Application of the WNN-Based SCG Optimization Algorithm for Predicting Soft Soil Foundation Engineering Settlement

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.

Spatial and temporal subsidence characteristics in Wuhan (China), during 2015–2019, inferred from Sentinel-1 synthetic aperture radar (SAR) interferometry

Ground subsidence is regarded as one of the most common geohazards, accompanied with the rapid urban expansion in recent years. In the last 2 decades, Wuhan, located in the alluvial Jianghan Plain, has experienced great urban expansion with increased subsidence issues, i.e., soft foundation subsidence and karst collapse. Here we investigated subsidence rates in Wuhan with 2015–2019 Sentinel-1 synthetic aperture radar (SAR) images. We found that the overall subsidence over the Wuhan region is significantly correlated with the distribution of engineering geological subregions (EGSs). We further validated the interferometric SAR (InSAR) measurements with better than 5 mm accuracy by comparing with leveling measurements. Subsidence centers in Qingling–Jiangdi, Houhu, Qingshan, and Dongxihu were identified with displacement rates of approximately 30 mm/yr. Our results demonstrated that the dominant driving factor is ongoing construction, and the fact that the subsidence centers shifted with construction intensities. The Qingling–Jiangdi area in our study is a well-known site of karst collapse. We find that the nonlinear subsidence of this area is correlated with the seasonal rainfall.

Numerical Analysis of the Preloading Consolidation of the Soft Foundation with a Plastic Drainage Plate

The permeability coefficient and void ratio are related to the stress state of the soil, which manifests as the temporal and spatial variability of consolidation. Based on the modified Cambridge model, this paper quotes the functional relationship between the soil permeability coefficient and void ratio and soil stress obtained by Tylor’s research and then uses finite-element software ABAQUS and its secondary development platform to establish a plastic drainage plate surcharge preloading numerical model that considers the influence of the void ratio and permeability coefficient with stress changes. The results show that considering the variability of the permeability coefficient in the consolidation process of the soft foundation has a great influence on the change of the consolidation state but has a small effect on the final consolidation state. For soft foundation surcharge preloading reinforcement treatment, if the strength of the soft foundation is low but the surcharge is large, the geometric nonlinearity and material nonlinearity should be considered. Using the theory of large deformation consolidation to calculate the consolidation of soil is closer to the actual situation.