Wavelet packet energy spectrum analysis-based water conveyance tunnel damage identification method

Abstract

The invention discloses a water conveyance tunnel damage identification method based on wavelet packet energy spectrum analysis. The method comprises the following steps of s1, collecting acceleration responses of monitoring points in damaged and non-damaged states; s2, adopting wavelet packet decomposition to select an optimal wavelet basis function and a decomposition layer number; s3, constructing a damage early warning index based on the wavelet packet energy spectrum; s4, the damage degree and position are judged according to the early warning index sudden change value; s5, fitting an identification result with a finite element simulation result; and s6, accurately quantifying the damage state corresponding to any given damage index sudden change value. According to the method, conversion from the early warning index damage sudden change value to the actual engineering damage is realized, and an effective method support is provided for health assessment and risk early warning of the tunnel lining structure. On-line long-time monitoring is not needed, and the damage position and degree can be identified.

Description

Technical Field

[0001] This invention relates to the field of signal analysis for water conveyance tunnels in the field of water conservancy engineering technology, specifically to a method for damage identification of water conveyance tunnels based on wavelet packet energy spectrum analysis. Background Technology

[0002] With continuous social progress and the ongoing improvement of infrastructure construction, the construction and application of water conveyance tunnels in water conservancy projects are becoming increasingly widespread. As an important component of water diversion and conveyance systems, water conveyance tunnels play a crucial role in hydropower stations, reservoir regulation, and urban water supply projects.

[0003] The Chinese mainland is bordered by the Circum-Pacific Plate's edge seismic belt to the east and the Eurasian Plate's intercontinental seismic belt to the west, resulting in frequent seismic activity. According to earthquake monitoring systems, my country ranks among the world's most seismically active countries in terms of both the number of earthquake events and the total energy released. Although the stress and response of underground structures during earthquakes are often influenced by above-ground structures, a review of tunnel damage in past earthquakes shows that tunnel structures themselves can also suffer significant damage. Common forms of damage include surrounding rock displacement, lining cracking, and structural joint failure, especially under adverse geological conditions, where the risk of damage to tunnel structures is even more pronounced. Traditional methods mainly include analytical methods, experimental methods, and numerical calculation methods. Due to the complexity of underground structures, including the variability of geological conditions, the diversity of tunnel shapes, and the complex propagation paths of seismic waves, accurate solutions using analytical methods face significant theoretical difficulties. While experimental methods can directly reflect the actual response of structures under seismic loading, they are too costly, difficult to control experimental conditions, and have long testing cycles. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a damage identification method based on wavelet packet energy spectrum, which can realize the conversion from early warning indicator damage mutation value to actual engineering damage, providing effective method support for health assessment and risk early warning of tunnel lining structures, and reducing damage to tunnel structures.

[0005] The objective of this invention is achieved through the following technical solution: A method for damage identification of water conveyance tunnels based on wavelet packet energy spectrum analysis includes the following steps: s1. Acquiring the acceleration response of monitoring points under damaged and undamaged states; s2. Selecting the optimal wavelet basis function and decomposition level using wavelet packet decomposition; s3. Construct a damage early warning index based on wavelet packet energy spectrum; s4. Determine the degree and location of damage based on the abrupt change value of the early warning index; s5. Fit the identification results with the finite element simulation results; s6. Accurately quantify the damage state corresponding to any given damage index mutation value.

[0006] The above-mentioned method for damage identification of water conveyance tunnels based on wavelet packet energy spectrum analysis includes the following specific steps in step s2: dbN (Daubechies N) and symN (Symlet N) are selected as wavelet functions for damage early warning based on wavelet packet energy spectrum, and wavelet packet decomposition is performed at 3 to 7 levels respectively; for each level of decomposition, the energy of all frequency bands is calculated; then, the norm entropy is calculated using the wavelet packet energy; these entropy values ​​are compared to find the optimal combination of decomposition level and wavelet basis function that best distinguishes between damaged and undamaged states, which is taken as the optimal decomposition level and wavelet basis function; however, as the number of decomposition levels increases, the overall computation time also increases, so the determination of the optimal decomposition level requires a comprehensive optimization method; this method should simultaneously meet two basic conditions: ensuring the computational accuracy required for damage identification; and completing the computation task within 10 seconds.

