Method for evaluating durability damage of pipe gallery concrete under high water pressure coupling environment

Abstract

The application discloses a pipe gallery concrete durability damage evaluation method under high water pressure coupling environment and relates to the technical field of concrete damage evaluation.The method comprises the following steps: collecting seepage flow, pore water pressure, structural strain and acoustic emission signals of multiple points of the pipe gallery in real time, obtaining multi-source monitoring data, generating a spatial multi-dimensional feature map of pipe gallery concrete damage, obtaining residual deviation, extracting structural damage net effect data under high water pressure coupling, generating a concrete durability damage evaluation dynamic threshold, dividing into non-triggering, local evolution and global early warning grades according to damage deviation, and outputting corresponding structural safety operation and maintenance decision instructions.The application solves the technical problem that the fusion depth of multi-source monitoring data is insufficient in the prior art, it is difficult to remove environmental background interference and single-point burr noise during evaluation, and the evaluation result deviates greatly from the actual situation, and achieves the technical effect of improving the reliability of durability damage evaluation.

Description

Technical Field

[0001] This invention relates to the field of concrete damage assessment technology, specifically to a method for assessing the durability damage of pipe gallery concrete under high water pressure coupling environment. Background Technology

[0002] With the large-scale construction and long-term operation of underground utility tunnels, their structural safety has received increasing attention from the engineering community. Especially in high-water-pressure coupled environments, such as deeply buried tunnels and high-water-level areas, underground concrete tunnels are subjected to complex and variable seepage, alternating dynamic and static water pressures, and changes in environmental temperature and humidity over long periods. Currently, conventional methods for monitoring and assessing concrete damage in utility tunnels mostly employ static thresholds or open-loop monitoring of single physical quantities, which are insufficient to effectively address the complex service conditions involving interwoven multi-source physical fields under harsh high-water-pressure environments.

[0003] Existing methods for assessing the health of underground utility tunnels often process various physical signals in isolation when dealing with massive amounts of multi-source monitoring data, lacking in-depth spatial geometric fusion of multi-source time-frequency domain characteristics. At the same time, they lack an effective spatiotemporal dual filtering mechanism to separate environmental noise from instantaneous burrs at single points on sensors. Furthermore, traditional damage assessment thresholds are mostly fixed constants, which cannot adaptively characterize the dynamic attenuation of material bearing capacity caused by long-term high water pressure coupling and structural fatigue evolution.

[0004] Existing technologies suffer from insufficient depth of multi-source monitoring data fusion, making it difficult to eliminate environmental background interference and single-point noise during assessment, resulting in significant discrepancies between the assessment and the actual situation. Summary of the Invention

[0005] This invention provides a method for assessing the durability damage of concrete in pipe gallery under high water pressure coupling environment. It is used to address the technical problem that existing technologies have insufficient fusion depth of multi-source monitoring data, making it difficult to remove environmental background interference and single-point burr noise during assessment, resulting in a large discrepancy between the assessment and the actual situation.

[0006] In view of the above problems, the present invention provides a method for assessing the durability damage of pipe gallery concrete under high water pressure coupling environment, the method comprising:

[0007] Real-time acquisition of seepage flow, pore water pressure, structural strain, and acoustic emission signals at multiple points in the utility tunnel yields multi-source monitoring data. Time-frequency domain feature extraction and standardization are performed on this multi-source monitoring data, followed by spatial geometric manifold reconstruction to generate a spatial multidimensional feature map of concrete damage in the utility tunnel. A dual difference comparison of time and spatial dimensions is performed on the spatial multidimensional feature map to obtain residual deviation. This residual deviation is then cross-verified using multi-source sensors to eliminate single-point noise and extract net structural damage data under high water pressure coupling. The current measured water pressure load and design ultimate water pressure are obtained, and a mapping function that exhibits negative correlation and exponential decay with water pressure level is established to generate a dynamic threshold for concrete durability damage assessment. The damage deviation of the net structural damage data relative to the dynamic threshold for concrete durability damage assessment is calculated. Based on the damage deviation, levels are categorized as non-triggered, local evolution, and global early warning, and corresponding structural safety operation and maintenance decision instructions are output.

[0008] In a preferred embodiment of the method for assessing the durability damage of pipe gallery concrete under high water pressure coupling environment according to the present invention, the multi-source monitoring data is subjected to time-frequency domain feature extraction and standardization, and spatial geometric manifold reconstruction is performed to generate a spatial multidimensional feature map of pipe gallery concrete damage. This includes: using wavelet packet transform to extract the energy spectral entropy of acoustic emission signals in different frequency bands from the multi-source monitoring data, and using Fourier transform to extract the root mean square value and peak factor of time-domain fluctuations of seepage flow, pore water pressure, and structural strain to obtain initial multi-source time-frequency domain feature values; performing zero-mean, unit variance standardization on the initial multi-source time-frequency domain feature values ​​to obtain multi-source time-frequency domain feature values; and performing spatial geometric manifold reconstruction based on the multi-source time-frequency domain feature values ​​to generate the spatial multidimensional feature map.

[0009] In a preferred embodiment of the method for assessing the durability damage of pipe gallery concrete under high water pressure coupling environment according to the present invention, the spatial geometric manifold reconstruction based on the multi-source time-frequency domain feature values ​​to generate the spatial multidimensional feature map includes: according to the physical spatial geometric position of each monitoring section of the pipe gallery, the multi-source time-frequency domain feature values ​​are spliced ​​into a multidimensional matrix to construct the feature distribution matrix of each measuring point in the spatiotemporal dimension; the feature distribution matrix is ​​linearly weighted and coordinate normalized to uniformly project the multi-physics field features onto a preset two-dimensional spatial plane coordinate system to generate the spatial multidimensional feature map.

