A method and system for detecting the quality of building concrete

Abstract

The application provides a building concrete quality detection method and system, through time sliding window segmentation processing of multi-physical field time sequence sampling data set in the early age hydration process of concrete, and based on the temperature field change signal, the ultrasonic propagation time delay signal and the resistivity response signal are temperature compensated and aligned, and the standardized multi-physical field time sequence is obtained; the standardized multi-physical field time sequence is analyzed for time consistency across the physical field, the dynamic time offset difference value of the ultrasonic propagation time delay sequence and the resistivity response sequence in the same time window is calculated, and the phase consistency deviation characteristic value is constructed accordingly; the phase consistency deviation characteristic value forms a spatial distribution phase drift characteristic field; based on the phase drift characteristic field, a concrete internal propagation consistency fracture discrimination structure is constructed, and then a concrete quality evaluation result is generated. By adopting the scheme, the time consistency deviation analysis of the multi-physical field signal can be realized under the non-uniform hydration heat environment.

Description

Technical Field

[0001] This application relates to the field of quality testing technology, and more specifically, to a method and system for testing the quality of building concrete. Background Technology

[0002] Concrete quality testing is the process of detecting and analyzing the internal density, uniformity, and mechanical property evolution of concrete structures using physical or physicochemical methods. Its goal is to assess whether the concrete structure meets design strength requirements, identify potential internal defects, and provide data support for project safety acceptance and maintenance decisions. As the most widely used structural material in construction projects, the early-age hydration process of concrete after pouring directly determines its final strength formation and long-term durability. Therefore, real-time perception and accurate assessment of the internal state of concrete during hydration are core aspects of project quality control.

[0003] However, in existing technologies, concrete quality testing methods typically employ sampling techniques such as pre-cured test blocks, rebound hammer testing, or core drilling. These methods can only perform discrete measurements at a limited number of locations at specific time points, making it difficult to comprehensively cover the state evolution of different regions within the concrete structure during the hydration process. Especially in large-volume concrete structures, the temperature gradient caused by uneven hydration heat release significantly affects the microstructure formation rate at different locations. Furthermore, existing testing methods cannot continuously capture and collaboratively analyze the multi-physics response signals within the concrete during hydration, resulting in test results that fail to accurately reflect the overall quality distribution of the structure. Non-uniformity makes it difficult to effectively identify potential internal fracture hazards caused by asynchronous hydration processes. Furthermore, existing technologies lack the means to perform time-series fusion analysis of multi-source physical field information such as ultrasound, resistivity, and temperature. The degree of synchronization between different physical signals cannot be quantitatively characterized, making it difficult to accurately determine whether there are quality defect areas inside the concrete caused by local hydration lag or overheating, thus affecting the comprehensiveness and accuracy of engineering quality assessment. Therefore, how to achieve time consistency deviation analysis of multi-physical field signals under non-uniform hydration thermal environment to accurately identify abnormal areas in the internal structure of concrete has become a challenge for the industry. Summary of the Invention

[0004] This application provides a method and system for testing the quality of building concrete, which can realize the time consistency deviation analysis of multi-physics field signals under non-uniform hydration heat environment.

[0005] In a first aspect, this application provides a method for testing the quality of building concrete, comprising the following steps: Before concrete pouring, an intelligent sensor array is pre-embedded in the main stress path nodes of the steel reinforcement skeleton and the non-uniform diffusion area of ​​hydration heat. The intelligent sensors synchronously collect ultrasonic propagation delay signals, resistivity response signals and temperature field change signals to construct a multi-physics field time-series sampling dataset. During the early-age hydration process of concrete, the multi-physics field time-series sampling dataset is segmented by a time sliding window, and the ultrasonic propagation delay signal and the resistivity response signal are aligned by temperature compensation based on the temperature field change signal to obtain a standardized multi-physics field time-series sequence. A cross-physics-field time consistency analysis is performed on the standardized multiphysics time series, the dynamic time offset difference between the ultrasonic propagation delay sequence and the resistivity response sequence within the same time window is calculated, and the deviation characteristic value of phase consistency is constructed accordingly. The deviation characteristic value of the phase consistency is mapped according to the spatial location of the smart sensor to form a spatially distributed phase drift characteristic field, which is used to characterize the difference in the degree of synchronization of multi-physics field evolution in different regions inside the concrete. Based on the spatial continuity abrupt change characteristics of the phase drift feature field, a consistent fracture discrimination structure for internal propagation in concrete is constructed, and the corresponding structural anomaly regions are identified, thereby generating concrete quality assessment results.

[0006] In conjunction with the first aspect, in one possible implementation, during the early-age hydration process of concrete, the multiphysics time-series sampling dataset is segmented by a time sliding window, and temperature compensation alignment is performed on the ultrasonic propagation delay signal and the resistivity response signal based on the temperature field change signal to obtain a standardized multiphysics time-series sequence. Specifically, this includes: Acquire the ultrasonic propagation delay signal, resistivity response signal, and temperature field change signal from the multiphysics temporal sampling dataset; The multiphysics temporal sampling dataset is segmented according to a preset time window length and sliding step size to obtain multiple multiphysics temporal sub-segments under consecutive time windows; Based on the temperature values ​​corresponding to the temperature field change signals within each time window, calculate the temperature drift correction amount of the ultrasonic propagation delay signal and resistivity response signal relative to the preset reference temperature within the same time window. Based on the temperature drift correction, the ultrasonic propagation delay signal and resistivity response signal within each time window are compensated and corrected point by point to obtain a standardized multiphysics time series.

[0007] In conjunction with the first aspect, in one possible implementation, performing cross-physics temporal consistency analysis on the standardized multiphysics time series, and calculating the dynamic time offset difference between the ultrasonic propagation delay sequence and the resistivity response sequence within the same time window, specifically includes: The ultrasonic propagation delay signal and resistivity response signal within each time window of the standardized multiphysics time series are obtained, and the ultrasonic propagation delay sequence and resistivity response sequence within each time window are constructed respectively. Dynamic matching of the ultrasonic propagation delay sequence and resistivity response sequence on the time axis is performed in each time window. The time offset of the two sequences at each time point is calculated to obtain the dynamic time offset difference in each time window.

