Slow-moving landslides, characterized by sustained destructive potential, are widely distributed in northwest China. However, research on the damage mechanisms of masonry buildings caused by slow-moving landslide-induced surface deformation is significantly lacking, which severely restricts the physical vulnerability assessment of masonry structures and the quantitative risk evaluation of slow-moving landslides. Through field investigations, CDEM numerical simulations, and statistical analyses, this study reveals the cooperative deformation characteristics and progressive failure mechanisms of masonry buildings subjected to ground cracks in slow-moving landslides, and establishes physical vulnerability curves for four distinct ground crack scenarios. The key findings indicate that masonry buildings affected by slow-moving landslides primarily exhibit transverse wall cracking and longitudinal wall inclination due to ground crack propagation. As crack propagation continues, the first-floor walls exhibit significantly higher Mises stresses compared to those on the second floor. Wall inclination rates demonstrate a distinct threshold effect during crack propagation: below the threshold, inclination increases linearly with crack displacement, while above the threshold, it exhibits exponential growth. Under identical crack displacement conditions, wall inclination rates decrease in the following order: horizontal tension, combined tension, settlement, and combined uplift scenarios. The differential effects of these scenarios on wall inclination become more pronounced with increasing crack displacement. Weibull functions were employed to fit vulnerability curves for masonry structures under four ground crack scenarios, revealing displacement thresholds of 22 cm, 26 cm, 27 cm, and 37 cm for complete structural vulnerability (V = 1) in each respective scenario. These findings provide valuable insights for vulnerability prediction and emergency rapid assessment of buildings subjected to slow-moving landslides across various disaster scenarios.

Typhoons are recurring meteorological phenomena in the southeastern coastal area of China, frequently triggering debris flows and other forms of slope failures that result in significant economic damage and loss of life in densely populated and economically active regions. Accurate prediction of typhoon-triggered debris flows and identification of high-risk zones are imperative for effective risk management. Surprisingly, little attention has been devoted to the construction of physical vulnerability curves in typhoon-affected areas, as a basis for risk assessment. To address this deficiency, this paper presents a quantitative method for developing physical vulnerability curves for buildings by modeling debris flow intensity and building damage characteristics. In this study, we selected the Wangzhuangwu watershed, in Zhejiang Province of China, which was impacted by a debris flow induced by Typhoon Lekima on August 10, 2019. We conducted detailed field surveys after interpreting remote sensing imagery to analyze the geological features and the mechanism of the debris flow and constructed a comprehensive database of building damage characteristics. To model the 2019 debris flow initiation, entrainment, and deposition processes, we applied the Soil Conservation Service-Curve Number (SCS-CN) approach and a two-dimensional debris flow model (FLO-2D). The reconstructed debris flow depth and extent were validated using observed debris flow data. We generated physical vulnerability curves for different types of building structures, taking into account both the degree of building damage and the modeled debris flow intensity, including flow depth and impact pressure. Based on calibrated rheological parameters, we modeled the potential intensity of future debris flows while considering various recurrence frequencies of triggering rainfall events. Subsequently, we calculated the vulnerability index and economic risk associated with buildings for different frequencies of debris flow events, employing diverse vulnerability functions that factored in uncertainty in both intensity indicators and building structures. We observed that the vulnerability function utilizing impact pressure as the intensity indicator tends to be more conservative than the one employing flow depth as a parameter. This comprehensive approach efficiently generated physical vulnerability curves and a debris flow risk map, providing valuable insights for effective disaster prevention in areas prone to debris flows.