[0010] In a preferred embodiment of the method for assessing the durability damage of pipe gallery concrete under high water pressure coupling environment according to the present invention, a dual difference comparison of time and space dimensions is performed on the spatial multidimensional feature map to obtain the residual deviation, including: in the time dimension, subtracting the mean value of each measuring point in the historical normal reference period without being affected by the sudden change of high water pressure from the values ​​in the spatial multidimensional feature map to obtain a time difference sequence; in the spatial dimension, subtracting the synchronous feature value of the normal reference section adjacent to it and not directly affected by high water pressure from the feature map value of the current measuring point to obtain a spatial difference sequence; combining and superimposing the time difference sequence and the spatial difference sequence to remove the background trend term caused by the change of normal environmental temperature and humidity and the periodic fluctuation of daily background water pressure to obtain the residual deviation.

[0011] In a preferred embodiment of the method for assessing the durability damage of pipe gallery concrete under high water pressure coupling environment according to the present invention, the residual deviation is cross-verified by multi-source sensors to eliminate single-point burr noise and extract the net effect data of structural damage under high water pressure coupling. This includes: for abnormal jump points appearing in the residual deviation, obtaining the original sampled values ​​of seepage flow, pore water pressure, structural strain and acoustic emission signal at the corresponding time of the abnormal jump point; determining whether the pore water pressure, structural strain and acoustic emission signal simultaneously produce a staged coordinated jump that conforms to the laws of seepage and elasticity conduction; if the abnormal jump point only shows isolated fluctuations of a single physical parameter or a single measuring point and the other sensors do not show multi-source linkage, then the abnormal jump point is determined to be single-point burr noise caused by electrical burrs or instantaneous sensor failure and is directly eliminated, retaining the effective residual deviation value with multi-source coordinated characteristics; and extracting data based on the effective residual deviation value to obtain the net effect data of structural damage.

[0012] In a preferred embodiment of the method for assessing the durability damage of pipe gallery concrete under high water pressure coupling environment according to the present invention, data extraction is performed based on the effective residual deviation value to obtain the net effect data of structural damage, including: traversing the effective residual deviation value, performing numerical reconstruction and alignment merging according to the spatial topological order of each measuring point in the pipe gallery; filtering out non-structural disturbances, and normalizing and smoothing the merged multidimensional matrix data to obtain the net effect data of structural damage.

[0013] In a preferred embodiment of the method for assessing the durability damage of pipe gallery concrete under high water pressure coupling environment according to the present invention, the current measured water pressure load and the design ultimate water pressure are obtained, a mapping function with negative correlation and exponential decay with water pressure level is established, and a dynamic threshold for assessing concrete durability damage is generated. This includes: constructing a negative correlation exponential decay function with the ratio of the current measured water pressure load to the design ultimate water pressure as the independent variable, wherein the decay rate of the exponential decay function is quantitatively controlled by a preset water pressure sensitivity decay coefficient; multiplying the foundation damage threshold by the negative correlation exponential decay function to dynamically generate a dynamic threshold for assessing concrete durability damage that matches the current high water pressure environment.

[0014] In a preferred embodiment of the method for assessing the durability damage of pipe gallery concrete under high water pressure coupling environment according to the present invention, the negative correlation exponential decay function is:

[0015] ;

[0016] in, This is the current measured water pressure load. To design the ultimate water pressure, Based on the damage threshold, The water pressure sensitivity attenuation coefficient, The service life of the utility tunnel. This is a preset annual linear degradation rate constant for material properties.

[0017] One or more technical solutions provided in this invention have at least the following technical effects or advantages:

[0018] This invention acquires multi-source monitoring data by real-time acquisition of seepage flow, pore water pressure, structural strain, and acoustic emission signals from multiple points in a utility tunnel. It then performs time-frequency domain feature extraction and standardization on the multi-source monitoring data, and reconstructs the spatial geometric manifold to generate a spatial multidimensional feature map of concrete damage in the utility tunnel. A dual difference comparison of time and spatial dimensions is performed on the spatial multidimensional feature map to obtain residual deviation. This residual deviation is then cross-verified using multi-source sensors to eliminate single-point noise and extract the net effect data of structural damage under high water pressure coupling. The current measured water pressure load and the design ultimate water pressure are obtained, and a mapping function that negatively correlates with the water pressure level and exhibits exponential decay is established to generate a dynamic threshold for concrete durability damage assessment. The damage deviation of the net effect data of structural damage relative to the dynamic threshold of concrete durability damage assessment is calculated. Based on the damage deviation, levels are classified as non-triggered, local evolution, and global early warning, and corresponding structural safety operation and maintenance decision instructions are output. This achieves the technical effect of improving the reliability of damage assessment. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the method for assessing the durability damage of pipe gallery concrete under high water pressure coupling environment provided in an embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram of the process for generating a spatial multidimensional feature map of concrete damage in a pipe gallery under high water pressure coupling environment, as provided in the embodiments of the present invention. Detailed Implementation

[0022] This invention provides a method for assessing the durability damage of pipe gallery concrete under high water pressure coupling environment. It addresses the technical problem that existing technologies suffer from insufficient fusion depth of multi-source monitoring data, making it difficult to remove environmental background interference and single-point noise during assessment, resulting in significant discrepancies between the assessment and the actual situation.

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0024] Example 1, please refer to the accompanying drawings in the instruction manual. Figures 1-2 As shown in the figure, the present invention provides a method for assessing the durability damage of pipe gallery concrete under high water pressure coupling environment, the method comprising:

[0025] Real-time acquisition of seepage flow, pore water pressure, structural strain, and acoustic emission signals at multiple points in the pipe gallery yields multi-source monitoring data.

