Available online at www.CivileJournal.org Civil Engineering Journal (E-ISSN: 2476-3055; ISSN: 2676-6957) Vol. 10, No. 01, January, 2024 249 Prediction of the Dynamic Properties of Concrete Using Artificial Neural Networks Amjad A. Yasin 1* 1 Department of Civil Engineering, Faculty of Engineering Technology, Al-Balqa Applied University, 11134 Amman, Jordan. Received 11 September 2023; Revised 03 December 2023; Accepted 09 December 2023; Published 01 January 2024 Abstract 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 time- consuming 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/m 3 . 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. Keywords: Concrete; Dynamic properties; Artificial Neural Networks; Regression Analysis. 1. Introduction Concrete, a key building material recognized for its strength and adaptability, is critical to contemporary society's infrastructure. Concrete's mechanical qualities, particularly its dynamic properties, are critical in determining the stability and performance of numerous civil engineering applications. The primary subject of much previous research on concrete was its dynamic compressive qualities, while tensile and Poisson's ratio properties were examined considerably less frequently [1]. During their service life, concrete buildings may be subjected to intensely dynamic loadings such as projectile impact and contact explosion, which would release a substantial amount of energy in a short period, emphasizing the need to investigate the dynamic characteristics of concrete [2]. Dynamic characteristics of concrete, such as dynamic shear modulus, dynamic modulus of elasticity, and dynamic Poisson's ratio, are important indications of how concrete buildings respond to external pressures like earthquakes and dynamic impacts. Estimating these qualities accurately is critical for ensuring the safety and lifespan of concrete structures. The dynamic characteristics of fine-grained concrete and foamed concrete were examined to assess these qualities and their usefulness in civil engineering construction [3, 4]. For testing these dynamic properties, the ultrasonic method is a beneficial, non-destructive tool. This approach gives critical insights into features such as dynamic shear modulus, dynamic modulus of elasticity, and dynamic Poisson's * Corresponding author: dr.amjad.yassin@bau.edu.jo http://dx.doi.org/10.28991/CEJ-2024-010-01-016 © 2024 by the authors. Licensee C.E.J, Tehran, Iran. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).