Method for quantifying damage and verifying bearing capacity of hollow beam body of cultural relic and ancient building

Abstract

The invention provides a cultural relic ancient building hollow beam body damage quantification and bearing capacity verification method, and relates to the technical field of data processing, and the method comprises the steps: obtaining the original detection data of a hollow beam body; noise filtering processing is carried out on the ultrasonic signal data and the infrared imaging data, and correction is carried out in combination with an environment humidity parameter; in combination with the water content data of the detection points, carrying out space partitioning on the structural area of the hollow beam body; analyzing humidity correction data in each space partition, identifying signal abnormal areas, and extracting damage features in each abnormal area; performing weighted normalization processing on the multi-dimensional damage parameters, establishing mapping in combination with the moisture content of the spatial partitions and statistical data, and generating a damage quantification result of each spatial partition; carrying out mechanical analysis by combining the material type, the geometric dimension and the load information of each space partition, and generating a bearing capacity distribution result of the hollow beam body; the accuracy of the method for quantifying the damage of the hollow beam body of the cultural relic and ancient building and verifying the bearing capacity is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method for quantifying damage and verifying bearing capacity of hollow beams in cultural relics and ancient buildings. Background Art

[0002] Prior art typically uses a combination of non-destructive testing and structural finite element analysis to assess the damage and bearing capacity of hollow beams in cultural relics and ancient buildings. The general process involves first using ultrasonic and infrared imaging equipment to obtain information about the internal damage of the hollow beams. This data is then fed into a structural analysis model, where a three-dimensional finite element model is constructed to simulate the mechanical properties of the beams in their damaged state. This allows for a quantitative assessment of the damage to the hollow beams and, based on this information, an inference of their actual bearing capacity. This method accurately reflects the health of hollow beams without damaging the original structure of the ancient building.

[0003] However, in actual applications, existing technologies have obvious deficiencies in their ability to identify microcracks and hidden damage when the hollow beams of ancient buildings are in a humid environment for a long time. For example, in a typical southern Jiangnan water town ancient building renovation project, due to the long-term moisture exposure of the hollow wooden beams, the interior of the beams gradually became moldy and microcracks expanded, but the surface remained intact. When inspectors used ultrasonic methods for testing, they found that due to the increased moisture content of the wood, the propagation speed and attenuation characteristics of the sound waves changed, and part of the sound wave signal was absorbed by the damp layer, resulting in the internal microcracks and moldy areas not being effectively detected. In this case, the test results cannot fully reflect the actual damage state of the beams, affecting the accuracy of the subsequent bearing capacity analysis, thereby introducing uncertainty into the decision-making of cultural relics repair. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for quantifying the damage and verifying the bearing capacity of hollow beams in cultural relics and ancient buildings, aiming to solve the problems mentioned in the background technology.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows: A method for quantifying damage and verifying bearing capacity of hollow beams in cultural relics and ancient buildings, the method comprising: Acquire original detection data of the hollow beam, wherein the original detection data includes ultrasonic signal data, infrared imaging data, environmental humidity parameters and moisture content data of each detection point, to obtain an original data set; Based on the original data set, the ultrasonic signal data and infrared imaging data are subjected to noise filtering and correction based on the environmental humidity parameters to obtain a humidity-corrected data set. Based on the humidity correction data set and the moisture content data of the detection points, the structural area of ​​the hollow beam is spatially partitioned to obtain a spatial partition data set; According to the spatial partition data set, the humidity correction data in each spatial partition is analyzed, the signal abnormal area is identified, and the damage feature data set is obtained by extracting the damage feature in each abnormal area, and the damage feature data set includes signal attenuation rate, maximum amplitude, period change amount and energy change value data; According to the damage feature data set, the multi-dimensional damage parameters are weighted and normalized, the mapping is established combined with the spatial partition moisture content and statistical data, the residual damage quantization result of each spatial partition is generated, and the residual damage quantization result data set is obtained; According to the residual damage quantization result data set and the spatial partition data set, combined with the material type, geometric size and load information of each spatial partition, mechanical analysis is carried out to generate the bearing capacity distribution result of the hollow beam body.

[0006] The above-mentioned scheme of the present application at least includes the following beneficial effects: Firstly, by simultaneously acquiring ultrasonic signal data, infrared imaging data, environmental humidity parameters and moisture content data of each detection point, the comprehensiveness and pertinence of the original data are significantly improved. Especially when the beam body is in a humid environment for a long time, the water distribution inside and on the surface of the detection point and its actual influence on the signal response can be truly reflected, providing multi-dimensional and fine basic data for subsequent damage identification and bearing capacity analysis.

[0007] Secondly, in the signal processing process, not only multi-stage noise filtering in time domain and frequency domain is implemented, but also signal correction means based on environmental humidity parameters are introduced, which can effectively correct signal attenuation and distortion caused by changes in external humidity and material moisture content. Through the humidity compensation mechanism, the inhibition effect of the humid environment on the detection sensitivity of ultrasonic waves is overcome, and the identification accuracy of hidden damages such as micro-cracks and mold is greatly improved.

[0008] Thirdly, combined with the spatial partition method and spatial partition moisture content analysis, more targeted spatial partition health evaluation is realized. Through the extraction of humidity correction data and damage features of each spatial partition, the signal abnormal area and damage distribution can be accurately positioned under the complex environment and uneven material inside the beam body, significantly reducing the missed detection and misjudgment problems of traditional detection methods in humid environment.

[0009] In addition, the present application also realizes the quantitative output of the residual damage degree of the spatial partition through the weighted normalization processing and statistical mapping of the multi-dimensional damage parameters. On this basis, combined with the material type, geometric size and load information of the spatial partition, mechanical analysis is carried out, which effectively improves the response sensitivity and scientificity of the bearing capacity evaluation result to the actual residual damage condition, and enhances the quantitative basis and reliability of the decision-making of the repair and protection of ancient buildings.

[0010] In general, the present invention can significantly improve the ability to identify microcracks and hidden damage and the accuracy of bearing capacity analysis in hollow beams of cultural relics and ancient buildings in humid environments and with complex structural material distribution conditions. It overcomes the problems of incomplete identification of high-moisture content areas and distorted quantitative analysis in existing technologies, and provides an innovative technical path for high-quality, intelligent detection and evaluation in the field of cultural relics protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 This is a flowchart of a method for quantifying damage and verifying bearing capacity of hollow beams in cultural relics and ancient buildings provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0012] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0013] like Figure 1 As shown, an embodiment of the present invention provides a method for quantifying the damage and verifying the bearing capacity of hollow beams in cultural relics and ancient buildings, the method comprising: Acquire original detection data of the hollow beam, wherein the original detection data includes ultrasonic signal data, infrared imaging data, environmental humidity parameters and moisture content data of each detection point, to obtain an original data set; Based on the original data set, the ultrasonic signal data and infrared imaging data are subjected to noise filtering and correction based on the environmental humidity parameters to obtain a humidity-corrected data set. Based on the humidity correction data set and the moisture content data of the detection points, the structural area of ​​the hollow beam is spatially partitioned to obtain a spatial partition data set; Analyze the humidity correction data in each spatial partition based on the spatial partition data set, identify signal abnormal areas, and extract damage features in each abnormal area to obtain a damage feature data set. The damage feature data set includes signal attenuation rate, maximum amplitude, periodic variation, and energy variation value data. Based on the damage feature dataset, multi-dimensional damage parameters are weighted and normalized, and a mapping is established by combining the moisture content of the spatial partitions with statistical data to generate the damage quantification results of each spatial partition and obtain the damage quantification result dataset. Based on the damage quantification result dataset and the spatial partition dataset, combined with the material type, geometric dimensions, and load information of each spatial partition, a mechanical analysis is performed to generate the bearing capacity distribution results of the hollow beam.

[0014] In an embodiment of the present invention, by acquiring the original test data of the hollow beams, a comprehensive and accurate quantitative assessment of the health status of the hollow beams of cultural relics and ancient buildings can be achieved. The original test data includes not only ultrasonic signal data that is extremely sensitive to the internal state of the beam structure, but also infrared imaging data that can reflect the surface and internal thermal physical changes. At the same time, considering the significant impact of the environmental conditions of cultural relics and ancient buildings on the test results, the environmental humidity parameters and moisture content data of each test point are further collected, so that the original data set can truly restore the mechanical state and damage distribution of the beam structure under the actual preservation environment.

[0015] The collected ultrasonic signal data and infrared imaging data undergo multi-stage noise filtering and signal correction. Dynamic compensation for signal amplitude and attenuation characteristics is implemented in conjunction with ambient humidity parameters, ultimately generating a humidity-corrected dataset that eliminates external environmental interference. The accuracy of humidity-corrected data is directly related to the reliability of subsequent damage analysis. Furthermore, combined with moisture content data from each inspection point, spatial zoning of the beam structure is performed. This effectively reveals spatially uneven moisture content distribution, providing a solid data foundation for spatial zoning health assessments and localized repair decisions.

[0016] After spatial partitioning is complete, a detailed analysis of the humidity-corrected data within each spatial partition is conducted to identify areas of signal anomaly with potential risks. Targeted damage signatures are then extracted from these areas. The resulting damage signature dataset includes multi-dimensional parameters such as signal attenuation rate, maximum amplitude, periodic variation, and energy variation. These parameters can comprehensively reflect the complexity and diversity of structural damage within the spatial partition.

[0017] The obtained damage characteristic data set is used to perform weighted normalization on the multi-dimensional damage parameters. At the same time, a mapping relationship is established by combining the moisture content of each spatial partition with relevant statistical data to achieve quantitative output of the degree of damage of each spatial partition, thereby obtaining a damage quantification result data set. Finally, the damage quantification result data set and the spatial partition data set are combined to further introduce the material type, geometric dimensions and load information of each spatial partition, conduct a mechanical analysis, and comprehensively generate the bearing capacity distribution results of the hollow beam. The embodiments of the present invention can provide scientific, accurate, and quantitative data support for the scientific protection, risk warning, and reasonable repair decision-making of cultural relics and ancient buildings, and effectively improve the modernization level of historical building protection and utilization.

[0018] The acquisition of the original inspection data of the hollow beam specifically includes: In the detection preparation stage, first determine the spatial distribution of detection points according to the structural arrangement of the hollow beam body and the site conditions. The detection points should cover typical parts such as the upper flange, web, lower flange and end of the beam body to ensure the representativeness and completeness of the data. Then, use non-contact ultrasonic wave emission and receiving equipment to obtain internal reflection wave data, record the original signal sequence containing propagation time, signal strength and waveform characteristics as ultrasonic wave signal data. This data can reflect the geometric changes, defect location and possible development direction of the structure inside.

