With the deepening development and utilization of urban underground space, the risk of urban road collapse is becoming increasingly prominent, which is a serious threat to the safety of life and property. Therefore, the risk assessment of urban road collapse has vital significance for the safety management of cities. The main idea is to predict ongoing accidents by analyzing historical accident cases in depth. This paper explores the combination of Interpretative Structural Modeling (ISM) and Bayesian Networks (BNs) to construct a risk assessment model of road collapse. First, the main risk factors of road collapse and their coupling relationships are identified, which is used to increase the low reliability of complex systems. Then, the risk factors of road collapse are logically divided by ISM to obtain the BN hierarchy. Finally, the BN node probabilities are evaluated by the Expectation–Maximization (EM) algorithm using the collected 92 real road collapse accident cases. The model can be used to quantify the coupling strength and influence degree of each risk factor on the occurrence of road collapse accidents, which in turn can predict the probability of road collapse accidents in a given scenario. This study can provide a theoretical basis for urban safety management and reduce the risk of road collapse and potential loss of life and property, which is important for the sustainable development of societies.

To investigate the influence of moisture content, density, and loading frequency on the dynamic strength and liquefaction characteristics of sandy soil, a series of vibration triaxial tests were designed and conducted. The tests were divided into incremental loading tests and cyclic loading failure tests. The results of the incremental loading tests indicated that the shear modulus decreases with increasing moisture content and density, while it increases with increasing loading frequency. The damping ratio of unsaturated samples showed no significant correlation with moisture content, whereas samples with a density above 50% exhibited an increasing trend in damping ratio with increasing density. The damping characteristics of the sandy soil were found to be related to the loading frequency, exhibiting the characteristics of viscous damping. The results of the cyclic loading tests revealed that the dynamic failure mode of the sand soil is the ultimate equilibrium failure mode. Increasing moisture content and decreasing density make the samples more susceptible to failure. During the process of cyclic loading leading to dynamic failure, the shear modulus of unsaturated samples remains constant, while the shear modulus of saturated samples gradually decreases. The damping ratio of saturated soil is significantly higher than that of unsaturated soil. During the process of cyclic loading leading to dynamic failure, the damping ratio of saturated soil shows no apparent correlation with loading frequency, but it decreases with increasing density.

The alteration of soil static and dynamic characteristics induced by clay content constitutes a crucial issue in the realm of disaster prevention and mitigation within geotechnical engineering. The static and dynamic characteristics of mixed soils with varying sand–clay contents were investigated through the design and implementation of static and dynamic triaxial tests. The relationship between clay content and soil resistance to liquefaction was investigated, with an analysis of the influence of clay content on soil strength and static liquefaction performance. Furthermore, the study examined the soil’s resistance to liquefaction under dynamic constitutive and cyclic loading conditions for soils with varying clay content. Results indicate that stress–strain curves for samples with varying clay content exhibit a consistent trend, with the lowest tangent modulus and peak strength observed in samples containing 30% clay. Increasing clay content diminishes soil’s resistance to liquefaction under static loading conditions. Higher confining pressures correspond to larger tangent moduli and peak deviating stresses in triaxial shear tests. Dynamic shear modulus decreases as clay content increases, whereas damping ratio decreases accordingly. Soil gradation significantly affects liquefaction-induced deformation, with the sample containing 30% clay experiencing the fastest increase in pore water pressure during testing failure, accompanied by fewer cyclic loading cycles until failure occurs. Improving soil gradation and adjusting the sand–clay ratio are beneficial for enhancing both soil strength and its resistance to liquefaction.