[0007] The above-mentioned method for damage identification of water conveyance tunnels based on wavelet packet energy spectrum analysis includes the following specific steps in step s3: After determining the wavelet function and decomposition level, the structural acceleration response signal is decomposed using the wavelet packet energy spectrum to construct a structural damage early warning index. The damage early warning index constructed using the wavelet packet energy spectrum is shown below: (1) For the original signal conduct Layer wavelet packet decomposition. This will produce Each sub-signal (or node), if Represents a node The first sub-signal For each sample, the energy It can be calculated as follows: #(1-1) In the formula: is the length (number of samples) of the sub-signal on the node; (2) Using the relative ratio of characteristic frequency band energy to the overall average energy as a damage early warning indicator has significant advantages over traditional absolute energy change monitoring; the energy ratio of each characteristic frequency band in the wavelet packet energy spectrum for structural damage early warning Defined as: # (1-2) In the formula: The wavelet packet energy spectrum of the structure in the damaged state is represented by the first wavelet packet energy spectrum. Energy value of each frequency band; This represents the total energy in the wavelet packet frequency band energy spectrum; This represents the average value of the total energy in the wavelet packet frequency band energy spectrum; it also represents the number of frequency bands corresponding to different decomposition levels. (3) The change in energy ratio of the first characteristic frequency band of the structure under damaged conditions: # (1-3) and The wavelet packet energy spectra of the structure in the undamaged and damaged states are respectively... The energy ratio of each characteristic frequency band; (4) In order to more intuitively and accurately demonstrate the condition of the structure under damage, based on Based on the damage early warning indicators, the energy ratio deviation was further defined. (Energy Ratio Variation Deviation, ERVD) is calculated by the following formula: # (1-4) In the formula: Indicates the energy ratio of each damage characteristic frequency band The average value.

[0008] The above-mentioned method for damage identification of water conveyance tunnels based on wavelet packet energy spectrum analysis includes the following specific steps in step s4: When damage occurs to the tunnel lining structure, the damage index values ​​near the damage location will exhibit significant abrupt changes. This abrupt change directly reflects the presence of damage and is crucial for the successful identification of the damage location by the damage index; by monitoring these abrupt changes, damage can be quickly located. The larger the damage value at the damage location, the greater the magnitude of the abrupt change in the corresponding damage index value.

[0009] The above-mentioned method for damage identification of water conveyance tunnels based on wavelet packet energy spectrum analysis includes the following specific steps in step s5: There is a certain correlation between the damage mutation value and the dynamic damage amount; when the structure is damaged, the abrupt change in its physical properties will lead to a change in the dynamic response, which will then be reflected in the modal parameters; therefore, by monitoring the changes in the modal parameters, the damage mutation value and dynamic damage amount of the structure can be indirectly inferred.

[0010] The above-mentioned method for identifying damage in water conveyance tunnels based on wavelet packet energy spectrum analysis includes the following specific step s6: Although there is a correlation between damage mutation values ​​and dynamic damage amounts, their quantitative relationship is often affected by various factors, such as the location, type, degree, and structural complexity of the damage. Therefore, in practical applications, it is usually necessary to combine specific experimental data or numerical simulation results to establish a quantitative relationship model between them. By constructing the exponential function y=a×e^x+b, the damage state of the tunnel lining structure corresponding to any given damage index mutation value can be accurately quantified. This provides us with a quantitative means to assess the health status of the tunnel lining structure.

[0011] Compared with the prior art, the present invention has the following technical effects: 1. It realizes the transformation from early warning indicator damage mutation value to actual engineering damage, and provides effective methodological support for health assessment and risk early warning of tunnel lining structures.

[0012] 2. No need for long-term online monitoring, and it can identify the location and extent of damage. Attached Figure Description

[0013] Figure 1 This is a finite element model diagram of a water conveyance tunnel.

[0014] Figure 2 It is an acceleration response diagram of the monitoring point.

[0015] Figure 3 This is a damage warning indicator map for monitoring points.

[0016] Figure 4 This is a distribution map of damage indicators along the tunnel lining.

[0017] Figure 5 This is a fitted curve of the dynamic damage amount of the lining corresponding to different damage mutation values. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0019] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0020] Example 1: A method for damage identification of water conveyance tunnels based on wavelet packet energy spectrum analysis, comprising the following steps: s1. Acquiring the acceleration response of monitoring points under damaged and undamaged states; s2. Selecting the optimal wavelet basis function and decomposition level using wavelet packet decomposition; s3. Construct a damage early warning index based on wavelet packet energy spectrum; s4. Determine the degree and location of damage based on the abrupt change value of the early warning index; s5. Fit the identification results with the finite element simulation results; s6. Accurately quantify the damage state corresponding to any given damage index mutation value.

