Surface surveying method and device for building cracks

Abstract

The invention is suitable for the technical field of building structure nondestructive testing, and provides a building crack surface surveying method and device.The building crack surface surveying method comprises the following steps that a physical property evaluation model of a tested material is built according to the water cement ratio, the water content and the minimum steel bar clear distance of the tested material, and the physical property evaluation model is used for evaluating the physical property of the tested material; outputting a physical property index of the material; constructing an expected accuracy evaluation model according to the physical property index of the material and the probe frequency of the current ultrasonic probe, and outputting an expected accuracy index; and constructing an actual measurement quality evaluation model according to the coupling uniformity between the current ultrasonic probe and the concrete surface, the signal-to-noise ratio of the actual measurement signal and the waveform definition, and outputting an actual measurement credibility index. According to the surface surveying method for the building cracks, a closed-loop feedback mechanism is formed by quantifying the material characteristics, predicting the expected accuracy, evaluating the actual measurement quality and intelligently adjusting the frequency of the probe.

Description

Technical Field

[0001] This invention belongs to the field of non-destructive testing technology for building structures, and particularly relates to a method and device for surface surveying of cracks in building structures. Background Technology

[0002] Currently, ultrasonic technology is the mainstream method for detecting concrete cracks. It relies on the diffraction effect of ultrasonic waves at the crack tip to estimate the crack depth through changes in acoustic time. However, in practical engineering applications, this technology faces multiple complex challenges.

[0003] Differences in the physical properties of the tested materials, such as water-cement ratio, moisture content, and minimum clear spacing of reinforcing bars, significantly alter the propagation speed and attenuation characteristics of ultrasonic waves. Traditional methods, employing fixed empirical parameters or single material models, cannot dynamically adapt to different material states, leading to deviations in the setting of basic acoustic parameters and introducing systematic errors. The selection of ultrasonic probe frequencies often relies on operator experience or fixed settings, lacking a mechanism for dynamic optimization based on material characteristics and crack conditions. Excessively high frequencies can cause excessive signal scattering and severe attenuation at coarse aggregates or cracks, while excessively low frequencies result in insufficient resolution, making it difficult to identify minute crack features.

[0004] In field testing, the uniformity of the coupling between the probe and the concrete surface, environmental noise interference, and other measured quality factors directly affect the reliability of the raw data. Existing methods only attempt to filter out noise in the later data processing or give general requirements in the specifications, but fail to quantify and evaluate the measured quality and provide real-time feedback to the testing process control.

[0005] In summary, existing ultrasonic surface surveying methods have significant shortcomings in terms of material adaptability and intelligent parameter adjustment, causing the stability and accuracy of test results to be highly dependent on the operator's subjective experience, making it difficult to achieve standardized and intelligent high-quality testing. Therefore, existing technologies urgently need improvement to address these issues. Summary of the Invention

[0006] The purpose of this invention is to provide a method for surface inspection of cracks in building structures, aiming to solve the above-mentioned problems.

[0007] This invention is implemented as follows: a method for surface inspection of cracks in a building, comprising the following steps:

[0008] S1: Construct a physical property evaluation model for the tested material based on its water-cement ratio, moisture content, and minimum clear spacing of reinforcing bars, and output the material's physical property index;

[0009] S2: Construct an expected accuracy assessment model based on the material physical property index and the current ultrasonic probe frequency, and output the expected accuracy index;

[0010] S3: Based on the coupling uniformity between the current ultrasonic probe and the concrete surface, the measured signal-to-noise ratio, and the waveform clarity, construct a measured quality assessment model and output the measured reliability index;

[0011] S4: Construct a probe frequency adjustment model based on the current probe frequency, probe signal stability, and expected accuracy index, and output the target probe frequency.

[0012] A further technical solution is provided in the physical property evaluation model of the tested material in step S1:

[0013] The water-cement ratio, moisture content, and minimum clear spacing of the reinforcing bars of the tested material are successively substituted into the maximum-minimum normalization formula for processing, and the water-cement ratio index, moisture content index, and minimum reinforcing bar spacing index are generated in sequence.

[0014] The material physical property index is obtained by multiplying the water-cement ratio index, the complement of the moisture content index (the value obtained by subtracting the corresponding index from 1), and the complement of the minimum rebar spacing index by the corresponding preset weight coefficients, and then summing them by weight. Finally, the material physical property index is obtained by exponentiation of the nonlinear adjustment factor.

[0015] A further technical solution, in the expected accuracy evaluation model of step S2:

[0016] The current ultrasonic probe frequency is substituted into the maximum-minimum normalization formula for processing, and the probe frequency index is generated.

[0017] The expected accuracy index is obtained by multiplying the material physical property index by a linear combination.

[0018] This linear combination is obtained by multiplying the preset frequency adjustment intensity coefficient by the probe frequency index and its complement, and then adding the preset basic accuracy coefficient.

[0019] A further technical solution, in the measured quality assessment model of step S3:

[0020] The coupling uniformity between the current ultrasonic probe and the concrete surface, the measured signal-to-noise ratio, and the waveform clarity are successively substituted into the maximum-minimum normalization formula for processing, and the coupling uniformity index, signal-to-noise ratio index, and waveform clarity index are generated in sequence.

[0021] The coupling uniformity index, signal-to-noise ratio index, and waveform clarity index are multiplied by their respective preset weighting coefficients and then summed. The result is then transformed by a negative exponential function with the natural constant as the base to obtain the measured reliability index. The exponential function includes a preset positive gain coefficient.

[0022] A further technical solution involves substituting the probe signal stability into the maximum-minimum normalization formula for processing, and generating a probe signal stability index.

[0023] In the probe frequency adjustment model of step S4:

[0024] The complements of the current probe frequency index, the probe signal stability index, and the expected accuracy index are multiplied by their respective preset weighting coefficients and summed. The result is then limited to the range of 0 to 1 and finally mapped to the preset probe frequency adjustment range to obtain the target probe frequency.

[0025] A further technical solution includes the following steps after step S4: S5: Construct a crack condition assessment model based on the average crack width detected by crack microscope, the angle between the main crack direction and the acoustic path, and the dust concentration in the crack, and output the crack condition index.

[0026] S6: Construct a measurement point spacing adjustment model based on the measured reliability index, preset measurement point spacing, and crack state index, and output the target measurement point spacing.

[0027] A further technical solution is provided in the crack state assessment model of step S5:

[0028] The average crack width, the angle between the main crack direction and the acoustic path, and the dust concentration in the crack are successively substituted into the maximum-minimum normalization formula for processing, and the average crack width index, the angle index, and the dust concentration index are generated in sequence.

[0029] The crack state index is obtained by multiplying the crack average width index, the included angle index, and the dust concentration index by their respective preset influence coefficients, and then multiplying them together. Finally, the crack state index is obtained by exponentiation of the overall nonlinear adjustment factor.

