The global construction industry urgently requires sustainable alternatives to ordinary Portland cement (OPC) to mitigate its immense carbon footprint. Red mud (RM), a highly alkaline bauxite residue, presents tremendous but challenging potential as a supplementary cementitious material. This review systematically bridges the gap between atomic-level interfacial bonding mechanisms and macroscopic engineering performance, highlighting how these properties are significantly dictated by specific RM sources (e.g., Bayer vs. Sintering processes). We first elucidate advanced pretreatment strategies, notably CO₂ mineralization, which synergistically mitigates extreme alkalinity and sequesters carbon. Crucially, the fundamental bonding mechanisms are decoded: beyond physical filling, RM integration induces significant micro-morphological densification via intense aluminosilicate depolymerization—evidenced by the Al[VI] to Al[IV] coordination shift—and the quantitative integration of approximately 40% reactive iron phases into stable Fe-S-H networks. By clearly distinguishing between traditional hydration and clinker-free alkali-activation pathways, we evaluate holistic structural parameters beyond mere 28-day compressive strength (40–67 MPa), explicitly addressing flexural capacity, modulus of elasticity, and volume stability. Environmental assessments confirm exceptional heavy metal immobilization (>95% efficiency, leaching < 0.010 mg/L) and a substantial 50–80% reduction in Global Warming Potential (GWP), provided the environmental burden of alkaline activators is rigorously accounted for. Furthermore, the long-term risk of Alkali–Silica Reaction (ASR) is evaluated as a primary durability concern. Finally, to overcome persistent rheological bottlenecks, this paper highlights transformative future trajectories, particularly data-driven Machine Learning (ML) for complex mix optimization and 3D concrete printing for advanced infrastructure. Ultimately, this review provides a robust theoretical foundation and a pragmatic roadmap for upcycling RM into safe, high-performance, and ultra-low-carbon building materials.

This study proposed a sea cliff stability model that accounted for the coupled effects of sea erosion and rainfall, offering an improved quantitative assessment of the toppling risk. The approach integrated the notch morphology (height and depth) and rainfall infiltration to quantify stability, validated by field data from six toppling sites near Da’ao Bay, where the maximum erosion distance error between model predictions and measurements ranged from 0.81% to 48.8% (with <20% error for Sites S2, S3, and S4). The results indicated that the notch morphology and rainfall exerted significant impacts on the sea cliff stability. Site S4 (the highest site) corresponded to a 17.5% decrease in K per 0.1 m notch depth increment. The rainfall infiltration reduced the maximum stable notch depth, decreasing by 8.86–21.92% during prolonged rainfall. This model can predict sea cliff stability and calculate the critical notch depth (e.g., 0.56–1.22 m for the study sites), providing a quantitative framework for coastal engineering applications and disaster mitigation strategies under climate change scenarios.