Abstract
Settlement prediction in soft soil foundation engineering is a newer technique. Predicting soft soil settling has long been one of the most challenging techniques due to difficulties in soft soil engineering. To overcome these challenges, the wavelet neural network (WNN) is mostly used. So, after assessing its estimate performance, two elements, early parameter selection and system training techniques, are chosen to optimize the traditional WNN difficulties of readily convergence to the local infinitesimal point, low speed, and poor approximation performance. The number of hidden layer nodes is determined using a self-adaptive adjustment technique. The wavelet neural network (WNN) is coupled with the scaled conjugate gradient (SCG) to increase the feasibility and accuracy of the soft fundamental engineering settlement prediction model, and a better wavelet network for the soft ground engineering settlement prediction is suggested in this paper. Furthermore, we have proposed the technique of locating the early parameters based on autocorrelation. The settlement of three types of traditional soft foundation engineering, including metro tunnels, highways, and high-rise building foundations, has been predicted using our proposed model. The findings revealed that the model is superior to the backpropagation neural network and the standard WNN for solving problems of approximation performance. As a result, the model is acceptable for soft foundation engineering settlement prediction and has substantial project referential value.
Highlights
Prediction of soft soil settlement has always been one of the technical problems in soft soil engineering
Optimizing the model and improving the prediction accuracy are an important content of the deformation prediction model
Ree neural networks, BP neural network, the traditional wavelet neural network (WNN) based on backpropagation (BP algorithm), and WNN based on scaled conjugate gradient (SCG) algorithm, are compared and analyzed comprehensively. is article designed an optimization of WNN built on the SCG method to predict soft soil foundation engineering settlement under the complex geology settings to overcome issues mentioned above. e results show that the optimization model achieves a better performance and is more suitable than the other two networks for soft soil foundation engineering settlement prediction
Summary
Prediction of soft soil settlement has always been one of the technical problems in soft soil engineering. As there are many factors affecting the settlement of soft foundations, how to predict the settlement of soft foundations correctly becomes a common problem for researchers in design and construction. Calculation and prediction based on measured data are a general method in engineering at present [1]. Used deformation prediction methods based on measured data include statistical analysis, time series analysis, grey system theory, Kalman filter, and neural networks, but these have their limitations [2, 3]. According to the actual application research, a single theory or model is difficult to accurately predict the magnitude of deformation. The inclusion of WNN simplifies the problem, it has several drawbacks in its current form
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