[0030] A further technical solution is provided in the measurement point spacing adjustment model in step S6:

[0031] The preset measuring point spacing is substituted into the maximum-minimum normalization formula for processing, and the preset measuring point spacing index is generated.

[0032] The complements of the preset measuring point spacing index, the measured reliability index, and the crack state index are multiplied by their respective preset weighting coefficients and then summed. The result is then limited to the range of 0 to 1 and finally mapped to the preset allowable range of measuring point spacing to obtain the target measuring point spacing.

[0033] A surface inspection device for cracks in a building structure, comprising:

[0034] Memory, used to store executable instructions;

[0035] The processor, when executing executable instructions stored in the memory, implements the surface survey method for building cracks as described in any one of claims 1-8.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0037] This invention provides a method for surface inspection of cracks in building structures. Through S1 quantification of material properties, S2 prediction of expected accuracy, S3 evaluation of measured quality, and S4 intelligent adjustment of probe frequency, a closed-loop feedback mechanism is formed. This mechanism enables the inspection process to dynamically optimize detection parameters according to actual working conditions, significantly improving the accuracy, stability, and automation level of surface inspection of cracks in building structures, reducing reliance on operator experience, and making a significant technological contribution.

[0038] This application enables precise quantification of probe signal stability and incorporates it as a key parameter into the probe frequency adjustment model, thereby solving the problem of limited frequency adjustment accuracy caused by insufficient quantification of probe signal quality in traditional methods. This scheme allows probe frequency adjustment to no longer rely solely on material properties and the current frequency, but to respond in real-time to the actual reliability of the signal. When signal stability decreases, the model can adjust the frequency accordingly to seek more stable detection conditions or better signal penetration, avoiding detection errors caused by poor signal quality. This adaptive frequency adjustment mechanism, based on comprehensive consideration of multiple factors, significantly improves the accuracy, reliability, and adaptability to complex detection environments in the surface survey of building cracks, reduces reliance on operator experience, and achieves intelligent optimization of the detection process.

[0039] By introducing crack condition assessment and adaptive adjustment of measuring point spacing, this method significantly improves the accuracy and efficiency of surface crack surveying in buildings. Specifically, step S5 quantifies the average crack width, the angle between the crack's main direction and the acoustic path, and the dust concentration within the crack, outputting a crack condition index. This allows the survey process to fully consider the complexity of the crack itself, avoiding detection biases caused by neglecting these key factors in traditional methods. Based on this, step S6 dynamically adjusts the measuring point spacing according to the measured reliability index, the preset measuring point spacing, and the crack condition index, achieving intelligent optimization of the detection strategy. In areas with complex cracks or low data reliability, denser measuring points can effectively capture subtle crack features and improve local detection accuracy; while in areas with simple cracks or high data reliability, measuring points can be appropriately sparsed to improve overall survey efficiency. This adaptive measuring point layout strategy allows for more rational allocation of detection resources, effectively solving the problem of balancing accuracy and efficiency in traditional fixed measuring point spacing, ultimately improving the reliability and practicality of surface crack survey results in buildings. Attached Figure Description

[0040] Figure 1 This is a schematic diagram illustrating the steps of a surface inspection method for cracks in a building. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0042] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0043] like Figure 1 As shown, a surface inspection method for building cracks according to an embodiment of the present invention includes the following steps:

[0044] S1: Construct a physical property evaluation model for the tested material based on its water-cement ratio, moisture content, and minimum clear spacing of reinforcing bars, and output the material's physical property index;

[0045] S2: Construct an expected accuracy assessment model based on the material physical property index and the current ultrasonic probe frequency, and output the expected accuracy index;

[0046] S3: Based on the coupling uniformity between the current ultrasonic probe and the concrete surface, the measured signal-to-noise ratio, and the waveform clarity, construct a measured quality assessment model and output the measured reliability index;

[0047] S4: Construct a probe frequency adjustment model based on the current probe frequency, probe signal stability, and expected accuracy index, and output the target probe frequency;

[0048] In this embodiment, the physical property evaluation model for the tested material serves to quantify the inherent characteristics of the building material being tested. These characteristics directly affect the speed, attenuation, and scattering behavior of ultrasonic waves propagating within it. By comprehensively considering multiple physical parameters of the material, this model aims to provide an objective and quantifiable indicator to reflect the degree of influence of the material on ultrasonic testing.

[0049] The material physical property index is a value calculated using a physical property evaluation model for the tested material. This index comprehensively reflects the influence of factors such as the water-cement ratio, moisture content, and minimum clear spacing of reinforcing bars on the ultrasonic wave propagation characteristics, providing basic data for subsequent adjustment of testing parameters.

[0050] The expected accuracy assessment model aims to predict the achievable accuracy of crack detection under specific material conditions and current ultrasonic probe frequencies. This model combines the inherent properties of the material with the current operating status of the equipment, providing a theoretical basis for optimizing the probe frequency.

[0051] The expected accuracy index is a numerical value calculated using the expected accuracy assessment model. This index reflects the expected level of accuracy for ultrasonic crack detection under current testing conditions and guides further adjustments to the probe frequency for better detection results.

[0052] The measured quality assessment model serves to evaluate the reliability of field data during ultrasonic surveying in real time. This model quantifies the trustworthiness of current measurement data by analyzing key indicators such as the coupling state between the probe and the measured surface, the quality of the received signal, and the clarity of the waveform.

[0053] The measured reliability index is a numerical value calculated using a measured quality assessment model. This index reflects the quality and reliability of current ultrasonic testing data, providing an important reference for subsequent adjustment of testing parameters and interpretation of results.

[0054] The probe frequency adjustment model dynamically adjusts the operating frequency of the ultrasonic probe based on current testing conditions and evaluation results. This model comprehensively considers factors such as the current probe frequency, probe signal stability, and expected accuracy, aiming to determine an optimal probe frequency to adapt to different testing needs and environments.

[0055] The target probe frequency represents a value calculated using a probe frequency adjustment model. This frequency is an optimized and adjusted operating frequency of the ultrasonic probe, designed to improve the penetration, resolution, and detection accuracy of ultrasonic waves under specific test materials and crack conditions.

[0056] The water-cement ratio represents the mass ratio of water to cement in concrete and is a key parameter affecting the strength, density, and ultrasonic wave propagation speed of concrete.

[0057] Moisture content indicates the amount of water in the material being tested, which affects the attenuation and propagation path of ultrasonic waves.

[0058] The minimum clear spacing between steel bars refers to the minimum distance between adjacent steel bars inside the concrete. Too small a spacing may cause scattering and interference of ultrasonic signals.

[0059] Coupling uniformity refers to the tightness and uniformity of the contact between the ultrasonic probe and the concrete surface being tested, which directly affects the effective transmission of ultrasonic energy.

[0060] The measured signal-to-noise ratio (SNR) represents the ratio of the received ultrasonic signal intensity to the background noise intensity, and is an important indicator for measuring signal quality.

