Identificación paramétrica de rigidez y amortiguamiento estructural mediante una Red Neuronal Evolutiva con optimización multiobjetivo

Abstract

Problem: the identification of structural parameters in multiple-degree-of-freedom systems using only acceleration data constitutes a coupled inverse problem, sensitive to noise and characterized by limited damping observability, in this context, objective: this research developed a parametric identification framework to estimate the stiffness 𝑘𝑖 and damping 𝑐𝑖 of the five stories of a reducedscale prototype building, relying exclusively on accelerometric signals, to this end, method: a physics-informed Evolutionary Artificial Neural Network was integrated with a two-phase NSGAII Multiobjective Genetic Algorithm, formulating the identification task as the simultaneous minimization of temporal fidelity, modal consistency, and physical regularization; the network mapped a feature vector of dimension D=660 to admissible structural parameters through bounded decoding. As results: using the 1985 Mexico City earthquake record, nine configurations were evaluated with 30 independent runs, and the best architecture (H1=32, H2=16, PS1=220) achieved a median RMSE of 0.1895 m/s², a median 𝑅 2 of 0.860, and coefficients of variation below 0.15 for both stiffness and damping; additionally, nonparametric validation showed significant differences compared with GA and PSO (p<0.001), and the Pareto front reached 98 solutions with HV=1.000±pm0.000; finally, discussion: the results indicate that the proposed framework reduces repeated identification to a single learning task, with millisecond-level inference and potential application in post-earthquake structural health monitoring.