Abstract
Ultrasonic detection has emerged as a rapid method for acquiring rock mass sound velocity and converting it into an elastic modulus parameter, a pivotal technique for investigating the in-situ mechanical properties of rock masses. Despite its significance, accurately deducing rock mass strength from elastic modulus remains a formidable challenge and a pressing issue in the realm of protorock parameter research. This study introduces an innovative artificial intelligence-driven methodology for transforming elastic modulus and strength parameters specific to coal measures through rigorous data analysis and experimental validation. By integrating two illustrative engineering cases, we explore the complexities of water inrush and floor heave issues encountered in tunnels traversing fault zones. The novel strength parameter calculation approach is benchmarked against previous studies, highlighting its superior advantages in terms of effectiveness and applicability. In essence, this research offers a comprehensive framework and practical workflow for translating in-situ acoustic parameter-derived elastic modulus into rock mass strength, serving as a valuable resource for future endeavors in mine water control research.