Concrete structures in marine environments are subjected to severe conditions that significantly compromise their durability and service life. Exposure to chloride penetration, sulfate attack, and physical erosion accelerates deterioration, leading to extensive maintenance requirements and high associated costs. To address these challenges, significant advancements in surface coatings and inspection technologies have been developed to enhance the longevity of concrete structures. This review examines recent progress in protective surface coatings that mitigate environmental damage and explores state-of-the-art inspection techniques for assessing structural integrity. By providing a comprehensive analysis of innovative materials, coating applications, and non-destructive evaluation methods, this paper aims to equip researchers and industry professionals with effective strategies for preserving concrete infrastructure in marine environments.

Epoxy-based vitrimers represent a paradigm shift in material science, offering an unprecedented combination of mechanical robustness, environmental sustainability, and reconfigurability. These dynamic polymer systems utilize associative dynamic covalent bonds (DCBs) such as transesterification to blend the structural integrity of thermosets with the recyclability and self-healing properties of thermoplastics. This unique combination makes vitrimers ideal candidates for high-performance applications in industries such as civil engineering, where material durability, repairability, and environmental compatibility are critical. Epoxy-based vitrimers, in particular, exhibit exceptional self-healing capabilities, allowing them to autonomously repair microcracks and damage, restoring mechanical properties under appropriate stimuli such as heat or light. Their recyclability further aligns with global sustainability goals by reducing material waste and lifecycle costs. Recent advancements have also integrated bio-based feedstocks and scalable manufacturing methods, enhancing the feasibility of these materials for industrial applications. This review explores the underlying self-healing mechanisms, dynamic recycling processes, and the emerging role of epoxy-based vitrimers in civil engineering. Challenges related to scalability, mechanical optimization, and regulatory acceptance are also discussed, with a focus on their potential to drive sustainable innovation in infrastructure materials.

Marine concrete structures are continuously exposed to harsh marine environments—salt, waves, and biological fouling—that accelerate corrosion and cracking, increasing maintenance costs. Traditional Non-Destructive Testing (NDT) techniques often fail to detect early damage due to signal attenuation and noise in underwater conditions. This study critically reviews recent advances in Artificial Intelligence-integrated NDT (AI-NDT) technologies for marine concrete, focusing on their quantitative performance improvements and practical applicability. To be specific, a systematic comparison of vision-based and signal-based AI-NDT techniques was carried out across reported field cases. It was confirmed that the integration of AI improved detection accuracy by 17–25%, on average, compared with traditional methods. Vision-based AI models such as YOLOX-DG, Cycle GAN, and MSDA increased mean mAP 0.5 by 4%, while signal-based methods using CNN, LSTM, and Random Forest enhanced prediction accuracy by 15–20% in GPR, AE, and ultrasonic data. These results confirm that AI effectively compensates for environmental distortions, corrects noise, and standardizes data interpretation across variable marine conditions. Lastly, the study highlights that AI-enabled NDT not only automates data interpretation but also establishes the foundation for predictive and preventive maintenance frameworks. By linking data acquisition, digital twin-based prediction, and lifecycle monitoring, AI-NDT can transform current reactive maintenance strategies into sustainable, intelligence-driven management for marine infrastructure.

The mechanical and durability performance of cementitious materials is fundamentally governed by the symmetry, anisotropy, and hierarchical organization of their microstructures. Conventional experimental characterization—based on imaging, spectroscopy, and physical testing—often struggles to capture these multiscale spatial patterns and their nonlinear correlations with macroscopic performance. Recent advances in machine learning (ML) provide unprecedented opportunities to interpret structural symmetry and anisotropy through data-driven analytics, computer vision, and physics-informed models. Furthermore, we summarize cases where symmetry-informed descriptors improve performance prediction accuracy in fiber- and nano-modified composites, demonstrating that ML-based symmetry analysis can substantially complement the limitations of conventional experimental-based characterization. We confirm that image-based models such as CNN and U-Net quantify the directionality and connectivity of pores and cracks, and that physically informative neural networks (PINNs) and heterogeneous data-based models enhance physical consistency and computational efficiency compared to conventional FEM and CFD. Finally, we present the conceptual and methodological foundation for developing AI-based microstructural symmetry analysis, aiming to go beyond simple prediction and establish a conceptual foundation for AI-driven cement design based on microstructure–performance causality.