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.

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