Geomechanical model tests are widely used in rock engineering design and construction, especially in water conservancy, mining, and tunneling. These tests are vital for understanding the stability of high arch dams and the reinforcement measures of dam foundations. However, traditional masonry techniques often rely on manual operation, which is time-consuming and labor-intensive and prone to inaccuracies in layout.

We proposed a rapid masonry technology that integrates rapid determination of similar material ratios, small-block pressing for model masonry, and guide-rail laser positioning. Through material experiments, we obtained 236 sets of similar material specimens. Utilizing these samples and a CPO-BP neural network, we developed a surrogate model with the rock deformation modulus, scaled according to geometric similarity, as the input and the mass ratios of four materials, namely barite powder, bentonite, water, and glue, as well as the specimen density, as output. This model enables quick calculation of the ratio of each component in the model and the density corresponding to their mechanical properties, achieving a mean absolute error of 2%. In addition, we designed compression equipment for the small blocks used in model masonry, which can simulate rock joints and fissures. The setup includes a support frame, air pressure top, block mold, and cover plate opening and closing device. Users can select molds based on their needs, allowing for automated pressing of small blocks with minimal effort, thereby enhancing compression efficiency and significantly reducing labor costs. Moreover, we developed a guide rail laser positioning device for accurate model layout. When coordinate information is input into the control box, the device uses a servo motor to drive a slider carrying a laser rangefinder to specified locations, enabling rapid and precise mapping of structural planes, valley terrain, and arch dam bodies.

Practical applications of this model demonstrate that this method significantly reduces the one-year cycle required by traditional model masonry methods to approximately four months, greatly improving the efficiency and accuracy of geomechanical model construction for arch dams. The findings provide valuable guidance for enhancing the speed and precision of geomechanical model testing while offering a reliable reference for optimizing model material usage.

With the continuous expansion and increasing complexity of water diversion tunnels in hydropower projects, their long-term structural safety has become a critical engineering challenge. Conventional safety evaluation methods often rely on qualitative assessments or multi-index systems, which are highly subjective and fail to adequately account for progressive material deterioration and time-dependent deformation. This study proposes an integrated quantitative framework that combines real-time inversion of mechanical parameters and deformation evolution analysis to dynamically evaluate the structural safety of tunnels.

The proposed framework integrates four major components: data preprocessing, parameter inversion, deformation simulation, and safety evaluation. First, the raw deformation monitoring data are preprocessed by imputing missing values using the K-nearest neighbors (KNN) algorithm, identifying and correcting outliers with a sliding-window Z-score method, and reducing noise through logarithmic trend fitting. Second, a physics-informed inversion approach combining deep learning architectures—fully connected layers (FCL) and gated recurrent units (GRUs)—with Bayesian optimization is established to infer the current mechanical parameters of the tunnel from preprocessed deformation data. Third, an elastoviscoplastic damage creep constitutive model based on internal variable thermodynamics is employed to simulate deformation behavior under various material-degradation scenarios, represented by different strength reduction coefficients (K_r). Finally, based on the analysis of material creep behavior and deformation evolution patterns, a time-dependent "3S" safety evaluation index system is established. This system comprises the long-term deformation acceleration safety coefficient (S₁), the nonlinear deformation initiation safety coefficient (S₂), and the short-term deformation acceleration safety coefficient (S₃). The safety state of the tunnel structure is quantified according to the relevant deformation evolution characteristics using the proposed 3S safety coefficients. The physical implications of these indices are as follows: S₁ characterizes the critical point at which the structure transitions into an accelerated creep phase under continuous strength attenuation, indicating the long-term instability risk; S₂ reflects the onset of deviation from the linear response during initial deformation, marking the beginning of dominance by nonlinear mechanical behavior; and S₃ indicates the threshold for notable acceleration of deformation within a defined short-term period, serving as an indicator of potential sudden instability.

