A method for testing the density of concrete during pouring construction

Abstract

This invention relates to the field of density testing technology, specifically to a method for testing the density of concrete during pouring construction. The method identifies abnormal sections based on the deviation of ultrasonic signal data points between monitoring points on a vertical line, analyzes the degree of matching deviation between abnormal sections and other monitoring points to obtain defect assessment indicators, and screens key sections. It analyzes the signal deviation and clustering distribution between key sections at monitoring points, and combines this with the defect assessment indicators to obtain defect attention levels. Based on the distribution of key sections, it identifies connected sections. Through the location distribution and proportion of connected sections, as well as defect attention levels, combined with the density and distance from the ground of monitoring points containing key sections, it obtains density assessment indicators and performs evaluation testing. This invention identifies associated defects and quantifies risks through multiple monitoring points along the wall thickness direction. By combining the spatial distribution of associated defects with quantified risks, it comprehensively assesses the wall density, making the test results more accurate and reliable.

Description

Technical Field

[0001] This invention relates to the field of density testing technology, and specifically to a method for testing the density of concrete during pouring construction. Background Technology

[0002] In engineering construction, hardened concrete should possess sufficient strength and durability to withstand loads and resist external environmental erosion. A crucial indicator of concrete durability is its density; higher density generally correlates with greater structural strength. Since concrete density reflects the uniformity and compactness of the material, it is a key indicator of concrete construction quality. Therefore, density monitoring of concrete walls is essential to prevent cracking and structural damage, ensuring the overall safety of the building.

[0003] During the pouring of concrete walls, vibration is typically required to ensure that air bubbles and voids within the concrete are effectively expelled. Insufficient vibration can lead to the formation of air bubbles and voids, thus affecting the density of the wall. Current technology often analyzes the density of concrete walls using ultrasonic signals from monitoring locations. However, because defects such as honeycomb, voids, or inclusions exist within the concrete wall, their locations exhibit spatial continuity. Single-point detection results only focus on the "presence" or "size" of defects, making it difficult to identify this cross-regional correlation. This can easily lead to misjudging systemic defects as scattered local problems. In reality, defects within concrete walls have spatial distribution characteristics. Failure to consider the spatial distribution characteristics of defects within the overall structure can result in high assessment errors, affecting the density evaluation of the concrete wall. Summary of the Invention

[0004] To address the technical problems in the prior art, the present invention aims to provide a method for detecting the compactness of concrete during concrete pouring construction. The specific technical solution adopted is as follows:

[0005] This invention provides a method for detecting the compactness of concrete during pouring construction, the method comprising:

[0006] At each monitoring point in the concrete wall, the direction perpendicular to the wall surface is taken as the extension direction; the ultrasonic signal sequence in the extension direction is acquired.

[0007] On the same vertical line, based on the signal value deviation of data points in the ultrasonic signal sequence between each monitoring point and other monitoring points, the abnormal segments of the monitoring points in the extension direction are determined; the degree of matching deviation between each abnormal segment of each monitoring point and the ultrasonic signal sequence of other monitoring points is analyzed to obtain the defect assessment index of the abnormal segments; based on the defect assessment index, key segments are screened from the abnormal segments.

[0008] At each monitoring point, the defect attention level of each monitoring point is obtained based on the degree of deviation of the ultrasonic signal sequence between key sections, the distribution and clustering of key sections from the center of the wall, and defect assessment indicators. On the same vertical line, connected sections are determined based on the distribution of key sections of all monitoring points in the extension direction. The overall risk of the wall is obtained based on the distribution and clustering of connected sections from the ground on each vertical line, the proportion of each connected section, and the defect attention level of the corresponding monitoring point.

[0009] The concentration of the wall's impact is obtained by analyzing the density and distance from the ground of monitoring points in key sections of the wall; and by combining the wall's overall risk and the concentration of its impact, a density assessment index is obtained.

[0010] Density assessment is conducted based on density assessment indicators.

[0011] Furthermore, the method for obtaining the abnormal segment includes:

[0012] For any given monitoring point, each other monitoring point on the same perpendicular line as the monitoring point is successively used as the analysis monitoring point;

[0013] The DTW algorithm was used to obtain matching pairs of each data point in the ultrasound signal sequence of the monitoring point and the analysis monitoring point. The distance between data points in each matching pair was calculated and negative correlation mapping was performed to obtain the matching value of each data point to the analysis monitoring point.

[0014] When the average matching value between the data point of the monitoring point and all other monitoring points on the same vertical line is less than the preset abnormal threshold, the corresponding data point is recorded as an abnormal data point.