[0008] In conjunction with the first aspect, in one possible implementation, constructing the deviation eigenvalues ​​for phase consistency specifically includes: Obtain the dynamic time offset difference corresponding to the position of each measuring point within each time window; The dynamic time offset difference of each measuring point within each time window is statistically processed to obtain the deviation characteristic value of the phase consistency of each measuring point within each time window.

[0009] In conjunction with the first aspect, in one possible implementation, mapping the deviation feature values ​​of phase consistency according to the spatial location of the smart sensor to form a spatially distributed phase drift feature field specifically includes: Obtain the deviation characteristic value of phase consistency of each smart sensor at its spatial location for each time window; Using the spatial coordinates of each smart sensor as a spatial reference, a spatial mapping relationship is established between the coordinates of each measuring point and the corresponding deviation feature value. Based on the spatial mapping relationship, spatial interpolation is performed on non-measurement point locations to generate a continuously spatially distributed phase drift feature field.

[0010] In conjunction with the first aspect, in one possible implementation, based on the spatial continuity abrupt change characteristics of the phase drift feature field, a concrete internal propagation consistency fracture discrimination structure is constructed, and the corresponding structural anomaly regions are identified, specifically including: Extract the gradient change between adjacent regions and feature values ​​at each spatial location in the phase drift feature field, and use the gradient change as a spatial continuity abrupt change feature; Based on the comparison results between the spatial continuity abrupt change characteristics and the preset fracture threshold, a fracture discrimination structure for consistent internal propagation in concrete is constructed. Based on the fracture discrimination structure, the spatial region in which a consistent propagation fracture occurs inside the concrete is determined, and the spatial region is identified as a structural anomaly region.

[0011] In conjunction with the first aspect, in one possible implementation, generating concrete quality assessment results specifically includes: Based on the spatial distribution range and continuity characteristics of the identified structural anomaly areas, the concrete hydration lag level is output. The concrete quality assessment results are generated based on the concrete hydration lag level.

[0012] Secondly, this application provides a building concrete quality testing system, comprising: The acquisition module is used to pre-embed the intelligent sensor array in the main stress path nodes of the steel reinforcement skeleton and the non-uniform diffusion area of ​​hydration heat before concrete pouring. The intelligent sensor synchronously acquires ultrasonic propagation delay signal, resistivity response signal and temperature field change signal to construct a multi-physics field time-series sampling dataset. The processing module is used to perform time sliding window segmentation processing on the multi-physics field time-series sampling dataset during the early-age hydration process of concrete, and to perform temperature compensation alignment on the ultrasonic propagation delay signal and the resistivity response signal based on the temperature field change signal to obtain a standardized multi-physics field time-series sequence. The processing module is also used to perform cross-physical field time consistency analysis on the standardized multi-physics time series, calculate the dynamic time offset difference between the ultrasonic propagation delay sequence and the resistivity response sequence within the same time window, and construct the phase consistency deviation characteristic value accordingly. The processing module is also used to map the deviation feature value of the phase consistency according to the spatial location of the smart sensor to form a spatially distributed phase drift feature field, which is used to characterize the difference in the degree of synchronization of multi-physics field evolution in different regions inside the concrete. The execution module is used to construct a concrete internal propagation consistency fracture discrimination structure based on the spatial continuity abrupt change characteristics of the phase drift feature field, identify the corresponding structural anomaly regions, and then generate concrete quality assessment results.

[0013] Thirdly, this application provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device performs the above-described method for testing the quality of building concrete.

[0014] Fourthly, this application provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the above-described method for detecting the quality of building concrete.

[0015] The technical solution provided in this application has the following beneficial effects: This application's solution, firstly, eliminates the interference of temperature drift caused by non-uniform release of hydration heat on ultrasonic propagation delay signals and resistivity response signals by performing sliding window segmentation and temperature compensation alignment on the multi-physics time-series sampling dataset. This ensures that the two physical field signals remain consistent on the temperature reference within the same time window, avoiding the temperature coupling effect from masking the abnormal response characteristics of the structure itself, and ensuring that subsequent analysis can truly reflect the internal state of the concrete rather than spurious changes caused by temperature fluctuations. Secondly, by calculating the dynamic time offset difference between the ultrasonic propagation delay sequence and the resistivity response sequence within the same time window and constructing a phase consistency deviation feature value, a quantitative characterization of the degree of synchronization of the two physical field signals on the time axis is achieved. This transforms the abstract phenomenon of asynchronous hydration processes within concrete into a calculable deviation index, enabling quantitative assessment of the inherent temporal correlation between multi-physics responses. Then, by further processing the deviation feature values ​​at each discrete measuring point according to the spatial location of the smart sensor... By mapping and interpolating to generate a continuous spatial distribution of phase drift feature fields, the originally isolated and discrete measurement point information is transformed into a spatial distribution picture reflecting the differences in the synchronization degree of multi-physics field evolution across the entire concrete domain. This comprehensively characterizes the non-uniform spatial distribution within the concrete, allowing the differences in hydration processes between different regions within the structure to be presented in a visualized field distribution form. Finally, by extracting the spatial continuity abrupt change features of the phase drift feature fields and constructing a fracture discrimination structure, the automatic identification of structurally consistent fracture regions caused by asynchronous hydration processes within the concrete is achieved. This transforms the multi-physics field temporal consistency deviation analysis into a spatial distribution of structurally abnormal regions that can be located and quantified, accurately delineating defect locations. This provides a clear spatial orientation for engineering quality assessment and defect localization, improving the practicality and reliability of the detection results. In summary, this scheme can achieve temporal consistency deviation analysis of multi-physics field signals under non-uniform hydration thermal environments to accurately identify structurally abnormal regions within concrete. Attached Figure Description

[0016] Figure 1 This is an exemplary flowchart of a method for testing the quality of building concrete according to some embodiments of this application; Figure 2 This is an exemplary flowchart illustrating the implementation of time consistency analysis according to some embodiments of this application; Figure 3 This is an exemplary flowchart illustrating the determination of a phase drift feature field according to some embodiments of this application; Figure 4 This is a structural schematic diagram of a building concrete quality inspection system according to some embodiments of this application; Figure 5 This is a structural schematic diagram of a computer device for implementing a method for testing the quality of building concrete, according to some embodiments of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] refer to Figure 1 The figure is an exemplary flowchart of a method for testing the quality of building concrete according to some embodiments of this application. The method for testing the quality of building concrete mainly includes the following steps: In step 101, before concrete pouring, an intelligent sensor array is pre-embedded in the main stress path nodes of the steel reinforcement skeleton and the non-uniform diffusion area of ​​hydration heat. The intelligent sensors synchronously collect ultrasonic propagation delay signals, resistivity response signals and temperature field change signals to construct a multi-physics field time-series sampling dataset.

