International Journal of Science, Engineering and TechnologyAuthors: Vansh Garg, Saksham Aggarwal, Anup Paul, Padmaja Panda, Chinmayee Tripathy
Abstract: Backscattering study of ultrasonic waves is a non-destructive technique used in characterization of cement mortar during hydration. Experimentally measured Attenuation, acoustic impedance, amplitude and velocity of ultrasonic waves at 25C, 32C, and 42C temperatures are considered for analysis. The present study explores different Machine learning models and expands the experimental data recorded at 25C, 32C and 42C to a range of 20C to 45C. Three machine learning models: linear regression, backpropagation-based neural network and linear discriminant analysis (LDA) are used for extrapolation. The critical phase transitions during hydration of cement mortar was detected. For this the angular relationship between attenuation and impedance vectors, was analysed by the Sliding Window Principal Component Analysis (SW-PCA) approach. Further volatility angle was analyzed to distinguish stable, transitional, and decoupled microstructural states of mortar. The SW-PCA approach again used to evaluate the extended datasets generated by various machine learning models. From ultrasonic wave velocity and impedance, density of mortar is estimated for both experimental and predicted datasets. This study was incorporated with angular volatility trends to obtain threshold temperatures for effective setting in mortar. The present study signifies that 25C supports a controlled and consistent solidification. 32C corresponds to a kinetic transition state exhibiting an elevated instability in microstructure and 42C leads to accelerated but structurally weaker densification. Further in the chosen machine learning models, linear regression captures overall trends, LDA and neural network models more appropriately represent non-uniform hydration behaviour and effective capture of crossover effects. The proposed SW-PCA analysis along with the ML framework captures an improved insight into temperature-dependent microstructural evolution during hydration.
DOI: http://doi.org/10.5281/zenodo.21716202
