Deep foundation pits, pipe gallery troughs, culverts, and other infrastructure often require backfilling operations. Soil-based controlled low-strength material (soil-based CLSM), with its advantages of self-compaction, self-leveling, and self-hardening, has garnered significant attention in recent years and shows potential as a replacement for traditional rolling compaction backfill materials. Based on the backfill project of the pipe gallery at the Xihong Bridge in Ningbo, this study investigates the unconfined compressive strength, permeability coefficient, compression characteristics, and flow behavior of soil-based CLSM with varying curing agent ratios, assessing its engineering feasibility through field testing. The results demonstrate that soil-based CLSM, particularly with polycarboxylate superplasticizer agent, exhibits substantially improved strength, permeability, construction workability, and other service performance. Additionally, a detailed simulation of the entire pipe gallery foundation pit construction process-including pipe gallery construction, trench backfilling, support removal, and road construction-was performed using the Hardening soil with small strain stiffness model of the soil. The deformation characteristics were analyzed under different backfill conditions to assess the suitability of soil-based CLSM for trench backfilling. The analysis also considered soil deformation under varying curing ages and upper load conditions. The optimized backfilling solution for soil-based CLSM was obtained and validated with field test data. The findings suggest that using soil-based CLSM for foundation trench backfilling can effectively mitigate settlement issues.

During the construction of deep and large foundation pits in floodplain areas, it is inevitable to cause stratum disturbance and endanger the safety of the surrounding environment. This paper focuses on the influence of deep foundation pit excavation on surrounding environment based on a soft soil deep foundation pit project in Nanjing floodplain area. A series of laboratory tests were conducted to obtain the parameters of the small strain hardening (HSS) model for the typical soil layers. Then PLAXIS 3D software is used to simulate the excavation process of the foundation pit. On the basis of field measurement and numerical model, the deformation characteristics of deep foundation pit and surrounding environment are analyzed. The HSS model and the appropriate model parameters can effectively simulate the deformation behavior during the excavation of the foundation pit. Aiming at the problem of excessive deformation of foundation pit and surrounding pipelines, the reinforcement effect of reinforced soil in active and passive areas under different reinforcement parameters is analyzed. The optimal reinforcement width and depth should be determined after reasonable analysis to obtain the best economic benefits.

The hardening soil model with small-strain stiffness (HSS model) is widely applied in deep foundation pit engineering in coastal soft-soil areas, yet it is characterized by a multitude of parameters that are relatively cumbersome to acquire. In this study, we incorporate a genetic algorithm and a back-propagation neural network (BPNN) model into an inversion analysis for HSS model parameters, with the objective of facilitating a more streamlined and accurate determination of these parameters in practical engineering. Utilizing horizontal displacement monitoring data from retaining structures, combined with local engineering, both a BPNN model and a BPNN optimized by a genetic algorithm (GA-BPNN) model were established to invert the stiffness modulus parameters of the HSS model for typical strata. Subsequently, numerical simulations were conducted based on the inverted parameters to analyze the deformation characteristics of the retaining structures. The performances of the BPNN and GA-BPNN models were evaluated using statistical metrics, including R2, MAE, MSE, WI, VAF, RAE, RRSE, and MAPE. The results demonstrate that the GA-BPNN model achieves significantly lower prediction errors, higher fitting accuracy, and predictive performance compared to the BPNN model. Based on the parameters inverted by the GA-BPNN model, the average compression modulus Es1-2, the reference tangent stiffness modulus Eoedref, the reference secant stiffness modulus E50ref, and the reference unloading-reloading stiffness modulus Eurref for gravelly cohesive soil were determined as Eoedref=0.83Es1-2 and Eurref=8.14E50ref; for fully weathered granite, Eoedref=1.54Es1-2 and Eurref=5.51E50ref. Numerical simulations conducted with these stiffness modulus parameters show excellent agreement with monitoring data, effectively describing the deformation characteristics of the retaining structures. In situations where relevant mechanical tests are unavailable, the application of the GA-BPNN model for the inversion analysis of HSS model parameters is both rational and effective, offering a reference for similar engineering projects.