Table 1_Performance, sustainability, and machine learning of UHPC with coarse aggregates: a critical review.docx

Machine learning methods, particularly Artificial Neural Networks (ANN) and Support Vector Machines (SVM), have demonstrated considerable potential for predicting mechanical properties and supporting durability- and sustainability-oriented performance optimization. Machine learning applications are emerging in UHPC research, particularly for performance prediction and mix-design optimization; however, their broader adoption remains limited by small and heterogeneous datasets, inconsistent validation procedures, and insufficient integration of life-cycle assessment parameters.

The major research gaps include physics-informed ML methods, optimization of aggregate gradation to enhance interfacial transition zone (ITZ) behaviour, and systematic long-term durability assessment under diverse environmental conditions. Based on these findings, a future research roadmap is proposed to support the development of sustainable, cost-effective, and intelligent UHPC systems. Unlike previous UHPC review articles that primarily focus on constituent materials, mechanical performance, or durability, this review integrates coarse aggregate incorporation, sustainability assessment, interfacial transition zone (ITZ) behaviour, and machine learning-based optimization within a single systematic framework, thereby providing an integrated roadmap for the development of sustainable and intelligent UHPC systems.</p>