A multitask SHM algorithm to identify damage with random severity and location in IPE beams using EMI technique

Existing electromechanical impedance (EMI) damage identification algorithms often face challenges in terms of generalizability. This paper presents a robust algorithm that can simultaneously estimate the region and severity of damage with random damage scenarios across a surface and any severity, rather than being limited to specific points and severities. The host structure was an I-beam. Simulated damage was introduced as a subtle added mass to evaluate the algorithm's effectiveness in early-stage damage identification. Initially, various EMI tests with different damage specifications were conducted, and validated through numerical simulation. Damage-sensitive features were extracted and were input into three ML models: support vector machine, random forest, and multilayer perceptron. An ensemble learning approach was employed to combine the individual predictions from these models. The algorithm achieved classification accuracies of 97.3 % and 94.4 % on the validation and test sets, respectively, for identifying damaged regions. The algorithm also quantifies damage severity, achieving R-squared values of 92 % and 88 % on the validation and test sets, respectively.