Analysis of Design-Build Contracts Utilising Machine Learning on the Axis of Time, Cost and Quality

Year 2025, Volume: 13 Issue: 4, 1455 - 1475, 30.10.2025

Anıl Demircan , Latif Onur Uğur

https://doi.org/10.29130/dubited.1664225

Abstract

In construction projects, contract management processes necessitate achieving a delicate equilibrium among time, cost, and quality. This work aims to analyse Design-Build standard construction contract texts by the application of machine learning (ML) methodologies to attain a more effective equilibrium among these three essential factors, hence enhancing the decision support process in contract management. The suggested model offers comprehensive predictions regarding time, cost, and quality in the management and risk assessment of construction projects where conventional methods are inadequate. A classification model employing text mining and ML algorithms is proposed in the study. The standard contract clauses of the FIDIC Conditions of Contract for Design, Build and Operate, as well as the JCT Design and Build Contract, have been analysed. In the realm of text mining, natural language processing (NLP) methodologies, including Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec (Continous Bag-of-Words (CBOW) and Skip-Gram), and Bag-of-Words (BoW), have been employed. Various ML algorithms, including Support Vector Machines (SVM), Decision Trees (DT), and Ensemble Learning (EL) techniques (XGBoost), have been employed to evaluate the efficacy of various text representation techniques. The models' performance was evaluated using 70%-30% and 80%-20% training-test data splits. The study concluded that the integration of the Skip-Gram approach with the XGBoost model yielded the highest accuracy (Acc) and F1 scores. In the 80%-20% train-test split, the F1 score was recorded at 0.8858 and the Acc at 0.8779, highlighting the significance of capturing contextual information. The primary constraint of the study is the inability to make a precise separation in the dataset, since the time and cost aspects in contract texts frequently overlap. This circumstance has led the model to erroneously categorise certain words into both the cost and time classifications, hence diminishing accuracy rates. The variety of legal and technical terminology in contracts has hindered the model's ability to appropriately evaluate some expressions. The results demonstrate that ML provides a novel approach to analysing construction contracts and has the capacity to enhance decision-making in contract management and negotiations. The model presented in this paper provides a framework for the more efficient management of time, cost, and quality factors.

Keywords

Standard Construction Contracts , Natural Language Processing , Machine Learning , Ensemble Learning , Text Mining

Ethical Statement

This study does not involve human or animal participants. All procedures followed scientific and ethical principles, and all referenced studies are appropriately cited

Zaman, Maliyet ve Kalite Ekseninde Makine Öğrenimini ile Tasarla-İnşa Et Sözleşmelerinin Analizi

Abstract

Bu çalışma, inşaat sektöründe yaygın olarak kullanılan Tasarla-İnşa Et (Design-Build) standart sözleşme metinlerinin analizinde makine öğrenimi tekniklerinin kullanımını incelemektedir. Zaman, maliyet ve kalite ekseninde gerçekleştirilen analiz, inşaat projelerinin yönetimi ve risk değerlendirmesi açısından önemli bir adım teşkil etmektedir. Çalışmada, metin madenciliği (text mining) ve makine öğrenimi algoritmalarından yararlanan bir sınıflandırma modeli önerilmektedir. Özellikle, FIDIC Conditions of Contract for Design, Build and Operate ve JCT Design and Build Contract standart sözleşme hükümleri incelenmiştir. Metin madenciliği bağlamında TF-IDF, Word2Vec (CBOW ve Skip-Gram) ve Bag-of-Words (BoW) gibi doğal dil işleme (NLP) teknikleri kullanılmıştır. Destek Vektör Makineleri (SVM), Karar Ağaçları (DT) ve Topluluk Öğrenmesi (XGBoost) gibi çeşitli makine öğrenimi algoritmaları ile farklı metin temsil yöntemlerinin performansları karşılaştırılmıştır. Modellerin başarımı, %70-%30 ve %80-%20 eğitim-test veri ayrımları altında analiz edilmiştir. Çalışma sonucunda, Skip-Gram yöntemi ile XGBoost modeli en yüksek doğruluk ve F1 skorlarını elde ederek en başarılı kombinasyon olarak belirlenmiştir. Özellikle %80-20 eğitim-test ayrımında F1 skoru 0.8858 ve doğruluk 0.8779 olarak ölçülmüştür, bu da bağlamsal bilgi yakalama yeteneğinin önemini göstermektedir. Ancak, çalışmanın en önemli sınırlılığı, sözleşme metinlerinde süre ve maliyet unsurlarının çoğu zaman iç içe geçmesi nedeniyle veri setinde kesin bir ayrımın yapılamamış olmasıdır. Bu durum, modelin bazı kelimeleri hem maliyet hem de süre kategorisine yanlış tahsis etmesine neden olarak doğruluk oranlarını düşürmüştür. Ayrıca, sözleşmelerde kullanılan hukuki ve teknik terimlerin çeşitliliği, modelin bazı ifadeleri doğru anlamlandırmasını zorlaştırmıştır. Elde edilen bulgular, makine öğreniminin inşaat sözleşmelerinin analizine dair yeni bir bakış açısı sunduğunu ve sözleşme yönetimi ile müzakerelerde karar verme süreçlerini iyileştirme potansiyeline sahip olduğunu göstermektedir. Bu çalışmada önerilen model, zaman, maliyet ve kalite unsurlarının daha verimli yönetilmesine yardımcı olacak bir çerçeve sunmaktadır.