[0015] If there are two or more consecutive abnormal data points in the ultrasound signal sequence of the monitoring point, they are merged into one abnormal time period; the propagation length position in the extension direction corresponding to each abnormal time period of the ultrasound signal sequence is taken as each abnormal segment.

[0016] Furthermore, the method for obtaining the defect assessment indicators includes:

[0017] For any abnormal segment at any monitoring point, calculate the similarity between the ultrasound signal sequence and the ultrasound signal sequences of other monitoring points on the same vertical line during the time period corresponding to the abnormal segment, and use it as the matching factor between the abnormal segment and each other monitoring point.

[0018] Other monitoring points with matching factors less than the preset matching threshold are used as difference monitoring points for this abnormal segment;

[0019] The ratio of the total number of differential monitoring points in the abnormal section to the total number of all other monitoring points on the same perpendicular line is taken as the matching deviation degree of the abnormal section.

[0020] The abnormal segment is negatively correlated with the mean of the matching factors of all difference monitoring points to obtain the matching abnormality degree of the abnormal segment;

[0021] By combining the matching deviation and matching anomaly of the abnormal section, the defect assessment index of the abnormal section is obtained.

[0022] Furthermore, the method for obtaining the defect attention level includes:

[0023] For any monitoring point with two or more key segments, calculate the similarity of the ultrasonic signal sequences between every two key segments at that monitoring point, and perform a negative correlation mapping on the mean of all similarities to obtain the defect complexity of that monitoring point.

[0024] Calculate the interval distance between every two adjacent key segments in the extended direction at the monitoring point, and perform a negative correlation mapping on the mean of all interval distances to obtain the clustering distribution degree of the monitoring point.

[0025] The ratio of the sum of the lengths of all key segments at the monitoring point to the total propagation length in the extended direction is taken as the segment distribution degree of the monitoring point.

[0026] The initial level of attention for the monitoring point is obtained by combining the defect complexity, clustering distribution, and segment distribution of the monitoring point.

[0027] Calculate the distance between each critical section at the monitoring point and the center of the wall and perform a negative correlation mapping to obtain the center threat level of each critical section; combine the center threat level of each critical section with the defect assessment index to obtain the hazard assessment index of each critical section.

[0028] By combining the hazard assessment indicators and initial attention levels of all key sections at the monitoring point, the defect attention level of the monitoring point is obtained.

[0029] Furthermore, the method for obtaining the connected segment includes:

[0030] For any vertical line, when there is a critical segment that overlaps in the distribution of the critical segment along the extension direction between each monitoring point and each adjacent monitoring point on the vertical line, the overlapping critical segments are merged into one overlapping segment.

[0031] After traversing the critical segments of all monitoring points along the vertical line, each overlapping segment is treated as a connected segment.

[0032] Furthermore, the method for obtaining the comprehensive risk includes:

[0033] For any connected segment on any vertical line, the ratio of the length of the connected segment in the extension direction to the total propagation length in the extension direction is used as the horizontal distribution index of each connected segment; the number of monitoring points contained in the connected segment is used as the vertical distribution index of each connected segment; the average defect attention of each connected segment containing monitoring points is used as the defect impact of each connected segment; combining the horizontal distribution index, vertical distribution index and defect impact of each connected segment, the compactness impact factor of each connected segment is obtained.

[0034] Calculate the minimum distance between each connected segment and every other connected segment on the vertical line, and use the mean of all minimum distances as the distribution spacing value of each connected segment; perform negative correlation mapping on the product of the distance of each connected segment from the wall and the ground and the distribution spacing value and normalize it to obtain the ground-distance distribution clustering index of each connected segment; combine the density influence factor and the ground-distance distribution clustering index of each connected segment to obtain the risk coefficient of each connected segment;

[0035] By combining the risk coefficients of all connected sections on the vertical lines, the overall risk of the wall is obtained.

[0036] Furthermore, the method for obtaining the concentration of influence includes:

[0037] Each monitoring point with a critical segment is designated as a critical point; the ratio between the total number of critical points and the total number of all monitoring points is taken as the critical percentage.

[0038] After calculating the distance of each key point from the wall and the ground, the average value is obtained to obtain the key load-bearing influence degree; after calculating the distance between each pair of key points, the average distance is obtained to obtain the key distance distribution degree; the product of the key load-bearing influence degree and the key distance distribution degree is negatively correlated to obtain the concentrated distribution influence degree of the key points.

[0039] By combining the degree of concentrated distribution of influence and the key proportion, the concentration of the wall's influence can be obtained.

