AI-Assisted Evaluation Of Pavement Material Performance Using Plate Load Test Data For Optimized Roadway Design

Abstract

Roadway design and pavement material evaluation are two areas of civil engineering that have been completely transformed by the quick development of artificial intelligence (AI). Plate Load Tests (PLT), a traditional method of determining subgrade strength, have proved time-consuming and interpretive. This paper explores the ways in which artificial intelligence (AI) methods, including fuzzy logic, ANN, and machine learning (ML), might improve the precision and effectiveness of PLT data interpretation for pavement design optimization. The study analyzes AI models, summarizes recent research, and talks about the benefits, drawbacks, and potential applications of AI.