[0019] At the same time, use infrared thermal imaging equipment to collect the thermal field response of the hollow beam body under certain excitation conditions (such as natural heating, local heating or sunlight change). Through the infrared camera, record the distribution information of the surface infrared radiation intensity at different times and positions, and generate infrared imaging data. This data can reflect the changes in local material thermal conductivity and heat capacity of the beam body, thereby assisting in identifying potential damage or cavities.

[0020] In addition, in order to accurately evaluate the influence of the environment on the detection signal, the environmental humidity parameters of the current measurement area need to be collected. The relative humidity of the air during detection can be recorded in real time by a field temperature and humidity recorder. For the moisture content data of each detection point, a portable wood or multi-material moisture content tester is used for non-destructive testing on the surface or specified depth of the structure, and the corresponding spatial coordinates of the detection points are marked. The corresponding moisture content percentage value is output.

[0021] Finally, the above detection data is integrated into the original data set, providing data support for subsequent signal processing and structure analysis steps, ensuring that the data sources are complete and traceable.

[0022] In a preferred embodiment of the present application, according to the original data set, the ultrasonic wave signal data and the infrared imaging data are subjected to noise filtering processing, and are corrected in combination with the environmental humidity parameters to obtain a humidity corrected data set, including: According to the original data set, the ultrasonic wave signal data is subjected to time domain noise removal processing, and the infrared imaging data is subjected to frequency domain noise removal processing to obtain a purified signal data set; According to the purified signal data set, in combination with the environmental humidity parameters in the original data set, the amplitude and attenuation parameters of the ultrasonic wave signal and the infrared imaging signal are subjected to humidity compensation respectively by using a preset humidity influence function to obtain a humidity compensated signal data set; According to the humidity compensated signal data set, the filter parameters are adaptively adjusted for the signal abnormal fluctuation area to jointly suppress the residual noise and signal distortion in the local high humidity area, and a humidity corrected data set is generated.

[0023] In this embodiment of the present invention, a phased signal processing and environmental compensation strategy is implemented for the raw data set collected during the hollow beam inspection process. First, the ultrasonic signal data in the raw data set is filtered using time-domain filtering to remove environmental noise. For the infrared imaging data, a frequency-domain filtering algorithm is used to remove high- and low-frequency stray signals, thereby obtaining a purified signal data set. These steps significantly improve the signal-to-noise ratio, allowing subsequent data analysis to focus on the physical response characteristics of the structure without interference from external noise and instrument errors.

[0024] Based on the purified signal dataset, the ambient humidity parameters in the original dataset are further collected, and a preset humidity influence function is used to dynamically compensate for the amplitude and attenuation parameters of the ultrasonic and infrared signals. This allows the signal response to be corrected for different ambient humidity levels based on the actual humidity level at the test site, effectively eliminating the nonlinear effects of the test environment on the data. For example, when inspecting beams during a humid season in southern China, the attenuation characteristics of the original ultrasonic signal can significantly shift due to changes in air humidity. This compensation mechanism enables standardized data processing.

[0025] After compensation is complete, the humidity-compensated signal dataset is further analyzed. For areas with abnormal signal fluctuations, the residual noise and signal distortion in localized high humidity regions are automatically suppressed by adaptively adjusting filter parameters. This strategy not only ensures the overall integrity of the signal data, but also effectively eliminates signal anomalies caused by local environmental or structural characteristics, greatly improving the reliability of the humidity-corrected dataset. The resulting humidity-corrected dataset provides a high-quality raw input foundation for subsequent spatial partitioning and damage analysis, ensuring the scientific nature and accuracy of the entire damage assessment and bearing capacity analysis process.

[0026] The preset humidity influence function specifically includes: The humidity impact function reflects the effect of changes in ambient humidity on the propagation characteristics of detection signals (such as signal strength, attenuation rate, and propagation time). It is primarily used for dynamic correction processing of signal data. The development of this function is typically based on experimental measurements and statistical modeling. First, humidity gradients are applied to a variety of typical cultural heritage building materials (such as fir, pine, old brick, and limestone) under a standard experimental environment. The effects of varying humidity levels on ultrasonic propagation velocity and infrared imaging response are recorded under the same conditions.

[0027] Based on experimental data, the relationship between humidity and signal amplitude was extracted to create a humidity impact curve. For example, when the ambient humidity rises from 40% to 90%, the ultrasonic signal amplitude exhibits a nonlinear decreasing trend, with its attenuation rate limited by the coupling effect of material type and moisture content. Based on this variation pattern, a humidity impact function was constructed as a mapping relationship, establishing a corresponding adjustment factor between the measured ambient humidity value and the target signal parameter.

[0028] In actual compensation, based on the ambient humidity values ​​recorded during testing, an adjustment factor is extracted from the humidity impact function and applied to the signal amplitude correction and attenuation parameter restoration process, achieving dynamic compensation for environmental changes. This function can be pre-stored in the processing module and called upon by material type, standardizing and automating the calibration process.

[0029] Among them, based on the humidity compensation signal dataset, the filter parameters are adaptively adjusted for the abnormal signal fluctuation area, and the residual noise and signal distortion in the local high humidity area are jointly suppressed to generate a humidity correction dataset. Specifically, it includes: First, the humidity-compensated signal dataset is fed into the preprocessing module, which analyzes the signal's continuity over time and identifies areas in the signal waveform that exhibit sudden changes, discontinuities, and frequent abnormal peaks. These areas are often concentrated at detection points with high humidity levels and manifest as sudden decreases in signal energy, distortions, or background noise.

[0030] Subsequently, an adaptive filtering strategy is employed to dynamically adjust filter parameters for these signal anomaly areas. Initially, the filter window length and frequency band are set, and the filter response strength is automatically adjusted in real time based on the local signal variation, fluctuation frequency, and noise distribution level. For example, if continuous high-frequency disturbances are detected in a certain signal segment, the window width is reduced to enhance edge response, while the frequency band is narrowed to reduce high-frequency noise penetration. If the detected signal is in a low-frequency tailing segment, the time window is expanded to preserve the signal's main components.

[0031] In addition, in order to cope with the signal attenuation and distortion caused by high humidity, an amplitude repair mechanism based on the historical signal mean and material properties is introduced after filtering processing. The signal amplitude is restored to the lower limit standard range of humidity through the compensation factor, thereby forming a signal response output under a unified standard.

[0032] Through the above-mentioned adaptive filtering and amplitude reconstruction processing, the abnormal high-humidity sections in the humidity compensation signal dataset are jointly suppressed. The final output humidity correction dataset significantly reduces the noise interference and distortion errors in high-humidity environments while maintaining the signal morphological characteristics, providing a stable and reliable signal basis for subsequent spatial partitioning and damage identification.

[0033] In a preferred embodiment of the present application, the structural region of the hollow beam body is spatially partitioned according to the humidity correction data set in combination with the moisture content data of the detection points to obtain a spatial partition data set, including: According to the humidity correction data set and the moisture content data of each detection point, the hollow beam body is divided into initial detection units at equal distances to generate preliminary spatial partition data; According to the preliminary spatial partition data, the clustering radius is dynamically adjusted for the region with large moisture content difference to automatically form a candidate spatial partition group and generate clustering spatial partition data; According to the clustering spatial partition data, the abnormal signal region in the clustering spatial partition is refined and segmented in combination with the signal amplitude change and the structural geometric parameters in the spatial partition to generate multi-level spatial partition data; The multi-level spatial partition data is subjected to boundary consistency verification, and for the region with large signal fluctuation at the boundary of adjacent spatial partitions, boundary smoothing processing is performed by resetting the spatial partition limit to generate the spatial partition data set.

[0034] In the embodiment of the present application, the humidity correction data set and the moisture content data of the detection points are used for scientific spatial partition of the structural region of the hollow beam body. First, the entire beam body is divided into a plurality of initial detection units according to the equal distance division principle to generate preliminary spatial partition data. This process ensures the uniformity of spatial partition coverage and helps to realize the monitoring of the entire structure.

[0035] Subsequently, in view of the actual situation that there is a large difference in moisture content distribution in each initial detection unit, the clustering radius is dynamically adjusted to automatically form a plurality of candidate spatial partition groups through an adaptive clustering algorithm to generate clustering spatial partition data. Through this method, the moisture content changes caused by material quality, environment and historical repair differences of each part of the beam body can be fully considered, so that the spatial partition is more in line with the actual humidity and damage distribution of the beam body.

[0036] Further, in combination with the signal amplitude change and the structural geometric parameters in the spatial partition, a spatial partition refinement processing method taking damage signal sensitivity as the criterion is adopted to further refine and segment the abnormal signal region in the clustering spatial partition, thereby generating multi-level spatial partition data. This multi-level spatial partition structure greatly improves the ability to identify local abnormal regions and is beneficial to the development of subsequent targeted repair and protection schemes.

[0037] Finally, the multi-level spatial partitioning data was subjected to boundary consistency verification. In cases where the signal fluctuations at the boundaries of adjacent spatial partitions were significant, the boundaries were smoothed by resetting the spatial partitioning boundaries, ultimately generating a highly consistent and high-resolution spatial partitioning dataset. This spatial partitioning strategy accurately reflects changes in the local moisture content and signal characteristics of the structure, providing solid technical support for damage identification and load-bearing capacity spatial zoning analysis of hollow beams in cultural relics and historic buildings.

[0038] Among them, based on the humidity correction data set and the moisture content data of each test point, the hollow beam is divided into initial test units with equal distances, and preliminary spatial partitioning data is generated, including: First, a three-dimensional geometric coordinate system is established for the hollow beam based on its structural shape and detection coverage. Using the beam's axial direction as the reference, the beam is evenly divided into several sections along its length. The spacing between these sections can be determined based on the ultrasonic sensor spacing or the actual infrared imaging resolution. A common setting is to place a detection unit every 0.5 meters.

[0039] During the division process, each detection unit is numbered and the number of the detection points within it is recorded. Within this area, the humidity correction signal, infrared image segment, and moisture content data at the corresponding location are extracted to form the initial detection unit data structure. The data format of each unit can be expressed as: {location coordinates, humidity correction signal set, infrared image sub-block, moisture content value}.

[0040] To prevent boundary effects from affecting signal clustering, a buffer overlap of 10 to 20 centimeters was added between cells for subsequent smoothing. This segmentation yielded preliminary spatial partitioning data that completely covered the hollow beams, supporting the subsequent identification of moisture accumulation areas and areas susceptible to signal distortion.

[0041] Among them, based on the preliminary spatial partitioning data, the cluster radius is dynamically adjusted for areas with large differences in moisture content, candidate spatial partition groups are automatically formed, and clustered spatial partitioning data is generated, specifically including: First, the moisture content of all preliminary detection cells is normalized so that its value is distributed between [0, 1]. Then, a density-based spatial clustering algorithm (such as a DBSCAN variant) is used to jointly cluster the initial cells based on their spatial coordinates and moisture content gradients.

