High Arch Dam Research Articles

Vibrator Rack Pose Estimation for Monitoring the Vibration Quality of Concrete Using Improved YOLOv8-Pose and Vanishing Points

Monitoring the actual vibration coverage is critical for preventing over- or under-vibration and ensuring concrete’s strength. However, the current manual methods and sensor techniques fail to meet the requirements of on-site construction. Consequently, this study proposes a novel approach for estimating the pose of concrete vibrator racks. This method integrates the Linear Spatial Kernel Aggregation (LSKA) module into the You Only Look Once (YOLO) framework to accurately detect the keypoints of the rack and then employs the vanishing point theorem to estimate the rotation angle of the rack without any 3D datasets. The method enables the monitoring of the vibration impact range for each vibrator’s activity and is applicable to various camera positions. Given that measuring the rotation angle of a rack in reality poses is challenging, this study proposes employing a simulation environment to validate both the feasibility and accuracy of the proposed method. The results demonstrate that the improved YOLOv8-Pose achieved a 1.4% increase in accuracy compared with YOLOv8-Pose, and the proposed method monitored the rotation angle with an average error of 6.97° while maintaining a working efficiency of over 35 frames per second. This methodology was successfully implemented at a construction site for a high-arch dam project in China.

Challenges of slope design for high cut slopes in jointed rock masses

AbstractThe construction of the 275 m high Yusufeli arch dam and auxiliary structures required the execution of very steep cut slopes with more than 400 m height. Uncertainties regarding the geotechnical conditions imposed a serious challenge for the cut slope design. The design approach evolved from initial back‐calculations to a robust methodology, which was constantly revised and updated as excavation proceeded. The article explains the modular design procedure that allowed continuous interaction, adjustments and updates of and between different design and construction activities. It briefly describes the process of geological data collection and parameter determination and elaborates on the calculation tools utilised for the design. Finally, the verification and updating of the design during construction is explained on a selected case study. The excavation of the Yusufeli Dam was completed in 2018, dam concreting and grouting in 2022, and the final impounding level was reached by the end of 2023.

Seismic performance assessment of high arch dams considering the pulse-like and directionality effects of near-fault ground motions

This paper investigates the pulse-like and directionality effects of near-fault horizontal ground-motion records on the seismic performance of high arch dams. A 3-dimensional arch dam-reservoir-foundation finite-element model is built. Two near-fault bidirectional ground-motion suites (i.e. ordinary and pulse-like suite) are collected. The fault-normal and fault-parallel orientations of the horizontal ground-motion records are rotated to reflect the directionality effect. Nonlinear dynamic analyses are further implemented for the high arch dam excited by both near-fault ground motions with different incidence angles. The results show that the pulse-containing ground-motion records can yield more notable dynamic response and damage for high arch dams as compared to the ordinary records. Moreover, with changing the incidence angles, both the dam dynamic response and damage metrics exhibit periodic variation and constant trends for the pulse-containing and ordinary ground-motion cases, respectively. Therefore, the directionality effect has a significant influence on the seismic performance of high arch dams excited by pulse-containing ground-motion records. Compared with the near-fault ordinary ground motions, the pulse-like ground motions will induce a 40 %-increase in the concrete damage volume of high arch dams. In addition, peak ground velocity could be regarded as a preferable intensity measure for assessing the seismic damage of high arch dams.

Dynamic damage evolution and energy characteristics of high arch dams under the action of near-field pulsed ground motions

To investigate the impact of near-field pulse ground motions on the dynamic damage evolution of concrete arch dams, in this study, a near-field pulse ground motion construction method based on the decomposition-superposition method is proposed. Focusing on a high arch dam in China, a comprehensive damage model encompassing both the dam body and foundation was established. Through nonlinear dynamic response analysis, the study explores the influence of pulse parameters in near-field ground motions on the seismic response of the high arch dam. The results demonstrate that as the pulse velocity of near-field earthquakes increases, the damage zones in both the dam body and foundation gradually expand. Specifically, the overall damage and plastic energy dissipation of the dam increase by 1.39 and 1.96 times, respectively, as the ratio of PGV to PGA of the seismic wave shifts from 0.07 s to 0.20 s. When the pulse frequency approaches the fundamental frequency of the dam, the impact on dam damage intensifies. In the nonlinear seismic response analysis of the arch dam project in this study, as the number of pulse waves n ≥ 4, the increase in the number of pulse waves demonstrates a more conspicuous impact on the dam's structural damage. Compared to the dam body, the foundation is more significantly influenced by variations in near-field ground motion pulse parameters, so it is necessary to pay more attention to the damage of the dam foundation for the high arch dam structure in the near-fault region.