Urban underground engineering is generally buried at a shallow depth and suffers long-term environmental water effects such as rainfall, rivers, underground pipeline leakage, and groundwater. The mechanical properties of the structures are affected by constant deterioration, which seriously hinders the safe, healthy, and sustainable development of the city. On the basis of on-site investigation of civil defense engineering, this article simulates the water environment conditions of mortar in underground engineering in the laboratory and conducts manual sample preparation in the laboratory. Then, water, H₂CO₃, NaCl, and Na₂CO₃ solution or wet–dry cycles are used to corrode the sample, respectively. A uniaxial compression test, Brazilian splitting test, analyses of the acoustic emission signals and electromagnetic signals, and magnetic imaging testing are performed, respectively. The results show that an increase in the action time of environmental water leads to a gradual increase in the uniaxial compressive strength, tensile strength, and elastic modulus of cement mortar, but it will decrease over a long period of time. Different environmental water components can also lead to a different performance of soaked mortar. The uniaxial compressive strength R, tensile strength σ_t, and elastic modulus E of mortar samples exhibit values in different solutions in the order of H₂CO₃ solution < NaCl solution < Na₂CO₃ solution < water. A moderate solution soak time can enhance the mechanical properties of the mortar, but this effect decreases at long time scales. The effect of wet–dry cycles on the mechanical properties and degradation process of mortar is significant. With the increase in wet–dry cycles, the porosity of mortar continuously increases. The cumulative ringing count, energy, amplitude, and impact number of acoustic emission signals always increase when the samples are loaded to failure. The uniaxial compressive strength, tensile strength, and elastic modulus first increase and then decrease. The experimental results lay the foundation for further investigating the performance changes in mortar under complex water environments in underground engineering.

The safety and sustainability of urban underground spaces have become crucial considerations in development projects. Seepage and cyclic loads are the principal reasons for the instability and failure of old underground space structures. This study investigates the variations in physical fields of underground spaces in cities under the coupling disturbance of seepage and cyclic loads, focusing on underground civil air defense engineering in Beijing as a case study. Different seepage conditions and the effects of seepage–cyclic load coupling were simulated using the numerical calculation software Plaxis 3D V20. The results show that change in groundwater can affect the deformation of underground space, and the severity is related to the quantity and intersection state of tunnels, the location of rivers above, and the strength of materials. The coupling effect of seepage–cyclic load on urban underground space structures is more serious than that of a single percolation. Decrease in material strength and high traffic loads are the principal reasons for the failure of underground structures. A 30% decrease in material strength causes the displacement to increase almost 1.5 times, and maximum displacement under different traffic loads can vary by 3 times. This study holds significant implications for the design, maintenance, and engineering management of underground spaces, emphasizing the importance of sustainable practices in urban development and infrastructure.

Gas pipeline leakages in urban underground utility tunnels pose a severe threat to public safety. Leakages of varying aperture sizes trigger differentiated risks of diffusion and explosion; thus, achieving precise identification of leakage hole size has become a critical issue in safety management. To address the difficulty of traditional methods in effectively separating the acoustic features of different leakage levels within complex utility tunnel environments, this paper proposes a gas pipeline leakage risk level identification method based on a lightweight Spatial–Temporal Graph Neural Network (ST-GNN). First, relying on a real utility tunnel simulation platform, acoustic signals under different pressures and leakage hole size are collected, and time-frequency magnitude features are constructed through Short-Time Fourier Transform (STFT). Furthermore, each acoustic sample is independently converted into a graph with STFT time frames as nodes, where temporal neighborhood edges and K-nearest neighbor edges jointly encode local dynamics and non-local spectral similarities. This transforms unstructured acoustic signals into graph-structured data that embodies spatial–temporal coupling relationships. Building upon this, a lightweight Chebyshev graph convolutional network is designed to progressively extract discriminative features strongly correlated with leakage levels using multi-layer convolution. Experimental results on the actual utility tunnel simulation platform dataset demonstrate that the proposed method achieves excellent performance in a three-level leakage classification task. The t-SNE visualization reveals the effective separation of features, progressing from complete mixing in the input layer to distinct separation in the output layer. Through multiple training statistics and ablation experiments, the impact of dataset size and the number of network layers on the identification performance is analyzed, validating the robustness of the proposed model under limited samples and the effectiveness of its lightweight structure. This provides a feasible solution for the automated and refined identification of gas pipeline leakage levels in underground utility tunnels.