Research on Neural Network Prediction Model of Whole Process Risk Management Based on Building Information Model
With the rapid development of China’s construction industry and the acceleration of urbanization, large-scale public building projects are becoming increasingly important in urban development, and the risk management problems of them should be pay more attention to. Based on the integration of back...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/5453113 |
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| _version_ | 1832544082606424064 |
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| author | Shihong Huang Chengye Liang Jiao Liu |
| author_facet | Shihong Huang Chengye Liang Jiao Liu |
| author_sort | Shihong Huang |
| collection | DOAJ |
| description | With the rapid development of China’s construction industry and the acceleration of urbanization, large-scale public building projects are becoming increasingly important in urban development, and the risk management problems of them should be pay more attention to. Based on the integration of back propagation (BP) neural network and building information model (BIM) technology, this paper carries out the research on risk management process of the whole life cycle of large public buildings and identifies the risk factors of large public buildings from the application dimension and the management dimension. The risk management evaluation index system is constructed and identified, and assessment, early warning, prevention, and control of risk management are applied and analyzed throughout the process. The international large public sports center project is used as a case study to establish a BIM model, while the BP neural network risk management model is used for prediction and calculation. The results of this study show that, first, the maximum deviation rate of the output indicators of the BP neural network risk model is 3.57% in the design period (B2) and the minimum deviation rate is 0.00% in the commissioning period (B4), which verifies the reliability of the training results of the model. Second, the best effect of risk management in the whole life cycle of the building is in the investment period (B1) and the highest risk is in the construction period (B3). Last, this paper constructs a new risk management framework to realise the risk management of the whole cycle of construction projects from design to operation, which helps to improve the management level and risk response ability of construction projects and ensure the smooth and sustainable development of the whole life cycle of construction. |
| format | Article |
| id | doaj-art-c3373b0bce204c5c8180111fa6de4415 |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-c3373b0bce204c5c8180111fa6de44152025-02-03T11:07:16ZengWileyAdvances in Civil Engineering1687-80942024-01-01202410.1155/2024/5453113Research on Neural Network Prediction Model of Whole Process Risk Management Based on Building Information ModelShihong Huang0Chengye Liang1Jiao Liu2Guangxi University of Finance and EconomicsBelarusian State UniversityGuangxi University of Finance and EconomicsWith the rapid development of China’s construction industry and the acceleration of urbanization, large-scale public building projects are becoming increasingly important in urban development, and the risk management problems of them should be pay more attention to. Based on the integration of back propagation (BP) neural network and building information model (BIM) technology, this paper carries out the research on risk management process of the whole life cycle of large public buildings and identifies the risk factors of large public buildings from the application dimension and the management dimension. The risk management evaluation index system is constructed and identified, and assessment, early warning, prevention, and control of risk management are applied and analyzed throughout the process. The international large public sports center project is used as a case study to establish a BIM model, while the BP neural network risk management model is used for prediction and calculation. The results of this study show that, first, the maximum deviation rate of the output indicators of the BP neural network risk model is 3.57% in the design period (B2) and the minimum deviation rate is 0.00% in the commissioning period (B4), which verifies the reliability of the training results of the model. Second, the best effect of risk management in the whole life cycle of the building is in the investment period (B1) and the highest risk is in the construction period (B3). Last, this paper constructs a new risk management framework to realise the risk management of the whole cycle of construction projects from design to operation, which helps to improve the management level and risk response ability of construction projects and ensure the smooth and sustainable development of the whole life cycle of construction.http://dx.doi.org/10.1155/2024/5453113 |
| spellingShingle | Shihong Huang Chengye Liang Jiao Liu Research on Neural Network Prediction Model of Whole Process Risk Management Based on Building Information Model Advances in Civil Engineering |
| title | Research on Neural Network Prediction Model of Whole Process Risk Management Based on Building Information Model |
| title_full | Research on Neural Network Prediction Model of Whole Process Risk Management Based on Building Information Model |
| title_fullStr | Research on Neural Network Prediction Model of Whole Process Risk Management Based on Building Information Model |
| title_full_unstemmed | Research on Neural Network Prediction Model of Whole Process Risk Management Based on Building Information Model |
| title_short | Research on Neural Network Prediction Model of Whole Process Risk Management Based on Building Information Model |
| title_sort | research on neural network prediction model of whole process risk management based on building information model |
| url | http://dx.doi.org/10.1155/2024/5453113 |
| work_keys_str_mv | AT shihonghuang researchonneuralnetworkpredictionmodelofwholeprocessriskmanagementbasedonbuildinginformationmodel AT chengyeliang researchonneuralnetworkpredictionmodelofwholeprocessriskmanagementbasedonbuildinginformationmodel AT jiaoliu researchonneuralnetworkpredictionmodelofwholeprocessriskmanagementbasedonbuildinginformationmodel |