[0061] Waveform clarity refers to the degree of clarity of the characteristics of the received ultrasonic waveform. A clear waveform helps to accurately identify crack information.

[0062] Probe signal stability refers to the consistency and reliability of the output signal of an ultrasonic probe during operation. A stable signal is the basis for accurate measurement.

[0063] This application proposes a surface inspection method for cracks in building structures, which aims to improve the accuracy and adaptability of ultrasonic testing through a series of interrelated assessment and adjustment steps.

[0064] In a preferred embodiment of the present invention, in the physical property evaluation model of the tested material in step S1:

[0065] The water-cement ratio, moisture content, and minimum clear spacing of the reinforcing bars of the tested material are successively substituted into the maximum-minimum normalization formula for processing, and the water-cement ratio index, moisture content index, and minimum reinforcing bar spacing index are generated in sequence.

[0066] ;

[0067] in This is the water-cement ratio weighting coefficient. This is the moisture content weighting coefficient. The minimum reinforcement spacing weighting coefficient. ,and , as well as All are greater than 0. The water-cement ratio index. The moisture content index, The minimum rebar spacing index. It is a non-linear adjustment factor. , This refers to the physical property index of the material.

[0068] In this embodiment, the type of the material being tested (such as C30 concrete, C40 concrete, etc.) can be manually input, and then the typical water-cement ratio, moisture content, and minimum clear spacing of the reinforcing bars for the corresponding material can be retrieved from a preset database to obtain this initial data; or the precise water-cement ratio, moisture content, and clear spacing of the reinforcing bars for the material being tested can be obtained by sampling and testing on site.

[0069] The water-cement ratio, moisture content, and minimum clear spacing of the reinforcing bars of the tested material are successively substituted into the maximum-minimum normalization formula for processing. The aim is to transform the original data with different dimensions and value ranges into a unified interval, typically [0,1], thereby eliminating the influence of dimensions and orders of magnitude and making different characteristics comparable. This can be achieved by subtracting the minimum value from the original data and then dividing by the difference between the maximum and minimum values. For example, for a parameter Y, its normalized exponent Y... norm =(YY min ) / (Y max -Y min This process generates the water-cement ratio index, moisture content index, and minimum rebar spacing index sequentially. These indices are dimensionless values ​​after maximum-minimum normalization, representing the relative levels of the water-cement ratio, moisture content, and minimum rebar spacing of the tested material within their respective preset ranges, providing standardized input for subsequent comprehensive evaluation.

[0070] Water-cement ratio weighting factor Moisture content weighting coefficient Minimum reinforcement spacing weighting coefficient Indices used to quantify the effects of water-cement ratio, moisture content, and minimum clear spacing of reinforcing bars on material physical properties. The relative importance or degree of influence of each factor. These coefficients can be determined through expert experience, historical data analysis, sensitivity analysis, or machine learning methods (such as regression analysis). Another way to determine them is through the analytic hierarchy process (AHP) or entropy weighting, which calculates them based on objective data on the influence of each factor on the physical properties of the material.

[0071] Nonlinear adjustment factor This is used to introduce nonlinear relationships to more accurately capture the complex interactions between material physical properties and various influencing factors. In real materials, the effects of various factors on ultrasonic wave propagation are often not a simple linear superposition. For example, when... When the value is less than 1, the index may become more sensitive to changes in certain factors, or exhibit a saturation effect within a specific range. This factor can be determined through experimental data fitting, numerical simulation, or empirical formulas based on materials science principles.

[0072] This application's solution addresses the inaccuracy of material parameter evaluation in traditional methods by refining and quantifying the water-cement ratio, moisture content, and minimum rebar spacing of the tested materials. Specifically, when performing surface inspection of building cracks, the original material parameters (water-cement ratio, moisture content, and minimum rebar spacing) are first converted into standardized dimensionless indices using a maximum-minimum normalization formula. This conversion process ensures the numerical comparability of different physical quantities and eliminates evaluation bias caused by dimensional differences. Subsequently, these standardized indices are incorporated into a material physical property evaluation model, which weights the importance of each material parameter by introducing weighting coefficients for water-cement ratio, moisture content, and minimum rebar spacing. This weighting mechanism allows the model to dynamically adjust the contribution of each parameter based on the actual material's influence on ultrasonic wave propagation. For example, if the water-cement ratio significantly affects ultrasonic wave attenuation, its weighting coefficient will be increased accordingly. Furthermore, the model introduces a nonlinear adjustment factor to capture the complex nonlinear relationships that may exist between material physical properties and these parameters, thereby generating a comprehensive and accurate material physical property index. This index can more accurately reflect the acoustic properties of the tested material, providing reliable basic data for subsequent ultrasonic probe frequency selection and crack detection, and significantly improving the adaptability of the entire survey method to different materials and the accuracy of assessment.

[0073] Through the above technical solution, this application effectively solves the systematic error problem existing in traditional methods for evaluating the physical properties of materials. By accurately normalizing and weighted nonlinearly combining the water-cement ratio, moisture content, and minimum clear spacing of reinforcing bars, this application can generate a material physical property index that is highly adaptable to material variability. This allows the ultrasonic survey method to move away from relying on fixed empirical parameters and instead dynamically adjust according to the actual characteristics of the material being tested, thereby significantly improving the accuracy and reliability of the material physical property evaluation. This provides a more solid and accurate foundation of data for subsequent ultrasonic testing, and ultimately enhances the overall accuracy of surface crack detection in the entire building structure.

[0074] In a preferred embodiment of the present invention, the expected accuracy evaluation model in step S2 is as follows:

[0075] The current ultrasonic probe frequency is substituted into the maximum-minimum normalization formula for processing, and the probe frequency index is generated.

[0076] ;

[0077] in Based on the accuracy coefficient, For frequency adjustment intensity coefficient, , For material physical property index, The probe frequency index. This represents the expected accuracy index.

[0078] In this embodiment, the current ultrasonic probe frequency is substituted into the maximum-minimum normalization formula for processing, and a probe frequency index is generated. This step aims to convert probe frequency data with different dimensions or ranges into a unified, dimensionless value, making them comparable within the model. Probe Frequency Index This is the normalized probe frequency value, representing the relative position or intensity of the current probe frequency within a preset frequency range. The probe frequency can be read directly from the built-in system registers.

[0079] Material physical property index It is determined based on parameters such as the water-cement ratio, moisture content, and minimum clear spacing of reinforcing bars of the tested material. It reflects the degree of influence of the tested material on the ultrasonic wave propagation characteristics and is an important basis for evaluating the accuracy of the test.

