Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network
The scientific and effective prediction of the water consumption of construction engineering is of great significance to the management of construction costs. To address the large water consumption and high uncertainty of water demand in project construction, a prediction model based on the back pro...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/8868817 |
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| author | Hao Peng Han Wu Junwu Wang |
| author_facet | Hao Peng Han Wu Junwu Wang |
| author_sort | Hao Peng |
| collection | DOAJ |
| description | The scientific and effective prediction of the water consumption of construction engineering is of great significance to the management of construction costs. To address the large water consumption and high uncertainty of water demand in project construction, a prediction model based on the back propagation (BP) neural network improved by particle swarm optimization (PSO) was proposed in the present work. To reduce the complexity of redundant input variables, this model determined the main influencing factors of water demand by grey relational analysis. The BP neural network optimized by PSO was used to obtain the predicted value of the output interval, which effectively solved the shortcomings of the BP neural network model, including its slow convergence speed and easy to fall into local optimum problems. In addition, the water consumption interval data of the Taiyangchen Project located in Xinyang, Henan Province, China, were simulated. According to the results of the case study, there were four main factors that affected the construction water consumption of the Taiyangchen Project, namely, the intraday amount of pouring concrete, the intraday weather, the number of workers, and the intraday amount of wood used. The predicted data were basically consistent with the actual data, the relative error was less than 5%, and the average error was only 2.66%. However, the errors of the BP neural network model, the BP neural network improved by genetic algorithm, and the pluralistic return were larger. Three conventional error analysis tools in machine learning (the coefficient of determination, the root mean squared error, and the mean absolute error) also highlight the feasibility and advancement of the proposed method. |
| format | Article |
| id | doaj-art-f416b4c41b3245f4a2344d1a9016814b |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-f416b4c41b3245f4a2344d1a9016814b2025-02-03T06:46:57ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/88688178868817Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural NetworkHao Peng0Han Wu1Junwu Wang2School of Architectural Engineering, Xinyang Vocational and Technical College, Xinyang 464000, ChinaSchool of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, ChinaThe scientific and effective prediction of the water consumption of construction engineering is of great significance to the management of construction costs. To address the large water consumption and high uncertainty of water demand in project construction, a prediction model based on the back propagation (BP) neural network improved by particle swarm optimization (PSO) was proposed in the present work. To reduce the complexity of redundant input variables, this model determined the main influencing factors of water demand by grey relational analysis. The BP neural network optimized by PSO was used to obtain the predicted value of the output interval, which effectively solved the shortcomings of the BP neural network model, including its slow convergence speed and easy to fall into local optimum problems. In addition, the water consumption interval data of the Taiyangchen Project located in Xinyang, Henan Province, China, were simulated. According to the results of the case study, there were four main factors that affected the construction water consumption of the Taiyangchen Project, namely, the intraday amount of pouring concrete, the intraday weather, the number of workers, and the intraday amount of wood used. The predicted data were basically consistent with the actual data, the relative error was less than 5%, and the average error was only 2.66%. However, the errors of the BP neural network model, the BP neural network improved by genetic algorithm, and the pluralistic return were larger. Three conventional error analysis tools in machine learning (the coefficient of determination, the root mean squared error, and the mean absolute error) also highlight the feasibility and advancement of the proposed method.http://dx.doi.org/10.1155/2020/8868817 |
| spellingShingle | Hao Peng Han Wu Junwu Wang Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network Advances in Civil Engineering |
| title | Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network |
| title_full | Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network |
| title_fullStr | Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network |
| title_full_unstemmed | Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network |
| title_short | Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network |
| title_sort | research on the prediction of the water demand of construction engineering based on the bp neural network |
| url | http://dx.doi.org/10.1155/2020/8868817 |
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