[0040] Furthermore, the method for obtaining the density assessment index includes:

[0041] The product of the wall's overall risk and the concentration of its impact is negatively correlated and normalized to obtain a density assessment index.

[0042] Furthermore, the density assessment based on the density assessment index includes:

[0043] When the density assessment index is greater than or equal to the preset first detection threshold, the density assessment result is recorded as qualified.

[0044] When the density assessment index is less than the preset first detection threshold and greater than the preset second detection threshold, the density assessment result is recorded as good.

[0045] When the density assessment index is less than or equal to the preset second detection threshold, the density assessment result is recorded as unqualified; the preset first detection threshold is greater than the preset second detection threshold.

[0046] Furthermore, the method for obtaining the key segment includes:

[0047] Abnormal sections where the defect assessment index exceeds the preset defect threshold are designated as critical sections.

[0048] The present invention has the following beneficial effects:

[0049] This invention uses ultrasonic signals to capture the internal structural features of a concrete wall along its thickness at multiple monitoring points, extending perpendicular to the wall surface. Along the same vertical line, abnormal sections are identified based on the deviation of ultrasonic signal data points between monitoring points. The degree of mismatch between these abnormal sections and other monitoring points is analyzed to obtain defect assessment indicators. This identifies potentially latent related defects within the same vertical line, quantifies the defect probability of abnormal sections, and screens key sections. High-risk sections selected in the initial screening are then used for subsequent hazard risk assessment, improving the comprehensive analysis of latent defects. At each monitoring point, the signal deviation and clustering distribution between key sections are analyzed to reflect defect complexity and locational influence. Combined with the defect assessment indicators, defect attention is obtained, and the defect risk of an individual monitoring point is measured.

[0050] Considering the spatial distribution of defects, connected sections are determined based on the distribution of key sections. The location distribution and proportion of these connected sections reflect the impact of defects on the load-bearing area and the scale of the defects. Combined with the attention given to defects at individual points, the overall risk is determined by reflecting local risks. The overall impact of defects in different vertical directions is systematically assessed. The density and distance from the ground of monitoring points in key sections reflect the clustering of defects and their impact on the load-bearing area, obtaining the concentration of impact. This further assesses the spatial clustering risk of defects, avoiding the neglect of potential hazards from concentrated defects. A density assessment index is obtained by combining the overall risk and the concentration of impact, providing a more comprehensive and accurate reflection of the overall wall density. This invention identifies associated defects and quantifies risks through multiple monitoring points along the wall thickness direction. By combining the spatial distribution of associated defects with quantified risks, the wall density is comprehensively assessed, making the test results more accurate and reliable. Attached Figure Description

[0051] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a flowchart of a method for detecting the compactness of concrete during pouring construction, provided by an embodiment of the present invention.

[0053] Figure 2 This is a schematic diagram of the distribution of monitoring points provided in one embodiment of the present invention;

[0054] Figure 3 This is a schematic diagram of an ultrasonic testing method for a wall, provided as an embodiment of the present invention.

[0055] Figure 4 This is a schematic diagram of the distribution of monitoring points on a vertical line according to one embodiment of the present invention;

[0056] Figure 5 A flowchart illustrating a method for obtaining defect concern according to an embodiment of the present invention;

[0057] Figure 6 This is a schematic diagram showing the distribution of key sections at monitoring points along the same vertical line, as provided in one embodiment of the present invention.

[0058] Figure 7 A flowchart illustrating a method for obtaining comprehensive risk according to an embodiment of the present invention. Detailed Implementation

[0059] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method for detecting the density of concrete during pouring construction according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0061] The following description, in conjunction with the accompanying drawings, details a specific scheme for a method for detecting the density of concrete during pouring construction provided by the present invention.

[0062] Please see Figure 1The diagram illustrates a flowchart of a method for detecting the compactness of concrete during pouring construction, according to an embodiment of the present invention. The method includes the following steps:

[0063] S1: At each monitoring point in the concrete wall, the direction perpendicular to the wall surface is taken as the extension direction; the ultrasonic signal sequence in the extension direction is acquired.

[0064] Once a concrete wall is constructed, destructive testing methods (such as cutting and drilling) can affect its integrity and structural strength. Ultrasonic compaction testing, however, emits ultrasonic waves and analyzes the reflected waves, causing no damage to the concrete and thus preserving the wall's integrity and ensuring its structural safety. Therefore, this embodiment uses ultrasonic non-destructive testing to determine the compaction of concrete during pouring.

