The Prediction of Metro Shield Construction Cost Based on a Backpropagation Neural Network Improved by Quantum Particle Swarm Optimization

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.

The Prediction of Pile Foundation Buried Depth Based on BP Neural Network Optimized by Quantum Particle Swarm Optimization

“…P id is the individual extremum of i-th variable at d-dimension; P gd is the global optimal solution at d-dimension. e weights and thresholds optimized by PSO can be assigned as initial value for training and prediction of the BP algorithm [40]. e detailed process is shown in Figure 8.…”

“…MAPE is mean absolute percentage error, which measures the relative errors between the average predicted value and the actual value on the test set [40]. It evaluates the model by the following criteria, the smaller the value of MAE, the smaller the error of the model and the higher the accuracy.…”

Analysis of Shield Tunneling Parameters and Research on Prediction Model of Tunneling Excavation Speed in Volcanic Ash Strata of Jakarta–Bandung High-Speed Railway Project

“…Predicting the shield machine tunneling excavation speed involves addressing the challenge of multivariate time series regression [15]. Machine-learning algorithms, including the Multiple Linear Regression [16,17] (MNR), Random Forest [18,19] (RF), Backpropagation Neural Network [20,21] (BPNN), Deep Neural Network [22,23] (DNN), Support Vector Machine [24] (SVM), and other algorithms, demonstrate notable advantages in processing time series data. The prediction of the shield tunneling excavation speed is achieved through the implementation of artificial intelligence prediction models.…”