A rapid and automated analysis procedure for seismic response of arch dams

The seismic safety of arch dams has long been a focal point of research. Due to the complexity of modeling and computation, analyzing the seismic response of arch dams using traditional finite element methods requires a considerable amount of time. In the event of a sudden earthquake, it is challenging to quickly obtain stress analysis results or conduct a safety assessment. To address these issues, a rapid and automated analysis procedure is proposed in this paper, providing seismic response of arch dams within hours after an earthquake. The procedure includes a pre-processing program, a computing program EACD-3D-2008, and a post-processing program, achieving a fully automated process from generating non-uniform earthquakes to analyzing dam dynamic responses and visualizing computation results. As a case study, the 294.5 m high Xiaowan arch dam in southwest China is analyzed, which is equipped with strong motion instruments that have recorded several small earthquakes. The case study revealed that accounting for the non-uniformity of the earthquake significantly improves simulation results, with maximum principal stresses typically occurring near the dam-foundation rock interface. Additionally, the procedure effectively compensates for missing data, allowing for the successful supplementation of the missing acceleration records. For stronger earthquakes, high-stress regions are clearly displayed in the result visualization, providing an effective reference for safety assessment. The case study validated the accuracy and wide applicability of the procedure, demonstrating its potential to offer valuable insights for similar analyses in various engineering projects.

Fragility analysis of high arch dam against far-field underwater blast

The security assessment of high arch dams is a significant topic that has been researched in recent years. It is troublesome to develop a security assessment for high arch dams under far-field underwater blasts because the high arch dam is a sophisticated statically indeterminate spatial shell structure, and the underwater blasting shock wave has the characteristics of a large peak value and high frequency. Hence, the fragility analysis of a 300-m-high arch dam against a far-field underwater blast based on the probabilistic method is conducted for safety evaluation with the index of downstream peak displacement (and Downstream Peak Displacement-Span ratio). The fragility analysis process of the high arch dam under the far-field underwater blast is proposed for the first time. The impact of foundation plasticity on the high arch dam under far-field underwater blast is discussed regarding dynamic response and damage patterns with the coupled acoustic-structural (CAS) method first. The damage limit states are presented based on different damage patterns and index values from enormous computational schemes. The fragility curves with different indexes of downstream peak displacement, upstream peak displacement, and damage volume ratio are also compared. The fragility curves of the high arch dam against the far-field underwater blast with different explosive distances of 50 to 200 m are established with the Artificial Neural Network method. The results demonstrate that the non-linear dynamic response and damage of the high arch dam under the far-field underwater blast are significantly influenced by the foundation plasticity, especially for vibration upstream and damage of the foundation face. The explosive situations with an explosive distance of 50–100 m and explosive weight of more than 12000 kg cause a greater threat to the security of the high arch dam with the probability of severe damage approaching 100 %. The explosive situations with an explosive distance of more than 150 m or explosive weight of less than 3000 kg have a small effect. The research results can be used as a reference when conducting safety assessments of high arch dams to blasting loads.

Progressive failure process-considered deformation safety diagnosis method for in-service high arch dam

Conventional methods cannot effectively characterize the progressive failure process, which limits their use in the deformation safety diagnosis of in-service high arch dam. This work proposes a novel method utilizing the frontier theories of mathematics, mechanics, and dam safety monitoring. First, considering the implicit assumption of traditional method, hybrid model (HM) is improved to calibrate the elastic deformation state of high arch dam. Second, the adaptive proportion selection and the neighborhood search strategy are applied to improve artificial bee colony (ABC) algorithm. The undetermined parameters of HM are optimized by the improved ABC. Third, the dangerous degrees of single-point deformation and multi-point deformation are characterized using HM analysis results and information entropy. On this basis, the progressive diagnosis criteria are constructed based on probability principle. Finally, two case studies are conducted to validate the proposed methodology. The analysis results demonstrate that the performance of HM is better than that of the statistical model (SM); the improved ABC promotes the HM performance; and the progressive diagnosis criteria compared with the traditional confidence interval criteria have stricter probabilistic and physical meanings. The 5-level safety control is realized, promoting the emergency response ability of high arch dam in operating condition.