[0021] The specific steps of step s2 are as follows: dbN (Daubechies N) and symN (Symlet N) are selected as wavelet functions for wavelet packet energy spectrum damage warning, and wavelet packet decomposition of 3 to 7 levels is performed respectively; for each level of decomposition, the energy of all frequency bands is calculated; then, the norm entropy is calculated using the wavelet packet energy; these entropy values ​​are compared to find which combination of decomposition level and wavelet basis function can best distinguish between damaged and undamaged states, which is taken as the optimal decomposition level and wavelet basis function; however, as the number of decomposition levels increases, the overall computation time also increases, so the determination of the optimal decomposition level needs to adopt a comprehensive optimization method; this method should simultaneously meet two basic conditions: it can ensure the computational accuracy required for damage identification; and it completes the computation task within an acceptable time range, i.e., within 10 seconds.

[0022] The specific steps of s3 are as follows: After determining the wavelet function and decomposition level, the structural acceleration response signal is decomposed using the wavelet packet energy spectrum to construct a structural damage early warning index. The damage early warning index constructed using the wavelet packet energy spectrum is shown below: (1) For the original signal conduct Layer wavelet packet decomposition. This will produce Each sub-signal (or node), if The sub-signal on the node represents the first... For each sample, the energy It can be calculated as follows: #(1-1)) In the formula: It is the length (number of samples) of the sub-signal on the node. (2) Using the relative ratio of characteristic frequency band energy to the overall average energy as a damage early warning indicator has significant advantages over traditional absolute energy change monitoring; the energy ratio of each characteristic frequency band in the wavelet packet energy spectrum for structural damage early warning Defined as: #(1-2)) In the formula: The wavelet packet energy spectrum of the structure in the damaged state is represented by the first wavelet packet energy spectrum. Energy value of each frequency band; This represents the total energy in the wavelet packet frequency band energy spectrum; This represents the average value of the total energy in the wavelet packet frequency band energy spectrum; This indicates the number of frequency bands corresponding to different decomposition levels; (3) The structure in the damaged state Energy ratio variation in each characteristic frequency band: # (1-3) and These represent the energy ratios of the i-th characteristic frequency bands of the wavelet packet energy spectrum in the undamaged and damaged states, respectively. (4) In order to more intuitively and accurately demonstrate the condition of the structure under damage, based on The damage warning index is based on the Energy Ratio Variation Deviation (ERVD), which is calculated by the following formula: # (1-4) In the formula: Indicates the energy ratio of each damage characteristic frequency band The average value.

[0023] The specific steps of S4 are as follows: When the tunnel lining structure is damaged, the damage index value near the damage location will show a significant abrupt change. This abrupt change is a direct reflection of the existence of damage and is also the key to the successful identification of the damage location by the damage index; by monitoring these abrupt changes, the damage can be quickly located. The larger the damage value at the damage location, the greater the magnitude of the abrupt change in the corresponding damage index value.

[0024] The specific steps of step s5 are as follows: There is a certain correlation between the damage mutation value and the dynamic damage amount; when the structure is damaged, the sudden change in its physical properties will lead to a change in the dynamic response, which will then be reflected in the modal parameters; therefore, by monitoring the changes in the modal parameters, the damage mutation value and dynamic damage amount of the structure can be indirectly inferred.

[0025] Step s6 is as follows: Although there is a correlation between damage mutation value and dynamic damage amount, their quantitative relationship is often affected by a variety of factors, such as the location, type, degree and complexity of the damage. Therefore, in practical applications, it is usually necessary to combine specific experimental data or numerical simulation results to establish a quantitative relationship model between them. By constructing the exponential function y=a×e^x+b, the damage state of the tunnel lining structure corresponding to any given damage index mutation value can be accurately quantified. This provides us with a quantitative means to evaluate the health status of the tunnel lining structure.

