The Prediction of Metro Shield Construction Cost Based on a Backpropagation Neural Network Improved by Quantum Particle Swarm Optimization
The prediction of construction cost of metro shield engineering is of great significance to project management. In this study, we used the rough set theory, a backpropagation (BP) neural network, and quantum particle swarm optimization (QPSO) to establish a prediction model for predicting the metro...
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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/10.1155/2020/6692130 |
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| _version_ | 1832546809998737408 |
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| author | Lanjun Liu Denghui Liu Han Wu Xinyu Wang |
| author_facet | Lanjun Liu Denghui Liu Han Wu Xinyu Wang |
| author_sort | Lanjun Liu |
| collection | DOAJ |
| description | The prediction of construction cost of metro shield engineering is of great significance to project management. In this study, we used the rough set theory, a backpropagation (BP) neural network, and quantum particle swarm optimization (QPSO) to establish a prediction model for predicting the metro shield construction costs. The model accounts for the complexity of metro shield construction and the nonlinear relationship between the construction cost factors. First, the factors affecting the construction cost were determined by referring to the Chinese National Standards and analysing the engineering practice of typical metro shield projects. The rough set theory was used to simplify the system of influencing factors to extract the dominant influencing factors and reduce the number of input variables in the BP neural network. Since the BP neural network easily falls into a local minimum and has a slow convergence speed, QPSO was used to optimize the weights and thresholds of the BP neural network. This method combined the strong nonlinear analysis capabilities of the BP and the global search capabilities of the QPSO. Finally, we selected 50 projects in China for a case analysis. The results showed the dominant factors affecting the construction cost of these projects included ten indicators, such as the type of tunnelling machine and the geological characteristics. The determination coefficient, mean absolute percentage error, root mean square error, and mean absolute error, which are frequently used error analysis tools, were used to analyse the calculation errors of different models (the proposed model, a multiple regression method, a traditional BP model, a BP model optimized by the genetic algorithm, and the BP model optimized by the particle swarm optimization). The results showed that the proposed method had the highest prediction accuracy and stability, demonstrating the effectiveness and excellent performance of this proposed method. |
| format | Article |
| id | doaj-art-69d98f2a77df445cbe759d251e459855 |
| 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-69d98f2a77df445cbe759d251e4598552025-02-03T06:46:59ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/66921306692130The Prediction of Metro Shield Construction Cost Based on a Backpropagation Neural Network Improved by Quantum Particle Swarm OptimizationLanjun Liu0Denghui Liu1Han Wu2Xinyu Wang3School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430070, ChinaChina Construction First Group Corporation Limited, Beijing 100161, ChinaSchool of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Architectural Engineering, Xinyang Vocational and Technical College, Xinyang 464000, ChinaThe prediction of construction cost of metro shield engineering is of great significance to project management. In this study, we used the rough set theory, a backpropagation (BP) neural network, and quantum particle swarm optimization (QPSO) to establish a prediction model for predicting the metro shield construction costs. The model accounts for the complexity of metro shield construction and the nonlinear relationship between the construction cost factors. First, the factors affecting the construction cost were determined by referring to the Chinese National Standards and analysing the engineering practice of typical metro shield projects. The rough set theory was used to simplify the system of influencing factors to extract the dominant influencing factors and reduce the number of input variables in the BP neural network. Since the BP neural network easily falls into a local minimum and has a slow convergence speed, QPSO was used to optimize the weights and thresholds of the BP neural network. This method combined the strong nonlinear analysis capabilities of the BP and the global search capabilities of the QPSO. Finally, we selected 50 projects in China for a case analysis. The results showed the dominant factors affecting the construction cost of these projects included ten indicators, such as the type of tunnelling machine and the geological characteristics. The determination coefficient, mean absolute percentage error, root mean square error, and mean absolute error, which are frequently used error analysis tools, were used to analyse the calculation errors of different models (the proposed model, a multiple regression method, a traditional BP model, a BP model optimized by the genetic algorithm, and the BP model optimized by the particle swarm optimization). The results showed that the proposed method had the highest prediction accuracy and stability, demonstrating the effectiveness and excellent performance of this proposed method.http://dx.doi.org/10.1155/2020/6692130 |
| spellingShingle | Lanjun Liu Denghui Liu Han Wu Xinyu Wang The Prediction of Metro Shield Construction Cost Based on a Backpropagation Neural Network Improved by Quantum Particle Swarm Optimization Advances in Civil Engineering |
| title | The Prediction of Metro Shield Construction Cost Based on a Backpropagation Neural Network Improved by Quantum Particle Swarm Optimization |
| title_full | The Prediction of Metro Shield Construction Cost Based on a Backpropagation Neural Network Improved by Quantum Particle Swarm Optimization |
| title_fullStr | The Prediction of Metro Shield Construction Cost Based on a Backpropagation Neural Network Improved by Quantum Particle Swarm Optimization |
| title_full_unstemmed | The Prediction of Metro Shield Construction Cost Based on a Backpropagation Neural Network Improved by Quantum Particle Swarm Optimization |
| title_short | The Prediction of Metro Shield Construction Cost Based on a Backpropagation Neural Network Improved by Quantum Particle Swarm Optimization |
| title_sort | prediction of metro shield construction cost based on a backpropagation neural network improved by quantum particle swarm optimization |
| url | http://dx.doi.org/10.1155/2020/6692130 |
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