[0026] In one embodiment, seepage flow rate refers to the volume of liquid flowing out through a specific leakage section or settlement joint of the pipe gallery per unit time, used to characterize the degree of local sealing failure. Pore water pressure refers to the fluid pressure borne by pore water embedded in the concrete wall or surrounding rock, used to dynamically capture the evolution of external water and soil pressure loads. Structural strain refers to the minute deformation and elastic deformation characteristics of the pipe gallery concrete under the coupled action of static and dynamic loads and high water pressure immersion. Acoustic emission signal refers to the broadband high-frequency electrical signal formed by the instantaneous elastic stress wave released due to the initiation, propagation, or penetration of microcracks within the material. After hardware physical alignment and unified timestamp marking of the spatial temporal responses of the above-mentioned multiple physical fields, multi-source monitoring data is constructed, which provides underlying data input support without information loss for the subsequent construction of multi-dimensional feature maps.

[0027] Acquire the raw sampling terminal voltage and pulse signals of the distributed fiber optic sensor network, electromagnetic flowmeter, vibrating wire piezometer, vibrating wire strain gauge, and broadband acoustic emission sensor array deployed along key sections of the utility tunnel, such as the K12+300 section. Preferably, a multi-channel synchronous industrial data acquisition instrument is used to acquire real-time seepage flow, pore water pressure, structural strain, and instantaneous acoustic emission amplitude of the current section in parallel at a sampling rate of 100kHz. At the hardware level, an absolute clock pulse is used to uniformly assign millisecond-level synchronization timestamps to all physical channels, thereby eliminating timing misalignment caused by the response delay of different sensor hardware.

[0028] Meanwhile, an abnormal level limiting and fault-tolerant module based on sliding standard deviation is set at the data access end to perform dynamic truncation compensation for instantaneous electrical pulse spikes caused by lightning strikes or power grid fluctuations, preventing extreme outliers from contaminating the original data stream, and finally outputting multi-source monitoring data containing spatial coordinate markers, physical dimension mapping relationships and unified clock alignment.

[0029] The multi-source monitoring data is subjected to time-frequency domain feature extraction and standardization, and spatial geometric manifold reconstruction is performed to generate a spatial multidimensional feature map of concrete damage in the pipe gallery.

[0030] Furthermore, such as Figure 2 As shown, the multi-source monitoring data undergoes time-frequency domain feature extraction and standardization, and spatial geometric manifold reconstruction is performed to generate a spatial multidimensional feature map of concrete damage in the pipe gallery. This embodiment of the invention also includes:

[0031] Wavelet packet transform was used to extract the energy spectral entropy of acoustic emission signals in different frequency bands from multi-source monitoring data, and Fourier transform was used to extract the root mean square value and peak factor of time-domain fluctuations of seepage flow, pore water pressure and structural strain, so as to obtain the initial multi-source time-frequency domain characteristic values.

[0032] The initial multi-source time-frequency domain feature values ​​are standardized with zero mean and unit variance to obtain the multi-source time-frequency domain feature values.

[0033] Based on the multi-source time-frequency domain feature values, spatial geometric manifold reconstruction is performed to generate the spatial multidimensional feature map.

[0034] Furthermore, based on the multi-source time-frequency domain feature values, spatial geometric manifold reconstruction is performed to generate the spatial multidimensional feature map. This embodiment of the invention also includes:

[0035] According to the physical spatial geometric position of each monitoring section of the utility tunnel, the multi-source time-frequency domain feature values ​​are spliced ​​into a multi-dimensional matrix to construct the feature distribution matrix of each measuring point in the spatiotemporal dimension.

[0036] The feature distribution matrix is ​​linearly weighted and normalized to project the multi-physics field features onto a preset two-dimensional spatial plane coordinate system, thereby generating a spatial multidimensional feature map.

[0037] In embodiments of the present invention, energy spectral entropy is used to quantitatively characterize the dispersion and complexity of the energy distribution of acoustic emission signals in different frequency bands. The root mean square value of time-domain pulsation refers to the statistical root mean square index reflecting the magnitude of energy fluctuations in a physical quantity. The peak factor refers to the ratio of the signal peak value to the effective value, used to characterize the sudden impact characteristics of the signal. Zero-mean unit variance standardization refers to transforming physical characteristic values ​​with different dimensions into standard quantities with a mean of 0 and a variance of 1 through mathematical transformation, thereby eliminating model bias caused by significant differences in magnitude.

[0038] The acoustic emission signal is decomposed into different frequency bands using an 8-layer wavelet packet transform. The relative energy proportion in each frequency band is calculated, and the energy spectral entropy of the acoustic emission signal is extracted, such as 0.85. For seepage flow, pore water pressure and structural strain, the root mean square value of time-domain fluctuation and peak factor are extracted within the sliding time window using fast Fourier transform to obtain the initial multi-source time-frequency domain characteristic values.

[0039] Next, the initial multi-source time-frequency domain feature values ​​are standardized using a mathematical formula with zero mean and unit variance, wherein the mathematical formula is: ,in, The original data values, The mean, Standard deviation, These are standardized characteristic values. This eliminates the data magnitude imbalance caused by the different dimensions of flow rate, pressure, and strain, thus obtaining multi-source time-frequency domain characteristic values.

[0040] Preferably, the acoustic emission signal is progressively split into eight layers of 256 independent frequency bands using an eight-layer wavelet packet transform. During the specific decomposition and calculation, the node signal from the previous layer is simultaneously input into a low-pass filter and a high-pass filter to filter out the corresponding approximate low-frequency sub-band components and detailed high-frequency sub-band components, respectively. These two components are then downsampled by half. The generated sub-band components are then used as new nodes and input into the low-pass and high-pass filters of the next layer. This filtering and downsampling cycle is repeated until the eighth layer decomposition is completed, thus comprehensively covering the high-frequency stress wave band generated by the propagation of concrete microcracks. Next, the sum of the squares of the signals in each independent frequency band is calculated as the absolute energy of that band. The absolute energies of all 256 frequency bands are summed to obtain the total energy. The relative energy percentage of each frequency band is calculated by dividing the absolute energy of each band by the total energy. This relative energy distribution is used as a probability distribution to calculate the energy spectral entropy of the acoustic emission signal, yielding a specific value such as 0.85, which characterizes the frequency domain energy dispersion during the propagation of concrete microcracks.