A Multi-Point Joint Prediction Model for High-Arch Dam Deformation Considering Spatial and Temporal Correlation

Deformation monitoring for mass concrete structures such as high-arch dams is crucial to their safe operation. However, structure deformations are influenced by many complex factors, and deformations at different positions tend to have spatiotemporal correlation and variability, increasing the difficulty of deformation monitoring. A novel deep learning-based monitoring model for high-arch dams considering multifactor influences and spatiotemporal data correlations is proposed in this paper. First, the measurement points are clustered to capture the spatial relationship. Successive multivariate mode decomposition is applied to extract the common mode components among the correlated points as spatial influencing factors. Second, the relationship between various factors and deformation components is extracted using factor screening. Finally, a deep learning prediction model is constructed with stacked components to obtain the final prediction. The model is validated based on practical engineering. In nearly one year of high-arch dam deformation prediction, the root mean square error is 0.344 and the R2 is 0.998, showing that the modules within the framework positively contribute to enhancing prediction performance. The prediction results of different measurement points as well as the comparison results with benchmark models show its superiority and generality, providing an advancing and practical approach for engineering structural health monitoring, particularly for high-arch dams.

Damage characteristics and dynamic responses of high arch dams under rockfall impact loads

Rockfall is a typical natural disaster that is sudden, high in speed, unpredictable, and can easily adversely affect the structures. High arch dams are usually constructed in deep valleys, where rockfall is a typical destructive load. However, there is currently a lack of research on the effects of rockfall on the safe operation of high arch dams. Therefore, the Wudongde arch dam in China is selected in this paper for exploring damage characteristics and dynamic responses of high arch dams under rockfall impact loads. To improve the authenticity of numerical simulation, a coupled model is established based on the actual dam shape and terrain characteristics of the dam. The concrete damaged plasticity model is used for dam concrete and the rockfall is described by the JH-2 model to simulate mechanical properties of materials and reflect strain rate effects. The trajectories of falling rocks at the dam site are first investigated to analyze the locations where they may impact the dam. Based on the obtained results, two impact positions are considered possible in the analysis, namely, the dam crest and the gate slot, and then the nonlinear dynamic responses of the dam impacted by rockfall at different positions are compared. Finally, a sensitivity analysis is carried out to evaluate the influence of the rockfall properties on the damage characteristics of the dam. The results indicate that the trajectory and impact velocity of falling rocks are related to the falling height, rock size, and initial velocity. The rockfall impact on the dam crest has little influence on the safety of the dam for the robust mass dam concrete, while the track beam near the gate slot is vulnerable to rockfall impact. The damage degree to the structure is related to rockfall properties. Compared to larger-sized rockfalls, rockfalls with a diameter of 1 m cause minimal damage to the structure and have little impact on its safety. Higher impact velocity and stiffness of the rock lead to more severe damage, while the effect is relatively smaller compared to the rockfall size. The high stiffness also reduces the damage to the rock itself.

Prediction model for high arch dam stress during the operation period using LightGBM with MSSA and SHAP

Dam stress is an important physical quantity to assess high arch dam safety during the operation period. To address the issues of low prediction capability and poor interpretability for high arch dam stress, a prediction model for high arch dam stress during the operation period incorporating the multi-strategy sparrow search algorithm (MSSA), light gradient boosting machine (LightGBM) and Shapley additive explanation (SHAP) is proposed utilizing the measurement data from the dam. First, the initial population diversity and quality of sparrows are improved by using Tent chaotic mapping and refractive inverse learning strategy, and the global search ability of the sparrow search algorithm (SSA) is enhanced by making sparrows jump out of local optimal positions based on adaptive t-distribution mutation. Second, the MSSA conducts optimization analysis on the LightGBM model, effectively solving the issue of the machine learning algorithm converging to local optimal solutions and enabling the determination of optimal hyperparameters. Finally, SHAP is introduced to enhance the interpretability of the MSSA-LightGBM model. The performance measurement indicates that the MSSA, which combines several excellent methods, outperforms SSA and particle swarm algorithm (PSO) in terms of global search ability. The MSSA-LightGBM dam stress model exhibits superior prediction precision compared to the SSA-LightGBM, PSO-LightGBM, LightGBM, and extreme gradient boosting models. SHAP can enhance the interpretability of the MSSA-LightGBM model and identify the crucial features affecting high arch dam stress. This research is highly significant in enhancing the prediction and analysis capabilities of high arch dam stress.