[0080] Base accuracy coefficient This represents the expected accuracy level under ideal or baseline conditions, without considering the effects of frequency tuning; it provides an initial accuracy baseline for the model. Frequency tuning strength coefficient. This is used to quantify the impact of probe frequency changes on the expected accuracy index. The larger the value, the more significant the impact of frequency changes on accuracy. , Initial values ​​can be set based on expert experience and then optimized. Specifically, multiple linear regression or nonlinear optimization algorithms (such as least squares method or gradient descent method) can be used to fit the values. and The optimal value is determined by the fact that in practical use, the system can learn online based on historical test results and dynamically fine-tune the coefficients.

[0081] This application's solution addresses the inadequacy of accuracy assessment caused by fixed probe frequencies by dynamically processing the probe frequency and combining it with material physical property indices to construct an expected accuracy assessment model. Specifically, the current ultrasonic probe frequency is substituted into a maximum-minimum normalization formula to generate a probe frequency index. This step standardizes the probe frequency, facilitating subsequent calculations and comparisons, adapting to different probe frequency ranges, and avoiding calculation errors caused by excessively large or small frequency values. Then, based on the material physical property index and the probe frequency index, the expected accuracy index is calculated using a formula. The basic accuracy coefficient provides a baseline accuracy level, while the frequency adjustment intensity coefficient, combined with the probe frequency index and its nonlinear term, dynamically adjusts the impact of frequency on accuracy, avoiding errors caused by extreme frequency values. The material physical property index acts as a multiplier, ensuring that the accuracy assessment is closely linked to material properties, thus achieving dynamic optimization of the probe frequency and intelligent accuracy assessment.

[0082] This model incorporates material physical property indices. Introduced as a multiplicative factor, it improves the expected accuracy. The evaluation results can directly reflect the inherent properties of the tested material. Simultaneously, by introducing the probe frequency index... and its nonlinear terms The model can capture the nonlinear effect of probe frequency on detection accuracy. For example, accuracy is high within a certain frequency range, but may decrease at excessively high or low frequencies. This design makes the expected accuracy assessment no longer a single, static value, but an intelligent assessment that can be dynamically adjusted according to the actual material properties and probe frequency, thus providing a more accurate basis for subsequent probe frequency adjustment.

[0083] Through the above technical solution, this application overcomes the problem of insufficient accuracy assessment caused by the fixed probe frequency in traditional methods. By normalizing the probe frequency and incorporating it into the expected accuracy assessment model, combined with material physical property indices, dynamic and intelligent assessment of detection accuracy is achieved. This allows the assessment results to more accurately reflect the actual accuracy under current detection conditions and material characteristics, providing a reliable basis for subsequent probe frequency optimization, thereby significantly improving the overall accuracy and reliability of surface surveying of cracks in buildings.

[0084] In a preferred embodiment of the present invention, in the measured quality assessment model of step S3:

[0085] The coupling uniformity between the current ultrasonic probe and the concrete surface, the measured signal-to-noise ratio, and the waveform clarity are successively substituted into the maximum-minimum normalization formula for processing, and the coupling uniformity index, signal-to-noise ratio index, and waveform clarity index are generated in sequence.

[0086] ;

[0087] in The coupling uniformity weighting coefficient is... The signal-to-noise ratio weighting coefficient. This is a waveform sharpness weighting coefficient. , , as well as All are greater than 0; This is the gain coefficient. , The coupling uniformity index, The signal-to-noise ratio is denoted as . The waveform sharpness index. This is the measured reliability index.

[0088] In this embodiment, coupling uniformity refers to the tightness and consistency of contact between the ultrasonic probe and the concrete surface being tested. Good coupling uniformity is the foundation for effective transmission of ultrasonic energy and directly affects the signal strength and quality. It can be achieved by applying a coupling agent (such as petroleum jelly, glycerin, or a special coupling gel) between the probe and the concrete surface, ensuring the probe contacts the surface with constant pressure and angle; or by designing a probe with a flexible contact surface or an adaptive clamping mechanism to adapt to uneven surfaces. Coupling uniformity can be quantified by the percentage of probe contact area or the uniformity of the coupling agent thickness.

[0089] The measured signal-to-noise ratio (SNR) refers to the ratio of effective signal power to noise power in an ultrasonic received signal. A high SNR means the signal is less susceptible to interference, has high data quality, and is beneficial for accurately identifying waveform characteristics. This can be achieved by using high-sensitivity sensors and low-noise amplifier circuits to enhance the signal; or by using digital signal processing techniques, such as filtering and averaging, to suppress noise. The measured SNR can be calculated as the ratio of the peak power of the received signal to the background noise power.

[0090] Waveform sharpness refers to the clarity of features such as boundaries, peaks, and troughs in a received ultrasonic signal waveform. A sharp waveform helps in accurately interpreting key parameters such as acoustic duration and amplitude. This can be achieved by optimizing the shape and width of the transmitted ultrasonic pulse to obtain a narrower pulse response, or by using signal processing algorithms such as deconvolution and wavelet transform to sharpen waveform features. Waveform sharpness can be measured by the steepness of the rising edge or the width of the main pulse.

[0091] Coupling uniformity index Signal-to-noise ratio index and waveform clarity index These are dimensionless values ​​obtained by normalizing the original coupling uniformity, measured signal-to-noise ratio, and waveform sharpness using a maximum-minimum method. These indices reflect the relative levels of their respective parameters between 0 and 1, with larger values ​​generally indicating better quality.

[0092] Based on this, this application uses the formula Calculate the measured reliability index Among them, the measured reliability index It is an indicator that comprehensively evaluates the quality of current ultrasonic testing data. Its value ranges from 0 to 1, and the larger the value, the more reliable the measured data.

[0093] Coupling uniformity weighting coefficient Signal-to-noise ratio weighting coefficient Waveform sharpness weighting coefficient These coefficients are used to quantify the relative importance of coupling uniformity, measured signal-to-noise ratio, and waveform sharpness in assessing measured reliability. They can be implemented by setting these coefficients using expert experience, determining the relative importance of each factor based on domain knowledge and practical testing experience; or by using data-driven methods, such as principal component analysis, regression analysis, or machine learning algorithms, to learn and optimize these weights from a large amount of historical data.

[0094] Gain coefficient It is a positive adjustment factor used to control the sensitivity or response strength of the measured confidence index to changes in input parameters. Its implementation can be determined through experimental testing and calibration to ensure... The output range and trend of change should meet actual needs; or be adjusted according to specific application scenarios and requirements for data reliability.

[0095] This scheme standardizes three key measured quality parameters—coupling uniformity between the ultrasonic probe and the concrete surface, signal-to-noise ratio, and waveform clarity—eliminating differences in their dimensions and value ranges and ensuring fairness in the evaluation. Subsequently, a nonlinear weighted combination model comprehensively considers the impact of these standardized parameters on the reliability of the measured data. The weighting coefficients are... , as well as This reflects the relative importance of each factor in actual detection, while the gain coefficient... This adjusts the model's sensitivity to quality changes, thus improving the measured reliability index of the final output. It can objectively and accurately reflect the overall quality of current ultrasonic testing data. This quantitative evaluation mechanism avoids the over-reliance on operator experience in traditional methods, and provides a reliable basis for subsequent adjustment of testing parameters (such as probe frequency or measuring point spacing), thereby improving the accuracy and automation level of surface surveying of building cracks.

