A Machine-Learning Approach for Extracting Modulus of Compacted Unbound Aggregate Base and Subgrade Materials Using Intelligent Compaction Technology

This study presents a rigorous approach for the extraction of modulus soil and unbound aggregate base materials quality management using intelligent compaction (IC) technology. The proposed makes use machine-learning methods in tandem with IC technology modulus-based spot testing as local calibration process to estimate mechanical properties compacted geomaterials. A calibrated three-dimensional finite element (FE) model that simulates proof-mapping geomaterials was used develop comprehensive database responses wide range single two-layered geosystems. then different inverse solvers artificial neural networks estimation from characteristics roller information about Several instrumented test sites were evaluation validation solvers. found promising IC. accuracy is enhanced if incorporated part program includes situ measurements devices laboratory resilient testing. Moreover, uniformity plays key role retrieval certainty. fuses intelligence mechanistic solutions position well suited materials.