| Abstrakt: |
This study explores how dynamic characteristics of concrete, such as dynamic shear modulus, dynamic modulus of elasticity, and dynamic Poisson's ratio, affect stability and performance in civil engineering applications. Traditional testing procedures, which include the time-consuming and costly process of mixing and casting specimens, are both timeconsuming and costly. The primary objective of this research is to improve efficiency by using Artificial Neural Networks (ANNs) and regression analysis to predict the dynamic properties of concrete, providing a machine-learning-based alternative to traditional experimental methodologies. A set of 72 concrete specimens was methodically built and evaluated, with compressive strengths of 50 MPa, aspect ratios ranging from 1 to 2.5, and an average density of 2400 kg/m3 . An input dataset and ANN targets were built using these samples. The ANN model, which used cutting-edge deep learning techniques, went through extensive training, validation, and testing, as well as statistical regression analysis. A comparison shows that the predicted dynamic modulus of elasticity and shear modulus using both ANN and regression approaches nearly match the experimental values, with a maximum error of 5%. Despite good forecasts for the dynamic Poisson's ratio, errors of up to 20% were detected on occasion, which were attributed to sample shape variations. [ABSTRACT FROM AUTHOR] |