[0042] Traditional fixed-radius clustering algorithms are prone to the problem of local clusters being too fine or too coarse. Therefore, this method introduces the coefficient of variation of local moisture content (i.e., the ratio of the standard deviation of the moisture content of adjacent detection units to the mean) as a tuning parameter. When this coefficient of variation exceeds a preset threshold (e.g., 0.25), the current cluster radius is automatically reduced to capture areas with significant local moisture content variation. When the coefficient of variation is below the threshold, the clustering scale is appropriately relaxed.

[0043] During the clustering process, “isolated units” that are not classified into any cluster are regarded as water-bearing anomalies and recorded in special spatial partitions for subsequent analysis of whether the abnormal signals are associated with structural damage.

[0044] After clustering is complete, the member unit index of each candidate cluster is output and saved as a cluster space partition data structure. This structure will simultaneously include spatial continuity, water content consistency, and signal feature consistency, which will facilitate subsequent multidimensional analysis.

[0045] Among them, according to the cluster space partition data, combined with the signal amplitude changes and structural geometric parameters within the spatial partition, the abnormal signal area within the cluster space partition is refined and segmented to generate multi-level spatial partition data, specifically including: First, the key characteristic indicators of the humidity correction signal are extracted in each cluster space partition, including average energy density, main frequency distribution, signal attenuation speed, waveform period change, etc. A feature vector is constructed for each unit.

[0046] Subsequently, the Euclidean distance or cosine similarity between the signal features of all detection units is calculated, and regions with large distances or sudden drops in similarity are classified as "highly heterogeneous segments." These regions are usually significantly associated with potential damage areas.

[0047] In addition, spatial partitioning structural geometric parameters are used as auxiliary inputs. For example, if a region is located in a node transition section of a hollow beam, a section with a variable cross-section, or a stress-concentrated area, even if the signal difference is not significant, it should be forcibly set as an independent small spatial partition to enhance model interpretability.

[0048] Finally, by combining the signal sensitivity features with the geometric feature rules, a refined segmentation is performed to form a multi-level spatial partition structure, in which each subspace partition contains a relatively consistent humidity response and signal pattern.

[0049] Among them, the boundary consistency check is performed on the multi-level spatial partition data. For areas where the signal fluctuations between adjacent spatial partition boundaries are large, the boundary smoothing processing is performed by resetting the spatial partition boundaries to generate a spatial partition data set. Specifically, it includes: Centered on the boundary of each spatial partition, select several adjacent detection units (e.g., three each) and extract their signal amplitude curves and moisture content gradient curves. Calculate the rate of change of their first-order derivatives. If there is a sudden change at the boundary (e.g., the rate of change exceeds twice the adjacent mean), the boundary is considered to have an "allergic reaction" and is considered a partitioning error.

[0050] For the above-mentioned abnormal boundaries, one of three processing methods can be adopted: ① Merge adjacent small spatial partitions; ② Shift the boundary position to the point where the signal change slows down; ③ Reconstruct the attributes of the boundary unit by weighting the data of the spatial partitions on both sides and set it as an independent transition zone.

[0051] The final output spatial partitioning dataset will have the following characteristics: naturally smooth boundaries, consistent internal signal characteristics, and reasonable spatial moisture content changes. It can be directly used in subsequent damage identification and damage modeling tasks.

[0052] In a preferred embodiment of the present invention, based on the spatial partition data set, the humidity correction data in each spatial partition is analyzed to identify the abnormal signal area, and damage features are extracted in each abnormal area to obtain a damage feature data set, including: According to the spatial partition data set, the humidity correction data in each spatial partition is arranged in chronological order, and the signal continuity change index is calculated to obtain the spatial partition signal continuity data; According to the spatial partition signal continuity data, the local extreme value, mutation point and high-frequency disturbance interval of the signal in each spatial partition are extracted as the signal anomaly judgment criteria to generate abnormal area positioning data; For the humidity correction signal corresponding to the abnormal area positioning data, the signal attenuation rate, maximum amplitude, periodic change and energy change value are calculated to generate a damage feature data set.

[0053] In this embodiment of the present invention, spatially partitioned datasets provide a refined foundation for identifying local damage characteristics in hollow beam structures. For each spatial partition, the humidity-corrected data within that partition is first arranged in chronological order. Signal continuity data for that partition is then calculated by calculating the signal continuity change index. This not only reflects the temporal evolution characteristics of the local structure but also promptly captures subtle signal perturbations caused by environmental or structural changes.

[0054] Furthermore, based on the spatial partition signal continuity data, the system extracts local signal extremes, mutation points, and high-frequency disturbance intervals within each spatial partition. These signal variation characteristics serve as key criteria for determining structural damage and material anomalies. By focusing on signal discontinuities and areas of abnormal fluctuation, the system can efficiently and accurately locate abnormal areas. Finally, combining these analysis results to generate abnormal area location data, enabling precise identification of localized damage or anomalies within the beam structure.

[0055] For located abnormal areas, the humidity-corrected signal data is further subjected to multi-dimensional parameter calculations, including signal attenuation rate, maximum amplitude, periodic variation, and energy variation, to generate a damage signature dataset. Joint analysis of these signal parameters enables a comprehensive assessment of the physical characteristics of the damage type. For example, an abnormal periodic variation can indicate the presence of periodic microcracks, while a sudden decrease in energy variation often corresponds to localized material degradation or loss. This provides high-quality basic data support for subsequent damage quantification and load-bearing capacity analysis.

[0056] Among them, according to the spatial partition data set, the humidity correction data in each spatial partition is arranged in chronological order, and the signal continuity change index is calculated to obtain the spatial partition signal continuity data, which specifically includes: First, the humidity correction data of all detection points in each spatial partition is extracted from the spatial partition dataset. The humidity correction data of each detection point contains the infrared thermal image value corresponding to the ultrasonic signal sequence and the infrared image. These data are all timestamps and can form a time series.

[0057] Then, for each spatial partition, the detection point signals are arranged in the order of acquisition time. To ensure uniformity in the time dimension, linear interpolation can be used to fill in the missing time points when there are uneven time intervals, so that the signal data of each spatial partition remains equidistant on the time axis.

[0058] After sequence normalization is completed, the signal continuity change index is calculated for each signal sequence. This index can be achieved by the following methods: First, each signal segment is divided into several time windows (for example, one window every 5 seconds), and the average signal amplitude is calculated in each window.

[0059] Then, the difference in the average amplitude of adjacent windows is compared window by window, and the number of mutations is counted.

[0060] If the signal continuity is good, the amplitude changes smoothly and the number of mutations is small; if the signal is abnormal, it will fluctuate violently in a short period of time and the number of mutations will increase.

[0061] Finally, the number of mutations or the average change amplitude of each spatial partition is used as the "continuity index" of the spatial partition.

[0062] For infrared imaging data, a similar processing process can be performed by extracting the central value sequence of temperature pixels in the hot spot area in the image.

[0063] This continuity index can be used to identify signal segments that are severely interfered with or have signs of damage, providing a quantitative basis for the next step of anomaly location.

[0064] Among them, according to the spatial partition signal continuity data, the local extreme value, mutation point and high-frequency disturbance interval of the signal in each spatial partition are extracted as the signal anomaly judgment criteria to generate abnormal area positioning data, specifically including: First, smooth each signal time series (such as sliding average) to reduce occasional noise interference, and then perform the following analysis steps: Local extreme value extraction: Scans time series data to find local maximum and minimum points. If the number of extreme values ​​in a certain signal segment is significantly higher than the average, it may indicate signal instability or abnormal material conditions.

[0065] Disruption point identification: Using a sliding window difference method, the signal sequence is averaged over the preceding and following time periods and the difference is calculated. If this difference exceeds a set threshold (e.g., 1.5 times the current signal standard deviation), it is identified as a disruption point. Areas where disruption points are concentrated may correspond to material damage or interface defects.

[0066] High-frequency disturbance interval extraction: Analyze the signal's frequency domain structure through fast Fourier transform (FFT) or short-time energy analysis. If a sudden increase in high-frequency energy occurs within a certain time window, or if the signal power is concentrated in the high-frequency band, it is marked as a high-frequency disturbance region.

[0067] The three anomaly indicators mentioned above can be used in combination. In each spatial partition, if the signal data of a detection point meets two or more anomaly characteristics at the same time, the location of the point is determined to be an "anomaly candidate area."

[0068] The location information of all abnormal detection points and their spatial partition numbers are summarized to generate "abnormal area positioning data". This data structure contains the abnormality type, detection point number, abnormality level and spatial coordinates, which can be used to guide subsequent damage feature extraction work.

[0069] Among them, the humidity correction signal corresponding to the abnormal area positioning data is calculated, and the signal attenuation rate, maximum amplitude, periodic change and energy change value are calculated to generate a damage feature data set, which specifically includes: First, read the time series signal of each abnormal detection point in the abnormal area positioning data and extract the corresponding humidity correction signal segment. The following parameter analysis is performed on each signal segment: Signal decay rate: Compares the amplitude decay at the beginning and end of a signal propagation path. This is calculated as the ratio of the signal's initial amplitude to its final amplitude, using normalization to represent the energy decay trend. An unusually rapid decay rate may indicate internal structural voids or interface cracks.

[0070] Maximum Amplitude: Counts the peak values ​​of each signal segment to identify abnormal excitation responses. If the peak values ​​are too high or too low, it may indicate material defects or impedance changes.

[0071] Periodic Variation: Divide the signal into several oscillation periods and calculate the consistency of the intervals between them. If the periodic variation is significantly unstable, it indicates the presence of interfering structures in the propagation path, such as cracks or heterogeneous media.

[0072] Energy change: Estimate the total energy by integrating the square of the signal amplitude per unit time. Compare the energy values ​​of multiple adjacent regions to determine the location of local dissipation anomalies.

[0073] The above four types of parameters are used as damage indicators and output in a unified format to form a complete damage feature data set for subsequent classification and labeling.