Keywords

Standart inşaat sözleşmeleri , Doğal dil işleme , Makine öğrenimi , Topluluk öğrenmesi , Metin madenciliği

References

• Ayhan, M., Dikmen, İ., & Birgonul, M. T. (2021). Predicting the occurrence of construction disputes using machine learning techniques. Journal of Construction Engineering and Management, 147(4). https://doi.org/10.1061/(ASCE)CO.1943-7862.0002019
• Bafna, P., Pramod, D., & Vaidya, A. (2016). Document clustering: TF-IDF approach. In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) (pp. 61–66). IEEE. https://doi.org/10.1109/ICEEOT.2016.7754750
• Başarslan, M. S., & Kayaalp, F. (2023). MBi-GRUMCONV: A novel Multi Bi-GRU and Multi CNN-based deep learning model for social media sentiment analysis. Journal of Cloud Computing, 12(1), 5. https://doi.org/10.1186/s13677-022-00440-z
• Başarslan, M. S., & Kayaalp, F. (2024). Sentiment analysis of coronavirus data with ensemble and machine learning methods. Turkish Journal of Engineering, 8(2), 175–185. https://doi.org/10.55560/tureng.1394101
• Ben Talha, M., Fatima-Zahrae, H., Benghabrit, A., & Eddine, R. (2024). A machine learning approach based on contract parameters for cost forecasting in construction. Journal of Construction Engineering and Management. Advance online publication. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002664
• Bogus, S., Migliaccio, G., & Jin, R. (2013). Study of the relationship between procurement duration and project performance in design–build projects: Comparison between water/wastewater and transportation sectors. Journal of Management in Engineering, 29(4), 382–391. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000207
• Cantarelli, C. C., van Wee, B., Molin, E. J. E., & Flyvbjerg, B. (2012). Different cost performance: Different determinants? Transport Policy, 22, 88–95. https://doi.org/10.1016/j.tranpol.2012.04.002
• Candaş, A. B., & Tokdemir, O. B. (2022). Automated identification of vagueness in the FIDIC Silver Book conditions of contract. Journal of Construction Engineering and Management, 148(4), Article 04022007. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002283
• Çaki, M. B., & Başarslan, M. S. (2024). Classification of fake news using machine learning and deep learning. Journal of Artificial Intelligence and Data Science, 4(1), 22–32. https://dergipark.org.tr/pub/jaida
• Debero, D. W., & Sinesilassie, E. G. (2024). Machine learning model for construction time prediction: A case of selected public building projects in Hosanna, Ethiopia. Journal of Engineering, 2024(1), Article 5653690. https://doi.org/10.1155/2024/5653690
• Deza, J. I., Ihshaish, H., & Mahdjoubi, L. (2022). A machine learning approach to classifying construction cost documents into the International Construction Measurement Standard. arXiv preprint. https://arxiv.org/abs/2203.05678
• Dikmen, I., Eken, G., Erol, H., & Birgonul, M. T. (2025). Automated construction contract analysis for risk and responsibility assessment using natural language processing and machine learning. Computers in Industry, 166, Article 104251. https://doi.org/10.1016/j.compind.2024.104251
• Du Plessis, H., & Oosthuizen, P. (2018). Construction project management through building contracts: A South African perspective. Acta Structilia, 25(1), 152–181.
• Etli, M. U., Yılmaz, A., Arı, B., & Turgut, Z. (2024). Evaluating deep learning techniques for detecting aneurysmal subarachnoid hemorrhage: A comparative analysis of convolutional neural network and transfer learning models. World Neurosurgery, 187, e807–e813. https://doi.org/10.1016/j.wneu.2024.04.025
• García, J., Villavicencio, G., Altimiras, F., Crawford, B., Soto, R., Minatogawa, V., Franco, M., Martínez-Muñoz, D., & Yepes Piqueras, V. (2022). Machine learning techniques applied to construction: A hybrid bibliometric analysis of advances and future directions. Automation in Construction, 142, Article 104532. https://doi.org/10.1016/j.autcon.2022.104532
• Hassan, F. ul, & Le, T. (2020). Automated requirements identification from construction contract documents using natural language processing. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 12(2), Article 02020002. https://doi.org/10.1061/(ASCE)LA.1943-4170.0000410
• Hassan, F. ul, & Le, T. (2021). Computer-assisted separation of design–build contract requirements to support subcontract drafting. Automation in Construction, 122, Article 103479. https://doi.org/10.1016/j.autcon.2020.103479
