Predicting construction project scheduling issues using LSTM neural network (long-term short-term memory)

Document Type : Research Article

Authors

1 Ph.D Candidate of Civil Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran

2 Department of Civil Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran

3 faculty of civil eng., yazd branch, islamic azad university, yazd,iran

Abstract

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.

Keywords

Main Subjects

References

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Amirkabir Journal of Civil Engineering
Volume 55, Issue 9
December 2023
Pages 1753-1764

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