[0096] In a preferred embodiment of the present invention, the probe signal stability is substituted into the maximum-minimum normalization formula for processing, and a probe signal stability index is generated.

[0097] In the probe frequency adjustment model of step S4:

[0098] ;

[0099] in This is the probe frequency weighting coefficient. The probe signal weighting coefficient is... The expected accuracy weighting coefficient, ,and , as well as All are greater than 0; This is the current probe frequency index. This is the probe signal stability index. This is the expected accuracy index. This is the minimum adjustable value for the probe frequency. This represents the maximum adjustable value of the probe frequency. The target probe frequency.

[0100] In this embodiment, Indicates will The range is limited to [0,1]. This refers to the probe frequency weighting coefficient. The probe signal weighting coefficient is... The expected accuracy weighting coefficient, ,and , as well as All are greater than 0; This represents the current probe frequency index. This is the probe signal stability index. This is the expected accuracy index. This is the minimum adjustable value for the probe frequency. This represents the maximum adjustable value of the probe frequency. The target probe frequency.

[0101] To better understand the above technical solution, the key technical features involved are described in detail below. Probe signal stability refers to the degree of fluctuation in the amplitude, frequency, phase, and other parameters of the transmitted or received signal of an ultrasonic probe over time under continuous operation or specific detection conditions. High signal stability means reliable signal quality, less susceptibility to interference, and high data reliability; conversely, poor stability may lead to increased measurement errors. This stability can be quantified in various ways. For example, it can be quantified by real-time monitoring of the peak voltage or power of the probe's output signal and calculating its standard deviation or coefficient of variation over a period of time; alternatively, it can be evaluated by analyzing the spectral purity or harmonic distortion of the signal.

[0102] The maximum-minimum normalization formula is a commonly used data preprocessing method used to linearly map raw data to a specified range (usually [0,1]). In practical applications, the historical maximum and minimum values ​​of probe signal stability can be preset or dynamically obtained in the data acquisition module, and then the currently measured stability data can be substituted into the formula for normalization. Alternatively, a sliding window approach can be used to update the maximum and minimum values ​​in real time to adapt to the trend of probe performance changes over time or in the environment, ensuring the real-time performance and accuracy of the normalization results.

[0103] Probe signal stability index This is a dimensionless value obtained by normalizing the original probe signal stability data using a maximum-minimum method. Its range is typically between [0,1]. A higher exponent indicates better probe signal stability. This exponent can be directly represented by the normalized value, or the normalized value can be reversed to be negatively correlated with signal instability, thus ensuring that a higher exponent represents better stability.

[0104] The probe frequency adjustment model is a mathematical model used to adjust the frequency based on multiple input parameters (current probe frequency index). Probe signal stability index Expected accuracy index Calculate and output an optimized target probe frequency. The model can be a linear or nonlinear regression model built based on empirical formulas or expert knowledge.

[0105] The function represents input values Restricted to a specified range, such as [0, 1]. If If less than 0, output 0; if If the value is greater than 1, output 1; otherwise, output In software programming, this can be achieved using conditional statements; at the hardware level, it can be achieved through limiting circuits or specific instructions of a digital signal processor (DSP) to truncate or saturate the values.

[0106] Weighting coefficient , as well as These represent the relative importance of the current probe frequency, probe signal stability, and expected accuracy in the probe frequency adjustment model, respectively. The sum of these coefficients is 1, and all are greater than 0, ensuring that all factors are considered and have a positive impact. These weighting coefficients can be determined through pre-training or parameter calibration; alternatively, they can be a model based on machine learning algorithms (such as neural networks or support vector machines), trained on a large amount of historical detection data, enabling it to adaptively learn and predict the optimal probe frequency.

[0107] Target probe frequency This is the optimized probe frequency calculated by the probe frequency adjustment model based on the input parameters for the current testing task. The calculated target frequency can be directly output through a digital-to-analog converter to control the frequency generator of the ultrasonic probe for adjustment; alternatively, the target frequency can be displayed as a suggested value to the operator, who can then manually adjust the probe frequency, or it can be used as the starting point for automatic frequency scanning.

[0108] The proposed solution quantifies the probe signal stability and integrates it as a key input parameter into the probe frequency adjustment model, thereby achieving adaptive optimization of the ultrasonic probe frequency. Specifically, firstly, the original probe signal stability data is normalized to its maximum and minimum values, transforming it into a dimensionless probe signal stability index. This standardization process ensures that input data with different dimensions can be processed uniformly by the model and avoids direct interference from fluctuations in the original data on the model's calculations. Subsequently, the probe's signal stability index... Compared with the current probe frequency index and expected accuracy index These are used together as inputs to the probe frequency adjustment model. The model uses preset weighting coefficients. , as well as The three input parameters are weighted and combined, where the probe frequency index is... Reflects the current operating status of the equipment, and the expected accuracy index. This comprehensively considers the physical properties of the material being tested, while the probe signal stability index... This directly quantifies the reliability of the signal. This comprehensive consideration of multiple factors allows the model to fully assess the current detection environment and equipment status. The calculation results are obtained through... The function was constrained to the valid range of [0,1], ensuring the rationality of subsequent frequency adjustments. Ultimately, this normalization result was mapped to the minimum adjustable value of the probe frequency. and maximum value This allows for the output of a precise target probe frequency. In this way, the solution of this application can dynamically adjust the probe frequency based on real-time signal quality feedback, enabling the detection process to better adapt to constantly changing detection conditions. This allows for optimization of detection parameters while maintaining signal reliability, thereby improving detection accuracy and efficiency.

[0109] As a preferred embodiment of the present invention, it further includes: S5: constructing a crack state assessment model based on the average crack width detected by crack microscope, the angle between the main crack direction and the acoustic path, and the dust concentration in the crack, and outputting the crack state index.

[0110] S6: Construct a measurement point spacing adjustment model based on the measured reliability index, preset measurement point spacing, and crack state index, and output the target measurement point spacing.

