Dispute Classification and Analysis: Deep Learning–Based Text Mining for Construction Contract Management
Publication: Journal of Construction Engineering and Management
Volume 150, Issue 1
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.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors would like to acknowledge the financial support provided by the National Key R&D Program of China (2022YFC3801700) and the National Natural Science Foundation of China (No. 72271106, No. U21A20151).
References
Abbaszadegan, A., and D. Grau. 2015. “Assessing the influence of automated data analytics on cost and schedule performance.” Procedia Eng. 123 (Jan): 3–6. https://doi.org/10.1016/j.proeng.2015.10.047.
Baker, H., M. R. Hallowell, and A. J. P. Tixier. 2020. “Automatically learning construction injury precursors from text.” Autom. Constr. 118 (Oct): 103145. https://doi.org/10.1016/j.autcon.2020.103145.
Bishop, C. M., and N. M. Nasrabadi. 2006. “Pattern recognition and machine learning.” Electron. Imaging 16 (Aug): 049901. https://doi.org/10.1016/10.1117/1.2819119.
Boella, G., L. di Caro, L. Humphreys, L. Robaldo, and L. E. van der Torre. 2012. “Eunomos, a legal document and knowledge management system for the Web to provide relevant, reliable and up-to-date information on the law.” Artif. Intell. Law 24: 245–283. https://doi.org/10.1007/s10506-016-9184-3.
Cakmak, E., and P. I. Cakmak. 2014. “An analysis of causes of disputes in the construction industry using analytical network process.” Procedia-Soc. Behav. Sci. 109 (12): 183–187. https://doi.org/10.1016/j.sbspro.2013.12.441.
Caled, D., M. Won, B. Martins, and M. J. Silva. 2019. “A hierarchical label network for multi-label EuroVoc classification of legislative contents.” In Proc., 23rd Int. Conf. on Theory and Practice of Digital Libraries Digital Libraries for Open Knowledge, 238–252. Berlin: Springer. https://doi.org/10.1007/978-3-030-30760-8_21.
Chen, G. X., M. Shan, A. P. Chan, X. Liu, and Y.-Q. Zhao. 2019. “Investigating the causes of delay in grain bin construction projects: The case of China.” Int. J. Constr. Manage. 19 (1): 1–14. https://doi.org/10.1080/15623599.2017.1354514.
Cheng, M. Y., H. C. Tsai, and Y. H. Chiu. 2009. “Fuzzy case-based reasoning for coping with construction disputes.” Expert Syst. Appl. 36 (2): 4106–4113. https://doi.org/10.1016/j.eswa.2008.03.025.
Fan, H., and H. Li. 2013. “Retrieving similar cases for alternative dispute resolution in construction accidents using text mining techniques.” Autom. Constr. 34 (Sep): 85–91. https://doi.org/10.1016/j.autcon.2012.10.014.
Goh, Y. M., and C. Ubeynarayana. 2017. “Construction accident narrative classification: An evaluation of text mining techniques.” Accid. Anal. Prev. 108 (Nov): 122–130. https://doi.org/10.1016/j.aap.2017.08.026.
Hassan, F., U. Le, and X. Lv. 2021. “Addressing legal and contractual matters in construction using natural language processing: A critical review.” J. Constr. Eng. Manage. 147 (9): 03121004. https://doi.org/10.1061/(ASCE)CO.1943-7862.000212.
Ilter, D. 2012. “Identification of the relations between dispute factors and dispute categories in construction projects.” Int. J. Law Built Environ. 4 (1): 45–59. https://doi.org/10.1108/17561451211211732.
Iyer, K., N. Chaphalkar, and G. Joshi. 2008. “Understanding time delay disputes in construction contracts.” Int. J. Project Manage. 26 (5): 174–184. https://doi.org/10.1016/j.ijproman.2007.05.002.
