Study on Settlement of Self-Compacting Solidified Soil in Foundation Pit Backfilling Based on GA-BP Neural Network Model

In this paper, based on Meyerhof’s theory of homogeneous foundation, the limit equilibrium analysis method and unified logarithmic spiral sliding surface assumption are used to derive the theoretical formula for the ultimate bearing capacity of a layered foundation when the foundation is completely rough. It should be noted that this formula is only applicable to strip foundations of upper soft clay and lower sandy soil. In addition, a comparative analysis is conducted between theoretical formulas and semiempirical formulas for layered foundations. On the basis of verifying the reliability of the theoretical formula results, numerical simulation is carried out to further explore and analyze the influence of the width to depth ratio of the foundation, the strength parameters of the double-layer soil, and the thickness of the upper soft soil on the bearing capacity of the foundation. Research has shown that the formula for the bearing capacity of a layered foundation derived in this paper has a certain degree of error compared to Meyerhof’s semiempirical formula, but it is in good agreement with numerical simulation results and Hansen’s weighted average method results. The ratio of the width to depth of the foundation, the ratio of the cohesive force of the double-layer soil, and the tangent ratio of the internal friction angle have a significant positive correlation with the ultimate bearing capacity of the foundation. The increase in thickness of the overlying cohesive soil has a negative impact on the ultimate bearing capacity of the foundation, and the thicker the soil, the smaller the foundation’s bearing capacity.

Section: Sensitivity Analysis Of Bearing Capacity Parameters Of a Lay...mentioning

confidence: 99%

Derivation of the Ultimate Bearing Capacity Formula for Layered Foundations Based on Meyerhof’s Theory

Liu,

Liu

2024

“…where ∆ is the difference coefficient matrix; X i is column matrix, representing the sequence of maximum settlements here; Y i (j) is i-th element of the j-th influencing factor; B ij is the correlation coefficient matrix; ∆ min is the minimum value in the ∆; ∆ max is the maximum value in the ∆, ∆ ij is the element in the i-th row and j-th column of the ∆; ρ is the resolution coefficient, with a value range of 0~1, 0.5 is taken [27].…”

Section: Grey Relational Grade Analysismentioning

confidence: 99%

Research on Settlement and Section Optimization of Cemented Sand and Gravel (CSG) Dam Based on BP Neural Network

Wang,

Yang,

Lin

2024

In order to predict the settlement and compressive stress of the cemented sand and gravel (CSG) dam, and optimize its section design, relying on a CSG dam in the design phase, using finite element software ANSYS, the influence of the dam’s own geometric dimensions and the material parameters of the overburden, including upstream and downstream slope coefficients of the first and the second stage of the dam body, the elastic modulus and the Poisson’s ratio of the overburden on the dam’s settlement and compressive stress are studied. An orthogonal experiment with six factors and three levels is conducted for a grey relational analysis of the dam’s maximum settlement and maximum compressive stress separately on these six parameters. Based on the BP neural network, the six selected factors are used as input layers for the neural network prediction model, and the maximum settlement and compressive stress of the dam are taken as the result to be output. The mapping relationship between the geometric dimensions of the dam body and the maximum settlement and the maximum compressive stress in the trained prediction model is combined with the global optimization tool Pattern Search in the MATLAB toolbox to optimize the section design of the dam. The results reveal that the six selected factors have a high correlation degree with the dam’s maximum settlement and maximum compressive stress. In dimension parameters, the downstream slope coefficient of the second stage of the dam has the greatest impact on the maximum settlement, with a grey correlation degree of 0.7367, and the upstream slope coefficient of the second stage of the dam has the greatest impact on the maximum compressive stress, with a grey correlation degree of 0.7012. The influence of the elastic modulus of the overburden on the maximum settlement and maximum compressive stress of the dam body is greater than its Poisson’s ratio. The BP neural network is applicable for predicting the dam’s settlement based on geometric dimension parameters of the dam and material parameters of the surrounding environment, with R2 reaching 0.9996 and RMSE only 0.0109 cm. Based on the optimization method combined with BP neural network, the material consumption is saved by 11.83%, the maximum settlement is reduced by 2.6%, the maximum compressive stress is reduced by 37.35%, and the optimization time is shortened by 40.92%, compared to the traditional method. The findings have certain reference value for site selection, dimension design, overburden treatment, and design optimization of CSG dams.

“…The spectral element method allows grouping layers of the same type within a structure as a single unit during dynamic analysis, reducing the number of elements significantly compared to finite element methods and thereby enhancing computational efficiency in dynamic analysis [26][27][28]. It also offers several advantages over conventional mechanical analysis methods, including faster computation, higher accuracy in results, greater performance efficiency, and overall precision [29,30].…”

Section: Introductionmentioning

confidence: 99%

Mechanical Analysis of Semi-Rigid Base Asphalt Pavement under the Influence of Groundwater with the Spectral Element Method

Zhang,

Wang,

Zhong

et al. 2024

Over prolonged exposure to groundwater conditions, semi-rigid base asphalt pavements can undergo significant changes in their internal moisture field, resulting in substantial variations in the pavement’s stiffness and, consequently, affecting the overall load-bearing capacity and stability of the road structure. This paper employs FWD non-destructive testing equipment to assess its mechanical performance and conduct data analysis and conducts a mechanical response study of asphalt road surfaces, considering the influence of roadbed moisture levels. Using the dynamic analytical theory, the fundamental equations and stiffness matrices for a linear elastic half-space model were established, leading to the development of a computational model for the mechanical response of semi-rigid base asphalt pavements under FWD dynamic loading, with an examination of the surface deflection in relation to changes in groundwater levels. Numerical examples and engineering applications were employed to validate the proposed model. The research findings indicate: With the passage of time, surface deflection values initially increase and then decrease, exhibiting a sinusoidal variation pattern similar to that of the applied load. As the distance from the loading center increases, the moment of peak deflection continually lags behind. The average absolute relative error between the results obtained using the method proposed in this study and the traditional ABAQUS finite element method was only 0.70%. The correlation coefficient between the theoretically computed deflection curve and the measured deflection curve using the spectral element method was greater than 0.9, with an average absolute relative error of 4.92% between the theoretical peak deflection and the measured peak deflection. As the groundwater level rises, surface deflection noticeably increases, with an approximately 40% increase in deflection values at the loading center. These research findings can be utilized to analyze the dynamic deflection of semi-rigid base asphalt pavements under various groundwater conditions, providing significant practical value for assessing road structural performance and serviceability.