[0019] In practice, before concrete pouring, the smart sensor array can be pre-embedded in the main stress path nodes and the non-uniform diffusion area of ​​hydration heat in the steel reinforcement cage. This can be achieved in the following way: First, determine the location of the main stress path nodes according to the steel reinforcement distribution diagram in the structural design drawings and determine the non-uniform diffusion area of ​​hydration heat according to the hydration heat simulation results. During the steel reinforcement cage binding process, make positioning marks on the steel reinforcement at the above-mentioned locations. Then, fix the ultrasonic sensor, resistivity sensor, and temperature sensor in the smart sensor array one by one at the marked positions according to their respective preset spacing. The fixing method is to use cable ties or wire binding. When fixing the ultrasonic sensor, ensure that the emitting surface of the ultrasonic sensor is in close contact with the surface of the reinforcing bar and coated with coupling agent. Keep the electrode part of the resistivity sensor insulated from the reinforcing bar. The temperature sensor's sensing end is suspended at the measuring point. Next, tie and lay the signal transmission cables of each sensor along the direction of the main reinforcing bar and lead them to the reserved interface of the data acquisition terminal outside the formwork. Protect the cables with protective sleeves when passing through areas with dense reinforcing bars or the edge of the formwork. Finally, before pouring concrete, perform channel verification and initial signal acquisition on all installed sensors. After confirming that each channel communicates normally and the data is readable, proceed with the concrete pouring operation.

[0020] It should be noted that the intelligent sensor array in this application refers to a multi-physics synchronous acquisition unit group composed of ultrasonic sensors, resistivity sensors, and temperature sensors arranged in a preset spatial layout; the main load path node in this application refers to a key monitoring location on the main load transmission path inside the concrete structure, where sensors are deployed to capture the multi-physics response signals of the core load-bearing area of ​​the structure during the early-age hydration process; the non-uniform diffusion region of hydration heat in this application refers to the spatial range in which the temperature field distribution is uneven due to differences in structural cross-sectional dimensions or different heat dissipation boundary conditions during the early-age hydration process of concrete, where sensors are deployed to capture the differences in multi-physics signals caused by temperature gradients at different locations.

[0021] In specific implementation, the construction of a multi-physics time-series sampling dataset by synchronously acquiring ultrasonic propagation delay signals, resistivity response signals, and temperature field change signals through the intelligent sensors can be achieved in the following way: First, after the concrete pouring is completed, the signal transmission cables of each sensor unit in the intelligent sensor array are connected to the data acquisition terminal. The data acquisition terminal has a built-in synchronization clock module, which is used to send a unified acquisition trigger pulse to each sensor unit to ensure that the ultrasonic sensor, resistivity sensor, and temperature sensor start data acquisition synchronously at a preset sampling frequency under the same clock reference. Then, at each sampling moment, the ultrasonic sensor emits ultrasonic pulses and receives the echo signals after passing through the concrete medium. The ultrasonic propagation delay signal is generated according to the time difference between the emission time and the echo reception time. At the same time, the resistivity sensor applies AC excitation to the interior of the concrete. The resistivity response signal is generated by measuring the response voltage between the electrodes and the excitation current. The temperature field change signal is generated by sensing the temperature at the measurement point through a temperature sensor via a thermocouple or thermistor. Then, the data acquisition terminal associates the signal values ​​output by each sensor at the same sampling time with the corresponding sensor spatial coordinates and the current sampling timestamp to generate a single-frame multiphysics sampling record. During continuous sampling, the single-frame records of each sampling time are arranged sequentially in time order to construct a three-dimensional data matrix containing time, space, and physical field dimensions as a multiphysics time-series sampling dataset. Finally, the multiphysics time-series sampling dataset is stored in the local memory of the data acquisition terminal and transmitted to the upper-level processing unit after each acquisition cycle. Other methods can also be used in other embodiments, which are not specifically limited here.

[0022] It should be noted that the ultrasonic propagation delay signal in this application refers to the signal generated by the ultrasonic sensor after emitting ultrasonic pulses and receiving echoes, based on the time difference between the emission and reception times, used to characterize the travel time change of ultrasonic waves along the propagation path inside concrete; the resistivity response signal in this application refers to the signal generated by the resistivity sensor after applying excitation current to the inside of concrete and measuring the response voltage between electrodes, used to characterize the change in the conductivity of the inside of concrete; the temperature field change signal in this application refers to the signal generated by the temperature sensor sensing temperature fluctuations at each measuring point, used to characterize the temperature change law during the early-age hydration process of concrete; the multiphysics time-series sampling dataset in this application refers to the data set formed by the data acquisition terminal synchronously collecting ultrasonic propagation delay signals, resistivity response signals, and temperature field change signals at each measuring point, and then arranging them in chronological order after associating them with spatial coordinates and sampling timestamps.

[0023] In step 102, during the early-age hydration process of concrete, the multiphysics time-series sampling dataset is segmented by a time sliding window, and the ultrasonic propagation delay signal and the resistivity response signal are aligned by temperature compensation based on the temperature field change signal to obtain a standardized multiphysics time-series sequence.

