High-performance and ultra-high-performance fiber-reinforced concretes (HPFRC/UHPFRC) have emerged as advanced cementitious composites capable of achieving superior mechanical performance, durability, and structural efficiency compared with conventional concrete. However, their widespread adoption remains challenged by relatively high material costs and significant embodied environmental impacts associated with elevated binder and fiber contents. This study presents a comprehensive life-cycle review of advanced high-performance cementitious composites, evaluating their sustainability from raw material extraction and mix design to structural application, service life, and end-of-life considerations. The review synthesizes current knowledge on material composition, production processes, structural performance, durability characteristics, and environmental impacts through the framework of life-cycle assessment (LCA). Particular attention is given to the influence of mix-design parameters, including binder composition, supplementary cementitious materials (SCMs), aggregate systems, and fiber type, on embodied carbon, energy demand, and mechanical performance. A dataset compiled from published experimental studies covering high-performance and ultra-high-performance concrete mixtures is analyzed to examine relationships between compressive strength, embodied energy, and carbon footprint, highlighting the dominant role of cementitious binders and fiber production in environmental impacts. Although advanced fiber-reinforced concretes generally exhibit higher cradle-to-gate emissions than conventional concrete, their superior mechanical properties, improved durability, reduced material demand, and extended service life can substantially reduce life-cycle environmental impacts at the structural level. The review further discusses emerging strategies for developing low-carbon high-performance cementitious composites, including clinker reduction, recycled and alternative fibers, optimized particle packing, and AI-assisted mix design. Finally, key research gaps are identified, particularly regarding standardized LCA methodologies, long-term durability data, harmonized performance-based functional units, and circular-economy strategies for material recycling and reuse. The findings highlight that performance-based life-cycle evaluation is essential for accurately assessing the sustainability potential of advanced high-performance cementitious composites in resilient and low-carbon infrastructure systems.

This paper presents a comprehensive review of bridge health identification (BHI) within the emerging paradigm of intelligent infrastructure, with a particular focus on modal analysis and artificial intelligence (AI)-driven methodologies. Aging bridge networks, increasing traffic demands, and environmental stressors have significantly accelerated structural deterioration, necessitating advanced monitoring and diagnostic frameworks. Modal parameters, including natural frequencies, mode shapes, and damping ratios, are widely recognized as reliable indicators of structural condition and form the foundation of vibration-based BHI. This study systematically reviews operational modal analysis (OMA) techniques, including frequency-domain, time-domain, and hybrid approaches, highlighting their capabilities and limitations under real-world conditions. Furthermore, the integration of AI and machine learning (ML) methods, ranging from supervised and unsupervised learning to deep learning (DL) and reinforcement learning (RL), is critically examined in the context of data-driven damage detection, feature extraction, and predictive maintenance. Special attention is given to Automated Operational Modal Analysis (AOMA), where recent advances in FDD- and SSI-based frameworks have enabled scalable and user-independent modal identification. Despite significant progress, key challenges remain, including environmental variability, data scarcity, lack of interpretability, and deployment constraints. Finally, the paper identifies major research gaps and outlines future directions toward physics-informed AI, multi-modal data fusion, uncertainty-aware decision-making, and digital twin integration. The study provides a unified perspective bridging structural dynamics and intelligent data-driven approaches, contributing to the development of next-generation smart bridge monitoring systems.