Application of the BP neural network algorithm to the ultimate bearing capacity test of building structures

Abstract

The ultimate bearing capacity test effect directly influences the safety performance of the design of building structures. To enhance the safety of building structures, applying the BP neural network algorithm to their ultimate bearing capacity test is studied to improve the test effect. The shear wave velocity of the building structure during stress is collected using the static probing technique. The input samples of the BP neural network are the building structure’s shear wave velocity and construction parameters. They are processed by dimensionality reduction through principal component analysis. The firework algorithm is used to optimize the weight of the BP neural network. An early termination training method is designed, and the optimal weight is combined to train the BP neural network. After training, the samples are input after dimensionality reduction, and the building structure’s ultimate bearing capacity test results are output. Experimental results show that this method can effectively collect the shear wave velocity of building structures and complete the dimensionality reduction of samples. Under different coaxial stresses, this method can effectively measure the ultimate bearing capacity, about 3800 kN. After parameter optimization, the test value of this method is very close to the target value; that is, the ultimate bearing capacity test precision is high.