Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete

Abstract

The work presents the results of an experimental campaign carried out on concrete elements in order to investigate the potential of using artificial neural networks (ANNs) to estimate the compressive strength based on relevant parameters, such as the water–cement ratio, aggregate–cement ratio, age of testing, and percentage cement/metakaolin ratios (5% and 10%). We prepared 162 cylindrical concrete specimens with dimensions of 10 cm in diameter and 20 cm in height and 27 prismatic specimens with cross sections measuring 25 and 50 cm in length, with 9 different concrete mixture proportions. A longitudinal transducer with a frequency of 54 kHz was used to measure the ultrasonic velocities. An ANN model was developed, different ANN configurations were tested and compared to identify the best ANN model. Using this model, it was possible to assess the contribution of each input variable to the compressive strength of the tested concretes. The results indicate an excellent performance of the ANN model developed to predict compressive strength from the input parameters studied, with an average error less than 5%. Together, the water–cement ratio and the percentage of metakaolin were shown to be the most influential factors for the compressive strength value predicted by the developed ANN model.

A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash

“…Artificial neural networks are algorithms simulating the microstructure (neurons) of a biological nervous system [27][28][29]. Their structure is similar to the biological connection between neurons in the human brain.…”

Estimación de la resistencia a la compresión del hormigón por medio del ensayo de Velocidad de Pulso Ultrasónico (VPU)

“…Si bien existen muchos estudios de regresión lineal entre la resistencia a la compresión y VPU, otras investigaciones han presentado relaciones exponenciales con igual o mejores resultados (Tharmaratnam y Tan, 1990). Incluso la utilización de redes neuronales puede estimar la resistencia a compresión con errores menores al 5% (Silva et al, 2021)…”

Metaheuristic Prediction of the Compressive Strength of Environmentally Friendly Concrete Modified with Eggshell Powder Using the Hybrid ANN-SFL Optimization Algorithm

“…In recent years, artificial intelligence (AI) systems have been increasingly used for solving regression and classification problems. This is due to the fact that more reliable results are obtained than when using conventional methods [37][38][39][40][41][42][43][44][45]. AI systems have generally shown a lot of potential for solving real-life tasks, particularly non-linear problems.…”