Concrete gravity dam strong shock damaged part quick identification method based on frequency errors

Abstract

The invention discloses a frequency error-based rapid identification method for strong earthquake damage parts of concrete gravity dams, which is suitable for the identification of strong earthquake damage parts of concrete gravity dams. The damage position identification method proposed by the invention can effectively reflect the frequency characteristics of the concrete gravity dam under different damage position conditions, and can quickly and accurately identify the damage position of the dam under the action of strong earthquakes. This method is simple to operate, without using a damage detector, and only needs to obtain the natural vibration frequencies of each order of the dam after the strong earthquake to quickly identify the damaged part of the strong earthquake, which has wide applicability.

Description

technical field [0001] The invention relates to the identification of strong earthquake damaged parts of concrete gravity dams, in particular to a rapid identification method of strong earthquake damaged parts of concrete gravity dams based on frequency error. Background technique [0002] Southwest China is an area where strong earthquakes frequently occur in China. A number of world-class concrete dams with a height of about 100m to 300m are being or will be constructed in strong earthquake areas. Earthquake action may cause damage to the dam. Over time, the damage accumulates, coupled with the continuous aging of materials (such as corrosion of steel bars, carbonization of concrete, etc.), and eventually the gravity dam is partially damaged or even collapsed. This potential damage may cause major disasters that threaten people's lives and property safety. Therefore, there is an urgent need to propose a practical and accurate rapid identification method for gravity dam da...

AI Technical Summary

Problems solved by technology

The main problems of non-destructive testing technology for dams are: (1) Generally, only small structures or some parts of structures can be tested; (2) There are no suitable specifications, procedures and standards to rely on; (3) The testing equipment is expensive and the testing The cost is high; (4) Temporary installation of instruments is required, and many positions of the dam cannot be reached, so it is difficult to implement

However, the neural network algorithm has shortcomings such as slow convergence speed, network structure selection depends on experience, and frequency characteristics applicable to all gravity dams cannot be obtained.

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