[0024] In some embodiments, during the early-age hydration process of concrete, the multiphysics time-series sampling dataset is segmented by a time sliding window, and the ultrasonic propagation delay signal and the resistivity response signal are aligned with temperature compensation based on the temperature field change signal to obtain a standardized multiphysics time-series sequence. This can be achieved through the following steps: Acquire the ultrasonic propagation delay signal, resistivity response signal, and temperature field change signal from the multiphysics temporal sampling dataset; The multiphysics temporal sampling dataset is segmented according to a preset time window length and sliding step size to obtain multiple multiphysics temporal sub-segments under consecutive time windows; Based on the temperature values ​​corresponding to the temperature field change signals within each time window, calculate the temperature drift correction amount of the ultrasonic propagation delay signal and resistivity response signal relative to the preset reference temperature within the same time window. Based on the temperature drift correction, the ultrasonic propagation delay signal and resistivity response signal within each time window are compensated and corrected point by point to obtain a standardized multiphysics time series.

[0025] In specific implementation, the multiphysics time-series sampling dataset is segmented according to a preset time window length and sliding step size to obtain multiple multiphysics time-series sub-segments under consecutive time windows. This can be achieved in the following way: retrieve the multiphysics time-series sampling dataset, set the time window length and sliding step size. The time window length can be set according to the early-age hydration reaction rate of concrete. The faster the hydration reaction rate, the shorter the time window length to ensure that the signal characteristics within the window have statistical stability. The sliding step size is smaller than the time window length to make the adjacent windows partially overlap, thereby avoiding the loss of signal characteristics at the window boundary. Starting from the initial sampling time of the dataset, a sampling frame is extracted according to the window length as the first sub-segment. Then, the starting boundary of the window is moved backward by one sliding step size and the next sub-segment is extracted. This process is repeated until the starting boundary of the window reaches the end of the dataset, thereby dividing the entire dataset into multiple time-series sub-segments with partially overlapping adjacent windows.

[0026] In specific implementation, the temperature drift correction of the ultrasonic propagation delay signal and resistivity response signal relative to the preset reference temperature within the same time window can be calculated based on the temperature value corresponding to the temperature field change signal within each time window. This can be achieved in the following way: For each time segment, the sampled value of the temperature field change signal is extracted and its arithmetic mean is calculated as the representative temperature of the window. The initial temperature at which the concrete is poured is taken as the preset reference temperature. The temperature drift coefficients corresponding to the ultrasonic propagation delay signal and resistivity response signal are obtained respectively. The difference between the representative temperature and the preset reference temperature is multiplied by the two temperature drift coefficients respectively to obtain the first temperature drift correction corresponding to the ultrasonic propagation delay signal and the second temperature drift correction corresponding to the resistivity response signal within the window.

[0027] In specific implementation, the ultrasonic propagation delay signal and resistivity response signal within each time window are compensated and corrected point by point according to the temperature drift correction amount to obtain the standardized multiphysics time series. This can be achieved in the following way: For each time series segment, the first temperature drift correction amount corresponding to the window is subtracted from each sample value of the ultrasonic propagation delay signal within the window, and the second temperature drift correction amount corresponding to the window is subtracted from each sample value of the resistivity response signal within the window. The two corrected signals are then re-associated with the original temperature field change signal and timestamp within the window. Finally, the segments are spliced ​​together according to their original time order, and duplicate frames in the overlapping areas are removed to obtain the standardized multiphysics time series.

[0028] It should be noted that, in this application, the multiphysics time-series segment refers to a time-series data segment containing continuous sampling frames, used as the basic analysis unit for temperature compensation alignment processing; the preset reference temperature in this application refers to a pre-set reference temperature value, used to measure the degree of drift caused by actual temperature changes to the ultrasonic propagation delay signal and resistivity response signal; the temperature drift correction amount in this application refers to the offset compensation value of the ultrasonic propagation delay signal or resistivity response signal caused by temperature changes, used to perform reverse correction on the actual acquired signal to eliminate the temperature effect; the standardized multiphysics time-series sequence in this application refers to a standardized data sequence formed by arranging the ultrasonic propagation delay signal and resistivity response signal, which have been freed from the influence of temperature drift, in chronological order after being associated with the temperature field change signal and timestamp.

[0029] In step 103, a cross-physical field time consistency analysis is performed on the standardized multiphysics time series, the dynamic time offset difference between the ultrasonic propagation delay sequence and the resistivity response sequence within the same time window is calculated, and the deviation characteristic value of phase consistency is constructed accordingly.

[0030] In some embodiments, reference Figure 2 As shown, this figure is an exemplary flowchart for implementing time consistency analysis in some embodiments of this application. The following steps can be used to perform cross-physics-field time consistency analysis on the standardized multiphysics time series, calculating the dynamic time offset difference between the ultrasonic propagation delay sequence and the resistivity response sequence within the same time window: The ultrasonic propagation delay signal and resistivity response signal within each time window of the standardized multiphysics time series are obtained, and the ultrasonic propagation delay sequence and resistivity response sequence within each time window are constructed respectively. Dynamic matching of the ultrasonic propagation delay sequence and resistivity response sequence on the time axis is performed in each time window. The time offset of the two sequences at each time point is calculated to obtain the dynamic time offset difference in each time window.

[0031] In specific implementation, the ultrasonic propagation delay signal and resistivity response signal within each time window of the standardized multiphysics time series can be obtained and the ultrasonic propagation delay sequence and resistivity response sequence within each time window can be constructed in the following way: Standardized data records corresponding to each time window are extracted from the standardized multiphysics time series. The data records within each window are arranged according to the sampling time sequence, and each record contains the ultrasonic propagation delay correction value, resistivity response correction value, and corresponding timestamp at that moment. For each time window, the ultrasonic propagation delay correction values ​​at all sampling moments within the window are extracted sequentially according to the sampling order to form the ultrasonic propagation delay sequence for that window. Simultaneously, the resistivity response correction values ​​at all sampling moments within the window are extracted sequentially according to the same sampling order to form the resistivity response sequence for that window. Elements with the same sequence number in both sequences have the same sampling timestamp correspondence, thereby completing the construction of the two matching sequences within each time window.