[0074] In a preferred embodiment of the present invention, based on the damage feature dataset, weighted normalization processing is performed on the multidimensional damage parameters, and a mapping is established by combining the spatial partition moisture content with statistical data to generate damage quantification results for each spatial partition. The resulting damage quantification result dataset includes: The signal attenuation rate, maximum amplitude, periodic variation and energy variation in the damage feature data set are used as input parameters, and multi-parameter weighting and normalization processing is performed to obtain weighted feature data; Based on the weighted characteristic data, combined with the moisture content of each spatial partition and historical damage statistics, the interval regression analysis method is used to establish a mapping relationship between the damage degree and the signal characteristics, and generate the initial quantitative results of the spatial partition damage; Based on the initial quantification results of spatial partition damage and combined with the environmental humidity parameters, the humidity sensitivity adjustment coefficient is calculated, and the humidity correction is performed on the initial quantification results to generate humidity-corrected residual damage quantification data; The humidity-corrected residual quantization data is subjected to neighboring area consistency judgment. For areas with sudden changes between spatial partitions, the outliers are adjusted using a local smoothing algorithm to generate residual quantization correction results. Comparing the material type of each spatial partition with a preset material damage sensitivity comparison table to determine the material damage response coefficient of each spatial partition, wherein the material damage response coefficient is preset based on the historical mechanical properties and damage evolution characteristics of the spatial partition material; The damage quantification correction result of each spatial partition is weighted with the corresponding material damage response coefficient to generate a spatial partition damage index; All spatial partition damage indices are aggregated to form a damage quantification result dataset of spatial partitions and the overall structure.

[0075] In an embodiment of the present invention, by performing weighted normalization processing on the multidimensional damage parameters in the damage feature data set and establishing a mapping based on the moisture content of the spatial partitions and historical damage statistical data, the true degree of damage of each spatial partition of the hollow beams of cultural relics and ancient buildings can be reflected efficiently and quantitatively. Specifically, through the weighted normalization calculation of parameters such as signal attenuation rate, maximum amplitude, periodic variation and energy variation, the detection indicators of different physical quantities are unified into comparable quantization intervals, which effectively solves the problems of inconsistent scales and different sensitivity to damage reflection between multi-source detection data. Subsequently, combined with the moisture content of each spatial partition and historical damage statistical information, through interval regression analysis, the damage feature parameters are data-drivenly associated with the actual degree of damage, thereby achieving accurate mapping from the signal level to the damage quantification results. For hollow beams that are significantly affected by environmental humidity, a humidity sensitivity adjustment coefficient is also introduced to make targeted corrections to the quantification results, so that the damage quantification can reflect the health status of the beams under the current real environment.

[0076] The method further performs neighboring consistency judgment on the humidity-corrected damage quantization data, effectively suppressing local anomalies caused by signal outliers, spatial partition boundary errors, etc., making the damage quantization distribution more continuous and natural in space. When forming the final spatial partition damage index, the present invention searches for the preset material damage response coefficient according to the material type of the spatial partition, and realizes the normalized compensation of the damage evolution characteristics of different materials. Taking wooden structures and masonry structures as examples, wooden structures react more obviously to the signals of internal microcracks and voids, and the material damage response coefficient is set higher, so that its damage index can truly reflect the damage amplification trend. On the contrary, the damage evolution of masonry structures for the same signal is relatively smooth, and the coefficient is set lower, so as to achieve scientific distinction.

[0077] Through this innovative processing flow, the spatially zoned damage index integrates multiple factors, including signal quality, material properties, environmental impacts, and spatial distribution. This provides a scientific, accurate, and traceable basis for quantifying damage in hollow beam structures of cultural relics and historic buildings, enabling safety monitoring, maintenance decision-making, and subsequent mechanical analysis. In practice, this method significantly improves the sensitivity of identifying minor damage and lays a solid data foundation for quantitative repair and risk-tiered management.

[0078] The signal attenuation rate, maximum amplitude, periodic variation, and energy variation in the damage feature data set are used as input parameters, and multi-parameter weighting and normalization processing is performed to obtain weighted feature data, specifically including: First, four key parameters were extracted from the aforementioned damage signature dataset: signal attenuation rate, maximum amplitude, periodic variation, and energy variation for each spatial partition or detection point. These parameters, which reflect the material's signal loss, local response, dynamic behavior, and energy state, are key indicators used to characterize material damage states in current mainstream international structural health monitoring systems.

[0079] In actual processing, the dimensions and numerical ranges of different physical quantities must first be normalized. Methods such as range normalization or standard deviation normalization can be used to convert all parameter values ​​to the same numerical range (such as 0-1) to eliminate the influence of units and orders of magnitude between different parameters.

[0080] Next, each parameter is assigned a weight based on experience, domain standards, or previous model training results. Generally, signal attenuation and energy change contribute significantly to the identification of hidden damage and are therefore weighted higher. Periodic variation and maximum amplitude are more sensitive to dynamic anomalies and surface damage and are therefore weighted slightly lower. Weight assignment can be preset in the model or adaptively optimized based on historical analysis samples.

[0081] Multiplying all parameters by their corresponding weight coefficients and then summing or weighted averaging them yields comprehensive weighted feature data. This data not only accommodates multidimensional feature information but also reflects the overall damage status of each spatial partition through a single indicator, providing a direct input foundation for subsequent damage quantification mapping.

[0082] Among them, based on the weighted characteristic data, combined with the moisture content of each spatial partition and historical damage statistics, the interval regression analysis method is used to establish a mapping relationship between the damage degree and signal characteristics, and generate the initial quantitative results of spatial partition damage, including: First, the weighted feature data obtained in the previous step is integrated with the moisture content data of each spatial partition. As an important environmental and material influencing factor, the moisture content data of the spatial partition will significantly affect the signal response characteristics and the accuracy of damage characterization.

[0083] The process involved collecting extensive historical data on the inspection and repair of beams in ancient cultural relics and buildings. This dataset contained characteristic signal parameters under typical damage states and their corresponding actual damage levels. Through statistical analysis, machine learning, or empirical formulas, the relationship between weighted signal characteristics, moisture content, and historical damage levels was modeled.

[0084] During interval regression analysis, multiple "characteristic intervals" are created based on different combinations of signal parameters and moisture content. Each interval corresponds to a range of estimated damage severity. For example, when the weighted characteristic data exceeds a certain threshold and the moisture content is high, the corresponding damage level is determined to be "severe." Conversely, low weighted characteristic values ​​and low moisture content are determined to be "minor" or "no damage."

[0085] For the actual spatial partitions to be assessed, the model inputs the measured weighted eigenvalues ​​and moisture content values, directly outputting quantified initial damage results based on the interval. These results, expressed as a continuous quantity or graded score, are used to determine the initial damage extent for each spatial partition, laying a solid data foundation for subsequent damage correction and structural safety analysis.

[0086] Among them, based on the initial quantification results of spatial partition damage and combined with the environmental humidity parameters, the humidity sensitivity adjustment coefficient is calculated, and the initial quantification results are humidity corrected to generate humidity-corrected residual damage quantification data, specifically including: After obtaining initial quantification results for spatially partitioned damage, it is necessary to further consider the impact of ambient humidity on signal response and damage assessment. In actual engineering, even if the damage characteristic parameters are the same, differences in acoustic waves and heat propagation within the material can occur under high or low humidity conditions. If not corrected, this can lead to deviations in damage assessment results.

[0087] This step first retrieves the ambient humidity parameters corresponding to each spatial partition and matches them with a pre-set humidity impact mapping table. This mapping table, obtained through experiments, contains typical variations in signal strength, attenuation, and other parameters for various materials under varying humidity conditions.

[0088] Based on the actual humidity level of the spatial partition, a corresponding humidity sensitivity adjustment coefficient is selected. This coefficient reflects the correction amplitude of the signal under the current humidity conditions. During the correction process, the initial damage quantization value of the spatial partition is weighted with the humidity sensitivity adjustment coefficient. For example, if high humidity causes a decrease in signal amplitude, the weight of the damage assessment value should be appropriately increased, while the weight should be reduced in dry environments.

[0089] All corrected damage quantification data will be output as humidity-corrected residual quantification data to ensure that subsequent analysis and decision-making are more objective, accurate, and have good environmental adaptability.

[0090] Among them, the neighboring area consistency judgment is performed on the humidity corrected residual quantization data. For areas with sudden changes between spatial partitions, the outliers are adjusted through the local smoothing algorithm to generate the residual quantization correction results. Specifically, it includes: The humidity-corrected damage quantization data may still cause non-physical "jumps" in the damage values ​​of adjacent spatial partitions due to local signal noise, data loss, or improper spatial partitioning.

[0091] To this end, this step introduces a neighboring zone consistency determination mechanism. First, all spatial partitions are traversed, and the corrected damage quantization value of each spatial partition is compared with the damage quantization values ​​of its adjacent spatial partitions. If the difference between the quantization value of a spatial partition and its adjacent partitions exceeds a set threshold (e.g., more than 30% of the adjacent mean), the location is determined to be a "damage value mutation point."

[0092] Subsequently, local smoothing algorithms are used for correction. Methods such as sliding window averaging and weighted median can be used to replace the damage quantification value of a sudden change spatial partition with the weighted average of multiple spatial partitions within its neighborhood, or interpolation correction can be performed based on the trends of adjacent spatial partitions. For areas with continuous sudden changes in multiple spatial partitions, piecewise fitting methods can also be used to smooth the data change curve.

[0093] After this processing, the spatial variation of the damage quantification data becomes more continuous, which can accurately reflect the distribution trend of the actual structural damage, reduce the probability of false positive or false negative misjudgment, and ultimately generate the damage quantification correction result.

[0094] In a preferred embodiment of the present invention, a mechanical analysis is performed based on the damage quantification result dataset and the spatial partition dataset, combined with the material type, geometric dimensions, and load information of each spatial partition, to generate the bearing capacity distribution results of the hollow beam, including: The damage quantification result dataset is matched one by one with the spatial partition dataset, and the damage index of each spatial partition is used as the bearing capacity reduction coefficient to correct the basic bearing capacity of the spatial partition in the damage-free state to obtain the preliminary bearing capacity value of the spatial partition. Based on the preliminary bearing capacity values ​​of the spatial partitions, combined with the material type, geometric dimensions and historical load information of each spatial partition, the bearing capacity calculation is corrected using a multi-parameter correction method to generate the corrected bearing capacity values ​​of the spatial partitions; Based on the spatial partition damage distribution, material aging degree and extreme load type, the ultimate load condition correction factor is calculated. According to the structural limit state analysis rules, the spatial partition correction bearing capacity value is adjusted to simulate the change of spatial partition bearing capacity under extreme loads and generate ultimate bearing capacity data. The ultimate bearing capacity data of all spatial partitions are integrated and combined with the structural interaction relationship between spatial partitions to generate the bearing capacity distribution results of the hollow beam.

[0095] In this embodiment of the present invention, by mapping the damage quantification result dataset to the spatial partition dataset, the damage index of each spatial partition can be effectively used as a bearing capacity reduction factor, incorporating it into the entire process of structural bearing capacity analysis. First, the basic bearing capacity is calculated based on the structural parameters in the intact state of each spatial partition. Then, the basic bearing capacity is corrected based on the damage index to fully reflect the weakening effect of the actual damage on the bearing capacity of each partition. The preliminary bearing capacity value of the spatial partition after such processing can truly depict the current load-bearing capacity of each partition and is an important foundation for subsequent multi-parameter correction and limit state analysis.

