This article explores the role of artificial intelligence (AI) in predicting nanomaterial properties, particularly its significance within geotechnical engineering. By analyzing multiple AI-based studies, the review concentrates on the forecasting of nanomaterial-altered soil characteristics and behaviors. Encouraging findings from these studies underscore AI's ability to accurately predict the geotechnical properties of nanomaterials, though challenges remain, particularly in quantifying nanomaterial percentages and their implications across various applications. Future research should address these challenges to enhance the accuracy of AI-based prediction models in geotechnical engineering. Nonetheless, the growing adoption of AI for predicting nanomaterial properties demonstrates its potential to revolutionize geotechnical engineering. AI's capacity to uncover intricate patterns and relationships beyond human capabilities enables more precise soil behavior predictions, fostering innovative solutions to geotechnical challenges. Its ability to process vast datasets, adapt to various scenarios, and continuously learn from new information makes AI an indispensable tool for understanding nanomaterial properties and their impact on soil behavior. In summary, the integration of AI and geotechnical engineering represents a pivotal advancement in comprehending nanomaterial properties and their practical applications. As research advances and AI technologies evolve, transformative progress in geotechnical engineering is expected. By harnessing AI's capabilities, researchers can unlock groundbreaking insights, drive innovation, and shape a more resilient and sustainable future for the geotechnical engineering industry.

The ultrasonic pulse velocity (UPV) correlates significantly with the density and pore size of subgrade filling materials. This research conducts numerous Proctor and UPV tests to examine how moisture and rock content affect compaction quality. The study measures the changes in UPV across dry density and compaction characteristics. The compacted specimens exhibit distinct microstructures and mechanical properties along the dry and wet sides of the compaction curve, primarily influenced by internal water molecules. The maximum dry density exhibits a positive correlation with the rock content, while the optimal moisture content demonstrates an inverse relationship. As the rock content increases, the relative error of UPV measurement rises. The UPV follows a hump-shaped pattern with the initial moisture content. Three intelligent models are established to forecast dry density. The measure of UPV and PSO-BP-NN model quickly assesses compaction quality. (c) 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).