The proposed method was implemented in the JLL Tunnel, a 20km-long underground structure located in Hunan Province, China, which features complex geological conditions. The mechanical parameters were successfully inverted from field monitoring data, with simulated deformation curves showing high agreement with the measured values. Numerical simulations under different K_r conditions revealed distinct deformation patterns. For K_r≥0.7, deformation stabilized after initial convergence. When 0.4≤K_r≤0.6, deformation exhibited slow growth, followed by an acceleration phase. For K_r≤0.3, deformation accelerated rapidly within a short time. The computed 3S safety coefficients were S₁=2.4-3.0, S₂=3.7-4.6, and S₃=5.7-7.3, indicating that the tunnel is currently in a safe state with sufficient safety margins. These results validated the method's effectiveness in distinguishing between short- and long-term risks and in providing early safety warnings through deformation trajectory analysis.

This study proposes an integrated quantitative framework for tunnel structural safety evaluation that effectively combines real-time monitoring data, physics-based modeling, and deformation evolution analysis. The established 3S index system provides a refined insight into structural behavior under material degradation and enables safety assessment across multiple time scales. Compared with conventional methods, the proposed framework enhances objectivity, supports the dynamic prediction of time-dependent performance, and facilitates lifecycle safety management and preventive maintenance of tunnel structures. The methodology demonstrates strong generalizability and offers remarkable practical value for risk prevention and sustainable operation in tunnel engineering.

High arch dams impose stringent requirements to ensure safety, requiring robust bearing capacity, deformation control, and resistance to seepage failure. The stability of the dam foundation serves as the cornerstone of the entire arch dam system. During operation, the enormous thrust generated by arch abutments acts on the dam-foundation interface, potentially inducing instability risks such as macroscopic fractures and shear sliding, particularly in weak foundation zones. These risks, if left unchecked, can compromise dam safety and may trigger catastrophic failure. Addressing weak zone reinforcement design in complex dam foundations poses a significant challenge, as no standardized system currently exists for prioritizing reinforcements or quantifying stability evaluation indicators.

To address this gap, this study proposes an energy-based method for stability evaluation and reinforcement design of weak dam foundation zones. A stability evolution analysis model was established using energy dissipation rate and domain integral variation, enabling the identification of critical weak zones and their evolutionary patterns. The study employed a three-dimensional numerical model of the arch dam-foundation system, accounting for complex geological factors such as faults, abutment slopes, and dam geometry. A thermodynamically driven creep constitutive model with internal variables was employed to conduct three-dimensional numerical simulations, revealing the stability evolution process of weak foundation zones. By analyzing energy dissipation rate curves and domain integrals, critical moments (marked by peak dissipation rates) and vulnerable areas (highlighted by energy concentration zones) were pinpointed. This method was then applied to parallel fault groups in a high arch dam foundation, with the reinforcement effectiveness analyzed in terms of energy dissipation rates, dam deformation, fault yield zones, and results from comparative testing using the super-water unit weight method.

Results indicate that energy dissipation rates and domain integrals for abutment faults initially increased rapidly after reservoir impoundment, gradually decreased, and eventually stabilized. The stability evolution of dam foundation faults under impoundment exhibits distinct time-dependent behavior, progressing through three phases: instability, transition, and stabilization. A significant observation is the delayed occurrence of peak energy dissipation rates in downstream faults, reflecting a spatiotemporal hysteresis in arch thrust transmission. During normal operations, the thrust from the arch extends its influence on deep foundation stability to a distance approximately twice the width of the arch abutment. However, its impact on downstream stability ranges between 2-3 times the abutment width. Comparative analysis using the super-water unit weight method demonstrated reduced dam deformation, improved fault yield zone distribution, and significant decreases in energy dissipation rates and domain integrals for critical faults after reinforcement.

The proposed method reveals spatiotemporal hysteresis in arch thrust transmission and its disturbance on structural stability. For multifault dam foundations, upstream faults exhibit less susceptibility to hydraulic disturbances when compared to downstream faults. Weak zones in downstream faults are primarily concentrated near their intersections with the dam abutment as well as along the strike direction. The f123 and f120 faults on the left bank were identified as critical to global stability, with key reinforcement areas at elevations of 2440-2470 m (f123) and 2395-2425 m (f120). Targeted reinforcement measures effectively enhanced fault and foundation stability, significantly improving the overall stability of the arch dam-foundation system.