[0111] In this embodiment, the average crack width detected by the crack microscope refers to the average size of the crack opening obtained by observing and measuring the crack surface using high-magnification optical equipment. Its function is to quantify the macroscopic geometric characteristics of the crack, as the crack width directly affects the scattering and attenuation of ultrasonic waves. Besides direct measurement using a crack microscope, automatic identification and measurement can also be performed using high-resolution image acquisition equipment combined with image processing algorithms, or non-contact scanning measurement can be performed using a laser displacement sensor. The angle between the main crack direction and the acoustic path refers to the angle between the main direction of the crack's extension on the building surface and the propagation path of the ultrasonic probe's sound beam. This angle significantly affects the reflection, refraction, and diffraction behavior of ultrasonic waves at the crack, thus affecting the signal reception quality and the accuracy of crack depth interpretation. Besides measurement by manual visual inspection combined with a protractor, the geometric model of the crack can be obtained through three-dimensional laser scanning, and acoustic path simulation calculations can be performed based on the placement position of the ultrasonic probe, or the relative relationship between the probe attitude and the crack direction can be obtained in real time using a probe positioning system based on an inertial measurement unit. The dust concentration within the crack refers to the content of dust particles in the filling material inside the crack. Dust inside cracks absorbs and scatters ultrasonic energy, causing signal attenuation and reducing detection accuracy. In addition to observing cracks with a microscope and combining image analysis for qualitative or semi-quantitative assessment, micro-sampling on the crack surface can be performed for laboratory analysis, or fiber optic sensors of specific wavelengths can be used to detect particle scattering inside the cracks.

[0112] A crack condition assessment model is a mathematical or logical model that comprehensively considers factors such as average crack width, the angle between the crack's main orientation and the acoustic path, and the dust concentration within the crack, and quantifies the complexity of the crack. This model aims to transform multi-dimensional crack characteristics into a unified index for subsequent decision-making. The crack condition index is a quantitative indicator output by the crack condition assessment model, used to characterize the complexity of the crack. This index can intuitively reflect the comprehensive influence of the crack on ultrasonic wave propagation.

[0113] The measured reliability index is the indicator output in step S3. In the measurement point spacing adjustment model, this index is used to reflect the reliability of the current detection data. A high reliability index indicates good data quality, while a low reliability index suggests that the data may have large errors.

[0114] The preset measuring point spacing is the initial spacing of measuring points set before surface inspection of cracks in a building, based on engineering experience, standard requirements, or preliminary survey results. This spacing serves as the basic reference value for the measuring point spacing adjustment model and is dynamically adjusted according to specific circumstances during actual testing. The measuring point spacing adjustment model is a mathematical or logical model that dynamically optimizes the measuring point layout spacing based on the measured reliability index, the preset measuring point spacing, and the crack state index.

[0115] The target measuring point spacing is the final measuring point layout interval output by the measuring point spacing adjustment model, used to guide actual survey operations. This spacing is an optimized value obtained after comprehensive evaluation, designed to ensure sufficiently accurate and efficient detection results under different crack conditions and actual measurement conditions.

[0116] In the surface survey method for building cracks proposed in this application, in order to overcome the shortcomings of traditional methods in terms of the complexity of crack conditions and the fixed spacing of measuring points, after optimizing and adjusting the probe frequency, a quantitative assessment of the crack's own condition and an adaptive adjustment mechanism for the measuring point spacing are further introduced.

[0117] Specifically, firstly, in step S5, key information such as the average width of the crack, the angle between its main orientation and the acoustic path, and the dust concentration within the crack are obtained using techniques such as crack microscopy. These parameters directly reflect the physical characteristics of the crack and their influence on ultrasonic wave propagation. Subsequently, this information is input into a crack condition assessment model, which comprehensively analyzes and quantifies these multi-dimensional characteristics to output a unified crack condition index. This index can objectively and accurately characterize the current complexity of the crack.

[0118] Building upon this, the process proceeds to step S6, which aims to dynamically optimize the measurement point spacing based on actual conditions. The measurement point spacing adjustment model receives three key inputs: first, the measured reliability index from step S3, which reflects the quality and reliability of the current detection data, ensuring subsequent adjustments are based on valid data; second, the preset measurement point spacing, serving as an initial reference benchmark; and finally, the crack state index output from step S5, which indicates the complexity of the crack. The measurement point spacing adjustment model comprehensively considers these three factors, calculating through internal logic or algorithms to output a target measurement point spacing. When the crack state is complex or the measured reliability is low, the model tends to reduce the measurement point spacing to increase the density of detection points and improve local detection accuracy; conversely, when the crack state is simple and the measured reliability is high, the model may appropriately increase the measurement point spacing to improve detection efficiency.

[0119] By introducing S5 and S6, this method enables intelligent and adaptive optimization of the crack detection process. It not only overcomes the shortcomings of traditional methods that ignore the influence of crack conditions, but also effectively solves the problems of insufficient accuracy in complex areas and low efficiency in simple areas when the measurement point spacing is fixed by dynamically adjusting the spacing. This mechanism, combined with the aforementioned steps of material physical property assessment, expected accuracy assessment, measured quality assessment, and probe frequency adjustment, forms a more comprehensive and robust system for detecting surface cracks in buildings. This allows the entire detection process to better adapt to various complex field conditions, significantly improving the accuracy, reliability, and efficiency of the detection results.

[0120] In a preferred embodiment of the present invention, in the crack state assessment model of step S5:

[0121] The average crack width, the angle between the main crack direction and the acoustic path, and the dust concentration in the crack are successively substituted into the maximum-minimum normalization formula for processing, and the average crack width index, the angle index, and the dust concentration index are generated in sequence.

[0122] ;

[0123] in This is the width influence coefficient. The angle influence coefficient is... The dust impact coefficient is... , as well as All are greater than 0. As the overall nonlinear adjustment factor, Greater than 0, The average crack width index. The angle index is The dust concentration index. This is the crack state index.

[0124] In this embodiment, the average crack width, the angle between the crack's main orientation and the acoustic path, and the dust concentration within the crack are key parameters affecting the propagation of ultrasonic waves within the crack. The average crack width can be obtained in various ways, such as direct measurement using a crack microscope or automatic identification and calculation through high-resolution image acquisition combined with image processing algorithms. The angle between the crack's main orientation and the acoustic path can be determined by geometric measurement and marking of the crack before surveying, or by obtaining the spatial distribution information of the crack through ultrasonic array probe scanning, and then calculating the relative angle between the probe's acoustic path and the crack's orientation. The dust concentration within the crack can be assessed through sampling analysis, endoscopic observation combined with image processing technology, or by real-time monitoring using miniature sensors placed inside the crack.

[0125] After normalization using the maximum-minimum normalization formula, the average crack width index, the included angle index, and the dust concentration index can be generated. These indices represent the relative influence of the corresponding crack parameters between 0 and 1.

[0126] Width Influence Coefficient Angle Influence Coefficient and dust impact coefficient It is used to adjust the crack condition index by adjusting the average crack width, the angle between the crack's main direction and the acoustic path, and the dust concentration within the crack. The weighting coefficients represent the relative contributions. These coefficients are all greater than 0 and are combined in a product form, indicating the combined influence of these factors. The values ​​of these coefficients can be determined based on a large amount of experimental data, expert experience, or by training and optimizing through machine learning algorithms (such as regression analysis and neural networks) to ensure that the model accurately reflects the importance of different crack parameters in actual ultrasonic testing.