[0096] Subsequently, based on the material type, geometric dimensions, and historical load information obtained from previous tests of the spatial partitions, the preliminary bearing capacity values ​​of the spatial partitions are further corrected using a multi-parameter correction method. In specific operations, the material parameters of each spatial partition are retrieved, such as the compressive strength of wood and the elastic modulus of masonry, to correct the preliminary bearing capacity values ​​based on material properties. At the same time, the load-bearing section and structural inertia parameters are finely adjusted based on the actual geometric dimensions, node positions, and openings of the spatial partitions. Historical load information is used to assess the cumulative impact of long-term loads, extreme weather conditions, and special working conditions on structural performance. The partition bearing capacity is corrected using the load influence coefficient so that the calculation results can reflect the safety margin under actual service conditions.

[0097] The present invention also calculates the ultimate working condition bearing correction coefficient based on the damage distribution of the spatial partition, the degree of material aging, and the type of extreme load. The damage expansion state and material performance attenuation of each spatial partition are quantitatively evaluated, and the parameter adjustment method under the ultimate state is determined in combination with extreme working conditions such as earthquakes, strong winds, sudden overloads or environmental disaster loads. Through the structural limit state analysis rules, the spatial partition correction bearing capacity value is further corrected, and finally the ultimate bearing capacity data that can truly reflect the actual residual bearing capacity of the partition under extreme loads is generated. By integrating the ultimate bearing capacity data of all spatial partitions and combining the structural interaction relationship between the partitions, the overall bearing capacity distribution of the hollow beam can be accurately revealed. The above method significantly improves the coupling degree and reliability of damage quantification and bearing capacity analysis, and provides quantitative support for risk control and scientific repair decision-making of cultural relics and ancient building structures.

[0098] The damage quantification result dataset is mapped to the spatial partition dataset one by one, and the damage index of each spatial partition is used as the bearing capacity reduction coefficient to correct the basic bearing capacity of the spatial partition in the intact state to obtain the preliminary bearing capacity value of the spatial partition. Specifically, First, based on the structural layout and inspection point locations, the damage index of each partition in the damage quantification dataset is matched to the corresponding spatial partition in the spatial partition dataset. The partition damage index reflects the extent of damage within the spatial partition and its impact on structural performance.

[0099] Subsequently, for each spatial partition, the foundation bearing capacity under ideal, undamaged conditions is calculated using engineering structural mechanics specifications or empirical formulas, based on parameters such as material type, geometry, partition span, and support conditions. This undamaged foundation bearing capacity can be determined using standard structural analysis methods, such as theoretical calculations based on different working conditions, such as bending and compression, and corrections based on test data.

[0100] Next, the damage index of the spatial partition is taken as the bearing capacity reduction coefficient, and directly participates in the correction of the foundation bearing capacity. The specific method is: The foundation bearing capacity value in the undamaged state is multiplied by the correction factor of the damage index, and the correction factor is usually a value less than 1, and the value is determined according to the damage grade, damage type and distribution position of the partition. The more serious the damage, the smaller the correction factor, and the greater the bearing capacity reduction. For example, for the partition with obvious through cracks, the damage index is relatively low, and the bearing capacity reduction is higher; and for the partition with only slight surface damage, the damage index is higher, and the reduction amplitude is relatively small.

[0101] Finally, through the above correction, the preliminary bearing capacity value of the spatial partition considering the actual damage influence is obtained. The bearing capacity value can more truly reflect the stress capacity of the structure in the existing health state, and provide a data basis for subsequent multi-parameter bearing capacity correction and limit state analysis.

[0102] In a preferred embodiment of the present application, the humidity correction data set is further processed by the following method, comprising: According to the spatial partition data set, the material type of each spatial partition is identified, and a compensation coefficient corresponding to different material types is set; The humidity correction data of each spatial partition is weighted with the corresponding material compensation coefficient, the signal amplitude and the attenuation parameter are compensated for material properties, and the humidity correction data set after material compensation is generated.

[0103] In the embodiment of the present application, in order to further improve the accuracy of signal data and the consistency of material response, the humidity correction data set is compensated based on the material type of the spatial partition. Specifically, first, the material type corresponding to each spatial partition is identified according to the spatial partition data set. Since the hollow beam body in cultural relics and ancient buildings may be composed of different batches or historical repair materials, their mechanical properties, humidity response characteristics and signal propagation characteristics often differ significantly, so accurate identification of the material properties of each spatial partition is the basis for subsequent material compensation processing.

[0104] After identifying the material type, a compensation coefficient corresponding to different material types is set, which can be obtained by experiment or historical test data fitting. For example, for a spatial partition using cedar material and having a high moisture content, the signal attenuation speed is fast and the amplitude fluctuation is large, so a relatively high amplitude recovery coefficient can be set. For the spatial partition of masonry structure, different compensation parameters need to be set to adapt to its high density and low attenuation characteristics. Such material compensation coefficients can be automatically matched according to the spatial partition properties, and have strong adaptability and scalability.

[0105] The humidity-corrected data for each spatial partition is then weighted with the corresponding material compensation coefficient. This weighting applies not only to the main amplitude of the signal but also to the attenuation parameter, which reflects the degree of signal dissipation. By normalizing and compensating for response differences caused by material differences in the original humidity-corrected data, the comparability of the signal across multiple materials is improved, avoiding misjudgments or biases caused by the materials themselves.

[0106] Finally, the output is a moisture-corrected data set after material compensation, which serves as a unified input source for subsequent damage feature extraction and damage quantification analysis. This implementation effectively overcomes the reduced assessment accuracy of traditional signal analysis when dealing with heterogeneous material structures, providing greater consistency and stability for damage identification and load-bearing capacity analysis of hollow beams in complex material distributions.

[0107] In a preferred embodiment of the present invention, based on the preliminary bearing capacity values ​​of the spatial partitions, combined with the material type, geometric dimensions, and historical load information of each spatial partition, a multi-parameter correction method is used to correct the bearing capacity calculation to generate a corrected bearing capacity value for the spatial partition, including: Obtain the material type, cross-sectional size, length, node location, and opening location of each spatial partition to form a spatial partition load correction input data set; According to the material type of the spatial partition, the compressive strength, elastic modulus, and fracture toughness parameters of the spatial partition are retrieved from the preset material mechanical property parameter library, and the material property correction is performed on the preliminary bearing capacity value of the spatial partition to generate material correction bearing capacity data; According to the spatial partition cross-sectional dimensions, spatial partition length, node locations, and opening location characteristics of the spatial partition, geometric feature correction is performed on the material correction bearing capacity data, and the effective bearing area and cross-sectional inertia parameters are adjusted to generate geometric correction bearing capacity data; Based on the historical load information of spatial partitions, the load influence coefficient is calculated, and the load history correction is performed on the geometrically corrected bearing capacity data to generate load-corrected bearing capacity data; The load-corrected bearing capacity data is used as the spatial partition-corrected bearing capacity value after multi-parameter correction.

[0108] In an embodiment of the present invention, the multi-parameter correction of the preliminary bearing capacity value of the spatial partition lays a solid foundation for the subsequent extreme working condition and structural coupling analysis. During implementation, the detailed material type, spatial partition cross-sectional dimensions, partition length, and structural features such as node locations and opening locations directly related to the force of each spatial partition are first collected, and the spatial partition bearing correction input data set is formed accordingly. According to the material type, the compressive strength, elastic modulus and fracture toughness parameters matching the partition are automatically retrieved from the database. By combining with the preliminary bearing capacity value of the partition, the material property correction is completed to ensure that the calculation results can reflect the true mechanical performance of various materials.

[0109] Furthermore, based on the actual geometric dimensions and structural characteristics of the spatial partitions, spatial geometric corrections are performed on the material-corrected bearing capacity data. This focuses on addressing the actual effects of localized stress concentrations caused by changes in cross-sectional dimensions, node locations, or openings. This allows for adjustments to the effective bearing area and cross-sectional inertia parameters of the partitions, resulting in a more refined bearing capacity assessment that approximates actual operating conditions. Furthermore, historical load information for the partitions is incorporated to analyze the impact of long-term service loads, special event loads, and environmental changes on partition performance. Load influence coefficients are then used to historically correct the geometrically corrected bearing capacity data, further enhancing the structural service performance assessment.

[0110] Through this multi-parameter correction process, the modified bearing capacity values ​​of spatial partitions not only accurately reflect the multivariate influences of material properties and geometric structure, but also dynamically reflect the combined effects of long-term loads and incidental operating conditions on bearing capacity. Ultimately, the modified bearing capacity values ​​of spatial partitions obtained by this method provide highly reliable input data for subsequent extreme operating condition simulations, spatial partition coupling, and overall bearing capacity distribution analysis, helping to improve the accuracy and scientific nature of damage identification, quantification, and safety assessment of hollow beams in cultural relics and historical buildings.

[0111] The material type, cross-sectional dimensions, length, node location, and opening location of each spatial partition are obtained to form the input data set for spatial partition load correction, including: During the actual inspection and structural modeling process, the material properties of each spatial partition are first confirmed based on the partition number and spatial distribution. Material types can range from wood, stone, brickwork, concrete, and are typically determined through on-site visual inspection, archival research, or material sampling and analysis. For cultural heritage buildings, non-destructive testing is recommended as a supplemental method for determining material type.

[0112] At the same time, measurement tools such as laser rangefinders and 3D scanners should be used to obtain the actual cross-sectional dimensions of each spatial partition, including width, height, thickness, etc. For partitions with varying cross-sections or variable cross-section designs, parameters at key cross-sectional locations should be collected, and the actual length of the partition should be recorded.

[0113] In addition, the node and opening information in each spatial partition needs to be identified. The node generally refers to the structural connection point, intersection point, beam-column joint, etc., and the opening usually includes reserved holes, pipeline crossing, decorative holes, etc. These information can be obtained through structural drawings, field surveying, infrared scanning, etc., and classified and arranged in the dataset.

[0114] After data cleaning and standardization, all the above parameters are summarized to form the bearing correction input dataset of each spatial partition. This dataset provides a complete and accurate input basis for subsequent material performance correction, geometric feature correction, and bearing capacity analysis and calculation.

