Exploration of carbonate aggregates in road construction using ultrasonic and artificial intelligence approaches
This work addresses the need for efficient quality assessment of carbonate aggregates in road construction, offering a potential tool to reduce reliance on tedious laboratory tests.
The study developed models to predict Los Angeles and Micro Deval coefficients for rock aggregates using ultrasonic and AI techniques, achieving predictions based on ultrasound velocity, porosity, and density to assess quality without lab analysis.
The COVID-19 pandemic has significantly impacted the construction sector, which is sensitive to economic cycles. In order to boost value and efficiency in this sector, the use of innovative exploration technologies such as ultrasonic and Artificial Intelligence techniques in building material research is becoming increasingly crucial. In this study, we developed two models for predicting the Los Angeles (LA) and Micro Deval (MDE) coefficients, two important geotechnical tests used to determine the quality of rock aggregates. These coefficients describe the resistance of aggregates to fragmentation and abrasion. The ultrasound velocity, porosity, and density of the rocks were determined and used as inputs to develop prediction models using multiple regression and an artificial neural network. These models may be used to assess the quality of rock aggregates at the exploration stage without the need for tedious laboratory analysis.