[0026] Example 2: A method for damage identification in water conveyance tunnels based on wavelet packet energy spectrum analysis, such as... Figure 1This paper studies the characteristics of wavelet packet energy spectrum using a water conveyance tunnel under numerical simulation of seismic loading as an example. The numerical simulation is of a water conveyance tunnel, with a lining structure made of C50 grade prestressed concrete, an outer diameter of 7.5 m, an inner diameter of 6.4 m, and a thickness of 0.55 m. The total longitudinal dimension is chosen to be 25.6 m, and the distance from the lower boundary of the model to the tunnel center is 40 m. The characteristics of the wavelet packet energy spectrum are comprehensively considered in the context of the surrounding rock-tunnel model structure. X , Y , Z The calculated dimensions are 100 × 25.6 × 80 (m), and the tunnel is located at the central axis of the soil mass. The density of the surrounding rock in Model III is 2620 kg·m³. -3 The elastic modulus is 7.25 GPa, and Poisson's ratio is 0.29. The density of the C50 concrete lining is 2600 kg·m³. -3 The elastic modulus is 34.5 GPa, and the Poisson's ratio is 0.2; the acceleration dynamic response of the water conveyance tunnel at key monitoring points under seismic loading was collected. Taking the arch crown as an example, from... Figure 2 As can be seen, the acceleration time history curves of the arch in the damaged and undamaged states are very similar, and the damage status cannot be determined solely from the acceleration time history curves. Therefore, wavelet packet analysis is required for both. First, wavelet packet analysis is performed on the acceleration time history curves of the damaged arch to find the optimal wavelet packet basis function and the optimal number of decomposition layers.

[0027] As shown in Table 1, analysis of the acceleration signal using wavelet packet decomposition reveals that the entropy of each wavelet packet gradually decreases with increasing decomposition level. This phenomenon indicates that as the decomposition level increases, the original signal is progressively refined and distributed across multiple frequency sub-bands. This process reduces the complexity of the signal within each sub-band, thereby decreasing the entropy. For wavelet basis functions with the same decomposition level, increasing the order of the wavelet basis does not always lead to a monotonic change in entropy, but the dbN series wavelet basis functions outperform the symN series in overall performance.

[0028] Table 2 shows that even in the most complex 7-level decomposition, the computation time is only about 2 seconds, indicating that wavelet packet decomposition has high computational efficiency. The final choice of the db25 wavelet basis function and the 7-level decomposition level was based on a comprehensive consideration of entropy, computation time, and signal characteristics. db25 exhibits good regularity and tight support, making it suitable for analyzing complex signals. The 7-level decomposition, on the other hand, provides sufficient frequency resolution while maintaining high computational efficiency.

[0029] Table 1. Entropy values ​​of different wavelet bases and decomposition levels of the vault under damage conditions.

[0030] Table 2 Time at each decomposition level Decomposition level N 3 4 5 6 7 Calculation time ＜1 s ＜1 s ＜1 s ＜2 s ＜2 s

[0031] The wavelet basis function db25 was selected to analyze the acceleration response. With a decomposition level of 7, wavelet packet decomposition was performed on the acceleration dynamic response at the damage site of the tunnel lining arch, and the wavelet packet frequency band energy spectrum was calculated. With 7 decomposition levels, wavelet packet decomposition of the acceleration dynamic response resulted in a total of 128 frequency band orders. Due to the large number of frequency band orders, only the frequency bands with higher energy were selected to form the characteristic frequency bands. The energy ratios obtained after wavelet packet decomposition at monitoring points on the tunnel lining structure were compared. There are 5 points in total.

[0032] Based on wavelet packet energy ratio Based on this, it also defined Damage early warning indicators will be used at each monitoring point Comparative analysis of damage early warning indicators revealed significant differences in the characteristic frequency band energy spectra at different monitoring points. These characteristic frequency band energy spectra can characterize the state of the structure. The structure at different monitoring points... The maximum values ​​have significant differences, through The maximum value can clearly distinguish the different states of the structure. At most frequency band orders, the monitoring points of the tunnel lining... The damage warning index values ​​are: crown > right arch waist > right side wall > (inverted arch, right wall corner), which verifies the magnitude of the damage values ​​simulated by the finite element method. This demonstrates that by constructing appropriate damage warning indices, effective early warning of structural damage can be achieved.

