Real-time inference of compacted soil properties using deflection tests: An AI-driven solution informed by unsaturated soil mechanics principles

Abstract

This paper presents a novel Artificial Intelligence (AI)-driven tool designed to convert deflection test results into crucial soil parameters essential for quality assurance in compaction projects. The accurate determination of these parameters, such as density and void ratio, is imperative for ensuring the structural integrity of infrastructures constructed on such soils. Moreover, it facilitates the utilization of modern non-destructive equipment in compaction endeavors. The determination of these parameters is notably challenging in unsaturated soils owing to the intricate interplay among factors such as suction, moisture content, void ratio, and resulting deflection.This paper presents a pioneering tool to address these challenges. By integrating unsaturated soil mechanics with advanced AI techniques, particularly reinforcement learning, the tool leverages a diverse array of inputs, including in-situ data, experimental observations, and physics-based modeling. This integration enables dynamic adaptation to changing field conditions and ensuring the tool’s real-time adaptability and predictive accuracy.Field trials validated the tool’s efficacy in predicting soil properties accurately without direct measurements of moisture content or suction, variables often unmeasured in practical soil compaction projects. This unique capability underscores significant advancements in real-time assessment of unsaturated soils, illustrating the transformative potential of AI in geotechnical engineering and unsaturated soil mechanics.