A Combined Prediction Model for Subgrade Settlement Based on Improved Set Pair Analysis

Abstract

Prediction of subgrade settlement is a complex problem involving various uncertainty factors. To overcome the defects and limitations of the single prediction model, a combined prediction model based on the improved set pair analysis was proposed to take into account the uncertainty and certainty of the single prediction model and make the combined prediction based on the certainty degree, and the criterion of set pair relationship was optimized. In the model, the set pair was first constructed to express the relationship between predicted and measured values. Then the risk of the set pair relationship identification was expressed based on the Bayesian decision theory, and the optimal criterion of set pair relationship was obtained by the adaptive search algorithm. Next, the relationship between the prediction model and the measured data was analyzed to get the certainty degree, and the weight coefficient was obtained according to the certainty degree. Finally the combination of single prediction models was carried out. A case study and comparison with other methods were conducted to confirm the reliability and validity of the proposed model. The result shows that this model fully considers the uncertainty and certainty of the single prediction model and also extends the method of determining the criterion of set pair relationship, which provides ideas for other combined prediction and evaluation problems.

A Three-Point Hyperbolic Combination Model for the Settlement Prediction of Subgrade Filled with Construction and Demolition Waste

“…For example, Yang et al [40] employed a hyperbolic model, a settlement rate model, and a settlement difference model to predict soft soil embankment settlement, and found that the improved settlement difference model had the best fitting effect. Fan [41] used a logistic model and a Gompertz model to propose a combination forecasting model for the settlement prediction of soft soil foundations, and Li et al [42] employed a combined prediction model based on an improved set pair analysis to predict subgrade settlement. Nejad et al [43] proposed a back propagation neural network model to test the feasibility of predicting pile foundation settlements using artificial neural networks.…”

A Remote Subgrade Settlement Monitoring System Based on Optical Method

“…Therefore, real-time monitoring of the subgrade settlement and timely alarm when the settlement exceeds the limit is of great significance for maintaining traffic safety and reducing property damage. In addition, the monitoring data also aids in the accurate prediction of the future subgrade settlement [7,8].…”