There have been numerous cases of dam failures caused by slope instability in water conservancy projects domestically and internationally, leading to significant casualties and property losses. Therefore, the stability of the slopes near the river banks is crucial for the operational safety of dams and hydropower stations. The Jinping Dam, features a massive engineering slope on its left bank. For approximately 10 years following the commencement of operations at the Jinping I Hydropower Station, certain areas near the dam on the left bank have exhibited persistent deformation, and the underlying mechanisms are still not fully understood. The presence of deep cracks on the left bank significantly influences the selection of the arch dam axis during the design phase.

This paper utilizes numerical simulation to analyze the variation characteristics of in-situ stress during the evolution of the river valley, aiming to clarify the influencing factors and mechanical mechanisms behind the formation of deep fractures. Additionally, by leveraging monitoring data on slope deformation and conducting a creep analysis of river valley evolution, this paper examines the relationship between deep fractures and the continuous deformation of the slope during operation.

This study found that the formation of deep fractures was closely related to valley incision, the complex geological conditions, and the tectonic stress of the valley slope. Owing to these factors, the unloading depth gradually decreased with a decrease in elevation. Consequently, when the rock mass at higher elevations experienced unloading at greater depths, the rapid release of strain energy occurred during the later stages of valley incision, which led to the unloading failure of the deep rock mass and the formation of deep fractures. Moreover, although the rock mass at lower elevations only experienced surface unloading failure, as the depth increased, the ratios of principal compressive stress and principal tensile stress decreased and the former remained at a high value. This resulted in compressive shear failure and the formation of deep fractures in the deep rock mass. Monitoring data showed that the arch thrust of the dam body hindered deformation in the empty direction, and displacement at high elevations was mainly due to gravity-driven tipping deformation. A comparison of apparent and deep deformation revealed that lamprophyre dikes, faults, and other weak zones were the main factors affecting slope deformation during operation, while the influence of deep fractures was not significant. Additionally, the tectonic stress of the mountain on the left bank had minimal impact on slope deformation. The results of the creep calculations were consistent with the observed deformation patterns of the slope during operation, further confirming that the influence of tectonic stress on slope deformation had dissipated.

In summary, this paper identifies the causes and mechanical mechanisms of deep fractures through finite element calculations and analysis of measured monitoring data, enhancing the engineering understanding of such slope issues. Compared with weak zones such as faults, deep fractures have less influence on the long-term deformation of the slope. The influence of tectonic stress on slope deformation has dissipated, which provides a foundation for further studies on the left bank slope of Jinping I Hydropower Station.

This study aims to address the significant challenge of predicting rockburst risks during the excavation of deep tunnels using tunnel boring machine (TBM) tunnel boring machine and develop a rapid prediction model to provide the basis for rockburst prevention and control, enhancing the safety and efficiency of deep tunnel construction. The proposed model leverages numerical samples and random forest (RF) algorithms to overcome the limitations of existing methods, which often do not achieve real-time and rapid prediction or consider the underlying mechanisms and factors influencing rockbursts.

Considering the Xianglushan Tunnel within the Dianzhong Water Diversion Project, we introduced a model that utilizes geostress and rock constitutive parameters as inputs and the elastic strain energy density of the surrounding rock as output. Numerical simulations of tunneling using the TBM under various working conditions arewere conducted, and 611 numerical samples were crafted through an orthogonal experimental design. We employed RF as the underlying classifier, with hyperparameters optimized through 10-fold cross-validation to create an efficient prediction model. The accuracy and applicability of the model were confirmed by comparing several machine learning algorithms.