[0041] Fast Fourier Transform (FFT) is used to calculate the root mean square (RMS) value and peak factor of time-domain pulsations within a set sliding time window to characterize the transient fluctuations and impact characteristics of the pipeline under high water pressure seepage. During feature extraction, the calculation source labels for each physical channel are retained, including direct measurement, sliding window mean interpolation, and dynamic amplitude limiting labels. If a physical parameter has incomplete data within the sliding window due to instantaneous packet loss, baseline alignment compensation is performed using the smoothing trend of adjacent time windows to prevent abrupt changes in feature values. Finally, a set of initial multi-source time-frequency domain feature values ​​with clear physical dimensions, spatiotemporal alignment, and elimination of instantaneous glitches is output, which can be directly used as a reliable input source for subsequent zero-mean unit variance standardization processing steps.

[0042] Spatial multidimensional feature map refers to a visualized multidimensional matrix image generated by projection, which intuitively represents the spatial geometric damage distribution of each measuring point along the entire pipeline under spatiotemporal coupling.

[0043] Based on the physical spatial geometric coordinates of each monitoring section along the utility tunnel, including longitudinal mileage, circumferential angle and radial burial depth, the acoustic emission energy spectrum entropy, the root mean square value of seepage flow time-domain fluctuation, the peak factor of pore water pressure and the time-frequency characteristics of structural strain are spliced ​​into a multi-dimensional matrix to construct the feature distribution matrix of each measuring point in the spatiotemporal dimension.

[0044] Next, linear weighting is performed on the feature distribution matrix. Based on the different weighting coefficients assigned by those skilled in the art according to the sensitivity of each physical field to concrete damage, the numerical range of the multi-physical field features is uniformly scaled to a preset two-dimensional spatial plane coordinate system using the minimum-maximum coordinate normalization method, so that each measuring point has a clear spatial geometric correspondence on the projected plane.

[0045] During manifold reconstruction and projection, the calculation source markers for each measurement point are retained, including direct measurement, spatial nearest interpolation, and restricted neighborhood smoothing. If a measurement point suffers from incomplete spatial coordinate data due to a brief communication interruption, local weighted interpolation is performed using the spatiotemporal correlation coefficient matrix of adjacent physical sections. The interpolation markers are recorded in the corresponding feature matrix without directly replacing the true response. The final output is an intuitive, continuous, and dimensionless multidimensional spatial feature map that eliminates interference from dimensions and glitches. Each measurement point in the map has a unique identifier, which can be a sequential number such as 1, 2, 3, 4, etc. The eigenvalues ​​of each measurement point in this map are closely related to the spatial topology of the measurement point and directly serve as a reliable input map for subsequent spatiotemporal double difference comparison and residual deviation acquisition steps.

[0046] The spatial multidimensional feature map is subjected to dual difference comparison of time and spatial dimensions to obtain residual deviation. The residual deviation is then cross-verified by multi-source sensors to remove single-point burr noise and extract the net effect data of structural damage under high water pressure coupling.

[0047] It should be noted that temporal dimension differencing refers to subtracting the current spatial multidimensional feature map value from the mean value of the historical normal reference period unaffected by high water pressure abrupt changes, point by point, to eliminate the long-term temperature and humidity evolution and seasonal variation baseline of the measuring point itself. Spatial dimension differencing refers to subtracting the current feature map value from the synchronous feature value of the adjacent normal reference section that is not directly affected by high water pressure, to eliminate the interference caused by the overall water pressure fluctuations in the entire pipe gallery environment. Residual deviation refers to the suspected deviation dataset retained after spatiotemporal dual differencing comparison and subtraction of conventional environmental and background trend terms. Multi-source sensor cross-verification refers to the mechanism of verifying the suspected deviation points through multi-physics field coordinated response by combining the physical transmission laws of seepage flow, pore water pressure, structural strain, and acoustic emission signals. Net structural damage effect data refers to the pure data set that truly reflects the structural damage under the coupling effect of high water pressure after completely removing environmental interference and single-point electrical glitch.

[0048] Furthermore, by performing a dual difference comparison of time and spatial dimension on the spatial multidimensional feature map, the residual deviation is obtained. This embodiment of the invention also includes:

[0049] In the time dimension, the mean value of each measuring point in the historical normal reference period without the impact of high water pressure sudden change is subtracted from the values ​​in the spatial multidimensional feature map to obtain the time difference sequence.

[0050] In the spatial dimension, the spatial difference sequence is obtained by subtracting the synchronous characteristic value of the normal reference section adjacent to it and not directly affected by high water pressure from the characteristic map value of the current measuring point.

[0051] The time difference sequence and the spatial difference sequence are combined and superimposed to remove the background trend term caused by the normal environmental temperature and humidity changes and the periodic fluctuations of daily background water pressure, so as to obtain the residual deviation.