[0127] Overall nonlinear adjustment factor This is used to introduce nonlinear relationships and further refine the evaluation of the crack state index. When When the value is greater than 0, the sensitivity of the crack state index can be adjusted according to the actual situation. For example, when the influence of the crack state on ultrasonic wave propagation exhibits nonlinear characteristics, the sensitivity can be adjusted... The value of can enable the model to better fit the actual situation, enhancing its robustness and accuracy. During the experiment, simulated crack samples with different widths, angles, and dust concentrations were designed to measure the ultrasonic signal attenuation rate. The relationship curve between the attenuation rate and each parameter was fitted; if it exhibits a significant nonlinearity (e.g., exponential or power-law type), then... Values ​​greater than 1 are acceptable (e.g., 1.2 to 2.0); if it is close to linear, then... ≈1. In actual value... The value can be determined by combining laboratory calibration and field verification, and can be designed as an adjustable parameter to meet the needs of different engineering scenarios.

[0128] The final generated crack state index It is a comprehensive indicator that quantifies the degree of influence of the overall state of the crack on ultrasonic detection. The larger the value, the more complex the crack state or the greater the interference with ultrasonic wave propagation.

[0129] The crack condition assessment model in this application systematically integrates multiple key physical parameters of cracks to generate a comprehensive index that accurately reflects crack complexity. After obtaining the normalized crack average width index, angle index, and dust concentration index, the model introduces width influence coefficient, angle influence coefficient, and dust influence coefficient. These coefficients serve as weights, and the three indices are fused through a product. This product-based fusion method ensures that extreme values ​​of any parameter can significantly impact the final result, better reflecting the actual comprehensive effect mechanism of cracks on ultrasonic wave propagation. For example, even with a small crack width, extremely high dust concentration can lead to severe attenuation of the ultrasonic signal. By adjusting these influence coefficients, the relative importance of each parameter in the assessment can be flexibly adjusted based on practical engineering experience or experimental data. Finally, the model introduces an overall nonlinear adjustment factor to perform a power operation on the fused result, generating the final crack condition index. This nonlinear adjustment factor allows the model to capture the nonlinear interactions that may exist between crack parameters. For example, when crack width and dust concentration both reach a certain level, their impact on ultrasonic wave propagation may not be a simple linear superposition but rather exhibits exponential growth. By adjusting... The value of can enable the model to better adapt to this complex nonlinear effect, thereby improving the crack state index. This model can more accurately and robustly reflect the true state of cracks. Combined with the measuring point spacing adjustment model in step S6 above, this crack state assessment model provides crucial adaptive capabilities for surface surveying methods of building cracks. By accurately quantifying the crack state, the model provides reliable input for subsequent measuring point spacing adjustments, allowing the adjustment to fully consider the complexity of the cracks themselves, thereby optimizing detection efficiency while ensuring detection accuracy.

[0130] In a preferred embodiment of the present invention, in the measurement point spacing adjustment model of step S6:

[0131] The preset measuring point spacing is substituted into the maximum-minimum normalization formula for processing, and the preset measuring point spacing index is generated.

[0132] ;

[0133] in The preset point spacing weighting coefficient, This is the weighting coefficient for the measured reliability. The crack state weighting coefficient is... , , as well as All are greater than 0; This represents the minimum allowable spacing between measuring points. This represents the maximum allowable spacing between measuring points. To preset the measuring point spacing index, This is a measured credibility index. This is a crack condition index. The distance between the target measurement points.

[0134] In this embodiment, the preset measurement point spacing is the initially set distance between measurement points used for ultrasonic testing. Maximum-minimum normalization is performed to transform the raw data with different dimensions or ranges into a unified [0,1] interval, forming a dimensionless preset measurement point spacing index. This allows this parameter to be compared and calculated with other indices on the same scale, eliminating the influence of dimensional differences on the model calculation results and providing standardized input for subsequent comprehensive evaluation.

[0135] The above formula is the core of the measuring point spacing adjustment model. It comprehensively considers the preset measuring point spacing index, the measured reliability index, and the crack state index. After weighted summation and clipping, the result is mapped to the actual allowable measuring point spacing range, thereby calculating the target measuring point spacing. Its function is to achieve adaptive adjustment of the spacing between measuring points in order to balance detection accuracy and efficiency.

[0136] Among them, the weighting coefficient , as well as These factors are used to quantify the relative importance of the preset measurement point spacing, measured reliability, and crack condition in the measurement point spacing adjustment model. Their sum is 1 and all are greater than 0, ensuring that all factors influence the final target measurement point spacing, and that this influence is controllable and interpretable. These weight coefficients can be determined using expert experience, analytic hierarchy process (AHP), or machine learning algorithms (such as regression analysis or genetic algorithm optimization) to reflect the importance of each factor in the measurement point spacing adjustment; alternatively, heuristic methods such as simulated annealing or particle swarm optimization can be used to search for the optimal weight combination within a preset parameter space. and The target measurement point spacing was defined. The effective range of values ​​reflects physical constraints and engineering specifications, ensuring that the calculated target measurement point spacing is feasible and meaningful in practical operation. For example, It can be set as the lower limit of the effective detection range of the ultrasonic probe, while It can be set as the maximum allowable spacing between measuring points, based on experience or standards, while ensuring that no cracks are missed. To preset the measuring point spacing index, This is a measured credibility index. These are crack state indices, which are input parameters of the model. They represent the preset detection density, the reliability of the current detection data, and the complexity of the crack itself, respectively, and are all normalized indices. Target measurement point spacing. This is the final output of the measurement point spacing adjustment model, representing the distance between measurement points recommended for subsequent detection after adaptive optimization.

[0137] This application's solution achieves intelligent optimization of measuring point spacing by establishing a quantitative model, solving the problem of the imbalance between accuracy and efficiency in adaptive adjustment. Specifically, the preset measuring point spacing is substituted into the maximum-minimum value normalization formula to generate a preset measuring point spacing index. This step standardizes the original spacing data to a uniform scale, eliminating dimensional differences and providing a comparable basis for subsequent dynamic calculations. Based on the preset measuring point spacing index, the measured reliability index, and the crack state index, the target measuring point spacing is calculated using weighted coefficients. The weighted coefficients satisfy a sum of 1 and are all greater than 0, ensuring a reasonable allocation of the influence weights of each factor and avoiding subjective bias. The clip function restricts the calculation results to the [0,1] interval, which is then mapped to the actual spacing range to ensure the output value is within the allowable boundaries and prevents invalid adjustments. In particular, based on the measured reliability index... The system participates in calculations, automatically reducing the spacing to compensate for data uncertainty when confidence levels are low; it also dynamically adjusts the spacing to adapt to different conditions based on the crack state index, which directly reflects crack complexity. Overall, through quantized weights and normalization, the model achieves precise adaptive spacing between measuring points, improving detection reliability and efficiency. This dynamic adjustment capability significantly enhances the intelligence level of detection, reduces reliance on operator experience, and effectively balances detection accuracy and efficiency.

