Amirkabir University of Technology Amirkabir Journal of Civil Engineering 2588-297X 55 9 2023 11 22 Predicting construction project scheduling issues using LSTM neural network (long-term short-term memory) Predicting construction project scheduling issues using LSTM neural network (long-term short-term memory) 1753 1764 5217 10.22060/ceej.2023.21383.7701 FA Erfan Farzad Ph.D Candidate of Civil Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran Hadi Dehghan Manshadi Department of Civil Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran Mohammad Ali Dashti Rahmatabadi faculty of civil eng., yazd branch, islamic azad university, yazd,iran Journal Article 2022 05 08 As the purpose of monitoring the project is to make accurate decisions that can have significant effects on the project’s success, predicting the project’s characteristics becomes more important. According to experts, schedule delays are a frequent issue in many construction projects. This research aims to propose a model that can address project scheduling problems. For this purpose, this study proposes new applications of recurrent neural network architectures based on short-term long-term memory (LSTM) prediction models. Subsequently, the prediction results of the presented models are compared and verified with the historical data of a real project. The data used in this study has been obtained from the South Extension Project of Tehran Metro Line 6. The project started in October 2016 and ended in July 2018, lasting for a total of 21 months. In this study, the training dataset consisted of the initial 14 months' data, which accounted for 83 percent of the total data. We used the construction project progress as a forecasting variable. To evaluate the performance of LSTM models, we used the mean square error (MSE) metric as the evaluation criterion. The results show that the model accurately forecasts the project’s future progress based on its past progress. As the purpose of monitoring the project is to make accurate decisions that can have significant effects on the project’s success, predicting the project’s characteristics becomes more important. According to experts, schedule delays are a frequent issue in many construction projects. This research aims to propose a model that can address project scheduling problems. For this purpose, this study proposes new applications of recurrent neural network architectures based on short-term long-term memory (LSTM) prediction models. Subsequently, the prediction results of the presented models are compared and verified with the historical data of a real project. The data used in this study has been obtained from the South Extension Project of Tehran Metro Line 6. The project started in October 2016 and ended in July 2018, lasting for a total of 21 months. In this study, the training dataset consisted of the initial 14 months' data, which accounted for 83 percent of the total data. We used the construction project progress as a forecasting variable. To evaluate the performance of LSTM models, we used the mean square error (MSE) metric as the evaluation criterion. The results show that the model accurately forecasts the project’s future progress based on its past progress. https://ceej.aut.ac.ir/article_5217_5a7b238ba0f6502e5d6be14424b20ded.pdf