[0065] In this embodiment of the invention, multiple monitoring points are uniformly arranged on the concrete wall to be tested, which has a uniform thickness. Please refer to [link to relevant documentation]. Figure 2 This diagram illustrates a monitoring point distribution according to an embodiment of the present invention. Using a direction perpendicular to the wall surface as the extension direction, a professional ultrasonic instrument transmits ultrasonic signals to the concrete wall, and the ultrasonic signals propagate along the extension direction. Specifically, a pair of transmitting and receiving transducers are coupled to two surfaces parallel to the component being measured, with the axes of the two transducers aligned on the same straight line. (See also...) Figure 3 The diagram illustrates an ultrasonic testing method for walls according to an embodiment of the present invention.

[0066] The ultrasonic signal sequence at each monitoring point is obtained. Due to the uniform thickness, the length of the ultrasonic signal sequence is consistent, meaning the total propagation length in the extension direction is consistent, equivalent to the wall thickness. It can be understood that since the propagation speed of ultrasound in concrete is fixed, and the horizontal axis of the ultrasonic signal sequence represents time, each moment actually corresponds to each propagation position inside the wall.

[0067] It should be noted that ultrasonic testing is a well-known technical method familiar to those skilled in the art, and will not be elaborated upon here.

[0068] S2: On the same vertical line, based on the signal value deviation of data points in the ultrasonic signal sequence between each monitoring point and other monitoring points, determine the abnormal segments of the monitoring points in the extension direction; analyze the degree of matching deviation between each abnormal segment of each monitoring point and the ultrasonic signal sequence of other monitoring points to obtain the defect assessment index of the abnormal segments; based on the defect assessment index, select key segments from the abnormal segments.

[0069] Concrete wall pouring is typically carried out in horizontal layers, with vibration operations moving vertically. Insufficient vibration in any layer will lead to generally low concrete density within that height range. In concrete wall structures, the ultrasonic signals at monitoring points should be essentially identical, exhibiting consistent reflection and scattering of ultrasonic waves; that is, the ultrasonic signal sequences at monitoring points should have similar signal values. However, defects such as honeycomb or voids at monitoring points can alter ultrasonic signal propagation. Therefore, analysis should be conducted along the same vertical line, first comparing the propagation of individual monitoring points within the wall to preliminarily assess the potential presence of lateral defects such as honeycomb or voids within the wall. Please refer to [link to relevant documentation]. Figure 4 The diagram illustrates a distribution of monitoring points on a vertical line according to an embodiment of the present invention, wherein the dashed line represents a vertical line.

[0070] Since the overall structure of concrete is usually continuous and relatively homogeneous, when the signals from most monitoring points are highly consistent, the appearance of a single point with a significant difference in the signal sequence is highly likely to reflect an abnormality in the concrete density at that point, such as quality problems like honeycomb, voids, or slag inclusions. Therefore, by analyzing the deviations of data points in the ultrasonic signal sequence, possible abnormal sections can be preliminarily identified.

[0071] Preferably, in this embodiment of the invention, the method for obtaining abnormal segments includes:

[0072] First, for any given monitoring point, each other monitoring point on the same perpendicular line as the monitoring point is sequentially taken as the analysis monitoring point, and the monitoring point is sequentially subjected to similarity analysis with each other. The DTW algorithm is used on the ultrasound signal sequences of the monitoring point and the analysis monitoring point to obtain matching pairs for each data point in the ultrasound signal sequence of the monitoring point. It should be noted that the method of obtaining sequence matching pairs using the DTW algorithm is a well-known technique in the art and will not be described in detail here.

[0073] For the matching pairs in the DTW algorithm, the distance between data points in each matching pair reflects the similarity. Therefore, the distance between data points in each matching pair is calculated and negative correlation mapping is performed to obtain the matching value of each data point to the analysis and monitoring point. The smaller the distance, the higher the degree of matching at the data point, that is, the more similar the signals. It should be noted that negative correlation mapping is a technique well known to those skilled in the art, such as using inverse proportional values ​​or negative exponentiation with the natural constant as the base, etc., and will not be elaborated further here.

[0074] Furthermore, when the average matching value between the data point of the monitoring point and all other monitoring points on the same vertical line is less than the preset anomaly threshold, the corresponding data point is recorded as an abnormal data point. Through matching analysis on the same vertical line, the worse the matching degree between the data point and the signals on all other monitoring points, that is, the smaller the average matching value, the more significant the anomaly at the data point. Threshold judgment is used for preliminary anomaly point screening. In this embodiment of the invention, the preset anomaly threshold can be set to 0.6. The specific value can be adjusted by the implementer according to the implementation scenario, and no restriction is imposed here.

