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

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