Construction contracts are vital for governing responsibilities in large-scale infrastructure projects, but their increasing complexity often leads to interpretation difficulties, disputes, and delays. Despite advances in natural language processing (NLP), automated analysis of construction contract clauses remains limited in project management. This study proposes a text classification framework integrating transformer-based contextual embeddings (BERT, ALBERT, RoBERTa, and DistilBERT) with machine learning and deep learning models (RNN, GRU, and LSTM) to analyze FIDIC and JCT contract provisions. Two multi-class classification tasks were defined: Dataset 1 (DS1) focusing on obligations, operational actions, optional provisions, general statements, and Dataset 2 (DS2) covering cost, quality, and time dimensions. Experimental results show that deep learning models consistently outperform traditional machine learning algorithms. Specifically, LSTM combined with RoBERTa and DistilBERT achieved the highest accuracy levels of 98.06% and 98.33% for DS1. The framework may support transparent contract governance by enabling faster and more consistent identification of contractual clauses. From a sustainability perspective, the findings suggest potential process-level contributions to economic efficiency, administrative workload reduction, and decision-making support throughout the project lifecycle.
Demircan et al. (Fri,) studied this question.