[0138] This application provides a surface surveying device for building cracks, including a memory and a processor. The memory stores executable instructions containing the programmed logic of a surface surveying method for building cracks, ensuring that the surveying process can be repeatedly invoked and avoiding errors introduced by empirical operations. When the processor executes the executable instructions stored in the memory, it implements the dynamic processing and optimization logic in the aforementioned method, thereby adapting to material properties in real time, optimizing equipment parameters, providing feedback on measured quality, and adaptively adapting to crack conditions.

[0139] Specifically, when the processor executes instructions, it first constructs a physical property evaluation model for the material being tested. Based on the input water-cement ratio, moisture content, and minimum rebar spacing, it generates a material physical property index, which quantitatively reflects the material's influence on the ultrasonic wave propagation speed and attenuation characteristics. Secondly, based on the material physical property index and the current ultrasonic probe frequency, it constructs an expected accuracy evaluation model, outputting an expected accuracy index to predict the predicted accuracy level of crack detection under specific material conditions. Thirdly, based on the coupling uniformity between the current ultrasonic probe and the concrete surface, the measured signal-to-noise ratio, and waveform clarity, it constructs a measured quality evaluation model, outputting a measured reliability index to quantify the reliability of the field data in real time. Finally, based on the current probe frequency, probe signal stability, and the expected accuracy index, it constructs a probe frequency adjustment model, outputting the target probe frequency to achieve dynamic optimization and adjustment of the probe's operating frequency.

[0140] The core innovation of this embodiment lies in integrating the memory and processor in hardware to store and execute executable instructions for surface inspection methods of building cracks. This allows for dynamic processing of input data and the application of evaluation models and adjustment logic, achieving adaptive optimization of material properties, equipment parameters, measured quality, and crack condition. For example, in the inspection of old concrete walls, the processor generates a material physical property index based on a high water-cement ratio and moisture content, and predicts a moderately low expected accuracy index based on an initial 50kHz probe frequency. Simultaneously, based on field data with moderate coupling uniformity, slightly low measured signal-to-noise ratio, and unclear waveform clarity, a moderate measured reliability index is evaluated. Finally, considering the current probe frequency, signal stability, and expected accuracy index, the target probe frequency is adjusted to 40kHz to enhance penetration and signal quality.

[0141] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for surface inspection of cracks in a building, characterized in that, Includes the following steps: S1: Construct a physical property evaluation model for the tested material based on its water-cement ratio, moisture content, and minimum clear spacing of reinforcing bars, and output the material's physical property index; S2: Construct an expected accuracy assessment model based on the material physical property index and the current ultrasonic probe frequency, and output the expected accuracy index; S3: Based on the coupling uniformity between the current ultrasonic probe and the concrete surface, the measured signal-to-noise ratio, and the waveform clarity, construct a measured quality assessment model and output the measured reliability index; S4: Construct a probe frequency adjustment model based on the current probe frequency, probe signal stability, and expected accuracy index, and output the target probe frequency.

2. The method for surface inspection of building cracks according to claim 1, characterized in that, In the physical property evaluation model of the tested material in step S1: The water-cement ratio, moisture content, and minimum clear spacing of the reinforcing bars of the tested material are successively substituted into the maximum-minimum normalization formula for processing, and the water-cement ratio index, moisture content index, and minimum reinforcing bar spacing index are generated in sequence. The material physical property index is obtained by multiplying the water-cement ratio index, the complement of the moisture content index (the value obtained by subtracting the corresponding index from 1), and the complement of the minimum rebar spacing index by the corresponding preset weight coefficients, and then summing them by weight. Finally, the material physical property index is obtained by exponentiation of the nonlinear adjustment factor.

3. The method for surface inspection of building cracks according to claim 2, characterized in that, In the expected accuracy evaluation model of step S2: The current ultrasonic probe frequency is substituted into the maximum-minimum normalization formula for processing, and the probe frequency index is generated. The expected accuracy index is obtained by multiplying the material physical property index by a linear combination. This linear combination is obtained by multiplying the preset frequency adjustment intensity coefficient by the probe frequency index and its complement, and then adding the preset basic accuracy coefficient.

4. The method for surface inspection of building cracks according to claim 1, characterized in that, In the measured quality assessment model of step S3: The coupling uniformity between the current ultrasonic probe and the concrete surface, the measured signal-to-noise ratio, and the waveform clarity are successively substituted into the maximum-minimum normalization formula for processing, and the coupling uniformity index, signal-to-noise ratio index, and waveform clarity index are generated in sequence. The coupling uniformity index, signal-to-noise ratio index, and waveform clarity index are multiplied by their respective preset weighting coefficients and then summed. The result is then transformed by a negative exponential function with the natural constant as the base to obtain the measured reliability index. The exponential function includes a preset positive gain coefficient.

5. The method for surface inspection of cracks in a building according to claim 1, characterized in that, The probe signal stability is substituted into the maximum-minimum normalization formula for processing, and the probe signal stability index is generated. In the probe frequency adjustment model of step S4: The complements of the current probe frequency index, the probe signal stability index, and the expected accuracy index are multiplied by their respective preset weighting coefficients and summed. The result is then limited to the range of 0 to 1 and finally mapped to the preset probe frequency adjustment range to obtain the target probe frequency.

6. The method for surface inspection of building cracks according to claim 1, characterized in that, After step S4, Includes the following steps: S5: Construct a crack condition assessment model based on the average crack width, the angle between the main crack direction and the acoustic path, and the dust concentration in the crack as detected by crack microscopy, and output the crack condition index. S6: Construct a measurement point spacing adjustment model based on the measured reliability index, preset measurement point spacing, and crack state index, and output the target measurement point spacing.

7. The method for surface inspection of building cracks according to claim 6, characterized in that, In the crack state assessment model of step S5: The average crack width, the angle between the main crack direction and the acoustic path, and the dust concentration in the crack are successively substituted into the maximum-minimum normalization formula for processing, and the average crack width index, the angle index, and the dust concentration index are generated in sequence. The crack state index is obtained by multiplying the crack average width index, the included angle index, and the dust concentration index by their respective preset influence coefficients, and then multiplying them together. Finally, the crack state index is obtained by exponentiation of the overall nonlinear adjustment factor.

8. The method for surface inspection of building cracks according to claim 7, characterized in that, In the measurement point spacing adjustment model of step S6: The preset measuring point spacing is substituted into the maximum-minimum normalization formula for processing, and the preset measuring point spacing index is generated. The complements of the preset measuring point spacing index, the measured reliability index, and the crack state index are multiplied by their respective preset weighting coefficients and then summed. The result is then limited to the range of 0 to 1 and finally mapped to the preset allowable range of measuring point spacing to obtain the target measuring point spacing.

9. A surface detection device for cracks in a building, characterized in that, include: Memory, used to store executable instructions; The processor, when executing executable instructions stored in the memory, implements the surface survey method for building cracks as described in any one of claims 1-8.

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