We conducted a series of numerical simulations of excavation using the TBM, employing an elastoviscoplastic constitutive model with internal variables. These simulations disclosed the energy evolution within the rock mass throughout the excavation process. Energy concentration occurred during transient unloading and the time-dependent deformation of the surrounding rock, leading to two distinct peaks in strain energy density. The second peak indicative the final energy storage during the creep phase of the surrounding rock postexcavation and unloading. Notably, a higher value at the tunnel wall—under identical conditions—correlated with an elevated risk of strainburst. We verified the rationality of the input and output parameters by analyzing energy evolution and correlation. The predictive accuracy and computational efficiency of the model were enhanced following the optimization of the hyperparameters using a 10-fold cross-validation. The input parameters partially mirrored the factors influencing rockburst, while the output parameters measured the energy storage status of the surrounding rock before potential rockburst failure. The RF-based rockburst risk prediction proxy model exhibited commendable performance on the training and testing sets, achieving accuracies of 99.75% and 82.02%, respectively. The performance of the RF-based rockburst risk prediction proxy model was superior to that of four other machine learning models—decision tree, K-nearest neighbors, support vector machine, and logistic regression—achieving prediction accuracies of 82.02%, 76.40%, 79.77%, 75.28%, and 76.40% for all samples, respectively. This result indicateds the robust predictive capability and generalization performance of the RF-based rockburst risk prediction proxy model in assessing rockburst risk levels.

We offer a novel approach and framework for the rapid prediction of rockburst risks during the excavation phase of deep tunnels. The RF-based rockburst risk prediction proxy model is reportedly an effective tool for rockburst risk prediction, marking a significant advancement in rockburst risk management. We provide a research path and framework for the rapid prediction of rockburst risk during the excavation period of deep tunnels.

Deep tunnels exhibit characteristics such as high ground stress and strong excavation disturbance. Especially in an intact hard rock environment, the original elastic strain energy stored in surrounding rocks is suddenly released due to the unloading effect during excavation, which can easily trigger rockburst disasters. Severe rockbursts pose a great threat to construction personnel and equipment such as tunnel boring machine (TBM), delaying the construction period and causing huge economic losses. Rockburst hazard is one of the key factors that affect the safety and efficiency of TBM excavation in deep tunnels. Active prevention is the most important technical method for controlling rockburst risk and ensuring personnel and equipment safety during construction.

With the deeply buried TBM section of the Xianglushan Tunnel in the Dianzhong Water Diversion Project as the engineering background, numerical simulations were performed to analyze the mechanisms and effects of two active rockburst control methods: destress blasting and pilot tunnel. A creep damage model with internal variables was employed to simulate the TBM continuous excavation process. Factors influencing the effectiveness of active rockburst control, such as the external insertion angle, number of advance blasting holes, and diameter and length of the pilot tunnel, were considered. The mechanisms of two active rockburst control methods were elucidated, and the release effects and spatiotemporal evolution processes of stress and energy of surrounding rocks under different construction parameters were studied.

The results demonstrated that active control had achieved the stress "peak-shaving" effect during TBM excavation by preconcentrating rock stress, thereby preventing sudden energy accumulation and reducing rockburst risk. Briefly, it had transferred rockburst risk during the secondary excavation to the construction process of destress blasting and pilot tunnel. The active control method and specific excavation parameters could be determined based on the actual rockburst risk level and on-site conditions. Destress blasting was suitable for local targeted stress release, ensuring that high stresses within the length of advanced boreholes are effectively released in a controlled manner. Increasing the number of advance blasting holes was more important for reducing rockburst risk than increasing the external insertion angle. Compared with destress blasting, pilot tunnel could better transfer and reduce the high stress of surrounding rocks and fully release energy from high-energy-storing rock masses. Furthermore, the overall stress and energy release effects demonstrated by pilot tunnel were better, and the technique's impact range was wider than those of destress blasting. Increasing the diameter and length of pilot tunnel could further reduce the risk of rockbursts. In conclusion, the construction of a pilot tunnel was more complex than that of destress blasting, but its stress release effect was generally better. In cases where a tunnel may have faced strong rockburst risk or other ineffective measures, pilot tunnels could be considered for realizing proactive prevention and rockburst control.

These research results can increase our understanding of the mechanism of rockburst prevention and offer a theoretical basis and a reference for rockburst active control and parameter optimization in practical engineering.