Prediction of Ultimate Bearing Capacity of Pile Foundation Based on Two Optimization Algorithm Models

Abstract

The determination of the bearing capacity of pile foundations is very important for their design. Due to the high uncertainty of various factors between the pile and the soil, many methods for predicting the ultimate bearing capacity of pile foundations focus on correlation with field tests. In recent years, artificial neural networks (ANN) have been successfully applied to various types of complex issues in geotechnical engineering, among which the back-propagation (BP) method is a relatively mature and widely used algorithm. However, it has inevitable shortcomings, resulting in large prediction errors and other issues. Based on this situation, this study was designed to accomplish two tasks: firstly, using the genetic algorithm (GA) and particle swarm optimization (PSO) to optimize the BP network. On this basis, the two optimization algorithms were improved to enhance the performance of the two optimization algorithms. Then, an adaptive genetic algorithm (AGA) and adaptive particle swarm optimization (APSO) were used to optimize a BP neural network to predict the ultimate bearing capacity of the pile foundation. Secondly, to test the performance of the two optimization models, the predicted results were compared and analyzed in relation to the traditional BP model and other network models of the same type in the literature based on the three most common statistical indicators. The models were evaluated using three common evaluation metrics, namely the coefficient of determination (R2), value account for (VAF), and the root mean square error (RMSE), and the evaluation metrics for the test set were obtained as AGA-BP (0.9772, 97.8348, 0.0436) and APSO-BP (0.9854, 98.4732, 0.0332). The results show that compared with the predicted results of the BP model and other models, the test set of the AGA-BP model and APSO-BP model achieved higher accuracy, and the APSO-BP model achieved higher accuracy and reliability, which provides a new method for the prediction of the ultimate bearing capacity of pile foundations.

Prediction of Ultimate Bearing Capacity of Soil–Cement Mixed Pile Composite Foundation Using SA-IRMO-BPNN Model

“…Therefore, a model with a topology of 8-10-1 is selected for simulation and prediction in this paper. N = w 1num + hidden num + w 2num + out num (18) 3.3.2. Parameter Optimization of SA-IRMO Optimization Algorithm…”

“…Table 7 lists the data used in the comparison. The static load experimental datasets of 20 groups of SCM pile composite foundations in the Pearl River Delta region, collected from [18], were divided into two parts: a training set (15) and a test set (5). These sets were then used for training and fitting by inputting them into the SA-IRMO-BPNN prediction model established in this paper.…”

“…EL et al [17] developed a novel multi-hybrid optimization model based on Design of Experiments (DoE), which combined reverse-propagated neural networks and genetic algorithms (GAs) to optimize the parameters of a BP neural network and process parameters. Ren et al [18] developed a new technique for calculating the UBC of pile foundations by optimizing BPNN utilizing the adaptive genetic algorithm and the adaptive particle swarm optimization algorithm. Shen et al [19] proposed a new group optimization approach for microchannel resistance factor prediction by combining BP with the Particle Swarm Evolution (PSE) algorithm.…”

Surface Deformation Prediction Model of High and Steep Open-Pit Slope Based on APSO and TWSVM

“…Compared to the support vector machine model, it not only has higher training speed, but also has better generalisation ability [11]. Since the TWSVM model is affected by parameters, the parameters must be optimised by combining the genetic algorithm [12], the particle swarm algorithm [13], the artificial fish swarm algorithm [14], and other search algorithms to improve the convergence speed and recognition accuracy of the model algorithm. Therefore, it is necessary to establish the combination prediction model strategy and make full use of the advantages of various models to improve the slope prediction accuracy.…”