Abstract
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.
Highlights
With the characteristics of energy-saving, land-saving, large transportation volume, low pollution, and low-operational risk, the metro plays an increasingly important role in the urban public transportation system [1]
We focus on the practices used during the construction of Chengdu Metro Line 11, Wuhan Metro Line 8, Tianjin Metro Line 7, and Wuhan Metro Line 21. is section provides a detailed description of the indicators and how to obtain the data, which is convenient for subsequent research on the selection of the index system and case analysis based on rough set theory
Two sets of data were required for the BP neural network to establish the construction cost prediction model of metro shield engineering, i.e., the training set and test set. e training set data was used to train the neural network, and the test set data was used to validate the neural network
Summary
With the characteristics of energy-saving, land-saving, large transportation volume, low pollution, and low-operational risk, the metro plays an increasingly important role in the urban public transportation system [1]. Nowadays, developing countries, represented by China, are at the peak of urban rail transit construction. According to the irteenth Five-Year Plan issued by the Chinese government, by 2020, the total length of the Chinese metro will reach 8,600 km, and the investment in metro construction will reach 300 billion dollars. E construction of metro projects includes the tunnel and metro stations. E cost of tunnel construction often accounts for more than 40% of the total construction costs [2]. Shield construction is the most commonly used method of urban metro tunnel construction [3].
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