Undrained Deformation Characteristics of Soft Marine Clay Subjected to One-Way Cyclic Loading with Variable Confining Pressure

To gain a better understanding of the undrained deformation characteristic of saturated marine clay soil subjected to vehicle cyclic traffic load, a sophisticated dynamic triaxial was used to conduct a variety of undrained one-way compression cyclic experiments with variable confining pressure (VCP) as well as constant confining pressure (CCP). The results indicate that, compared to CCP test results, VCP is helpful to raise the axial resilient modulus (Mr) and restrain the permanent plastic strain ( ε a p ) development of the specimens. By normalization analysis of the measured data of Mr and ε a p , the virtually unique correlation between normalized average resilient modulus, normalized permanent axial strain after 1,000 loading cycles, and normalized mean normal stress is established, respectively, regardless of the values of CSR. Additionally, the VCP influence on ε a p is quantified and fitted by a power law function, which can be used for subsoil deformation prediction and provides new insights into the mechanics of strain accumulation under undrained cyclic loading conditions.

Deformation Prediction of a Deep Foundation Pit Based on the Combination Model of Wavelet Transform and Gray BP Neural Network

The purpose of this study was to predict the deformation of a deep foundation pit based on a combination model of wavelet transform and gray BP neural network. Using a case of a deep foundation pit, a combination model of wavelet transform and gray BP neural network was used to predict the deformation of the deep foundation pit. The results show that compared with the traditional gray BP neural network model, the relative error of the combination model of wavelet transform and gray BP neural network was reduced by 2.38%. This verified that the combined model has high accuracy and reliability in the prediction of foundation pit deformation and also conforms to the actual situation of the project. The research results can provide a valuable reference for foundation pit deformation monitoring.

A Deformation Prediction Model of High Arch Dams in the Initial Operation Period Based on PSR-SVM-IGWO

The deformation prediction of the dam in the initial stage of operation is very important for the safety of high dams. A hybrid model integrating chaos theory, support vector machine (SVM), and an improved Grey Wolf Optimization (IGWO) algorithm is developed for deformation prediction of dam in the initial operation period. Firstly, the chaotic characteristics of the dam deformation time series will be identified, mainly using the Lyapunov exponent method, the correlation dimension method, and the Kolmogorov entropy method. Secondly, the SVM-IGWO model based on phase space reconstruction (PSR) is established for deformation forecasting of the dam in the initial operation period. Taking SVM as the core, the deformation time series is reconstructed in phase space to determine the input variables of SVM and the GWO algorithm is improved to realize the optimization of SVM parameters. Finally, take the actual monitoring displacement of Xiluodu super-high arch dam as an example. The engineering application example shows that, compared with the existing models, the prediction accuracy of the PSR-SVM-IGWO model established in this paper is improved.