• Jafari, P., Al Hattab, M., Mohamed, E., & AbouRizk, S. (2021). Automated extraction and time–cost prediction of contractual reporting requirements in construction using natural language processing and simulation. Applied Sciences, 11(13), Article 6188. https://doi.org/10.3390/app11136188
• Jiang, C., Li, X., Lin, J.-R., Liu, M., & Ma, Z. (2023). Adaptive control of resource flow to optimize construction work and cash flow via online deep reinforcement learning. Automation in Construction, 150, Article 104632. https://doi.org/10.1016/j.autcon.2023.104632
• Jiang, S., Hu, J., Magee, C. L., & Luo, J. (2024). Deep learning for technical document classification. IEEE Transactions on Engineering Management, 71, 1163–1179. https://doi.org/10.1109/TEM.2024.3389052
• Liu, J., Dietz, T., Carpenter, S. R., Alberti, M., Folke, C., Moran, E., Pell, A. N., Deadman, P., Kratz, T., Lubchenco, J., Ostrom, E., Ouyang, Z., Provencher, W., Redman, C. L., Schneider, S. H., & Taylor, W. W. (2007). Coupled human and natural systems. AMBIO: A Journal of the Human Environment, 36(8), 639–649. https://doi.org/10.1579/0044-7447(2007)36[639:CHANSP]2.0.CO;2
• Mewomo, M. C., Aigbavboa, C., & Lesalane, P. (2018). An examination of the key drivers of amendments to the standard forms of contract in the South African construction industry. Journal of Construction in Developing Countries, 23(1), 115–124.
• Moon, S., Lee, G., & Chi, S. (2022). Automated system for construction specification review using natural language processing. Advanced Engineering Informatics, 51, Article 101495. https://doi.org/10.1016/j.aei.2021.101495
• Moon, S., Lee, G., Chi, S., & Oh, H. (2021). Automated construction specification review with named entity recognition using natural language processing. Journal of Construction Engineering and Management, 147(1), Article 04020147. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001953
• Nguyen, P. T. (2021). Application of machine learning in construction management. TEM Journal, 10(4), 1385–1389. https://doi.org/10.18421/TEM104-23
• Nguyen Van, T., & Nguyen Quoc, T. (2021). Research trends on machine learning in construction management: A scientometric analysis. Journal of Applied Science and Technology Trends, 2(2), 124–132. https://doi.org/10.38094/jastt2021211
• Ola, A. A. (2018). Cohesion between cost, time, and quality performance of civil engineering projects: Evidence from contractor prequalification criteria. Urban Studies and Public Administration, 1(2), 210-219. https://doi.org/10.22158/uspa.v1n2p210
• Öztürk, T., Turgut, Z., Akgün, G., & Köse, C. (2022). Machine learning-based intrusion detection for SCADA systems in healthcare. Network Modeling Analysis in Health Informatics and Bioinformatics, 11(1), Article 47. https://doi.org/10.1007/s13721-022-00372-1
• Parmar, M., & Tiwari, A. (2024). Enhancing text classification performance using stacking ensemble method with TF-IDF feature extraction. In 2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) (pp. 166–174). IEEE.
• Sanni-Anibire, M. O., Zin, R. M., & Olatunji, S. O. (2021). Machine learning-based framework for construction delay mitigation. Journal of Information Technology in Construction, 26, 303–318. https://doi.org/10.36680/j.itcon.2021.017
• Son, B.-Y., & Lee, E.-B. (2019). Using text mining to estimate schedule delay risk of 13 offshore oil and gas EPC case studies during the bidding process. Energies, 12(10), Article 1956. https://doi.org/10.3390/en12101956
• Tayefeh Hashemi, S., Ebadati, O. M., & Kaur, H. (2020). Cost estimation and prediction in construction projects: A systematic review on machine learning techniques. SN Applied Sciences, 2(10), Article 1703. https://doi.org/10.1007/s42452-020-03497-1
• Üstebay, S., Turgut, Z., Odabaşı, Ş. D., Aydın, M. A., & Sertbaş, A. (2023). A machine learning approach based on indoor target positioning by using sensor data fusion and improved cosine similarity. Electrica, 24(1), 218-227. Advance online publication. https://doi.org/10.5152/electrica.2023.23051
• Zhong, B., Shen, L., Pan, X., Zhong, X., & He, W. (2024). Dispute classification and analysis: Deep learning–based text mining for construction contract management. Journal of Construction Engineering and Management, 150(1), Article 04023151. http://doi.org/10.1061/JCEMD4.COENG-14080
• Zhou, H., Gao, B., Tang, S., Li, B., & Wang, S. (2023). Intelligent detection on construction project contract missing clauses based on deep learning and NLP. Engineering, Construction and Architectural Management. Advance online publication. https://doi.org/10.1108/ECAM-10-2022-0983