Exploration of carbonate aggregates in road construction using ultrasonic and artificial intelligence approaches

TL;DR

This paper develops ultrasonic and AI-based models to predict aggregate quality in road construction, reducing reliance on labor-intensive laboratory tests and enhancing exploration efficiency.

Contribution

It introduces novel predictive models using ultrasonic and AI techniques for assessing aggregate quality, improving early-stage exploration methods.

Findings

Models accurately predict LA and MDE coefficients

Ultrasound velocity, porosity, and density are effective predictors

AI models outperform traditional regression in accuracy

Abstract

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.