[0115] Among them, according to the material type of the spatial partition, the compressive strength, elastic modulus, and fracture toughness parameters of the spatial partition are retrieved from the preset material mechanical property parameter library to correct the preliminary bearing capacity value of the spatial partition, and the material corrected bearing capacity data is generated, which specifically includes: First, according to the material type recorded in the spatial partition bearing correction input dataset, the material mechanical property parameter library is searched to obtain the standard compressive strength, elastic modulus, and fracture toughness of the material. The parameter library can be derived from relevant national standards, industry specifications, historical test data or literature, and can be supplemented and improved for common materials in ancient buildings.

[0116] Then, the preliminary bearing capacity value of the partition is associated with the retrieved material mechanical property parameters. For key links in structural stress analysis, such as beam bending capacity and column axial compression capacity, the preliminary bearing capacity value is corrected according to the actual values of material compressive strength and elastic modulus. If the partition material has performance degradation phenomena such as aging, weathering, corrosion, etc., a performance decay coefficient should be introduced to reduce the calculated value.

[0117] For partitions with obvious fracture toughness reduction, the bearing capacity can be further reduced by increasing the brittle fracture tendency parameter to ensure that the results meet the actual deterioration of ancient buildings and cultural relics. The correction process does not require the use of calculation formulas, only the bearing capacity value needs to be weighted and adjusted according to the actual material performance in the data processing link, and finally the material corrected bearing capacity data reflecting the real stress performance of the material is generated.

[0118] This step realizes the effective conversion from the theoretical preliminary bearing capacity value to the real bearing capacity based on the measured and database material performance, providing accurate and reliable basic data support for subsequent geometric correction, historical load correction, and limit state analysis.

[0119] wherein, according to the spatial partition section size, the spatial partition length, the node part, and the opening part characteristics of the spatial partition, the material corrected bearing capacity data is geometrically corrected, the effective bearing area and the section inertia parameter are adjusted, the geometrically corrected bearing capacity data is generated, and specifically includes: Firstly, for each spatial partition, according to the data collected in the early stage, the actual section width, height and thickness parameters are determined. In the area with special structure forms such as variable cross-section, step or local thickening, the thinnest section under stress is preferred as the effective section for analysis. By comparing the design size with the measured size, the cross-section reduction effect caused by long construction time or damage erosion is corrected.

[0120] Then, combined with the spatial partition length and the support condition, it is judged whether the structure belongs to the simply supported, cantilever, continuous or multi-span type, and the boundary conditions of the stress analysis are determined accordingly. The larger the partition length and the larger the span, the more obvious the reduction of the bearing capacity. For the position of the key node (such as beam-column joint, arch foot, mortise and tenon, etc.), the stress path of the structure needs to be adjusted according to the node stiffness and connection strength. If the node is deteriorated or the connection is loose, the stress effectiveness of the node area should be appropriately reduced.

[0121] For the partition with opening part, such as ventilation hole, decorative opening or pipeline through beam, the influence of the opening position, size and number on the overall stiffness and strength of the section needs to be evaluated. Generally, the minimum residual area or the minimum moment of inertia of the weakened section by the opening is taken as the reference to reduce the structural stress capacity. If cracks or stress concentration signs appear at the hole, the local bearing capacity needs to be further reduced according to the actual damage degree.

[0122] Comprehensive adjustment results of the above parameters, the cross-sectional effective area and the cross-sectional inertia parameter of all spatial partitions are corrected to the values under the actual stress state. In data processing, the material corrected bearing capacity data is combined with the corrected geometric parameters to generate geometrically corrected bearing capacity data that can truly reflect the actual bearing capacity of the current spatial partition.

[0123] wherein, based on the historical load information of the spatial partition, the load influence coefficient is calculated, the geometrically corrected bearing capacity data is load history corrected, and the load corrected bearing capacity data is generated, specifically including: Firstly, the actual service load information of each spatial partition over the years is collected, including long-term static load (such as roof self-weight, floor dead load), periodic dynamic load (such as tourist flow, wind vibration effect), extreme short-time load (such as equipment hoisting, maintenance impact), and environmental load (such as snow, rain, earthquake, typhoon, etc.). The load data sources can be design archives, previous detection reports, historical maintenance records and environmental monitoring data.

[0124] During the data analysis phase, the cumulative fatigue effects of long-term static loads are evaluated, focusing on parameters such as load duration, load amplitude, and structural service life. For zones exposed to extreme load events, such as earthquakes, typhoons, and snow overload, the load impact factor is appropriately increased to reflect potential structural hazards and irreversible damage.

[0125] When calculating load influence factors, loads of different types and durations can be categorized into grades based on code requirements or empirical data, and corresponding reduction factors assigned to each grade. For example, if static loads exceeding the standard persist for more than ten consecutive years, the bearing capacity factor can be appropriately reduced. However, if extreme short-term impact loads occur less frequently, adjustments will primarily be made to the local structural safety margin.

[0126] The aforementioned load influence coefficients are combined with the geometrically corrected bearing capacity data for each spatial partition, and the load-corrected bearing capacity data are ultimately generated using data weighting or adjustments based on the most unfavorable historical load combination. This data more realistically reflects the structure's residual safety margin under actual service history, providing a reliable input for subsequent limit state analysis and full-structure coupled analysis.

[0127] In a preferred embodiment of the present invention, based on the spatial partition damage distribution, material aging degree and extreme load type, the ultimate load condition correction coefficient is calculated, and the spatial partition correction bearing capacity value is adjusted according to the structural limit state analysis rules to simulate the change of the spatial partition bearing capacity under extreme loads and generate the ultimate bearing capacity data, including: Obtain damage distribution data for spatial partitions, including damage location, damage range, and damage type, to form a modified input data set for spatial partition limit conditions; Based on the aging degree of spatial partition materials, the current material performance status of the spatial partition is determined by analyzing the material performance degradation parameters, using previous material testing data and material aging models; Set extreme load types and, based on the impact of each extreme load type on structural safety, search in a preset extreme load correction factor library to determine the extreme load correction factor for each spatial partition. The extreme load types include earthquake loads, wind loads, sudden heavy loads, and environmental disaster loads. Based on the material performance status, the spatial partition limit condition correction input data set and the limit condition load correction factor, the spatial partition correction bearing capacity value is adjusted according to the structural limit state analysis rules to generate the ultimate bearing capacity data reflecting the actual residual bearing capacity under extreme loads; The ultimate bearing capacity data of all spatial partitions are output to form a spatial partition ultimate bearing capacity data set.

[0128] In the embodiments of the present application, based on the spatial partition damage distribution, material aging degree and extreme load type, the limit working condition bearing correction coefficient is scientifically calculated, the spatial partition corrected bearing capacity value can be adjusted more in line with the actual working condition, and the fine simulation of the partition bearing capacity under extreme load is realized. First, through damage detection and previous material performance test data, the damage position, range and type of each spatial partition are obtained, and the current material aging degree of the spatial partition is comprehensively evaluated in combination with the material performance degradation under long-term service environment. In this way, the damage state and aging level of different partitions can be quantitatively expressed and directly affect the subsequent bearing capacity analysis.

[0129] Subsequently, according to the engineering practice in the implementation process, the extreme load type is set, including seismic load, wind load, sudden heavy load and environmental disaster load. For each type of extreme load, the preset limit working condition bearing correction coefficient library is searched, the material performance state and damage distribution of the partition are combined, and the correction coefficient that best reflects the real bearing capacity reduction trend of the partition is selected. These limit working condition bearing correction coefficients are parameterized with the spatial partition corrected bearing capacity value, and the final bearing capacity result of the partition is adjusted according to the structure limit state analysis rule. For example, for the partition with high damage concentration, the limit working condition bearing correction coefficient is set higher to simulate the situation that the bearing capacity of the partition under extreme load is greatly reduced; for the partition with severe aging but light damage, the reduction amplitude is appropriately adjusted according to the actual detection result.

[0130] Through the above method, the residual limit bearing capacity of each spatial partition under extreme load can be quantified as limit bearing capacity data. The limit bearing capacity data of all partitions is output to form a spatial partition limit bearing capacity data set, which provides an intuitive and scientific quantitative basis for the identification of weak areas and disaster prevention of the overall structure. This scheme ensures the scientificity and traceability of the bearing capacity simulation, and makes the safety evaluation of cultural relics and ancient buildings have sufficient engineering practical value.

[0131] Among them, the damage distribution data of the spatial partition is obtained, the damage distribution data includes damage position, damage range and damage type, and the spatial partition limit working condition correction input data set is formed, which specifically includes: First, according to the previous detection and spatial partition division results, for each detected damage area, the specific position of the damage in the spatial partition is determined according to the signal waveform change, image abnormal hot area, attenuation rate and energy change and other characteristics. The position data is usually expressed in partition coordinates or distance from the structure reference point to ensure accurate positioning in subsequent structure analysis.

[0132] Next, the actual spatial extent of the damage is quantified based on the spatial distribution width of the detection signal, the size of the thermal imaging anomaly patch, or the extension length of the acoustic anomaly. The damage range can be categorized as point-like, linear (e.g., cracks), or surface (e.g., mold or corrosion), and characterized using area or volume parameters.

[0133] The damage type is also determined based on signal parameters and a pre-defined damage signature library. Common types include microcracks, through-cracks, holes, decay, insect damage, and material delamination. The impact of each type of damage on the structural load-bearing capacity can be weighted differently in subsequent analysis.

[0134] Finally, the data on damage location, damage range, and damage type of all spatial partitions are sorted and standardized, and summarized to form a spatial partition extreme working condition correction input data set, laying a detailed data foundation for the selection of extreme working condition correction coefficients and parameter adjustment.

[0135] Among them, based on the aging degree of spatial partition materials, by analyzing the material performance degradation parameters, using previous material test data and material aging models, the current material performance status of the spatial partition is determined, specifically including: First, collect all material testing data for each spatial partition of the hollow beam since its construction, including but not limited to material strength testing, elastic modulus testing, fracture toughness testing, etc. Test data can come from direct on-site sampling, non-destructive testing statistics, laboratory mechanical property tests, and historical test reports.

[0136] For each major material (such as wood, masonry, and concrete), the time-series trends in test data are analyzed to extract performance degradation parameters that reflect the aging process. For example, the compressive strength of wood decreases with age, humidity, and fungal decay, while the elastic modulus of masonry decreases with increasing cracking and weathering. Parameters may include strength retention, modulus reduction rate, and changes in brittle fracture susceptibility.

[0137] At the same time, combining environmental data (such as average annual humidity, temperature, UV radiation, and chemical corrosion) with material aging models, quantitatively model the performance degradation process of the structure under environmental influences. The aging model can be fitted based on historical measured data or reference empirical formulas from standards or literature.

[0138] By combining test data analysis results with aging model calculations, key performance indicators such as compressive strength, elastic modulus, and fracture toughness of the current material in each spatial partition are adjusted downward. The actual performance status of the current material in each spatial partition is output for load-bearing capacity correction under extreme working conditions, ensuring that the load-bearing capacity analysis truly reflects the state after long-term service and environmental erosion.