[0033] To more intuitively and accurately demonstrate the structure's condition under damage, based on Based on the damage early warning indicators, the energy ratio deviation was further defined. .from Figure 3 It is evident that the damage warning index values ​​of the lining structure exhibit a certain degree of abrupt change at the arch crown, right arch waist, and right side wall locations, and the damage identification results are consistent with... Figure 4 The numerical simulations show a good match in the location of the damage distribution, and the damage index identification effect is relatively good. In the lining structure, the damage warning index values ​​at damaged and undamaged locations change as damage occurs, and the damage warning index value at each node is different and not zero.

[0034] The magnitude of damage to the lining structure is related to the abrupt changes in damage index values. From Figure 3 It can be seen that there are corresponding abrupt changes in the damage index values ​​at the crown, right arch waist, and right side wall. The abrupt change value at the crown is 0.1352, while the abrupt change at the right arch waist is slightly smaller than that at the crown, with a value of 0.0702. Two abrupt changes are observed near the right side wall, with values ​​of 0.0450 and 0.0618 respectively. Figure 4The damage values ​​extracted from the numerical simulation show that the damage amount for the arch crown monitoring unit is 0.0803, for the right arch waist monitoring unit it is 0.0379, and for the right side wall monitoring units it is 0.0183 and 0.0208. This indicates a direct proportionality between the damage condition of the tunnel lining structure and the damage index value; specifically, the higher the degree of damage to the tunnel lining, the greater the abrupt change in the damage index value. Figure 3 It can be seen that the damage index values ​​near the invert arch did not show significant abrupt changes, with a change value of only 0.0048. Figure 4 The data also shows that the invert arch was not damaged. This further verifies the rationality of the damage index values.

[0035] When damage occurs to the tunnel lining structure, significant abrupt changes in damage index values ​​occur near the damage location. These abrupt changes directly reflect the presence of damage and are crucial for the successful identification of damage locations by the damage index. By monitoring these abrupt changes, damage can be quickly located. The larger the damage value at the damage location, the greater the magnitude of the corresponding abrupt change in the damage index value. Therefore, monitoring damage early warning indicators is essential. The changes in values ​​are used to determine the location and severity of damage, enabling timely detection of potential damage risks and the implementation of necessary preventative measures before damage occurs. By comparing and analyzing the damage mutation values ​​with the lining damage distribution obtained from numerical simulation, the accuracy of the damage identification technology is demonstrated, and the feasibility of wavelet packet analysis in tunnel lining damage monitoring is also shown.

[0036] Although there is a correlation between damage mutation values ​​and dynamic damage amounts, their quantitative relationship is often influenced by various factors, such as the location, type, and extent of damage, as well as the complexity of the structure. Therefore, in practical applications, it is usually necessary to combine specific experimental data or numerical simulation results to establish a quantitative relationship model between them. Damage early warning indicators for monitoring points in tunnel lining structures. The correspondence between the dynamic damage of the tunnel lining structure and the finite element simulation is as follows: Figure 4 As shown.

[0037] Table 3 Damage mutation values ​​corresponding to dynamic damage. Damage mutation value 0.0048 0.0450 0.0515 0.0618 0.0702 0.1126 0.1352 Dynamic damage 0 0.0183 0.0213 0.0208 0.0379 0.0617 0.0803 Damage mutation value 0.1380 0.1454 0.1529 0.2259 0.2883 0.3150 - Dynamic damage 0.0847 0.0925 0.1010 0.2771 0.5998 0.8300 - Based on the data in Table 3, the data is fitted using the following exponential function, which is shown below: In the formula: y Indicates the amount of dynamic damage; A 1. t 1. y 0 indicates an undetermined coefficient; x This is represented as a damage mutation value.

[0038] The fitted exponential function is as follows Figure 5 As shown in the figure, the fitting analysis based on a limited dataset indicates that the theoretical maximum value of dynamic damage is 0.83. Further research revealed that when the damage mutation value exceeds the critical point of 0.331, the function curve exhibits a significant increasing slope, indicating that the dynamic damage has entered an accelerated development stage. The final dynamic damage value after standardization was set to 1. This upper limit was determined considering both the actual damage situation and the requirements of data analysis standardization. The functional relationship between dynamic damage and damage mutation value is shown in the following equation: As can be seen from fitting function 2, there is a significant positive correlation between the damage mutation value and the dynamic damage amount, and it exhibits a clear increasing function characteristic. With the continuous increase of the damage mutation value, not only does the dynamic damage amount increase accordingly, but its growth rate also shows a gradually increasing trend. The constructed fitting function can accurately quantify the damage state of the tunnel lining structure corresponding to any given damage index mutation value, providing us with a quantitative means to assess the health status of the tunnel lining structure.

