2571-1075ISSN (Online)
Volume 11 , Issue 2 , PP: 13-24, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Rania Bashir 1 * , Marek Salamak 2 , Sonia Ahmed 3
Doi: https://doi.org/10.54216/IJBES.110202
The study put forward an integrated artificial intelligence-based approach to the analysis and prediction of contracting disputes in Engineering Projects, especially through Machine Learning methods and Deep Learning methods. Current ways of managing contracts cannot effectively deal with the complicated nature of Legal Texts and do not provide for early identification of potential disputes. This developed System was built using the Python Programming Language, using key libraries for Natural Language Processing (NLP) and Machine Learning (ML). The cache of Contract Documents in all formats was transformed into numerical vectors using TF-IDF once all Document Processing and Clean-up Procedures were completed. Multiple Models were built, with trained versions of each, including Logistic Regression, SVM, Voting Classifiers and an MLP (Multi-Layer Perceptron) based Neural Network model. Since each Contracting Dispute was modelled separately to improve overall prediction accuracy, initial recommendations for resolution are generated. Results show that the MLP performed in a SUPERIOR fashion, with an Overall Model Accuracy of 88%, and F1 Score of 0.874, effectively classifying Contracting Disputes relating to Delays, Payments and Scope Variations. The application of this framework to an actual example taken from the construction industry in Syria reaffirmed the capability of automating contract text review and improving risk management. This reinforces the importance of artificial intelligence as a tool for increasing proactive decision-making and minimizing conflict in engineering projects.
Engineering Contract , Building Information Modeling (BIM) , Artificial Intelligence (AI) , , Legal Dispute Resolution , Machine Learning , Dispute Prediction
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