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Uneven stowage of wharf is one of the main external loads during its service period. The main load-bearing structure of the overhead vertical wharf is the lower pile foundation. Under the action of the upper pile load, the pile foundation will be damaged to varying degrees, and the local damage may cause adverse effects on the overall structural safety of the wharf. In order to realize the inversion analysis of the adverse damage inducement of wharf stowage, the stress detection results of the lower pile foundation are taken as inversion data samples, and 10,000 sets of stress data are obtained by establishing a parameterized numerical calculation model. After normalization and dimension reduction, they are input into the established neural network model, and the action position and strength of stowage damage inducement are identified. The results show that the inversion analysis samples obtained by the parameterized model in this paper have higher accuracy and generalization in the calculation of neural network model. Article Highlights The difference of recognition indexes between training samples and test samples is small, and there is no lack of fitting ability, and the generalization ability is strong. The regression learner network has two hidden layers, and the number of nodes is 10 and 4, respectively. The parameter setting with the activation function as identity can meet the inversion needs of the heap damage inducement. Inversion calculation of wharf damage incentives needs to establish samples with large data space, while materialization model calculation takes up a lot of computer memory and takes time.

Inversion of damage inducement of wharf pile foundation under heaped load based on parametric model