[0032] In specific implementation, dynamic matching of the ultrasonic propagation delay sequence and resistivity response sequence within each time window is performed on the time axis. The time offset of the two sequences at each time point is calculated, and the dynamic time offset difference within each time window can be obtained in the following way: For each time window, the ultrasonic propagation delay sequence within that window is used as the reference sequence, and the resistivity response sequence is used as the sequence to be matched. Within a preset time offset search range, the sequence to be matched is shifted forward or backward one by one on the time axis. For each step, the overall similarity or error value of the two sequences within the overlapping time interval at the current offset is calculated. After traversing all the offsets within the search range, the offset corresponding to the minimum overall error or maximum similarity between the two sequences is selected as the dynamic time offset difference between the two sequences within that time window. The above search and matching process is performed on each sampling point within that time window to obtain the dynamic time offset difference corresponding to each sampling time point within that window. After arranging them in the sampling order, a sequence of dynamic time offset differences for that window is formed. The above operation is performed on all time windows one by one to obtain the dynamic time offset difference corresponding to each time window.

[0033] It should be noted that the ultrasonic propagation delay sequence in this application refers to a data sequence formed by arranging the ultrasonic propagation delay correction values ​​at each sampling time within the same time window in the order of sampling time, used for dynamic matching with the resistivity response sequence on the time axis; the resistivity response sequence in this application refers to a data sequence formed by arranging the resistivity response correction values ​​at each sampling time within the same time window in the order of sampling time, used for dynamic matching with the ultrasonic propagation delay sequence on the time axis; the time offset in this application refers to the amount of displacement of the sequence to be matched relative to the reference sequence on the time axis during the dynamic matching process on the time axis, used to characterize the degree of time deviation between the two sequences at the same time point; the dynamic time offset difference in this application refers to the time offset corresponding to the minimum overall error of the two sequences in the overlapping interval after dynamic matching of the ultrasonic propagation delay sequence and the resistivity response sequence within the preset time offset search range, used to characterize the degree of time consistency deviation between the ultrasonic response and the resistivity response within the same time window.

[0034] In some embodiments, constructing the deviation eigenvalues ​​for phase consistency can be achieved using the following steps: Obtain the dynamic time offset difference corresponding to the position of each measuring point within each time window; The dynamic time offset difference of each measuring point within each time window is statistically processed to obtain the deviation characteristic value of the phase consistency of each measuring point within each time window.

[0035] In practice, the dynamic time offset difference corresponding to each measuring point location within each time window can be obtained in the following way: In the dynamic time offset difference corresponding to each time window, the data is collected according to the measuring point location. The dynamic time offset difference of the same measuring point location within all time windows is arranged in the window time order to obtain the dynamic time offset difference corresponding to the measuring point location in each time window. The above collection operation is performed on all measuring point locations one by one to complete the acquisition of the dynamic time offset difference of each measuring point location within each time window.

[0036] In specific implementation, the dynamic time offset difference of each measuring point location within each time window is statistically processed to obtain the deviation characteristic value of phase consistency of each measuring point location within each time window. This can be achieved in the following way: For the dynamic time offset difference corresponding to each measuring point location within each time window, the root mean square or standard deviation of the dynamic time offset difference at all sampling time points within that window is calculated. This root mean square or standard deviation is used as the deviation characteristic value of phase consistency of the measuring point location within that time window. The deviation characteristic value is used to quantify the overall deviation of the ultrasonic propagation delay signal and the resistivity response signal on the time axis within that time window. The larger the deviation characteristic value, the worse the time consistency of the two physical field signals at that location. The smaller the deviation characteristic value, the better the time consistency of the two physical field signals at that location. The above statistical calculation is performed on all measuring point locations and all time windows one by one to obtain the deviation characteristic value of phase consistency of each measuring point location within each time window.

[0037] It should be noted that the deviation characteristic value in this application refers to a quantitative index used to characterize the overall deviation of the ultrasonic propagation delay signal and the resistivity response signal on the time axis within the same time window at the same measurement point.

[0038] In step 104, the deviation characteristic value of the phase consistency is mapped according to the spatial location of the smart sensor to form a spatially distributed phase drift characteristic field, which is used to characterize the difference in the degree of synchronization of multi-physics field evolution in different regions inside the concrete.

[0039] In some embodiments, reference Figure 3 As shown, this figure is an exemplary flowchart for determining the phase drift feature field in some embodiments of this application. In this embodiment, the deviation feature values ​​of phase consistency are mapped according to the spatial location of the smart sensor to form a spatially distributed phase drift feature field, which can be achieved by the following steps: In step 1041, the deviation characteristic value of phase consistency of each smart sensor at its spatial location for each time window is obtained; In step 1042, the spatial coordinates of each smart sensor are used as the spatial reference to establish a spatial mapping relationship between the coordinates of each measuring point and the corresponding deviation feature value. In step 1043, spatial interpolation is performed on the non-measurement point positions based on the spatial mapping relationship to generate a continuously spatially distributed phase drift feature field.

[0040] In practice, the deviation feature values ​​of phase consistency corresponding to each time window at each spatial location of each smart sensor can be obtained in the following way: extract the deviation feature values ​​of each measurement point position within each time window according to the smart sensor number and spatial coordinates, extract the deviation feature values ​​corresponding to all time windows at the spatial location of each smart sensor in the window time order, forming a set of deviation feature values ​​that correspond one-to-one with the time window at that spatial location, and perform the above extraction operation on all smart sensors one by one, thereby completing the acquisition of the deviation feature values ​​of each smart sensor corresponding to each time window at its spatial location.

[0041] In practice, the spatial mapping relationship between the position coordinates of each smart sensor and the corresponding deviation feature value can be established by taking the spatial position coordinates of each measuring point as the spatial reference. This can be achieved by obtaining the three-dimensional spatial coordinates of each smart sensor within the concrete structure, associating and binding each deviation feature value in the set of deviation feature values ​​corresponding to each smart sensor with the three-dimensional spatial coordinates of that sensor, forming discrete spatial data points composed of spatial coordinates and deviation feature values. The above association and binding is performed on all smart sensors and all time windows respectively, thereby establishing the spatial mapping relationship between the position coordinates of each measuring point and the corresponding deviation feature value.