[0052] Preferably, the historical normal reference period refers to the period during which the utility tunnel is not subjected to abnormal external high water pressure disturbance and the structure is in a stable service state. Typical historical characteristic data can be collected during the historical normal reference period. For example, the database retrieval interface extracts raw multi-source monitoring data from the pipe gallery over a continuous historical service period without external high water pressure surges, extreme weather disturbances, and stable structural monitoring indicators, such as the past 90 to 180 days. Combined with meteorological logs and historical inspection records, abnormal period data caused by routine equipment maintenance, extreme rainstorms, or sudden changes in temperature and humidity are automatically removed. Subsequently, for the selected clean historical data segments, a dynamic sliding time window is used to perform point-by-point alignment and parallel accumulation of multi-source time-frequency domain characteristic values ​​on a daily basis. The arithmetic mean of acoustic emission energy spectrum entropy, root mean square value of seepage flow, peak factor of pore water pressure, and structural strain characteristics of each measuring point at each historical moment is calculated. During the calculation process, a robust statistical filtering mechanism based on the absolute deviation of the median is introduced. If individual values ​​in a certain historical time window deviate from the normal range due to instantaneous sensor jitter, the extreme value is removed and the local mean is recalculated. This results in a set of historical normal benchmark mean values ​​that can reflect the long-term service evolution of the measuring point and completely eliminate occasional glitch interference, which can be directly used as stable base data for subsequent time dimension differential comparison. The time difference sequence is obtained by subtracting the corresponding historical normal baseline mean from the values ​​in the spatial multidimensional feature map at the current moment. This eliminates the long-term temperature and humidity evolution and seasonal variation of the measurement point itself, and directly serves as a reliable input baseline for subsequent spatial difference combination and background trend separation.

[0053] Preferably, by using the geographic information and spatial topology database of the utility tunnel, standard normal reference sections that are adjacent to both sides of the current monitoring point and have been confirmed by historical inspections and multi-source status checks to be unaffected by high water pressure are automatically retrieved and located. Subsequently, spatial topology alignment is performed, matching the characteristic map values ​​of the current monitoring section with the synchronous characteristic values ​​of the normal reference section point by point in the corresponding spatial grid nodes, both horizontally and vertically, to ensure that the two are completely synchronized at the sampling time and in the physical channel. Then, the synchronous characteristic values ​​of the normal reference section are subtracted point by point from the characteristic map values ​​of the current monitoring point to calculate the spatial difference sequence, thereby completely eliminating the common interference caused by the overall water pressure fluctuations and changes in macroscopic geological conditions of the entire utility tunnel environment.

[0054] During the spatial difference calculation process, the calculation source markers for each measuring point are retained, including direct spatial difference, interpolation of adjacent sections, and spatial smoothing markers. If some data is missing due to the temporary offline status of a local measuring point in an adjacent reference section, spatial weighted interpolation is performed using the spatiotemporal correlation characteristics of the normal sections on both sides. The interpolation markers are recorded in the corresponding difference matrix without directly replacing the true difference response. Finally, a set of clean and intuitive spatial difference sequences is output.

[0055] First, a matrix weighted fusion method is employed. Based on weights pre-defined by those skilled in the art, the temporal and spatial difference sequences are combined and superimposed at corresponding spatiotemporal grid nodes to construct a joint difference matrix encompassing the spatiotemporal evolution characteristics of the entire line. Subsequently, a combination of spatial low-pass filtering and temporal moving average processing is applied to this joint difference matrix. The low-pass filter removes high-frequency spikes in the spatial dimension, and the time-sliding window is used to calculate and subtract the gradually changing trend term, thereby completely eliminating background trend interference caused by alternating environmental temperature and humidity changes and periodic fluctuations in daily background water pressure. During the superposition and trend stripping process, the calculation source markers for each measuring point are retained, including direct superposition fusion, neighborhood filtering interpolation, and boundary smoothing markers. If a local area experiences boundary effects due to spatial matrix alignment, causing data fluctuations, linear compensation is performed using the trend gradient of neighboring normal grids. The interpolation markers are recorded in the corresponding matrix without directly replacing the true values. Finally, a set of pure, intuitive, and completely eliminated residual deviations, representing the true deviation state of the pipe gallery concrete structure deviating from conventional fluctuation patterns under the interweaving of multiple physical fields, is output.

[0056] Furthermore, by cross-verifying the residual deviation using multi-source sensors to eliminate single-point burr noise and extracting the net structural damage effect data under high water pressure coupling, this embodiment of the invention also includes:

[0057] For the abnormal jump points that appear in the residual deviation, the original sampled values ​​of seepage flow, pore water pressure, structural strain and acoustic emission signal at the corresponding time of the abnormal jump point are obtained.

[0058] Determine whether the pore water pressure, structural strain, and acoustic emission signal simultaneously produce a staged coordinated leap that conforms to the laws of seepage and elasticity conduction;

[0059] If the abnormal jump point is only manifested as an isolated fluctuation of a single physical parameter or a single measuring point and the other sensors do not show multi-source linkage, then the abnormal jump point is determined to be a single-point glitch noise caused by electrical glitch or instantaneous sensor failure and is directly eliminated, while retaining the effective residual deviation value with multi-source collaborative characteristics.

[0060] Data is extracted based on the effective residual deviation value to obtain the net effect data of structural damage.

[0061] Furthermore, based on the effective residual deviation value, data is extracted to obtain the net effect data of structural damage. This embodiment of the invention also includes:

[0062] Traverse the valid residual deviation values ​​and perform numerical reconstruction and alignment merging according to the spatial topological order of each measuring point in the utility tunnel;

[0063] After filtering out non-structural disturbances, the merged multidimensional matrix data is normalized and smoothed to obtain the net effect data of structural damage.

[0064] It should be noted that the abnormal jump point refers to the moment when the residual deviation exceeds the normal fluctuation threshold and manifests as a sudden numerical deviation. Multi-source sensor cross-verification refers to the mechanism of verifying the suspected deviation point through multi-physics field coordinated response by combining the physical transmission laws of seepage flow, pore water pressure, structural strain, and acoustic emission signals. The seepage and elasticity transmission laws refer to the inherent physical causal and temporal relationship between increased water pressure, sudden increase in pore water pressure, microcracks in the concrete matrix releasing acoustic emission signals, and local strain deformation in the structure. The net structural damage effect data refers to the pure data set that truly reflects the structural damage under the coupling effect of high water pressure after completely eliminating environmental interference and single-point electrical burrs.

