A comparative analysis of LSTM, GRU, and Transformer models for construction cost prediction with multidimensional feature integration
Construction cost prediction remains a complex challenge due to the multidimensional nature of construction data and external factors. The objective of this study is to identify the most effective deep learning model for accurately predicting construction costs by comparing the performance of LSTM,...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-01-01
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| Series: | Journal of Asian Architecture and Building Engineering |
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| Online Access: | http://dx.doi.org/10.1080/13467581.2025.2455034 |
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| author | Tang Shi Kazuya Shide |
| author_facet | Tang Shi Kazuya Shide |
| author_sort | Tang Shi |
| collection | DOAJ |
| description | Construction cost prediction remains a complex challenge due to the multidimensional nature of construction data and external factors. The objective of this study is to identify the most effective deep learning model for accurately predicting construction costs by comparing the performance of LSTM, GRU, and Transformer models. Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer are advanced machine learning regression models widely utilized for data prediction tasks. This study investigates these models’ performance for construction cost prediction using a multidimensional feature framework. Through comprehensive evaluation and comparison, the Transformer model demonstrated superior performance, particularly excelling in handling complex feature interactions and long-sequence data. The LSTM model, while effective in capturing temporal dependencies, shows reliable performance but lags behind the Transformer in accuracy. The GRU model, although faster in training, proved less accurate and is less effective in handling outliers. Key features such as Total Area (TA), Site Area (SA), and Number of Floors (NF) were identified as significant predictors across all models, with the Transformer model proving particularly adept at capturing complex interactions. By integrating these features, this study contributes to improved cost management, thereby enhancing prediction accuracy and reliability. |
| format | Article |
| id | doaj-art-bd0accfe94f241d3bb05fb0bd6ce9acb |
| institution | Kabale University |
| issn | 1347-2852 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Asian Architecture and Building Engineering |
| spelling | doaj-art-bd0accfe94f241d3bb05fb0bd6ce9acb2025-01-20T14:37:59ZengTaylor & Francis GroupJournal of Asian Architecture and Building Engineering1347-28522025-01-010011610.1080/13467581.2025.24550342455034A comparative analysis of LSTM, GRU, and Transformer models for construction cost prediction with multidimensional feature integrationTang Shi0Kazuya Shide1Shibaura Institute of TechnologyShibaura Institute of TechnologyConstruction cost prediction remains a complex challenge due to the multidimensional nature of construction data and external factors. The objective of this study is to identify the most effective deep learning model for accurately predicting construction costs by comparing the performance of LSTM, GRU, and Transformer models. Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer are advanced machine learning regression models widely utilized for data prediction tasks. This study investigates these models’ performance for construction cost prediction using a multidimensional feature framework. Through comprehensive evaluation and comparison, the Transformer model demonstrated superior performance, particularly excelling in handling complex feature interactions and long-sequence data. The LSTM model, while effective in capturing temporal dependencies, shows reliable performance but lags behind the Transformer in accuracy. The GRU model, although faster in training, proved less accurate and is less effective in handling outliers. Key features such as Total Area (TA), Site Area (SA), and Number of Floors (NF) were identified as significant predictors across all models, with the Transformer model proving particularly adept at capturing complex interactions. By integrating these features, this study contributes to improved cost management, thereby enhancing prediction accuracy and reliability.http://dx.doi.org/10.1080/13467581.2025.2455034construction costcost predictiondeep learningcost managementmultidimensional feature integration |
| spellingShingle | Tang Shi Kazuya Shide A comparative analysis of LSTM, GRU, and Transformer models for construction cost prediction with multidimensional feature integration Journal of Asian Architecture and Building Engineering construction cost cost prediction deep learning cost management multidimensional feature integration |
| title | A comparative analysis of LSTM, GRU, and Transformer models for construction cost prediction with multidimensional feature integration |
| title_full | A comparative analysis of LSTM, GRU, and Transformer models for construction cost prediction with multidimensional feature integration |
| title_fullStr | A comparative analysis of LSTM, GRU, and Transformer models for construction cost prediction with multidimensional feature integration |
| title_full_unstemmed | A comparative analysis of LSTM, GRU, and Transformer models for construction cost prediction with multidimensional feature integration |
| title_short | A comparative analysis of LSTM, GRU, and Transformer models for construction cost prediction with multidimensional feature integration |
| title_sort | comparative analysis of lstm gru and transformer models for construction cost prediction with multidimensional feature integration |
| topic | construction cost cost prediction deep learning cost management multidimensional feature integration |
| url | http://dx.doi.org/10.1080/13467581.2025.2455034 |
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