The coupling effect of mechanical and environmental loads is the main cause of deterioration in the performance of reinforced concrete structures. However, most studies on the durability of reinforced concrete structures have focused only on mechanical or environmental loads. As a result, it is difficult to fully capture the effects of dry shrinkage and creep caused by temperature and humidity changes within the structure, as well as their impact on the overall deformation of the structure. To address this gap, this paper presents a numerical simulation of underground culvert projects using the DuCOM-COM3D analysis software. The results of the simulation demonstrate that this approach offers a more precise characterization of the porosity, temperature, and humidity inside the concrete, resulting in improved accuracy in predicting the long-term deflection, crack width, and other macro-mechanical indices of the structure. Despite these advantages, some discrepancies were observed between the calculated and measured long-term deflection values. Additionally, a limit analysis was conducted to investigate the potential causes of the large deformation observed in the measurements. Overall, the results contribute to a better understanding of the complex mechanical and environmental loads affecting reinforced concrete structures and provides a methodology for accurately simulating their long-term behavior.

Hydropower dams are subjected to soft water penetration during their service lives. Concrete deterioration due to calcium leaching will decrease the durability of concrete and affect dam safety. The long-term performance of concrete dams due to calcium leaching should be evaluated and predicted accurately to complete reinforcement work in a timely manner. In this paper, a methodology that combined microscopic tests and numerical analysis to evaluate the long-term performance of dam concrete due to calcium leaching is proposed. The current state of concrete is evaluated by analyzing the components of sediments and seepage water through microscopic and spectroscopic tests, such as X-ray photoelectron spectroscopy, scanning electron microscopy, and inductively coupled plasma mass spectrometry. The long-term degradation of concrete was predicted by utilizing a multi-scale model of calcium leaching, which considered the micro-pore structure of cement hydrates flux with time. The simulated results using this calcium leaching model showed a good agreement with other experiments. Finally, a real case study including field inspection was performed and the long-term durability of dam concrete was predicted through microscopic tests and finite element analysis method. It implies that the proposed method could provide calculation and theoretical basis for the durability analysis of concrete dams due to calcium leaching.

The Jing-Mi Diversion Canal is a large-scale water diversion project in Beijing. Routine monitoring is crucial for the reliability and stability of urban water supply. Compared with traditional monitoring methods, interferometric synthetic aperture radar (InSAR) has the advantages of large scale and high accuracy. Based on the small baseline subset InSAR, 187 ascending and 102 descending SAR images obtained from Sentinel-1 were used to detect the deformation along the diversion canal from 2017 to 2023. The results show that there was a sinking trend along the diversion canal. The subsidence was serious in the first half of the canal, and continued to sink from 2019 to 2020. The subsidence was alleviated in 2023. Combined with leveling measurements, the InSAR deformation monitoring results of important pumping station buildings were verified. The measurement accuracy of InSAR can reach the millimeter level. We extracted the groundwater level time series and subsidence for risky canal segments. Through pixel-by-pixel comparison, it was found that fluctuations in groundwater level would have some impact on surface deformation. Severe local subsidence or uplift deformation occasionally occurred. To ensure the safety of water diversion, the monitoring and maintenance of relevant pump station buildings in risky areas should be increased in the future.

This study proposes a human–computer interactive agent collaboration framework for reservoir dam seepage safety assessment. This is to address the large volume of safety assessment reports, inconsistent textual descriptions, low efficiency of manual statistics, and difficulty in maintaining consistent classification criteria. Using a large language model as the core for semantic understanding, the framework decomposes the report review process into subtasks including data preprocessing, knowledge retrieval, semantic classification, result feedback, and manual review. Structured collaboration among multiple agents is achieved through an “entity location-safety hazard” knowledge primary key, semantic rule constraints, and the Model Context Protocol (MCP). While preserving expert control, the system transforms original texts into aggregable and traceable risk statistics, forming a closed-loop mechanism of “machine-enabled efficiency, human oversight, and knowledge accumulation”. Application validation was conducted using more than 2000 reservoir safety assessment reports, from which 2724 valid severe-risk records were extracted. The results show that severe risks were mainly concentrated in key locations, including dam bodies, culverts, spillways, dam foundations, downstream dams, drainage facilities, and abutments. The main risk types were leakage, abnormal seepage behavior, construction quality problems, noncompliant seepage control, structural damage, and noncompliant drainage facilities. The findings indicate that the proposed framework can improve the efficiency of text processing for seepage safety assessments and the consistency of risk statistics, thereby providing intelligent support for reservoir dam hazard screening and scientific decision-making.

To address the large volume of reservoir dam seepage safety assessment reports, inconsistent textual descriptions, low efficiency of manual statistics, and difficulty in maintaining consistent classification criteria, this study proposes a human-computer interactive agent collaboration framework for dam seepage safety assessment. Using a large language model as the core for semantic understanding, the framework decomposes the report review process into subtasks including data preprocessing, knowledge retrieval, semantic classification, result feedback, and manual review. Structured collaboration among multiple agents is achieved through an "entity location-safety hazard" knowledge primary key, semantic rule constraints, and the Model Context Protocol (MCP). While preserving expert control, the system transforms original texts into aggregable and traceable risk statistics, forming a closed-loop mechanism of "machine-enabled efficiency, human oversight, and knowledge accumulation". Application validation was conducted using more than 2,000 reservoir safety assessment reports, from which 2,804 severe-risk records were extracted. The results show that severe risks were mainly concentrated in key locations including the dam body, culverts, spillway, dam foundation, downstream dam, drainage facilities, and abutments. The main risk types were leakage, abnormal seepage behavior, construction quality problems, noncompliant seepage control, structural damage, and noncompliant drainage facilities. The findings indicate that the proposed framework can improve the efficiency of seepage safety assessment text processing and the consistency of risk statistics, thereby providing intelligent support for reservoir dam hazard screening and scientific decision-making.