Effect of attached outlets on the dynamic response of arch dams

Attached outlets are integral components of arch dams to discharge flood. However, they are usually neglected in the current seismic analysis of arch dams for simplicity. This paper aims to analyze the effect of attached outlets on the nonlinear seismic response of arch dams. For this purpose, a 235 m high arch dam is analyzed as a case study. The results indicate that the effect of attached outlets on the seismic response of arch dams depends on the ground motion intensity. For the ground motions with a low peak ground acceleration (PGA), the effect of attached outlets on the concrete damage is slight and local, which is consistent with the existing perception. However, as the ground motion intensity increases, their effect extends to the entire dam body. In contrast, the influence of attached outlets on the joint opening is typically observed at the middle contraction joints. Furthermore, the model analysis demonstrates that the inertia effect of attached structures is the primary contribution to these phenomena. Finally, a simplified simulation approach is used for incorporating the influence of attached outlets by coalescing the mass to the dam surface.

Inverse analysis of deformation moduli for high arch dams using the displacement reconstruction technique and multi‐objective optimization

AbstractThe inverse analysis of the deformation moduli of high arch dams based on displacement monitoring data is essential for structural safety assessment. In traditional inverse analysis methods, the deformation moduli are identified based on the single‐objective optimization and the hydrostatic component derived from the statistical model. This type of method has two main shortcomings: First, it treats the essential multi‐objective optimization problem as a single‐objective problem; second, the extracted hydrostatic component may be biased due to the multicollinearity of variables in the statistical model. This paper presents a methodology for the inverse analysis of the deformation moduli of high arch dams under a multi‐objective optimization strategy. The methodology employs empirical mode decomposition to extract the aging component from displacement monitoring data. Then, thermomechanical analysis is used to reconstruct the remaining hydrostatic and temperature components, thereby avoiding the biases encountered in solving the statistical model. The adaptive polynomial chaos expansion method is embedded in the NSGA‐III algorithm to establish and solve multi‐objective functions in the inverse analysis. Additionally, a composite decision index considering errors and test information is proposed to determine acceptable deformation moduli from the Pareto solution set. A high arch dam is selected to illustrate this methodology with static and dynamic monitoring data. The results show that the identified deformation moduli have errors of 3.8% and 7.2% in displacement and acceleration, respectively. The proposed methodology can yield deformation modulus values that are more consistent with the physical implications than those of the single‐objective optimization method.

Vertical crack identification of arch dam under underwater explosion based on mode transition

The failure process of concrete arch dam subjected to underwater explosion has been the research hotspot in recent years, however, there is a lack of an effective method to identify the crack in dam body. To solve this problem, the crack identification of arch dam caused by underwater explosions has been studied in this paper. Firstly, an analysis method to identify the vertical crack in dam body is proposed, according to a comparison of the modal characteristics of the arch dam with and without crack. Secondly, the reliability of the proposed method is verified by a small-scale arch dam that cracking along the crown beam attacked by foundation explosion. The modal change of the dam induced by cracking is captured in both experiment and numerical simulation. Thirdly, this method is applied to analyze the influence of vertical crack on the modal of a 240 m high arch dam. Some findings need be noted as follows: vertical cracks lead to modal transition of arch dam of two adjacent dense modes, such as orders between 4 and 5, 7 and 8, and 11 and 12 respectively in this case, the ratio of modal transition is 6.04% in the first 8 modes and it is 14.1% in the first 12 modes; The effect of cracks on the mode of arch dam is related to the spatial position of the vibration mode peak, the nearer the two are, the more significant the change in that order of mode is. This finding indicates that the vertical cracks may lead to mode transition or mode loss of arch dams, it should be considered that the change of arch-cantilever system vibration mode sequence in the identification of the crack of arch dam caused by underwater explosion.

Construction and selection of deformation monitoring model for high arch dam using separate modeling technique and composite decision criterion

Deformation prediction is important to ensure the safe and stable operation of arch dams. Statistical models are extensively applied in arch dam deformation monitoring models, which generally include hydrostatic pressure component, temperature component, and aging (irrecoverable) component. In traditional statistical models, aging component is misset, which will cause unreasonable mutual compensation of each component, resulting in overfitting of the overall model. In this paper, the deformation model based on separate modeling technology is, therefore, proposed mitigating the overfitting problem caused by misspecification of the expression of the aging component in traditional statistical models. Dam deformation components related to different effects are extracted from the deformation monitoring sequence with improved complete ensemble empirical mode decomposition with adaptive noise algorithm and equal water level condition. The correct components of the monitoring model are constructed separately. On the one hand, the fitting accuracy of the model is reflected by the coefficient of determination (R2); on the other hand, the overfitting degree of the model is quantitatively evaluated by the overfitting coefficient (OC), so that the model with high fitting accuracy and prediction accuracy is determined, that is, the optimal model is selected by using the R2-OC criterion. In this paper, displacement monitoring data from measurement points are used for analysis. The results show that the deformation monitoring model based on the separated modeling technique exhibits higher prediction accuracy and lower false alarm rate. The R2-OC criterion better reflects the degree of overfitting of the monitoring model and the real situation of arch dam monitoring and warning, which improves the accuracy of model selection.

