A multitask SHM algorithm to identify damage with random severity and location in IPE beams using EMI technique - Zamanian, Mehrab; Sepehry, Naserodin; Zahrai, Seyed Mehdi (Structures (Oxford), 2024)
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.