  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.
