Non-Destructive Characterization and AI-Based Damage Prediction in Ceresit Mortar Using Experimental Modal Analysis and Hybrid SAINT-HBA Model

In the present study, mechanical behavior of Ceresit 20 mortar is investigated through non-destructive experimental modal testing on a mortar beam. The objective was to determine the Young's modulus (E) in three principal directions longitudinal, transverse, and vertical in terms of bending frequencies. Torsion mode data was utilized in order to calculate the shear modulus (G) and to calculate Poisson's ratio (ν). A finite element model was used for validation, with very good correlation with experiment. Besides, a new Machine Learning (ML) model, Self-Attention Interpretable Neural Transformer optimized by Honey Badger Algorithm (SAINT-HBA), was introduced to predict crack depth of mortar beams. Utilizing bending and torsion frequencies of failed specimens, the model predicted single and multiple crack scenarios very well. SAINT-HBA outperformed hybrid SAINT models that used Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Ecosystem-based Optimization (AEO) and was found to be useful for structural health monitoring applications.