide constructive model selection suggestions in practical text mining applications. Results show that the text convolution neural network model with 256 filter number and [1,2,3,4] filter size is a suitable backbone architecture for classifying construction dispute cases, which produced the best performance with the P@1(%), P@3(%), P@5(%), NDCG@1(%), NDCG@3(%), and NDCG@5(%) by 65.99, 54.60, 44.32, 65.99, 62.41, and 65.09. In conclusion, the contributions of this research mainly cover the following: (1) exploring and evaluating several multilabel classification models in construction dispute classification tasks and making further model optimizations and (2) the automatic generation of government-issued statutes enabling contract administrators to understand and evaluate the worth of their claims prior to taking it to litigation and therefore put in place strategies to reduce and resolve dispute in construction contract management.