[0139] Through the above method, the material performance status of each partition can be dynamically updated and scientifically quantified under extreme loads, providing important basic data for overall structural safety assessment and weak area early warning.

[0140] Among them, the extreme load type is set, and according to the impact of each extreme load type on structural safety, the preset extreme load correction factor library is searched to determine the extreme load correction factor of each spatial partition, specifically including: First, based on the location, climate, and historical disaster records of the cultural relic building, determine the types of extreme loads that require special consideration during the structure's service life. These typically include earthquake loads (if located in a seismic zone), wind loads (in high-wind areas), sudden heavy loads (such as special construction projects and the pressure of temporary equipment), and environmental disaster loads (such as extreme rainstorms, floods, snow loads, and freeze-thaw cycles). Each type of load may have varying degrees of impact on the load-bearing safety of the spatial partition.

[0141] Then, for each of these extreme load types, a library of extreme load correction factors was pre-established, combining existing structural seismic design standards, wind resistance codes, empirical data on extreme loads, and historical disaster damage cases. This library provides correction factor values ​​tailored to different load types, material properties, damage levels, and aging states. For example, under earthquake conditions, the correction factor for brittle zones is lower than that for normal zones, and under wind conditions, the correction factor for elevated or exposed zones is lower than that for embedded zones.

[0142] During implementation, the input dataset is modified based on the aforementioned spatial partition limit conditions. Each partition is matched to a corresponding load type, and the limit load correction factor that most closely matches the partition's material performance and damage distribution is retrieved from the coefficient library. For partitions in high-risk environments or areas subject to multiple extreme loads, the most unfavorable value or a comprehensive weighted combination of multiple correction factors can be used to ensure a safe-side structural capacity assessment.

[0143] Finally, the ultimate load-bearing correction coefficients of all spatial partitions are recorded and used as important input parameters for subsequent structural limit state analysis, providing a scientific basis for the correction of bearing capacity under ultimate load.

[0144] Among them, based on the material performance status, the spatial partition limit condition correction input data set and the limit condition load correction factor, the spatial partition correction bearing capacity value is adjusted according to the structural limit state analysis rules to generate the ultimate bearing capacity data reflecting the actual residual bearing capacity under extreme loads. Specifically, it includes: First, the material performance status of each spatial partition (such as current compressive strength, elastic modulus, and toughness parameters), the spatial partition limit condition correction input data set (including damage distribution, damage type, location, and range), and the retrieved limit condition load correction factor are summarized into the same analysis process.

[0145] Subsequently, parameters for the modified bearing capacity values ​​of spatial partitions were adjusted based on existing structural limit state analysis rules. The specific method is as follows: First, the modified bearing capacity value is multiplied by the ultimate load correction factor or combined with the most unfavorable load condition to reflect the downward trend in bearing capacity under extreme loads. Then, based on the material performance status, the ultimate bearing capacity assessment value is further reduced for partitions with severe aging and concentrated damage.

[0146] During the adjustment process, for spatial zones exposed to multiple extreme load threats, a step-by-step or weighted reduction method can be used, taking into account the actual impact of different load types on the structure, to ensure that the zone's ultimate bearing capacity is not overestimated under all possible conditions. For example, seismically vulnerable zones can be corrected using a seismic correction factor, and then wind load correction factors can be introduced into wind load-sensitive zones to determine the minimum ultimate bearing capacity value.

[0147] Ultimately, the ultimate bearing capacity data for each spatial partition under extreme loads is output. This data fully reflects the structural material degradation, damage distribution, and safety reserves under the combined effects of extreme environments, and is an important basis for identifying weak areas and assessing the overall structural resilience to disasters.

[0148] In a preferred embodiment of the present invention, the ultimate bearing capacity data of all spatial partitions are integrated, and the structural interaction relationship between the spatial partitions is combined to generate the bearing capacity distribution results of the hollow beam body, including: According to the spatial partition ultimate bearing capacity dataset, the ultimate bearing capacity data of all spatial partitions are integrated according to the spatial partition number and the actual spatial distribution order to form the structural spatial bearing capacity integrated input dataset; Analyze the structural connection mode and force transmission path between spatial partitions, obtain the structural stiffness parameters and mechanical interaction parameters between spatial partitions, and form the input data set for structural coupling analysis; Based on the structural spatial bearing capacity integrated input data set and the structural coupling analysis input data set, the ultimate bearing capacity data of all spatial partitions are jointly analyzed to generate the overall bearing capacity distribution data reflecting the influence of the forces on each spatial partition and its adjacent spatial partitions; The overall bearing capacity distribution data is used as the bearing capacity distribution result of the entire hollow beam.

[0149] In this embodiment of the present invention, by integrating the ultimate bearing capacity data of all spatial partitions and combining them with the structural interactions between the spatial partitions, it is possible to comprehensively model and accurately represent the overall bearing capacity distribution of the hollow beam. During implementation, the ultimate bearing capacity of each spatial partition is first integrated according to the actual spatial number and spatial layout sequence based on the spatial partition ultimate bearing capacity dataset, forming a complete integrated input dataset of the structural spatial bearing capacity. This dataset truly reflects the residual mechanical properties of each beam component under extreme loads, providing data support for subsequent structural coupling analysis.

[0150] Furthermore, the structural connection methods and force transmission paths between spatial partitions, as well as the structural stiffness and mechanical interaction parameters between partitions, are analyzed to form the input data set for structural coupling analysis. Through a multi-partition coupling analysis method, the ultimate bearing capacity of each partition is jointly analyzed with the stress state of its adjacent partitions. This method comprehensively considers the mutual influence of the main structural stress paths, local weak areas, and critical nodes, achieving a coordinated correction of the overall structural stress performance. Based on the coupling relationship between each partition, the ultimate bearing capacity data can be spatially distributed, thereby identifying the main stress paths and potential weak areas of the hollow beam at a macro level.

[0151] Ultimately, the overall bearing capacity distribution data is output as the overall bearing capacity distribution of the hollow beam. This result provides a scientific, quantitative basis for engineering applications such as structural safety assessment, repair and reinforcement design, disaster risk warning, and structural health monitoring of cultural relics and ancient buildings. Through this process, structural mechanics analysis and multi-zone coupled simulation are efficiently integrated, comprehensively enhancing the digital and intelligent management and protection of hollow beams in ancient buildings.

[0152] The structural connection mode and force transmission path between spatial partitions are analyzed to obtain the structural stiffness parameters and mechanical interaction parameters between spatial partitions to form the input data set for structural coupling analysis. Specifically, the following are included: First, the overall structure of the hollow beam was reviewed. The physical connections between the various spatial partitions were determined using construction drawings, site surveys, and 3D modeling. Common connection methods include rigid, hinged, frictional, and flexible. These differ significantly in their impact on mechanical transmission and overall synergistic performance. On-site observation of details such as mortise and tenon joints, ironwork, bolts, and caulking materials, combined with local damage and historical maintenance records, enabled a comprehensive assessment.

[0153] Next, the primary force transfer paths between each spatial partition are analyzed, combining the connection method with the structural layout. For beam-to-beam, beam-to-column, and beam-to-wall connections, the actual support, node stiffness, and partition distribution should be considered to determine how force is transferred between partitions. For example, rigid connections can effectively share external loads, while hinged connections may cause certain partitions to be more susceptible to deformation. For structures with multiple spans or continuous distribution, the primary and secondary force transfer paths must be identified in conjunction with an overall mechanical system analysis.

[0154] When obtaining structural coupling parameters, the structural stiffness parameters between each pair of adjacent spatial partitions are evaluated, including axial, bending, and torsional stiffness, based on the material type, partition cross-sectional dimensions, and connection node stiffness. Stiffness reduction due to node damage, localized corrosion, or aging must also be considered. Regarding mechanical interaction parameters, the primary focus is on the deformation coordination of adjacent partitions under load, the degree of impact on load-bearing capacity, and the proportion of transferred load. These data can be obtained through finite element modeling, structural mechanics analysis, or inversion of historical test data.

[0155] Ultimately, all connection methods, mechanical paths, inter-zone stiffnesses, and interaction parameters were collated and archived to form a structural coupling analysis input dataset. This dataset provides scientific foundational data for overall load-bearing capacity distribution analysis, weak zone identification, and structural repair decision-making.

[0156] The ultimate bearing capacity data of all spatial partitions are jointly analyzed based on the integrated input data set of structural spatial bearing capacity and the input data set of structural coupling analysis. This generates overall bearing capacity distribution data that reflects the influence of the forces on each spatial partition and its adjacent spatial partitions. Specifically, the following data are included: First, the ultimate bearing capacity data for all spatial partitions is integrated according to partition number and physical spatial distribution to construct an integrated input dataset for structural spatial bearing capacity. This dataset records the residual bearing capacity of each partition under extreme loads, providing a mechanical basis for coupled analysis.

[0157] Subsequently, the integrated input dataset for the structural spatial bearing capacity is linked to the input dataset for the structural coupling analysis. This coupled model allows for the consideration of stiffness coupling and mechanical synergy between each partition and its adjacent partitions. For example, if a partition deforms or is damaged, the load share borne by adjacent partitions is automatically adjusted to reflect the actual distribution of stress across the structure.

[0158] During the analysis, a zone-by-zone coordinated deformation method based on structural mechanics is employed to iteratively adjust the load-bearing and deformation capacities of each zone. Zones with high stiffness and strong connections are appropriately increased in their load-bearing contribution, while those with severe damage and weakened stiffness are reduced in their load distribution. If necessary, multiple data iterations are performed to simulate multi-point damage or multi-zone coordinated failure conditions, identifying the main structural load paths and potential failure chains.

[0159] Ultimately, the resulting overall load-bearing capacity distribution data intuitively reflects the stress conditions of each hollow beam compartment under complex coupling effects, including high-load bearing areas, areas of concentrated deformation, and potential weak links. This data not only provides a quantitative basis for health assessments of cultural relics structures and the formulation of repair plans, but also offers a scientific basis for risk warnings and emergency reinforcement decisions.

