A distributed dynamic load identification method based on the hierarchical-clustering-oriented radial basis function framework using acceleration signals under convex-fuzzy hybrid uncertainties

Load identification is a hotly studied topic due to the widespread recognition of its importance in structural design and health monitoring. This paper explores an effective identification method for the distributed dynamic load (DDL) varying in both time progress and space dimensions using limited acceleration responses. As for the reconstruction of spatial distribution, the radial basis function (RBF) interpolation strategy, whose hyper-parameters are determined by a hierarchical clustering algorithm, is applied to approximate the DDL and then transform the continuous function into finite dimensions. In the time domain, based on the inverse Newmark iteration, the RBF coefficients at each discrete instant are obtained by the least square solution of the modal forces. Considering the multi-source uncertainties lacking exact probability distributions, a multi-dimensional interval model is developed to quantify convex parameters and fuzzy parameters uniformly. Further, a Chebyshev-interval surrogate model with different orders is constructed to obtain the fuzzy-interval boundaries of DDLs. Eventually, three examples are discussed to demonstrate the feasibility of the developed DDL identification approach considering hybrid uncertainties. The results suggest its promising applications in different structures and loading conditions.

Automated summary

This paper explores an effective method for identifying the distributed dynamic load (DDL) that varies in both time and space dimensions using limited acceleration responses. The spatial distribution of the DDL is approximated using a radial basis function (RBF) interpolation strategy, and the temporal distribution is obtained through an inverse Newmark iteration. A multi-dimensional interval model is developed to quantify uncertainties, and a Chebyshev-interval surrogate model is constructed to obtain the fuzzy-interval boundaries of the DDL. The feasibility of this approach is demonstrated through three examples, showing promising applications in different structures and loading conditions.