[0065] For any abnormal jumps in the residual deviation, the processing program extracts the original sampled values ​​of seepage flow, pore water pressure, structural strain, and acoustic emission energy spectrum entropy within the corresponding time t0 and its preceding and following sliding time windows (e.g., t0 is 5 seconds). The program first constructs a cooperative response verification matrix for the multi-source physical fields to determine whether the pore water pressure, structural strain, and acoustic emission signal simultaneously exhibit a phased cooperative jump consistent with the laws of seepage and elasticity conduction.

[0066] Assuming that the residual deviation reaches its peak at time t0 at the k-th monitoring section, the processing program calculates the pore water pressure increment ΔP and the structural strain increment Δ And the acoustic emission energy spectrum entropy increment ΔH. A multi-source cooperative decision function S is defined. cross as follows:

[0067] ;

[0068] in, , , These represent the standard deviations of the corresponding parameters under normal service conditions. , , This is the weighting coefficient. If set... =0.35, =0.35, =0.30, when a certain measuring point =4.2, =3.8, When = 4.0, the calculation yields =3.99, which is much greater than the preset collaborative threshold value such as 2.5, indicating that the sharp increase in pore water pressure caused the structural strain to increase and was accompanied by the release of high-frequency acoustic emission energy, which is consistent with the physical conduction law. Therefore, this jump point is determined to be the real damage response and is retained.

[0069] If a certain anomalous jump point at time t0 only manifests as an isolated sharp increase in structural strain or acoustic emission signal, for example... =5.5, while the changes in pore water pressure and seepage flow at the same location and adjacent measuring points are close to 0, i.e. =0.1, calculated as follows =1.685<2.5 indicates that the other sensors do not exhibit multi-source physical linkage. The processing program determines that the abnormal jump point is a single-point glitch noise caused by electrical glitch or instantaneous sensor failure, and sets it to zero or removes it from the residual deviation matrix, retaining the effective residual deviation value with multi-source collaborative characteristics.

[0070] Spatial topological order refers to the order of measuring point nodes arranged according to their physical geometric positions, such as longitudinal mileage, circumferential angle, and radial burial depth. The unique identifiers of the measuring points are also arranged in this order. The process involves iterating through the effective residual deviation values, reconstructing and aligning the numerical data according to the spatial topological order of each measuring point in the utility tunnel, filtering out non-structural disturbances, and normalizing and smoothing the merged multidimensional matrix data. When obtaining the net structural damage effect data, the processing program inputs the effective residual deviation values ​​retained in the previous step. First, it iterates through all effective residual deviation values ​​that have passed multi-source collaborative verification. Based on the physical spatial geometric coordinates of each measuring point in the utility tunnel, including longitudinal mileage, circumferential angle, and radial coordinates, the discrete characteristic values ​​are rearranged according to the topological structure and assembled into a spatiotemporal characteristic distribution matrix, achieving precise alignment of data from different spatial measuring points under the same spatiotemporal reference.

[0071] Subsequently, the processing program searches the matrix along the spatial grid, sets a threshold range for minor fluctuations, and directly filters out and zeroes non-structural weak disturbances below the threshold range to avoid interference from minor sensor drift or background mechanical vibrations in subsequent analysis. Next, for the filtered multidimensional matrix data, a smoothing algorithm based on spatial neighborhood distance is used to calculate a weighted average of the values ​​of each node and its neighboring nodes within the matrix. The calculation results are then uniformly normalized and scaled according to the maximum and minimum values ​​of the matrix to eliminate abrupt changes in local amplitude caused by differences in sensor sampling.

[0072] During the numerical reconstruction, filtering, and smoothing process, the calculation source markers of each node are retained. If a local measuring point loses some cooperative signals due to a single-channel sensor failure, the processing program performs restricted interpolation based on the spatial correlation of adjacent normal measuring points. The interpolation markers are recorded in the corresponding matrix without directly replacing the actual evolution response. Finally, a set of net structural damage effect data with clear physical dimensions, spatiotemporal topological alignment, and elimination of non-structural noise interference is output, which can be directly used as a reliable input source for subsequent dynamic threshold generation and operation and maintenance decision-making steps.

[0073] Obtain the current measured water pressure load and the design ultimate water pressure, establish a mapping function that is negatively correlated with the water pressure level and decreases exponentially, and generate a dynamic threshold for assessing concrete durability damage.

[0074] Furthermore, by obtaining the current measured water pressure load and the design ultimate water pressure, establishing a mapping function that exhibits a negative correlation with the water pressure level and exponentially decays, and generating a dynamic threshold for assessing concrete durability damage, this embodiment of the invention also includes:

[0075] A negative correlation exponential decay function is constructed with the ratio of the current measured water pressure load to the design ultimate water pressure as the independent variable, wherein the decay rate of the exponential decay function is quantitatively controlled by a preset water pressure sensitivity decay coefficient.

[0076] By multiplying the basic damage threshold by the negative correlation exponential decay function, a dynamic threshold for assessing concrete durability damage that matches the current high water pressure environment is dynamically generated.

[0077] Furthermore, the exponential decay function for negative correlation is:

[0078] ;

[0079] in, This is the current measured water pressure load. To design the ultimate water pressure, Based on the damage threshold, The water pressure sensitivity attenuation coefficient, The service life of the utility tunnel. This is a preset annual linear degradation rate constant for material properties.