Kingma, D. P., and J. Ba. 2014. “Adam: A method for stochastic optimization.” Preprint, submitted December 22, 2014. http://arxiv.org/abs/1412.6980.
Ko, T., H. D. Jeong, and G. Lee. 2021. “Natural language processing–Driven model to extract contract change reasons and altered work items for advanced retrieval of change orders.” J. Constr. Eng. Manage. 147 (11): 04021147. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002172.
LeCun, Y., Y. Bengio, and G. Hinton. 2015. “Deep learning.” Nature 521 (7533): 436–444. https://doi.org/10.1038/nature14539.
Lee, C. K., T. W. Yiu, and S. O. Cheung. 2016. “Selection and use of alternative dispute resolution (ADR) in construction projects-past and future research.” Int. J. Project Manage. 34 (3): 494–507. https://doi.org/10.1016/j.ijproman.2015.12.008.
Leitner, E., G. Rehm, and J. Moreno-Schneider. 2019. “Fine-grained named entity recognition in legal documents.” In Proc., Int. Conf. on Semantic Systems, 272–287. Berlin: Springer. https://doi.org/10.1007/978-3-030-33220-4_20.
Li, C., J. Ge, K. Cheng, B. Luo, and V. Chang. 2022. “Statute recommendation: Re-ranking statutes by modeling case-statute relation with interpretable hand-crafted features.” Inf. Sci. 607 (Aug): 1023–1040. https://doi.org/10.1016/j.ins.2022.06.042.
Li, S., H. Zhang, L. Ye, X. Guo, and B. Fang. 2019. “Mann: A multichannel attentive neural network for legal judgment prediction.” IEEE Access 7 (Oct): 151144–151155. https://doi.org/10.1109/ACCESS.2019.2945771.
Liao, P. C., H. Shi, Y. Su, and X. Luo. 2018. “Development of data-driven influence model to relate the workplace environment to human error.” J. Constr. Eng. Manage. 144 (3): 04018003. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001448.
Liu, J., H. Li, M. Skitmore, and Y. Zhang. 2019. “Experience mining based on case-based reasoning for dispute settlement of international construction projects.” Autom. Constr. 97 (Jan): 181–191. https://doi.org/10.1016/j.autcon.2018.11.006.
Mahfouz, T., A. Kandil, and S. Davlyatov. 2018. “Identification of latent legal knowledge in differing site condition (DSC) litigations.” Autom. Constr. 94 (Oct): 104–111. https://doi.org/10.1016/j.autcon.2018.06.011.
Mitropoulos, P., and G. Howell. 2001. “Model for understanding, preventing, and resolving project disputes.” J. Constr. Eng. Manage. 127 (3): 223–231. https://doi.org/10.1061/(ASCE)0733-9364(2001)127:3(223).
Na, X., M. Ling, Q. Liu, W. Li, and Y. Deng. 2021. “An improved text mining approach to extract safety risk factors from construction accident reports.” Saf. Sci. 138 (Jun): 105216. https://doi.org/10.1016/j.ssci.2021.105216.
Nguyen, T.-S., L. M. Nguyen, S. Tojo, K. Satoh, and A. Shimazu. 2018. “Recurrent neural network-based models for recognizing requisite and effectuation parts in legal texts.” Artif. Intell. Law 26 (Jun): 169–199. https://doi.org/10.1007/s10506-018-9225-1.
Occhipinti, A., L. Rogers, and C. Angione. 2022. “A pipeline and comparative study of 12 machine learning models for text classification.” Expert Syst. Appl. 201 (Sep): 117193. https://doi.org/10.1016/j.eswa.2022.117193.
Pan, X., B. Zhong, Y. Wang, and L. Shen. 2022. “Identification of accident-injury type and bodypart factors from construction accident reports: A graph-based deep learning framework.” Adv. Eng. Inf. 54 (Oct): 101752. https://doi.org/10.1016/j.aei.2022.101752.
