Earticle

Due to the normal forecasting methods for subgrade settlement using observation data have different explicabilities, and the predicting results has bigger volatility and lower accuracy. The Combined forecasting model of subgrade settlement based on forecasting availability and real-coded quantum evolutionary algorithm (RQEA) is put forward in this paper. At the first, according to the basic settlement law of subgrade and characteristics of settlement curve, the growth curve with the S-type characteristics are chosen as single forecasting model; Then, to get the weights of each single forecasting model, objective function is build on the basis of standard of forecasting availability maximization, and RQEA is employed to solve the objective function, and to construct the combined forecasting model of subgrade settlement. The result of engineering practice shows that the proposed method has better prediction accuracy and stability, and can meet engineering demand.

Due to the normal forecasting methods for subgrade settlement using observation data have different applicability and disadvantages, The Combined forecasting model is put forward based on support vector machine (SVM) and real-coded quantum evolutionary algorithm (RQEA) in this paper. Its core is that, according to the basic settlement law of subgrade and characteristics of settlement curve, the growth curve which has S-type characteristic are chosen as single forecasting model, then support vector machine is used to combine the predicting results of each single forecasting model, at the same time, RQEA is adopted to optimize support vector machine parameter to improve the SVM’s performance. The analytical result of engineering practice indicates that the proposed combined forecasting model of subgrade settlement base on SVM and RQEA can not only improve the predicting accuracy, but also reduce the predicting risk, and can meet engineering demand.

Due to the normal forecasting methods for subgrade settlement using observation data have different applicabilities, and the predicting results has bigger volatility and lower accuracy. In view of the above problems, a method based on least square support vector regression (LSTSVR) and real-coded quantum evolutionary algorithm (RQEA) is proposed. Firstly, the LSTSVR parameter is chosen as a combinatorial optimization problem, and determining the objective function of the combinatorial optimization problem, then, using RQEA to solve the combinatorial optimization problem and optimize the LSTSVR parameters, Finaly, LSTSVR-RQEA is used to sovle the prediction of subgrade settlement. The simulation results show that RQEA is an effective method to select LSTSVR parameters, and has excellent performance when applied to the prediction of subgrade settlement.