[0080] It should be noted that the measured water pressure load refers to the actual water pressure value acting on the pipe gallery structure at the current moment, collected by the pipe gallery water pressure sensor. The design limit water pressure refers to the nominal maximum limit water pressure that the pipe gallery structure can withstand during its design. The water pressure sensitivity attenuation coefficient is a control parameter used to characterize the sensitivity of the concrete structure's damage threshold to rising water pressure. The foundation damage threshold is the initial benchmark threshold for structural damage assessment when there is no high water pressure and material aging is not considered. Service life refers to the cumulative service time of the pipe gallery from its commissioning to the current sampling time. The annual linear decay rate constant of material performance refers to the average annual decay rate reflecting the performance decline of concrete materials due to natural aging over time. The dynamic threshold for concrete durability damage assessment is a real-time threshold value dynamically generated after comprehensively considering the impact of the current water pressure load and the natural aging of the material, used to determine whether the structure has suffered damage.

[0081] First, the current measured water pressure load is extracted through the sensor data interface. Then, the system's preset database is used to retrieve the design ultimate water pressure, foundation damage threshold, water pressure sensitivity attenuation coefficient, service life of the pipe gallery, and preset annual linear decay rate constant for material properties. Next, the processing program constructs a negatively correlated exponential decay function with the ratio of the current measured water pressure load to the design ultimate water pressure as the independent variable. This function calculates the water pressure ratio by dividing the current measured water pressure load by the design ultimate water pressure. The water pressure sensitivity attenuation coefficient is multiplied by this ratio, and the negative value is used as the exponent of the exponential function, resulting in a decay factor that decreases exponentially with water pressure levels. This ensures that the attenuation factor decreases as the water pressure load increases, thus quantitatively reflecting the reduction effect of high water pressure on the structural durability threshold. Simultaneously, the preset annual linear decay rate constant for material properties is multiplied by the service life, and the product is subtracted from the value of 1 to calculate the material property aging decay coefficient that evolves over service time. Subsequently, the basic damage threshold, the negative correlation index decay factor with water pressure, and the material performance aging decay coefficient are multiplied to calculate a dynamic threshold for assessing concrete durability damage that matches the current high water pressure environment and service life. During the threshold generation process, the calculation source markers for each input parameter are retained, including real-time water pressure acquisition markers, historical service life markers, and decay coefficient adjustment markers. If the measured water pressure suddenly increases, the dynamic threshold is refreshed in real time, so that it is dynamically adjusted downward as the water pressure increases, thereby achieving early warning of the risk of microcrack propagation and durability deterioration in concrete under high water pressure.

[0082] The damage deviation of the net effect data of structural damage relative to the dynamic threshold of concrete durability damage assessment is calculated. Based on the damage deviation, it is divided into non-triggered, local evolution and global early warning levels, and the corresponding structural safety operation and maintenance decision instructions are output.

[0083] In embodiments of the present invention, damage deviation refers to a scalar ratio between the net effect data of structural damage and the dynamic threshold for assessing concrete durability damage at the current moment. Structural safety operation and maintenance decision instructions refer to safety control instructions automatically dispatched based on the defined damage deviation level, including maintaining routine monitoring, issuing local inspection and reinforcement tasks, or initiating high-pressure emergency decompression and personnel evacuation.

[0084] The damage deviation of the net structural damage effect data relative to the dynamic threshold is calculated point-by-point at each spatial topology grid node. This is achieved by directly dividing the net structural damage effect data of the current node by the current dynamic threshold for concrete durability damage assessment. Subsequently, the calculated damage deviation is compared in a stepped interval manner with the system's preset first safety threshold, such as 0.8, and second warning threshold, such as 1.

[0085] If the damage deviation is less than the first set safety threshold, the node is determined to be in the non-triggered level, and the structural safety operation and maintenance decision instruction is to maintain the normal sampling frequency and store historical logs. If the damage deviation is greater than or equal to the first set safety threshold and less than the second set warning threshold, the node is determined to be in the local evolution level, and the structural safety operation and maintenance decision instruction is to automatically increase the sensor sampling frequency of the section where the node is located, generate a local inspection work order containing specific mileage coordinates and circumferential position, and send it to the operation and maintenance terminal.

[0086] If the damage deviation is greater than or equal to the second set warning threshold, the node is determined to have reached the global warning level. The structural safety operation and maintenance decision instruction is to immediately trigger the audible and visual alarm, and coordinate the control of the drainage valve of the pipe gallery to carry out emergency pressure reduction. At the same time, control instructions for emergency evacuation and structural reinforcement are issued.

[0087] For example, if the net structural damage effect data for a key measuring point calculated in the previous step is 75 MPa, and the currently calculated dynamic threshold for concrete durability damage assessment is 70.38 MPa, the processing program divides 75 by 70.38, calculating the damage deviation of the measuring point to be approximately 1.0656. Since 1.0656 is greater than or equal to the second preset warning threshold, such as 1, the measuring point is determined to be at the global warning level, and a high water pressure emergency decompression and structural reinforcement decision command is immediately triggered. During the judgment and command output process, the judgment source and decision timestamp are retained for each grid node. If the deviation calculation is abnormal in a certain area due to the failure of individual sensors, the processing program adopts the highest conservative level in the neighborhood as a fallback constraint to ensure that no missed reports occur, ultimately forming a closed-loop structural safety operation and maintenance decision output.

[0088] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Specific embodiments of this specification have been described above. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0089] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0090] This specification and accompanying drawings are merely illustrative examples of the invention and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its scope. Therefore, if such modifications and variations fall within the scope of the invention and its equivalents, the invention is intended to include these modifications and variations.

