Water inrush disasters in karst tunnels pose a significant threat to construction safety, project timelines, and the long-term sustainability of infrastructure. Effective risk assessment is crucial for mitigating these hazards and ensuring the resilient development of underground transportation networks. This study proposes a quantitative risk assessment model that integrates the entropy weight method with attribute interval recognition theory to address the uncertainties inherent in complex geological environments. First, a hierarchical evaluation index system is established based on four primary controlling factors: stratigraphy, geological structure, topography, and hydrogeology. Subsequently, the entropy weight method is employed to objectively determine the weight of each index, thereby minimizing human bias. The attribute interval recognition model is applied to calculate the comprehensive attribute measure for each tunnel segment, effectively managing the fuzziness of risk classification boundaries. The risk grade is ultimately determined using the confidence criterion. The proposed model is applied to the Qigan Mountain karst tunnel in Chongqing, China, which is divided into 56 segments for detailed analysis. Results show 41.32% (4088 m) high-risk, 40.14% (3971 m) medium-risk and 18.55% (1835 m) low-risk sections, which are highly consistent with the theoretical water inflow calculation results. The model realizes accurate and quantitative water inrush risk assessment, providing a scientific basis for disaster prevention and control in karst tunnel construction, and further promoting the sustainability and safety of underground engineering in karst areas.

Coal slag is a common byproduct during the operation of fossil-fired power plants. It often becomes a type of solid waste if no suitable utilization is performed. This study experimentally investigated the dynamic characteristics of mixtures of Q2 Lishi loess incorporating coal slag, with the aim of developing a sustainable method for loess improvement and waste disposal associated with the Sustainable Development Goals (SDGs). The loess samples underwent unconfined compressive strength (UCS) testing and dynamic triaxial assessments to obtain the mechanical performance at coal slag proportions of 0%, 1%, 3%, and 5%. Microstructural characteristics were examined using field-emission scanning electron microscopy (FE-SEM). The results indicate that the inclusion of coal slag significantly enhances both UCS and dynamic modulus, with strength improvements reaching up to 73.6% at a 5% slag content. A minimum slag content of 3% effectively reduced pore connectivity and facilitated improved load transfer within the soil matrix, whereas further increases in slag content produced marginal gains in mechanical properties. This approach directly addresses the challenge of solid waste disposal by repurposing industrial by-products, thereby reducing environmental footprint. Environmental assessments identified limited leaching risks, underscoring the need for appropriate mitigation measures to ensure environmental compatibility. The findings suggest that incorporating 3–5% coal slag optimally stabilizes loess soils, which contribute to SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Consumption and Production) by offering a sustainable and resource-efficient alternative to conventional stabilization techniques.