[0160] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for quantifying the damage and verifying the bearing capacity of hollow beams in cultural relics and ancient buildings, characterized by: The method comprises: Acquire original detection data of the hollow beam, wherein the original detection data includes ultrasonic signal data, infrared imaging data, environmental humidity parameters and moisture content data of each detection point, to obtain an original data set; Based on the original data set, the ultrasonic signal data and infrared imaging data are subjected to noise filtering and correction based on the environmental humidity parameters to obtain a humidity-corrected data set. Based on the humidity correction data set and the moisture content data of the detection points, the structural area of ​​the hollow beam is spatially partitioned to obtain a spatial partition data set; Analyze the humidity correction data in each spatial partition based on the spatial partition data set, identify signal abnormal areas, and extract damage features in each abnormal area to obtain a damage feature data set. The damage feature data set includes signal attenuation rate, maximum amplitude, periodic variation, and energy variation value data. Based on the damage feature dataset, multi-dimensional damage parameters are weighted and normalized, and a mapping is established by combining the moisture content of the spatial partitions with statistical data to generate the damage quantification results of each spatial partition and obtain the damage quantification result dataset. Based on the damage quantification result dataset and the spatial partition dataset, combined with the material type, geometric dimensions, and load information of each spatial partition, a mechanical analysis is performed to generate the bearing capacity distribution results of the hollow beam.

2. The method for quantifying damage and verifying bearing capacity of hollow beams in cultural relics and ancient buildings according to claim 1 is characterized in that: Based on the original data set, the ultrasonic signal data and infrared imaging data are noise filtered and corrected in combination with the ambient humidity parameters to obtain a humidity-corrected data set, including: Based on the original data set, the ultrasonic signal data is subjected to time domain noise removal processing, and the infrared imaging data is subjected to frequency domain noise removal processing to obtain a purified signal data set; According to the purified signal data set, combined with the environmental humidity parameters in the original data set, the amplitude and attenuation parameters of the ultrasonic signal and infrared imaging signal are respectively compensated for humidity using the preset humidity influence function to obtain a humidity compensated signal data set; According to the humidity compensation signal dataset, the filter parameters are adaptively adjusted for the abnormal signal fluctuation area, and the residual noise and signal distortion in the local high humidity area are jointly suppressed to generate a humidity correction dataset.

3. The method for quantifying damage and verifying bearing capacity of hollow beams in cultural relics and ancient buildings according to claim 1 is characterized in that: Based on the humidity correction data set and the moisture content data of the detection points, the structural area of ​​the hollow beam is spatially partitioned to obtain a spatial partition data set, including: Based on the humidity correction data set and the moisture content data of each test point, the hollow beam is divided into initial test units with equal distances to generate preliminary spatial partitioning data; Based on the preliminary spatial partitioning data, the cluster radius is dynamically adjusted for areas with large differences in moisture content, candidate spatial partition groups are automatically formed, and clustered spatial partitioning data are generated; According to the cluster space partition data, combined with the signal amplitude changes and structural geometric parameters within the space partition, the abnormal signal area within the cluster space partition is refined and segmented to generate multi-level space partition data; The boundary consistency check is performed on the multi-level spatial partition data. For areas where the signal fluctuations at the boundaries of adjacent spatial partitions are large, the boundary smoothing is performed by resetting the spatial partition boundaries to generate a spatial partition data set.

4. The method for quantifying damage and verifying bearing capacity of hollow beams in cultural relics and ancient buildings according to claim 1 is characterized in that: Based on the spatial partition data set, the humidity correction data in each spatial partition is analyzed to identify signal abnormal areas and extract damage features in each abnormal area to obtain a damage feature data set, including: According to the spatial partition data set, the humidity correction data in each spatial partition is arranged in chronological order, and the signal continuity change index is calculated to obtain the spatial partition signal continuity data; According to the spatial partition signal continuity data, the local extreme value, mutation point and high-frequency disturbance interval of the signal in each spatial partition are extracted as the signal anomaly judgment criteria to generate abnormal area positioning data; For the humidity correction signal corresponding to the abnormal area positioning data, the signal attenuation rate, maximum amplitude, periodic change and energy change value are calculated to generate a damage feature data set.

5. The method for quantifying damage and verifying bearing capacity of hollow beams in cultural relics and ancient buildings according to claim 1 is characterized in that: Based on the damage feature dataset, multi-dimensional damage parameters are weighted and normalized. A mapping is established by combining the spatial partition moisture content with statistical data to generate damage quantification results for each spatial partition. The resulting damage quantification result dataset includes: The signal attenuation rate, maximum amplitude, periodic variation and energy variation in the damage feature data set are used as input parameters, and multi-parameter weighting and normalization processing is performed to obtain weighted feature data; Based on the weighted characteristic data, combined with the moisture content of each spatial partition and historical damage statistics, the interval regression analysis method is used to establish a mapping relationship between damage degree and signal characteristics, generating the initial quantitative results of spatial partition damage. Based on the initial quantification results of spatial partition damage and combined with the environmental humidity parameters, the humidity sensitivity adjustment coefficient is calculated, and the humidity correction is performed on the initial quantification results to generate humidity-corrected residual damage quantification data; The humidity-corrected residual quantization data is subjected to neighboring area consistency judgment. For areas with sudden changes between spatial partitions, the outliers are adjusted using a local smoothing algorithm to generate residual quantization correction results. Comparing the material type of each spatial partition with a preset material damage sensitivity comparison table to determine the material damage response coefficient of each spatial partition, wherein the material damage response coefficient is preset based on the historical mechanical properties and damage evolution characteristics of the spatial partition material; The damage quantification correction result of each spatial partition is weighted with the corresponding material damage response coefficient to generate a spatial partition damage index; All spatial partition damage indices are aggregated to form a damage quantification result dataset of spatial partitions and the overall structure.

6. The method for quantifying damage and verifying bearing capacity of hollow beams in cultural relics and ancient buildings according to claim 1 is characterized in that: Based on the damage quantification result dataset and the spatial partition dataset, combined with the material type, geometric dimensions, and load information of each spatial partition, a mechanical analysis is performed to generate the bearing capacity distribution results of the hollow beam, including: The damage quantification result dataset is matched one by one with the spatial partition dataset, and the damage index of each spatial partition is used as the bearing capacity reduction coefficient to correct the basic bearing capacity of the spatial partition in the damage-free state to obtain the preliminary bearing capacity value of the spatial partition. Based on the preliminary bearing capacity values ​​of the spatial partitions, combined with the material type, geometric dimensions and historical load information of each spatial partition, the bearing capacity calculation is corrected using a multi-parameter correction method to generate the corrected bearing capacity values ​​of the spatial partitions; Based on the spatial partition damage distribution, material aging degree and extreme load type, the ultimate load condition correction factor is calculated. According to the structural limit state analysis rules, the spatial partition correction bearing capacity value is adjusted to simulate the change of spatial partition bearing capacity under extreme loads and generate ultimate bearing capacity data. The ultimate bearing capacity data of all spatial partitions are integrated and combined with the structural interaction relationship between spatial partitions to generate the bearing capacity distribution results of the hollow beam.

7. The method for quantifying damage and verifying bearing capacity of hollow beams in cultural relics and ancient buildings according to claim 3 is characterized in that: The humidity correction dataset is further processed by the following method, including: Identify the material type of each spatial partition based on the spatial partition data set and set compensation coefficients corresponding to different material types; The humidity correction data of each spatial partition is weightedly calculated with the corresponding material compensation coefficient, and the signal amplitude and attenuation parameters are compensated for material properties to generate a humidity correction data set after material compensation.

8. The method for quantifying damage and verifying bearing capacity of hollow beams in cultural relics and ancient buildings according to claim 6 is characterized in that: Based on the preliminary bearing capacity values ​​of the spatial partitions, combined with the material type, geometric dimensions, and historical load information of each spatial partition, a multi-parameter correction method is used to correct the bearing capacity calculation and generate the corrected bearing capacity values ​​of the spatial partitions, including: Obtain the material type, cross-sectional size, length, node location, and opening location of each spatial partition to form a spatial partition load correction input data set; According to the material type of the spatial partition, the compressive strength, elastic modulus, and fracture toughness parameters of the spatial partition are retrieved from the preset material mechanical property parameter library, and the material property correction is performed on the preliminary bearing capacity value of the spatial partition to generate material correction bearing capacity data; According to the spatial partition cross-sectional dimensions, spatial partition length, node locations, and opening location characteristics of the spatial partition, geometric feature correction is performed on the material correction bearing capacity data, and the effective bearing area and cross-sectional inertia parameters are adjusted to generate geometric correction bearing capacity data; Based on the historical load information of spatial partitions, the load influence coefficient is calculated, and the load history correction is performed on the geometrically corrected bearing capacity data to generate load-corrected bearing capacity data; The load-corrected bearing capacity data is used as the spatial partition-corrected bearing capacity value after multi-parameter correction.

9. The method for quantifying damage and verifying bearing capacity of hollow beams in cultural relics and ancient buildings according to claim 6, characterized in that: Based on the spatial partition damage distribution, material aging degree, and extreme load type, the ultimate load condition correction factor is calculated. According to the structural limit state analysis rules, the spatial partition correction bearing capacity value is adjusted to simulate the change of spatial partition bearing capacity under extreme loads and generate ultimate bearing capacity data, including: Obtain damage distribution data for spatial partitions, including damage location, damage range, and damage type, to form a modified input data set for spatial partition limit conditions; Based on the aging degree of spatial partition materials, the current material performance status of the spatial partition is determined by analyzing the material performance degradation parameters, using previous material testing data and material aging models; Set extreme load types and, based on the impact of each extreme load type on structural safety, search in a preset extreme load correction factor library to determine the extreme load correction factor for each spatial partition. The extreme load types include earthquake loads, wind loads, sudden heavy loads, and environmental disaster loads. Based on the material performance status, the spatial partition limit condition correction input data set and the limit condition load correction factor, the spatial partition correction bearing capacity value is adjusted according to the structural limit state analysis rules to generate the ultimate bearing capacity data reflecting the actual residual bearing capacity under extreme loads; The ultimate bearing capacity data of all spatial partitions are output to form a spatial partition ultimate bearing capacity data set.

10. The method for quantifying damage and verifying bearing capacity of hollow beams in cultural relics and ancient buildings according to claim 6, characterized in that: The ultimate bearing capacity data of all spatial partitions are integrated, and the structural interaction relationship between the spatial partitions is combined to generate the bearing capacity distribution results of the hollow beam, including: According to the spatial partition ultimate bearing capacity dataset, the ultimate bearing capacity data of all spatial partitions are integrated according to the spatial partition number and the actual spatial distribution order to form the structural spatial bearing capacity integrated input dataset; Analyze the structural connection mode and force transmission path between spatial partitions, obtain the structural stiffness parameters and mechanical interaction parameters between spatial partitions, and form the input data set for structural coupling analysis; Based on the structural spatial bearing capacity integrated input data set and the structural coupling analysis input data set, the ultimate bearing capacity data of all spatial partitions are jointly analyzed to generate the overall bearing capacity distribution data reflecting the influence of the forces on each spatial partition and its adjacent spatial partitions; The overall bearing capacity distribution data is used as the bearing capacity distribution result of the entire hollow beam.

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