[0039] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several changes and improvements without departing from the overall concept of the present invention, and these should also be considered within the scope of protection of the present invention.

Claims

1. A method for damage identification of water conveyance tunnels based on wavelet packet energy spectrum analysis, characterized in that: Includes the following steps: s1. Collect the acceleration response of monitoring points under damaged and undamaged conditions; s2. Wavelet packet decomposition is used to select the optimal wavelet basis function and the number of decomposition levels; s3. Construct a damage early warning index based on wavelet packet energy spectrum; s4. Determine the extent and location of damage based on the sudden change values ​​of early warning indicators; s5. Fitting the identification results with the finite element simulation results; s6. Precisely quantify the damage state corresponding to any given mutation value of a damage index.

2. The method for damage identification of water conveyance tunnels based on wavelet packet energy spectrum analysis according to claim 1, characterized in that: The specific steps of step s2 are as follows: dbN and symN are selected as wavelet functions for wavelet packet energy spectrum damage early warning, and wavelet packet decomposition of 3 to 7 levels is performed respectively; for each level of decomposition, the energy of all frequency bands is calculated. Then, the norm entropy is calculated using wavelet packet energy. These entropy values ​​are compared to find the optimal combination of decomposition level and wavelet basis function to best distinguish between damaged and undamaged states. However, as the number of decomposition levels increases, the overall computation time also increases. Therefore, the determination of the optimal number of decomposition levels requires a comprehensive optimization method. This method satisfies two basic conditions: it can ensure the computational accuracy required for damage identification and complete the computation task within 10 seconds.

3. The method for damage identification of water conveyance tunnels based on wavelet packet energy spectrum analysis according to claim 1, characterized in that: The specific steps of s3 are as follows: After determining the wavelet function and decomposition level, the structural acceleration response signal is decomposed using the wavelet packet energy spectrum to construct a structural damage early warning index. The damage early warning index constructed using the wavelet packet energy spectrum is shown below: For the original signal conduct Layer wavelet packet decomposition; this will produce If a node, Represents a node The first sub-signal For each sample, the energy is calculated as follows: In the formula: It is a node The length of the upper sub-signal; Based on the relative ratio of characteristic frequency band energy to the overall energy mean as a damage early warning index, the energy ratio of each characteristic frequency band in the wavelet packet energy spectrum for structural damage early warning is used. Defined as: In the formula: The wavelet packet energy spectrum of the structure in the damaged state is represented by the first wavelet packet energy spectrum. Energy value of each frequency band; This represents the total energy in the wavelet packet frequency band energy spectrum; This represents the average value of the total energy in the wavelet packet frequency band energy spectrum; This indicates the number of frequency bands corresponding to different decomposition levels; The structure in the damaged state Energy ratio variation in each characteristic frequency band: and The wavelet packet energy spectra of the structure in the undamaged and damaged states are respectively... The energy ratio of each characteristic frequency band; (4) In order to more intuitively and accurately demonstrate the condition of the structure under damage, based on Based on the damage early warning indicators, the energy ratio deviation was further defined. Calculated by the following formula: In the formula: Indicates the energy ratio of each damage characteristic frequency band The average value.

4. The method for damage identification of water conveyance tunnels based on wavelet packet energy spectrum analysis according to claim 1, characterized in that: As mentioned in s4, when the tunnel lining structure is damaged, the damage index value near the damage location will show obvious abrupt changes. This abrupt change is a direct reflection of the existence of damage and is also the key to the damage index being able to successfully identify the damage location. By monitoring these abrupt changes, the damage can be quickly located. The larger the damage value at the damage location, the greater the abrupt change in the corresponding damage index value.

5. The method for damage identification of water conveyance tunnels based on wavelet packet energy spectrum analysis according to claim 1, characterized in that: The specific steps of step s5 are as follows: By monitoring the changes in modal parameters, the damage mutation value and dynamic damage amount of the structure are indirectly inferred.

6. The method for damage identification of water conveyance tunnels based on wavelet packet energy spectrum analysis according to claim 1, characterized in that: The specific steps of step s6 are as follows: By constructing the exponential function y=a×e^x+b, the damage state of the tunnel lining structure corresponding to any given mutation value of the damage index can be accurately quantified.

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