Harnessing the Power of Improved Deep Learning for Precise Building Material Price Predictions
Accurate forecasting of construction material prices is essential for effective cost control and risk management in construction projects. However, due to the influence of various complex factors, building material prices exhibit high nonlinearity and instability, often making traditional prediction...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/6/873 |
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| author | Zhilong Guo Yayong Luo Tongqiang Yi Xiangnan Jing Jing Ma |
| author_facet | Zhilong Guo Yayong Luo Tongqiang Yi Xiangnan Jing Jing Ma |
| author_sort | Zhilong Guo |
| collection | DOAJ |
| description | Accurate forecasting of construction material prices is essential for effective cost control and risk management in construction projects. However, due to the influence of various complex factors, building material prices exhibit high nonlinearity and instability, often making traditional prediction methods inadequate for achieving optimal results. This study introduces an innovative prediction model, CEEMDAN-VMD-GRU-ARIMA, specifically designed for forecasting the price of prestressed steel bars. This model uniquely combines CEEMDAN and VMD to address nonlinear characteristics, and it innovatively incorporates sample entropy for the adaptive selection of either GRU or ARIMA for prediction. Additionally, a VMD decomposition mode number K value optimization method, based on a sparse index, is proposed. Experimental results demonstrate that the model performs exceptionally well, achieving an adjusted R-squared value of 81.10%, with various error indicators significantly surpassing the results for the baseline model. This approach offers new insights for short-term price prediction of building materials and contributes to enhancing the economic benefits and management efficiency of construction projects. |
| format | Article |
| id | doaj-art-670fd886668f4a2ca5c6b30bdd4721f3 |
| institution | OA Journals |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-670fd886668f4a2ca5c6b30bdd4721f32025-08-20T02:11:15ZengMDPI AGBuildings2075-53092025-03-0115687310.3390/buildings15060873Harnessing the Power of Improved Deep Learning for Precise Building Material Price PredictionsZhilong Guo0Yayong Luo1Tongqiang Yi2Xiangnan Jing3Jing Ma4Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaNaiman Desertification Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaSchool of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, ChinaSchool of Economics and Management, City University of Hefei, Hefei 231137, ChinaDepartment of Psychology, Beijing Normal University, Beijing 100875, ChinaAccurate forecasting of construction material prices is essential for effective cost control and risk management in construction projects. However, due to the influence of various complex factors, building material prices exhibit high nonlinearity and instability, often making traditional prediction methods inadequate for achieving optimal results. This study introduces an innovative prediction model, CEEMDAN-VMD-GRU-ARIMA, specifically designed for forecasting the price of prestressed steel bars. This model uniquely combines CEEMDAN and VMD to address nonlinear characteristics, and it innovatively incorporates sample entropy for the adaptive selection of either GRU or ARIMA for prediction. Additionally, a VMD decomposition mode number K value optimization method, based on a sparse index, is proposed. Experimental results demonstrate that the model performs exceptionally well, achieving an adjusted R-squared value of 81.10%, with various error indicators significantly surpassing the results for the baseline model. This approach offers new insights for short-term price prediction of building materials and contributes to enhancing the economic benefits and management efficiency of construction projects.https://www.mdpi.com/2075-5309/15/6/873construction material price forecastingCEEMDAN-VMD-GRU-ARIMAnonlinear time seriessample entropyprestressed steel bars (PSB) |
| spellingShingle | Zhilong Guo Yayong Luo Tongqiang Yi Xiangnan Jing Jing Ma Harnessing the Power of Improved Deep Learning for Precise Building Material Price Predictions Buildings construction material price forecasting CEEMDAN-VMD-GRU-ARIMA nonlinear time series sample entropy prestressed steel bars (PSB) |
| title | Harnessing the Power of Improved Deep Learning for Precise Building Material Price Predictions |
| title_full | Harnessing the Power of Improved Deep Learning for Precise Building Material Price Predictions |
| title_fullStr | Harnessing the Power of Improved Deep Learning for Precise Building Material Price Predictions |
| title_full_unstemmed | Harnessing the Power of Improved Deep Learning for Precise Building Material Price Predictions |
| title_short | Harnessing the Power of Improved Deep Learning for Precise Building Material Price Predictions |
| title_sort | harnessing the power of improved deep learning for precise building material price predictions |
| topic | construction material price forecasting CEEMDAN-VMD-GRU-ARIMA nonlinear time series sample entropy prestressed steel bars (PSB) |
| url | https://www.mdpi.com/2075-5309/15/6/873 |
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