Research on HC-LSSVM Model for Soft Soil Settlement Prediction Based on Homotopy Continuation Method

Abstract

Prediction of soft soil settlement is an important research topic in the field of civil engineering, and the least square support vector machine is one of the commonly used prediction methods at present. Nonetheless, the existing LSSVM models have problems of low search efficiency in the search process and lack of global optimal solution in the search results. In order to solve this problem, based on the leave-one-out cross-validation method, the homotopy continuation method was used to optimize the LSSVM model parameters, and then the HC-LSSVM model was constructed with the goal of minimizing the sum of squares of the prediction error of the full sample retention one. Finally, the rationality and correctness of the model are verified by engineering application. The results show that the HC-LSSVM model constructed in this study can accurately predict the settlement of soft ground, which is superior to the common LSSVM model and solves the problem that the parameters of LSSVM model cannot be solved optimally. The research results provide a new method for prediction of soft soil settlement.

ANN-Based Dynamic Prediction of Daily Ground Settlement of Foundation Pit Considering Time-Dependent Influence Factors

“…Specifically, machine learning-based methods aim at establishing the highly nonlinear relationships between inputs and the target output, i.e., ground settlements. Four machine learning algorithms have been widely used for the prediction of ground settlements: ANNs [3,20,[28][29][30][31][32], support vector machines (SVMs) [9,[33][34][35], random forest methods (RFMs) [36], and decision trees (DTs) [37]. Leu and Lo [20] developed an ANN to predict the intensity and the location of the maximum ground settlement in multiple construction stages based on three inputs: excavation areas, construction methods, and excavation depth.…”

Early Warning of the Construction Safety Risk of a Subway Station Based on the LSSVM Optimized by QPSO

“…Zeng et al [14] used trial and error (TE), the gravity search algorithm (GSA), and the whale optimization algorithm (WOA) to find the best parameters of the LSSVM. Because the settlement of soft soil is a complex nonlinear system, Cui et al [15] used the cross-validation method to get the best LSSVM model. The results showed that the selection of the control parameters had a significant impact on the predictive performance of the LSSVM.…”

“…The optimization model of QPSO is initialized according to Equations ( 13)- (15), and the nonlinear early warning model of the LSSVM is initialized according to Equations ( 16)-( 18).…”

“…(2) Find the optimal parameter combination of the LSSVM based on QPSO The normalized data and preset QPSO parameters were introduced into Equations ( 13)- (15) to initialize the optimization model of QPSO. Then, the normalized data and preset LSSVM calculation parameters were introduced into Equations ( 16)- (18) to initialize the LSSVM-based nonlinear early warning model.…”

“…The least-squares support vector machine (LSSVM) is an improved SVM, and the core of the improvement is the solution of a set of linear equations obtained by the Kuhn-Tucker condition [12]. The LSSVM reduces the solution difficulty and improves the solution speed, and it is a new application in complex problems such as power demand prediction [13], slope safety factor prediction [14], and soft soil settlement prediction [15]. In addition, in the application research of machine learning modeling, different algorithms reach different conclusions for different research problems.…”