[0042] In specific implementation, the generation of a continuously spatially distributed phase drift feature field by performing spatial interpolation processing on non-measurement point locations based on the spatial mapping relationship can be achieved in the following way: For each time window, the spatial coordinates of each measurement point location and the corresponding deviation feature value within that time window are used as known discrete points. The deviation feature value at non-measurement point locations other than the measurement point locations is estimated using a spatial interpolation method. This spatial interpolation method can determine the estimated value of the non-measurement point location by performing weighted calculation based on the spatial distance between the known point and the point to be estimated. Interpolation calculation is performed one by one for each time window and each non-measurement point location to obtain the estimation result of the deviation feature value at all locations in a continuously distributed spatial distribution under each time window. The interpolation results of each time window are arranged in chronological order to form a spatiotemporally joint phase drift feature field.

[0043] It should be noted that the spatial mapping relationship in this application refers to the data correspondence between the deviation characteristic value at a known measuring point and the spatial location; the non-measuring point location in this application refers to the spatial location inside the concrete where no smart sensor is pre-embedded; the phase drift characteristic field in this application refers to the spatial distribution field formed by continuously arranging the estimation results of the deviation characteristic values ​​at all spatial locations under each time window according to spatial coordinates, which is used to characterize the difference in the degree of synchronization of the evolution of multi-physics fields in different regions inside the concrete.

[0044] In step 105, based on the spatial continuity abrupt change characteristics of the phase drift feature field, a concrete internal propagation consistency fracture discrimination structure is constructed, and the corresponding structural anomaly region is identified, thereby generating concrete quality assessment results.

[0045] In some embodiments, based on the spatial continuity abrupt change characteristics of the phase drift feature field, constructing a consistent fracture discrimination structure propagating within concrete and identifying the corresponding structural anomaly regions can be achieved through the following steps: Extract the gradient change between adjacent regions and feature values ​​at each spatial location in the phase drift feature field, and use the gradient change as a spatial continuity abrupt change feature; Based on the comparison results between the spatial continuity abrupt change characteristics and the preset fracture threshold, a fracture discrimination structure for consistent internal propagation in concrete is constructed. Based on the fracture discrimination structure, the spatial region in which a consistent propagation fracture occurs inside the concrete is determined, and the spatial region is identified as a structural anomaly region.

[0046] In specific implementation, the gradient change between adjacent regions and the eigenvalue at each spatial location in the phase drift feature field is extracted. Using the gradient change as a spatial continuity abrupt change feature can be achieved in the following way: For the phase drift feature field under the current time window, the deviation eigenvalue at each spatial location and the deviation eigenvalue at its adjacent spatial locations are extracted. The difference between the deviation eigenvalue between the center location and its adjacent locations is calculated. The spatial gradient operator is used to synthesize the differences in each direction to obtain the gradient magnitude at that location as the spatial continuity abrupt change feature at that location. The above calculation is performed on each spatial location one by one to obtain the gradient change distribution corresponding to all locations. Among them, the larger the gradient magnitude, the more drastic the change in deviation eigenvalue between that location and its surrounding area, indicating that the synchronization degree of multiphysics evolution at that location has abruptly changed.

[0047] In specific implementation, the concrete internal propagation consistency fracture discrimination structure can be constructed by comparing the spatial continuity abrupt change characteristics with the preset fracture threshold in the following way: Obtain the preset fracture threshold, which can be determined based on the statistical distribution of the spatial continuity abrupt change characteristics at all spatial locations within the phase drift feature field. Specifically, calculate the mean and standard deviation of the spatial continuity abrupt change characteristics at all locations, and use the sum of the mean and standard deviation by a preset multiple as the preset fracture threshold. Then, compare the calculated spatial continuity abrupt change characteristics at each spatial location with the preset fracture threshold one by one. When the spatial continuity abrupt change characteristic at a certain location is greater than or equal to the preset fracture threshold, the location is determined as a fracture location and assigned a first label value. When it is less than the preset fracture threshold, the location is determined as a non-fracture location and assigned a second label value. After comparing and labeling all spatial locations, the spatial label distribution formed by arranging the label values ​​of all locations according to spatial coordinates serves as the concrete internal propagation consistency fracture discrimination structure.

[0048] In specific implementation, the spatial region where a propagating and consistent fracture occurs inside the concrete is determined based on the fracture discrimination structure. The spatial region is identified as a structurally abnormal region in the following way: According to the label value of each spatial position in the fracture discrimination structure, the positions marked with the first label value and spatially consecutive and adjacent are clustered together. Each connected fracture position is clustered as a propagating and consistent fracture spatial region. The geometric range of each fracture spatial region is extracted, and the concrete internal region corresponding to the spatial position within the geometric range is identified as a structurally abnormal region.

[0049] It should be noted that, in this application, the gradient change amount refers to a quantitative value used to measure the degree of change in deviation from the eigenvalue of a certain spatial location and its adjacent regions in the phase drift feature field; the spatial continuity abrupt change feature in this application refers to a feature obtained by characterizing the gradient change amount of each spatial location in the phase drift feature field, used to identify whether the deviation from the eigenvalue at the corresponding location has changed drastically relative to its surrounding region, that is, to characterize the degree of spatial discontinuity of the phase drift feature field; the preset fracture threshold in this application refers to a critical discrimination criterion used to determine whether the spatial continuity abrupt change feature has reached the degree of fracture, that is, as a judgment boundary value to distinguish fracture location from non-fracture location; the fracture discrimination structure in this application refers to the spatial range in the phase drift feature field where spatial continuity abrupt change occurs; and the structural anomaly region in this application refers to the continuous spatial range in the concrete interior where consistent fracture occurs, determined based on the fracture discrimination structure.

[0050] In some embodiments, generating concrete quality assessment results can be achieved through the following steps: Based on the spatial distribution range and continuity characteristics of the identified structural anomaly areas, the concrete hydration lag level is output. The concrete quality assessment results are generated based on the concrete hydration lag level.

[0051] In specific implementation, the concrete hydration lag level can be output based on the spatial distribution range and continuity characteristics of the identified structural anomaly areas in the following manner: The geometric boundaries of each fracture spatial region are determined according to the spatial location marked with the first marker value in the fracture discrimination structure, and the volume ratio of each fracture spatial region in the overall concrete structure is calculated. A preset hydration lag level classification rule is established, which refers to a rule that divides the degree of concrete hydration lag into multiple levels based on the volume ratio range of the structural anomaly areas in the overall structure. This rule includes multiple hydration lag levels and a threshold range for the volume ratio of the anomaly areas corresponding to each level. The threshold range for the volume ratio can be obtained by calibrating multiple sets of concrete specimens of known quality grades after conducting actual measurements and statistics on the volume ratio of the fracture areas. The calculated volume ratio is then matched with the threshold range corresponding to each level, and the successfully matched level is used as the output concrete hydration lag level.

