The invention discloses a
shallow foundation bearing capacity calculation method fusing
machine learning and a non-
local algorithm, which belongs to
geotechnical engineering, and comprises the following steps: 1, judging foundation
soil type and strain hardening type foundation soil according to an indoor triaxial test result, 2, carrying out 3 and 2, establishing a finite
element model, carrying out refined
simulation, and then carrying out 7, carrying out 4, carrying out 5, carrying out 6, carrying out 7, carrying out 7, carrying out 7, carrying out 7, carrying out 7, carrying out 7, carrying out 7, carrying out 7, carrying out 7, carrying out 7, and carrying out 7; 3, performing refined
simulation by using a constitutive model combined with a non-
local algorithm, and then turning to 4 and 4 to calibrate a
shallow foundation analysis influence range DLf and a
softening rate control parameter beta, 5, judging whether the DLf is appropriate, turning to 7 if the DLf is appropriate, and turning to 6 and 6 to construct and
train a
machine learning agent model if the DLf is not appropriate, and finally obtaining appropriate DL'f, beta 1 and 7 to perform
shallow foundation analysis, and finally, performing the shallow foundation analysis. By the adoption of the method, efficient inversion of non-local parameters is achieved, and therefore the reasonable shallow foundation
bearing capacity prediction value is obtained.