As delays in construction projects escalate costs, timely project completion stands as a pivotal criterion for success in construction endeavors. Accurate scheduling duration estimates play a vital role in averting additional expenses and mitigating the risk of disputes among employers, contractors, and clients. Experts assert that delays are a common occurrence in the majority of civil engineering projects, emphasizing the critical role of time management in these endeavors. Project scheduling often faces constraints related to activity precedence relationships, project completion time, budget, and various resources like tools, equipment, machinery, or limited human resources. In the realm of construction project control, neural networks emerge as potent and innovative tools. Leveraging machine learning capabilities and analyzing intricate data, these tools contribute significantly to enhancing the management and control of construction processes. This article introduces a model for addressing project scheduling challenges, proposing a novel application of the Long Short-Term Memory (LSTM) neural network. Results demonstrate that LSTM outperforms other Recurrent Neural Networks (RNNs) in handling time series problems. Furthermore, this study advances our understanding of GPT models' application, offering insights into research prospects for implementing GPT models within the construction industry. تفاصيل المقالة