Qiao, J., C. Wang, S. Guan, and L. Shuran. 2022. “Construction-accident narrative classification using shallow and deep learning.” J. Constr. Eng. Manage. 148 (9): 04022088. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002354.
Raza, M., F. K. Hussain, O. K. Hussain, M. Zhao, and Z. Rehman. 2019. “A comparative analysis of machine learning models for quality pillar assessment of SaaS services by multi-class text classification of users’ reviews.” Future Gener. Comput. Syst. 101 (Dec): 341–371. https://doi.org/10.1016/j.future.2019.06.022.
Sulea, O. M., M. Zampieri, S. Malmasi, M. Vela, L. P. Dinu, and J. Van Genabith. 2017. “Exploring the use of text classification in the legal domain.” Preprint, submitted October 25, 2017. https://doi.org/10.48550/arXiv.1710.09306.
Tariq, J., and S. S. S. Gardezi. 2023. “Study the delays and conflicts for construction projects and their mutual relationship: A review.” Ain Shams Eng. J. 14 (1): 101815. https://doi.org/10.1016/j.asej.2022.101815.
The Supreme People’s Court of the People’s Republic of China. 2023. “China judgments online.” Accessed May 8, 2023. https://wenshu.court.gov.cn/.
Tian, D., M. Li, S. Han, and Y. Shen. 2022a. “A novel and intelligent safety-hazard classification method with syntactic and semantic features for large-scale construction projects.” J. Constr. Eng. Manage. 148 (10): 04022109. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002382.
Tian, D., H. Liu, S. Chen, M. Li, and C. Liu. 2022b. “Human error analysis for hydraulic engineering: Comprehensive system to reveal accident evolution process with text knowledge.” J. Constr. Eng. Manage. 148 (9): 04022093. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002366.
Tixier, A. J. P., M. R. Hallowell, B. Rajagopalan, and D. Bowman. 2016. “Automated content analysis for construction safety: A natural language processing system to extract precursors and outcomes from unstructured injury reports.” Autom. Constr. 62 (Feb): 45–56. https://doi.org/10.1016/j.autcon.2015.11.001.
Xu, Y., Y. Zhou, P. Sekula, and L. Ding. 2021. “Machine learning in construction: From shallow to deep learning.” Dev. Built Environ. 6 (May): 100045. https://doi.org/10.1016/j.dibe.2021.100045.
Yang, Z. 2012. “The study on law disputes in construction project contract relationship.” Phys. Procedia 33 (Jan): 1999–2004. https://doi.org/10.1016/j.phpro.2012.05.314.
Zhang, F., H. Fleyeh, X. Wang, and M. Lu. 2019. “Construction site accident analysis using text mining and natural language processing techniques.” Autom. Constr. 99 (Mar): 238–248. https://doi.org/10.1016/j.autcon.2018.12.016.
Zhong, B., X. Pan, P. E. Love, L. Ding, and W. Fang. 2020. “Deep learning and network analysis: Classifying and visualizing accident narratives in construction.” Autom. Constr. 113 (May): 103089. https://doi.org/10.1016/j.autcon.2020.103089.
Zhong, B., X. Xing, P. Love, X. Wang, and H. Luo. 2019. “Convolutional neural network: Deep learning-based classification of building quality problems.” Adv. Eng. Inf. 40 (Apr): 46–57. https://doi.org/10.1016/j.aei.2019.02.009.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 150 • Issue 1 • January 2024
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jun 1, 2023
Accepted: Sep 7, 2023
Published online: Oct 31, 2023
Published in print: Jan 1, 2024
Discussion open until: Mar 31, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Automation and robotics
- Business management
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction management
- Contracts and subcontracts
- Dispute resolution
- Engineering fundamentals
- Environmental engineering
- Filters
- Filtration
- Government
- Legal affairs
- Models (by type)
- Neural networks
- Optimization models
- Organizations
- Practice and Profession
- Systems engineering
- Water treatment
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.