A causal prediction model for the measured temperature field of high arch dams with dual simulation of lag influencing mechanism

Mathematical models using measured dam temperature as modelling factors are frequently used in displacement-based structural health monitoring of high arch dams. However, when these models are used to predict future displacement, dam temperature is also unknown, thus it is necessary to predict the temperature field of the dam body in the first step. The main purpose of this paper is to improve the accuracy and rationality of data-driven models in simulating the complex lag influencing mechanism of temperature field in high arch dams. Two strategies are integrated from the perspective of modelling factors and modelling methods. First, based on massive temperature monitoring data, lag influencing times and laws of air temperature and upstream reservoir water level on dam temperature are quantified using cosine similarity and Shapley additive explanation. Based on the main influencing factors and the optimal lag time, dam temperature field zoning and modelling factor optimization methods are proposed. On this basis, a causal prediction model is established for the measured temperature field of high arch dams using the nonlinear autoregressive with exogenous inputs (NARX) neural network, by which the lag influencing mechanism preliminarily simulated through modelling factors can be further optimized. The results of the Jinping-I arch dam indicate that the causal mechanism of arch dam temperature field is nonlinear and hysteresis. The proposed method has achieved the quantification standards of temperature field zoning and modelling factor optimization for high arch dams based on the lag influencing mechanism, and the obtained temperature field zones of the dam body are consistent with the actual situation. Compared with the frequently used support vector machine model, the NARX model can significantly improve the prediction accuracy of dam temperature field, where the root mean square error of all 134 temperature monitoring points on the dam body decreases with an average rate of 76%.

A novel deformation monitoring model for high arch dams using impulse response-based equivalent temperature and machine learning-aided separate modeling

The anatomy of service status through health monitoring models is essential to long-term structural safety. Since deformation is the intuitive representation of the dam's operating condition, it is crucial to investigate the deformation monitoring model with high accuracy and strong interpretability for the safety management of high arch dams. A novel deformation monitoring model is proposed by incorporating impulse response-based equivalent temperature (ET) and machine learning-aided separate modeling technique (SMT). This methodology has three main sources of novelty. First, the impulse response-based equivalent temperature and corresponding temperature component are derived from heat transfer and temperature convolution theories. Then, the model parameters are identified by an improved firefly algorithm. Second, the components in the monitored displacement are progressively stripped out by clustering analysis, separation under equal water level conditions, and a robust signal decomposition technique. Third, the separated deformation components and environmental factors are modeled by the multi-output deep extreme learning machine (DELM) with autoencoder, and the monitoring model ET-SMT-DELM is thus established. The world's highest arch dam is selected to illustrate the proposed model, and the prediction accuracy, early warning performance, component shares, and impulse response mechanism are comprehensively investigated by comparing with several typical baseline models. The results show that the overfitting of the proposed model is reduced, and the prediction and early warning performance is significantly improved. The resulting temperature impulse response function and component shares imply that the interpretability of the model is also enhanced.

Study on valley deformation of high arch dams based on nonlinear rheological damage model

The scale of the high arch dam project is large, and the water level is high. The construction and water storage effect will significantly impact the rock mass of the high slopes on both sides, especially on the structural planes such as internal faults. Based on the Jinping Ⅰ high arch dam, an improved Nishihara rheological damage model was established to address the phenomenon of valley shrinkage deformation. The stress and deformation states of the rock mass and dam in the reservoir area were calculated, and the results were compared with the conventional elastic–plastic finite element method. The results indicate that the improved parallel Nishihara rheological damage model can better conform to the deformation properties of the internal structural planes of the rock mass, and the calculation results using this model are closer to the actual deformation situation. When considering the structural plane's rheological characteristics, the valley shrinkage deformation law is consistent with the conventional elastic–plastic finite element calculation results, but the magnitude is too large. In addition, when considering the rheological characteristics of the structural plane, the increase in valley shrinkage deformation value further increases the stress and deformation of the high arch dam, which can better reflect the dam's working behavior and ensure the dam's safe operation.

