A shallow foundation bearing capacity calculation method fusing machine learning and non-local algorithm

By integrating machine learning and nonlocal algorithms, combined with finite element simulation and triaxial experiments, the influence range and softening rate parameters are calibrated, solving the deviation problem in bearing capacity calculation in traditional methods, and realizing efficient and low-cost bearing capacity prediction.

CN120470845BActive Publication Date: 2026-02-17BEIHANG UNIV
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Patent Information

Application Number
CN202510556486.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-04-29
Publication Date
2026-02-17
Estimated Expiration
2045-04-29

AI Technical Summary

Technical Problem

Traditional geotechnical engineering methods for calculating the bearing capacity of shallow foundations are based on the theory of elastic half-space, which leads to large deviations between the calculated results and the actual situation. Furthermore, the values ​​of nonlocal algorithm parameters affect the residual state of the load-displacement curve, making it impossible to obtain reasonable predicted values ​​of the foundation bearing capacity.

Method used

By integrating machine learning and nonlocal algorithms, the influence range and softening rate control parameters are calibrated through indoor triaxial experiments. Machine learning technology is used to enhance efficiency and achieve efficient inversion of nonlocal parameters. Combined with finite element simulation, reasonable load-bearing capacity prediction values ​​are obtained.

Benefits of technology

It solves the problem of the influence of nonlocal parameter values, realizes efficient and low-cost bearing capacity prediction, takes into account the bearing capacity prediction of hardened foundation soil, and reduces manpower and economic costs.

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Abstract

This invention discloses a method for calculating the bearing capacity of shallow foundations by integrating machine learning and nonlocal algorithms, belonging to geotechnical engineering. 1. The type of foundation soil is determined through indoor triaxial tests; strain-hardening foundation soils are analyzed; 2. Strain-softening foundation soils are analyzed; 3. A finite element model is established for refined simulation, followed by 7. 3. A constitutive model combined with a nonlocal algorithm is used for refined simulation, then proceeds to 4. 4. The influence range (DL) of the shallow foundation analysis is calibrated. f DL is determined by the softening rate control parameter β. f If suitable, proceed to step 7; otherwise, proceed to step 6. Step 6 involves building and training a machine learning agent model to ultimately obtain a suitable deep learning (DL) algorithm. f Shallow foundation analysis was performed with β1 and 7 to obtain the predicted value of the foundation bearing capacity. The present invention uses the above method to achieve efficient inversion of nonlocal parameters, thereby obtaining a reasonable predicted value of the shallow foundation bearing capacity.
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