The condition of drainage pipes greatly affects the urban environment and human health. However, it is difficult to carry out economical and efficient pipeline investigation and evaluation due to the location and structure of drainage pipes. Herein, the four most-commonly used drainage pipeline evaluation standards have been synthesized and analyzed to summarize the deterioration and breakage patterns of drainage pipes. The common pipe breakage patterns are also summarized by integrating the literature and engineering experience. To systematically describe the condition of drainage pipes, a system of influencing factors for the condition of pipes, including physical, environmental, and operational factors, has been established, and the mechanism of action of each influencing factor has been summarized. Physical, statistical, and AI models and their corresponding representative models have been categorized, and the research progress of current mainstream drainage-pipe deterioration and breakage prediction models are reviewed in terms of their principles and progress in their application.

As a geological disaster widely distributed in the southern regions of China, rainfall-induced shallow landslides pose a significant threat to affected areas. Timely detection of landslides is crucial in the effective response to such disasters. However, landslide detection faces adverse impacts from various factors, such as insufficient sample data, complex model structures, and limitations in detection accuracy during the actual detection process. In this study, high-quality image samples were collected from multiple landslide disaster areas in southern China, and a rainfall-induced shallow landslide sample database was constructed in the region. Based on this, a lightweight attention-guided YOLO model (LA-YOLO) was proposed to improve the detection performance of YOLO model for rainfall-induced shallow landslides. First, CG block is introduced to enhance the C2f module, enriching the feature representation capability through multiscale feature fusion and reducing the model’s parameters and computational complexity. Second, the SimAM attention module is used to focus on the target regions, improving feature extraction effectiveness. Experimental results show that the model parameters of LA-YOLO were reduced by approximately 30%, with precision, recall, and mean average precision (mAP) on the landslide sample dataset increasing by 2.6%, 0.7%, and 2.2%, respectively. While ensuring model detection performance, the model structure was significantly optimized, achieving both lightweight and accuracy goals, confirming the model’s superiority in monitoring rainfall-induced shallow landslide disasters.

Soft soil foundations in marine environments are under coupling actions of seawater erosion and dry–wet cycles due to tides. Ferronickel slag is a solid waste produced in the smelting of ferronickel alloys. To recycle industrial solid waste and conserve energy, ferronickel slag is partially substituted for cement to solidify the soft soil foundations in marine environments. Unconfined compression tests were conducted for soil–cement mixed with ferronickel slag of various proportions to investigate its apparent erosion characteristics and mechanical characteristics under dry–wet cycles. In the tests, the corresponding numbers of cycles were set to 0, 6, 12, and 18. To further investigate the microscopic action mechanism of ferronickel slag on soil–cement, a nuclear magnetic resonance (NMR) device was utilized to analyze the microstructure of the soil–cement. According to the testing results, the unconfined compressive strength of soil–cement first increased and then decreased when the number of cycles of seawater erosion increased. With other conditions being the same, the addition of ferronickel slag can improve soil–cement strength, and changes in soil–cement strength were more significant than that with no ferronickel slag mixed. Moreover, the optimal amount of admixture was proved to be 45%. As the number of dry–wet cycles increased, the mass of soil–cement first increased and then decreased. With the same number of dry–wet cycles, soil–cement mixed with ferronickel slag had a smaller mass loss rate than that with no ferronickel slag added. After six dry–wet cycles, apparent erosion of soil–cement becomes increasingly serious, including the absence of edges and corners, deformation of surfaces, and even spalling and cracking. The NMR analysis revealed that dry–wet cycles can promote the evolution of small pores into larger ones within the soil–cement, thereby increasing the number of larger pores, leading to an increase in porosity, a decrease in the compactness of the soil–cement, and a reduction in strength.