Seismic response of concrete gravity dam reinforced with FRP sheets on dam surface

This paper aims at exploring the effects of anti-seismic reinforcement with the fiber-reinforced polymer （FRP） material bonded to the dam surface in dam engineering. Time-history analysis was performed to simulate the seismic failure process of a gravity dam that was assumed to be reinforced at the locations of slope discontinuity at the downstream surface, part of the upstream face, and the dam heel. A damage model considering the influence of concrete heterogeneity was used to model the nonlinearity of concrete. A bond-slip model was applied to the interface between FRP and concrete, and the reinforcement mechanism was analyzed through the bond stress and the stress in FRP. The results of the crack pattern, displacement, and acceleration of the reinforced dam were compared with those of the original one. It is shown that FRP, as a reinforcement material, postpones the occurrence of cracks and slows the crack propagation, and that cracks emanating from the upstream surface and downstream surface are not connected, meaning that the reinforced dam can retain water-impounding function when subjected to the earthquake. Anti-seismic reinforcement with FRP is therefore beneficial to improving the seismic resistant capability of concrete dams.

Dynamic probability evaluation of safety levels of earth-rockfill dams using Bayesian approach

In order to accurately predict and control the aging process of dams, new information should be collected continuously to renew the quantitative evaluation of dam safety levels. Owing to the complex structural characteristics of dams, it is quite difficult to predict the time-varying factors affecting their safety levels. It is not feasible to employ dynamic reliability indices to evaluate the actual safety levels of dams. Based on the relevant regulations for dam safety classification in China, a dynamic probability description of dam safety levels was developed. Using the Bayesian approach and effective information mining, as well as real-time information, this study achieved more rational evaluation and prediction of dam safety levels. With the Bayesian expression of discrete stochastic variables, the a priori probabilities of the dam safety levels determined by experts were combined wfth the likelihood probability of the real-time check information, and the probability information for the evaluation of dam safety levels was renewed. The probability index was then applied to dam rehabilitation decision-making. This method helps reduce the difficulty and uncertainty of the evaluation of dam safety levels and complies with the current safe decision-making regulations for dams in China. It also enhances the application of current risk analysis methods for dam safety levels.