[0052] In specific implementation, the generation of concrete quality assessment results based on the concrete hydration lag level can be achieved in the following way: A preset quality assessment rule base is obtained, which stores the quality assessment conclusions corresponding to each hydration lag level. These conclusions are obtained through actual measurement and calibration of multiple sets of concrete specimens with known quality levels. Specifically, this can involve obtaining the concrete hydration lag level of each set of specimens and its corresponding known actual quality level, establishing a mapping relationship between each hydration lag level and the actual quality level, and using this mapping relationship as the quality assessment rule base. Then, based on the output concrete hydration lag level, a matching query is performed in the quality assessment rule base to extract the quality assessment conclusion corresponding to that hydration lag level as the concrete quality assessment result.

[0053] It should be noted that, in this application, the spatial distribution range refers to an index characterizing the spatial location and geometric boundary coverage of the structurally abnormal region within the overall concrete structure; the continuity characteristic refers to a comprehensive characteristic index used to quantify the spatial distribution pattern, aggregation degree, and connectivity scale of the structurally abnormal region; the concrete hydration lag level refers to a grade index used to characterize the synchronicity level of the early-age hydration process of concrete; and the concrete quality assessment result refers to the final conclusion output after evaluating the overall quality of the concrete structure based on the concrete hydration lag level.

[0054] In another aspect, in some embodiments, this application provides a building concrete quality testing system, with reference to... Figure 4 The figure is a schematic diagram of the structure of a building concrete quality inspection system according to some embodiments of this application. The building concrete quality inspection system 400 includes: a data acquisition module 401, a processing module 402, and an execution module 403, which are described below: The acquisition module 401 in this application is mainly used to pre-embed the intelligent sensor array in the main stress path node of the steel skeleton and the non-uniform diffusion area of ​​hydration heat before concrete pouring. The intelligent sensor synchronously acquires ultrasonic propagation delay signal, resistivity response signal and temperature field change signal to construct a multi-physics field time-series sampling dataset. Processing module 402 in this application is mainly used to perform time sliding window segmentation processing on the multi-physics field time-series sampling dataset during the early-age hydration process of concrete, and to perform temperature compensation alignment on the ultrasonic propagation delay signal and the resistivity response signal based on the temperature field change signal to obtain a standardized multi-physics field time-series sequence. The processing module 402 described in this application is also used to perform cross-physical field time consistency analysis on the standardized multi-physics time series, calculate the dynamic time offset difference between the ultrasonic propagation delay sequence and the resistivity response sequence within the same time window, and construct the phase consistency deviation characteristic value accordingly. The processing module 402 described in this application is also used to map the deviation feature value of the phase consistency according to the spatial location of the smart sensor to form a spatially distributed phase drift feature field, which is used to characterize the difference in the degree of synchronization of multi-physics field evolution in different regions inside the concrete. The execution module 403 in this application is mainly used to construct a concrete internal propagation consistency fracture discrimination structure based on the spatial continuity abrupt change characteristics of the phase drift feature field, identify the corresponding structural abnormal area, and then generate concrete quality assessment results.

[0055] The foregoing has detailed examples of the building concrete quality testing method and system provided in the embodiments of this application. It is understood that the corresponding apparatus, in order to achieve the above functions, includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0056] In some embodiments, this application also provides a computer device, the computer device including a memory and a processor, the memory for storing a computer program, and the processor for calling and running the computer program from the memory, so that the computer device performs the above-described method for testing the quality of building concrete.

[0057] In some embodiments, reference Figure 5 The dashed lines in the figure indicate that the unit or module is optional. This figure is a structural schematic diagram of the computer equipment used to implement the building concrete quality testing method of this application. The building concrete quality testing method in the above embodiments can be achieved through... Figure 5 The computer device 500 shown is used to implement this, and the computer device 500 includes at least one processor 501, a memory 502 and at least one communication unit 505. The computer device 500 may be a terminal device, a server or a chip.

[0058] The processor 501 can be a general-purpose processor or a special-purpose processor. For example, the processor 501 can be a central processing unit (CPU). The CPU can be used to control the computer device 500, execute software programs, and process data from the software programs. The computer device 500 may also include a communication unit 505 for inputting (receiving) and outputting (transmitting) signals.

[0059] For example, computer device 500 may be a chip, communication unit 505 may be the input and / or output circuit of the chip, or communication unit 505 may be the communication interface of the chip, and the chip may be a component of terminal device, network device or other device.

[0060] For example, computer device 500 may be a terminal device or a server, and communication unit 505 may be a transceiver of the terminal device or the server, or communication unit 505 may be a transceiver circuit of the terminal device or the server.

[0061] The computer device 500 may include one or more memories 502 storing a program 504. The program 504 can be executed by a processor 501 to generate instructions 503, causing the processor 501 to perform the methods described in the above method embodiments according to the instructions 503. Optionally, the memory 502 may also store data (such as a target audit model). Optionally, the processor 501 may also read data stored in the memory 502, which may be stored at the same storage address as the program 504, or the data may be stored at a different storage address than the program 504.

[0062] The processor 501 and memory 502 can be configured separately or integrated together, for example, integrated on the system-on-chip (SOC) of the terminal device.

[0063] It should be understood that each step of the above method embodiment can be completed by hardware logic circuits or software instructions in the processor 501. The processor 501 can be a CPU, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0064] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0065] For example, in some embodiments, this application also provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to perform the above-described method for detecting the quality of building concrete.