Deep learning model for displacement monitoring of super high arch dams based on measured temperature data

Dam displacement, an important indicator for the health monitoring of dam structures, can effectively reflect its operational status. Displacement prediction models based on measured data are currently an important tool for dam safety monitoring. However, a significant portion of current models rely on statistical or shallow machine-based approaches, posing challenges in extracting comprehensive and impactful features from environmental factors. To address the above problems, this paper proposes a novel deep learning model that combines Inception architectures with residual connections (Inception-ResNet) and Gate Recurrent Units (GRU). This model employs improved Inception-ResNet blocks with channel attention and spatial attention modules to extract features from dam deformation-related environmental factors sequences at multiple scales. Subsequently, GRU is utilized to learn from long-term dependencies. The proposed model fully combines the remarkable feature extraction capability of the Inception-ResNet block with the learning capability of GRU for long-term dependencies. The availability of the proposed model is tested with measured data of a super high arch dam. The experimental results show that the proposed model outperforms two typical shallow machine learning methods and two typical deep learning models in the four typical monitoring points selected, which demonstrates convincingly that the proposed model is able to predict dam deformation with high accuracy and robustness for dam structure safety monitoring.

Analysis framework for the seismic performance of a concrete high-arch dam considering the incident direction and oblique incident angle of near-fault SV-waves

This study aims to develop an analytical framework to assess the seismic performance of high-arch dams by considering the incident direction and input angle of near-fault (NF) seismic waves. Based on this comprehensive analysis framework, we aim to accomplish the following objectives: (1) Comparison of the discrepancies in the variability of dynamic indices with increasing incident angles along different incident directions of seismic waves; (2) Estimation of the global damage responses based on the seismic characteristic parameters and incident angles; and (3) Assessment of the influences of the impulsive characteristics of NF earthquake waves with different incident directions on the correlations between the response indices. To this end, the effects of the incident direction and incident angle of NF SV-waves (including pulse-like waves and non-pulse-like waves) on the nonlinear responses and correlation of dynamic indicators of arch dams were investigated. The simulation results revealed that the changing pattern of the dynamic behavior of the dam with the incident angle differed significantly along the different incident directions of NF SV-waves. The peak ground velocity/peak ground acceleration was found to have a good correlation with the overall damage volume ratio. Further, along various input directions of NF SV-waves, the damage index was found to have a strong positive correlation with the upstream center joint opening, whereas the degree of correlation between the damage and displacement indices was not reduced owing to the impulsive characteristics of NF SV-waves. Finally, a two-variable damage model that considered both the input angle and seismic characteristic parameters was established and used to roughly predict the global damage index of the dam.

Seismic damage characteristics of high arch dams under oblique incidence of SV waves

This study investigates the seismic damage characteristics of high arch dams considering the oblique incidence of seismic waves. Based on the dynamic implicit finite element method, a viscous-spring artificial boundary is applied to consider radiation damping of the infinite foundation, the seismic wave input is converted into an equivalent nodal force of the artificial boundary, and the equivalent nodal loads of the wave input under oblique incidence of the SV wave is deduced. Taking JP Ⅰ arch dam as an example, combined with the concrete damaged plasticity (CDP) model, the nonlinear dynamic response of the dam under 25° oblique incidence of the SV wave is conducted considering the geometrical nonlinearity of contraction joints and the dynamic mechanical properties of concrete materials. Based on the incremental dynamic analysis (IDA) method, the relative displacement and transverse joint opening are selected as the damage measures for fragility analysis of the arch dam. According to the damage distribution from the analysis of a large number of seismic time-history responses, the damage levels of the arch dam are classified. Three limit states of slight damage, moderate damage and severe damage are proposed for the arch dam. The average responses of the 10 earthquakes are considered as the limit values to quantitatively describe the damage of the arch dam. By fitting the results of incremental dynamic analysis, the seismic probabilistic demand model and seismic fragility curves for the two damage measures to predict the probability of the arch dam reaching damage levels under different seismic actions have been established. The fragility curve shows that the probability of severe damage is almost zero under the maximum design earthquake (PGA = 0.1971 g) and less than 5% under the maximum credible earthquake (PGA = 0.2299 g). It is concluded that JP Ⅰ arch dam has a high degree of safety redundancy and fully satisfies the “two levels” design requirements.