A Data-Driven Framework for Explainable Artificial Intelligence in Pavement Distress Analysis and Decision Support: Integrating Clustering Models and Principal Component Analysis

Abstract

The increasing complexity of transportation infrastructure demands advanced, data-driven approaches for early pavement distress detection and maintenance decision-making. Traditional assessment methods often fail to provide reliable, interpretable, and proactive insights into pavement degradation. This study introduces an Explainable Artificial Intelligence (XAI) framework that integrates clustering algorithms with principal component analysis (PCA) to improve early-stage pavement distress analysis. The proposed framework leverages K-means, Gaussian mixture models (GMMs), and hierarchical clustering, applied to a customized dataset encompassing pavement performance metrics, geospatial information, and aggregate properties. By incorporating ground-truth validation, our approach not only differentiates between high-quality and deteriorating pavement sections but also reveals underlying factors contributing to distress, overcoming the opacity of traditional machine learning (ML) models. Results demonstrate that this transparent, interpretable AI-driven framework enhances infrastructure resilience by enabling data-informed decision-making for predictive maintenance. Beyond transportation engineering, the methodology establishes a scalable paradigm for explainable AI applications in civil infrastructure, advancing the intersection of ML, geospatial analysis, and material science.