[0066] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0067] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for detecting the quality of construction concrete, characterized in that, Includes the following steps: Before concrete pouring, an intelligent sensor array is pre-embedded in the main stress path nodes of the steel reinforcement skeleton and the non-uniform diffusion area of ​​hydration heat. The intelligent sensors synchronously collect ultrasonic propagation delay signals, resistivity response signals and temperature field change signals to construct a multi-physics field time-series sampling dataset. During the early-age hydration process of concrete, the multi-physics field time-series sampling dataset is segmented by a time sliding window, and the ultrasonic propagation delay signal and the resistivity response signal are aligned by temperature compensation based on the temperature field change signal to obtain a standardized multi-physics field time-series sequence. A cross-physics temporal consistency analysis is performed on the standardized multiphysics time series, the dynamic time offset difference between the ultrasonic propagation delay sequence and the resistivity response sequence within the same time window is calculated, and the deviation characteristic value of phase consistency is constructed accordingly. The deviation characteristic value of the phase consistency is mapped according to the spatial location of the smart sensor to form a spatially distributed phase drift characteristic field, which is used to characterize the difference in the degree of synchronization of multi-physics field evolution in different regions inside the concrete. Based on the spatial continuity abrupt change characteristics of the phase drift feature field, a consistent fracture discrimination structure for internal propagation in concrete is constructed, and the corresponding structural anomaly regions are identified, thereby generating concrete quality assessment results.

2. The method as described in claim 1, characterized in that, During the early-age hydration process of concrete, the multiphysics time-series sampling dataset is segmented by a time sliding window, and temperature compensation alignment is performed on the ultrasonic propagation delay signal and the resistivity response signal based on the temperature field change signal to obtain a standardized multiphysics time-series sequence, specifically including: Acquire the ultrasonic propagation delay signal, resistivity response signal, and temperature field change signal from the multiphysics temporal sampling dataset; The multiphysics temporal sampling dataset is segmented according to a preset time window length and sliding step size to obtain multiple multiphysics temporal sub-segments under consecutive time windows; Based on the temperature values ​​corresponding to the temperature field change signals within each time window, calculate the temperature drift correction amount of the ultrasonic propagation delay signal and resistivity response signal relative to the preset reference temperature within the same time window. Based on the temperature drift correction, the ultrasonic propagation delay signal and resistivity response signal within each time window are compensated and corrected point by point to obtain a standardized multiphysics time series.

3. The method as described in claim 1, characterized in that, Performing cross-physics temporal consistency analysis on the standardized multiphysics time series, specifically calculating the dynamic time offset difference between the ultrasonic propagation delay sequence and the resistivity response sequence within the same time window, includes: The ultrasonic propagation delay signal and resistivity response signal within each time window of the standardized multiphysics time series are obtained, and the ultrasonic propagation delay sequence and resistivity response sequence within each time window are constructed respectively. Dynamic matching of the ultrasonic propagation delay sequence and resistivity response sequence on the time axis is performed in each time window. The time offset of the two sequences at each time point is calculated to obtain the dynamic time offset difference in each time window.

4. The method as described in claim 1, characterized in that, The specific components of constructing the deviation eigenvalues ​​for phase consistency include: Obtain the dynamic time offset difference corresponding to the position of each measuring point within each time window; The dynamic time offset difference of each measuring point within each time window is statistically processed to obtain the deviation characteristic value of the phase consistency of each measuring point within each time window.

5. The method as described in claim 1, characterized in that, Mapping the phase consistency deviation feature values ​​according to the spatial location of the smart sensor to form a spatially distributed phase drift feature field specifically includes: Obtain the deviation characteristic value of phase consistency of each smart sensor at its spatial location for each time window; Using the spatial coordinates of each smart sensor as a spatial reference, a spatial mapping relationship is established between the coordinates of each measuring point and the corresponding deviation feature value. Based on the spatial mapping relationship, spatial interpolation is performed on non-measurement point locations to generate a continuously spatially distributed phase drift feature field.

6. The method as described in claim 1, characterized in that, Based on the spatial continuity abrupt change characteristics of the phase drift feature field, a structural discrimination structure for consistent fracture propagation within concrete is constructed, and the corresponding structural anomaly regions are identified, specifically including: Extract the gradient change between adjacent regions and feature values ​​at each spatial location in the phase drift feature field, and use the gradient change as a spatial continuity abrupt change feature; Based on the comparison results between the spatial continuity abrupt change characteristics and the preset fracture threshold, a fracture discrimination structure for consistent propagation inside concrete is constructed. Based on the fracture discrimination structure, the spatial region where a consistent propagation fracture occurs inside the concrete is determined, and the spatial region is identified as a structural anomaly region.

7. The method as described in claim 1, characterized in that, The concrete quality assessment results specifically include: Based on the spatial distribution range and continuity characteristics of the identified structural anomaly areas, the concrete hydration lag level is output. The concrete quality assessment results are generated based on the concrete hydration lag level.

8. A building concrete quality inspection system, characterized in that, include: The acquisition module is used to pre-embed the intelligent sensor array in the main stress path nodes of the steel reinforcement skeleton and the non-uniform diffusion area of ​​hydration heat before concrete pouring. The intelligent sensor synchronously acquires ultrasonic propagation delay signal, resistivity response signal and temperature field change signal to construct a multi-physics field time-series sampling dataset. The processing module is used to perform time sliding window segmentation processing on the multi-physics field time-series sampling dataset during the early-age hydration process of concrete, and to perform temperature compensation alignment on the ultrasonic propagation delay signal and the resistivity response signal based on the temperature field change signal to obtain a standardized multi-physics field time-series sequence. The processing module is also used to perform cross-physical field time consistency analysis on the standardized multi-physics time series, calculate the dynamic time offset difference between the ultrasonic propagation delay sequence and the resistivity response sequence within the same time window, and construct the phase consistency deviation characteristic value accordingly. The processing module is also used to map the deviation feature value of the phase consistency according to the spatial location of the smart sensor to form a spatially distributed phase drift feature field, which is used to characterize the difference in the degree of synchronization of multi-physics field evolution in different regions inside the concrete. The execution module is used to construct a concrete internal propagation consistency fracture discrimination structure based on the spatial continuity abrupt change characteristics of the phase drift feature field, identify the corresponding structural anomaly regions, and then generate concrete quality assessment results.

9. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to call and run the computer programs from the memory, so that the computer device performs the building concrete quality testing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions or code that, when executed on a computer, cause the computer to implement the building concrete quality testing method as described in any one of claims 1 to 7.

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