This study presents a systematic review of Explainable Artificial Intelligence (XAI) applications in Transportation Infrastructure Management (TIM), focusing on predictive maintenance of safety-critical assets such as railways and bridges. A predefined review protocol was implemented, and peer-reviewed literature was systematically retrieved from Web of Science and Scopus covering the period 2015 to March 2025. Using structured Boolean search logic and clearly defined inclusion and exclusion criteria—requiring explicit integration of explainability within AI-driven infrastructure maintenance—450 records were initially identified, screened in multiple stages, and refined to 163 eligible studies for detailed analysis. Through structured data extraction and thematic synthesis, the review develops a taxonomy of model-specific, model-agnostic, hybrid, and human-centered XAI approaches while identifying recurring challenges including heterogeneous multi-modal data environments, lack of standardized interpretability metrics, computational constraints in real-time deployment, limited robustness validation under field conditions, and unresolved performance–interpretability trade-offs. The findings demonstrate systematic growth in XAI-driven predictive maintenance research and highlight the need for domain-specific benchmarks, hybrid interpretable architectures, digital twin-assisted validation, and edge-enabled explainable systems to enable scalable, transparent, and regulation-ready infrastructure management aligned with Industry 5.0.
Hu et al. (Sat,) studied this question.