This study aims to evaluate the effectiveness of machine learning (ML) models in predicting concrete damage using electromechanical impedance (EMI) data. From numerous experimental evidence, the damaged mortar sample with surface-mounted piezoelectric (PZT) material connected to the EMI response was assessed. This work involved the different ML models to identify the accurate model for concrete damage detection using EMI data. Each model has been evaluated with evaluation metrics with the prediction/true class and each class is classified into three levels for testing and trained data. Experimental findings indicate that as damage to the structure increases, the responsiveness of PZT decreases. Therefore, examined the ability of ML models trained on existing experimental data to predict concrete damage using the EMI data. The current work successfully identified the approximately close ML models for predicting damage detection in mortar samples. The proposed ML models not only streamline the identification of key input parameters with models but also offer cost-saving benefits by reducing the need for multiple trials in experiments. Lastly, the results demonstrate the capability of the model to produce precise predictions.

The current research focuses on the implementation of the fuzzy logic approach for the prediction of base pressure as a function of the input parameters. The relationship of base pressure (β ) with input parameters, namely, Mach number (M), nozzle pressure ratio (η), area ratio (α), length to diameter ratio (ξ ), and jet control (ϑ ) is analyzed. The precise fuzzy modeling approach based on Takagi and Sugeno’s fuzzy system has been used along with linear and non-linear type membership functions (MFs), to evaluate the effectiveness of the developed model. Additionally, the generated models were tested with 20 test cases that were different from the training data. The proposed fuzzy logic method removes the requirement for several trials to determine the most critical input parameters. This will expedite and minimize the expense of experiments. The findings indicate that the developed model can generate accurate predictions