Claims

1. A method for assessing the durability damage of pipe gallery concrete under high water pressure coupling environment, characterized in that, The method includes: Real-time acquisition of seepage flow, pore water pressure, structural strain and acoustic emission signals at multiple points in the pipe gallery to obtain multi-source monitoring data; The multi-source monitoring data is subjected to time-frequency domain feature extraction and standardization, and spatial geometric manifold reconstruction is performed to generate a spatial multidimensional feature map of concrete damage in the pipe gallery. The spatial multidimensional feature map is subjected to a dual difference comparison of time and spatial dimensions to obtain the residual deviation. The residual deviation is then cross-verified by a multi-source sensor to remove single-point burr noise and extract the net effect data of structural damage under high water pressure coupling. Obtain the current measured water pressure load and the design ultimate water pressure, establish a mapping function that is negatively correlated with the water pressure level and decreases exponentially, and generate a dynamic threshold for assessing concrete durability damage. The damage deviation of the net effect data of structural damage relative to the dynamic threshold of concrete durability damage assessment is calculated. The damage deviation is divided into non-triggered, local evolution and global early warning levels, and the corresponding structural safety operation and maintenance decision instructions are output.

2. The method for assessing the durability damage of pipe gallery concrete under high water pressure coupling environment as described in claim 1, characterized in that, The multi-source monitoring data undergoes time-frequency domain feature extraction and standardization, followed by spatial geometric manifold reconstruction to generate a spatial multidimensional feature map of concrete damage in the utility tunnel, including: Wavelet packet transform was used to extract the energy spectral entropy of acoustic emission signals in different frequency bands from multi-source monitoring data, and Fourier transform was used to extract the root mean square value and peak factor of time-domain fluctuations of seepage flow, pore water pressure and structural strain, so as to obtain the initial multi-source time-frequency domain characteristic values. The initial multi-source time-frequency domain feature values ​​are standardized with zero mean and unit variance to obtain the multi-source time-frequency domain feature values. Based on the multi-source time-frequency domain feature values, spatial geometric manifold reconstruction is performed to generate the spatial multidimensional feature map.

3. The method for assessing the durability damage of pipe gallery concrete under high water pressure coupling environment as described in claim 2, characterized in that, Based on the multi-source time-frequency domain feature values, spatial geometric manifold reconstruction is performed to generate the spatial multidimensional feature map, including: According to the physical spatial geometric position of each monitoring section of the utility tunnel, the multi-source time-frequency domain feature values ​​are spliced ​​into a multi-dimensional matrix to construct the feature distribution matrix of each measuring point in the spatiotemporal dimension. The feature distribution matrix is ​​linearly weighted and normalized to project the multi-physics field features onto a preset two-dimensional spatial plane coordinate system, thereby generating a spatial multidimensional feature map.

4. The method for assessing the durability damage of pipe gallery concrete under high water pressure coupling environment as described in claim 1, characterized in that, Perform a dual difference comparison of time and spatial dimensions on the spatial multidimensional feature map to obtain the residual deviation, including: In the time dimension, the mean value of each measuring point in the historical normal reference period without the impact of high water pressure sudden change is subtracted from the values ​​in the spatial multidimensional feature map to obtain the time difference sequence. In the spatial dimension, the spatial difference sequence is obtained by subtracting the synchronous characteristic value of the normal reference section adjacent to it and not directly affected by high water pressure from the characteristic map value of the current measuring point. The time difference sequence and the spatial difference sequence are combined and superimposed to remove the background trend term caused by the normal environmental temperature and humidity changes and the periodic fluctuations of daily background water pressure, so as to obtain the residual deviation.

5. The method for assessing the durability damage of pipe gallery concrete under high water pressure coupling environment as described in claim 4, characterized in that, The residual deviation was cross-verified using multi-source sensors to eliminate single-point noise and extract the net structural damage effect data under high water pressure coupling, including: For the abnormal jump points that appear in the residual deviation, the original sampled values ​​of seepage flow, pore water pressure, structural strain and acoustic emission signal at the corresponding time of the abnormal jump point are obtained. Determine whether the pore water pressure, structural strain, and acoustic emission signal simultaneously produce a staged coordinated leap that conforms to the laws of seepage and elasticity conduction; If the abnormal jump point is only manifested as an isolated fluctuation of a single physical parameter or a single measuring point and the other sensors do not show multi-source linkage, then the abnormal jump point is determined to be a single-point glitch noise caused by electrical glitch or instantaneous sensor failure and is directly eliminated, while retaining the effective residual deviation value with multi-source collaborative characteristics. Data is extracted based on the effective residual deviation value to obtain the net effect data of structural damage.

6. The method for assessing the durability damage of pipe gallery concrete under high water pressure coupling environment as described in claim 5, characterized in that, Data extraction is performed based on the effective residual deviation value to obtain the net effect data of structural damage, including: Traverse the valid residual deviation values ​​and perform numerical reconstruction and alignment merging according to the spatial topological order of each measuring point in the utility tunnel; After filtering out non-structural disturbances, the merged multidimensional matrix data is normalized and smoothed to obtain the net effect data of structural damage.

7. The method for assessing the durability damage of pipe gallery concrete under high water pressure coupling environment as described in claim 1, characterized in that, Obtain the current measured water pressure load and the design ultimate water pressure, establish a mapping function that is negatively correlated with the water pressure level and decreases exponentially, and generate a dynamic threshold for assessing concrete durability damage, including: A negative correlation exponential decay function is constructed with the ratio of the current measured water pressure load to the design ultimate water pressure as the independent variable, wherein the decay rate of the exponential decay function is quantitatively controlled by a preset water pressure sensitivity decay coefficient. By multiplying the basic damage threshold by the negative correlation exponential decay function, a dynamic threshold for assessing concrete durability damage that matches the current high water pressure environment is dynamically generated.

8. The method for assessing the durability damage of pipe gallery concrete under high water pressure coupling environment as described in claim 7, characterized in that, The negative correlation exponential decay function is: ； in, This is the current measured water pressure load. To design the ultimate water pressure, Based on the damage threshold, The water pressure sensitivity attenuation coefficient, The service life of the utility tunnel. This is a preset annual linear degradation rate constant for material properties.

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