[0075] Finally, if there are two or more consecutive abnormal data points on the ultrasound signal sequence of the monitoring point, they are merged into one abnormal time period. Since the existence of abnormalities usually has a certain range, a single abnormal data point on the ultrasound signal sequence is very likely to be an error point. Therefore, the local propagation period that may be defective is only identified from the signal by the distribution of consecutive abnormal data points.

[0076] Furthermore, the propagation length position corresponding to each abnormal time period in the ultrasonic signal sequence along the extension direction is taken as each abnormal segment. In a specific embodiment of the present invention, since the ultrasonic velocity is fixed and known, the time in the signal sequence can be converted into the horizontal position along the extension direction, i.e., the depth from the wall surface, by the velocity. The specific position distance can be obtained by the formula s=v×t, where s represents the position in the extension direction, v is the propagation speed of the ultrasonic wave in the concrete, and t is the time. By obtaining the first and last positions in the extension direction corresponding to the first and last times of each abnormal time period, the length between the first and last positions is the abnormal segment.

[0077] Because abnormal sections may exhibit varying degrees of defect manifestation, they possess different levels of defect risk. Low-risk areas may consist of extremely small, localized anomalous data points, and their overall signal deviation may not be significant, constituting localized interference regions that could introduce errors during subsequent defect assessment. Therefore, further quantitative analysis is needed to determine the likelihood of actual defects occurring in potentially anomalous sections, and sections with potentially significant defects should be selected for further analysis.

[0078] Preferably, in this embodiment of the invention, the method for obtaining defect assessment indicators includes:

[0079] For any abnormal segment at any monitoring point, the similarity between the ultrasound signal sequence corresponding to the abnormal segment and the ultrasound signal sequences of other monitoring points along the same perpendicular line is calculated within the time period of the ultrasound signal sequence corresponding to the abnormal segment. This similarity serves as the matching factor between the abnormal segment and each other monitoring point. Matching detection is then performed on the corresponding segment of the abnormal segment, and the similarity of other monitoring points within this segment is analyzed. It should be noted that obtaining the similarity between sequences is a technique well-known to those skilled in the art, and methods such as the DTW algorithm, Pearson correlation coefficient, or Manhattan distance can be used, without further limitation or elaboration here.

[0080] Furthermore, other monitoring points with matching factors less than a preset matching threshold are designated as difference monitoring points for the abnormal segment. A smaller matching factor indicates a more significant defect in the abnormal segment compared to other monitoring points. Therefore, difference monitoring points with poor matching are determined using the threshold. The more difference monitoring points there are, the stronger the defect in the abnormal segment. In this embodiment, the matching threshold can be set to 0.7; the specific value can be adjusted by the implementer and is not limited here.

[0081] Furthermore, the ratio of the total number of differential monitoring points in the abnormal section to the total number of all other monitoring points along the same vertical line is used as the matching deviation degree of the abnormal section. A larger matching deviation degree indicates a greater signal difference between the abnormal section and more monitoring points, and a more significant abnormal defect in the abnormal section. A negative correlation is then established between the mean of the matching factors of the abnormal section and all differential monitoring points to obtain the matching anomaly degree of the abnormal section. A smaller overall matching factor between the abnormal section and the differential monitoring points indicates a more significant difference and a stronger defect manifestation.

[0082] Finally, by combining the matching deviation and matching anomaly of the abnormal segment, a defect assessment index for the abnormal segment is obtained. In this embodiment of the invention, both the matching deviation and matching anomaly are positively correlated with the defect assessment index of the abnormal segment. The product of the matching deviation and matching anomaly of the abnormal segment is normalized and used as the defect assessment index for the abnormal segment. The larger the defect assessment index, the higher the probability of a defect.

[0083] It should be noted that normalization is a technique well known to those skilled in the art. The choice of normalization can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0084] By using defect assessment indicators, defective sections requiring special attention are selected from abnormal sections; these are high-probability defective sections that have a significant impact on the wall's density. In this embodiment of the invention, abnormal sections with defect assessment indicators exceeding a preset defect threshold are designated as critical sections. The preset defect threshold can be set to 0.7, which can be adjusted by the implementer.

[0085] S3: At each monitoring point, based on the degree of deviation of the ultrasonic signal sequence between key sections, the distribution and clustering of key sections from the center of the wall, and defect assessment indicators, the defect attention level of each monitoring point is obtained; on the same vertical line, based on the distribution of key sections of all monitoring points in the extension direction, connected sections are determined; based on the distribution and clustering of connected sections from the ground on each vertical line, the proportion of each connected section, and the defect attention level of the corresponding monitoring point, the overall risk of the wall is obtained.

[0086] During concrete pouring, the distribution of defects is related to the construction process. If vibration is uniform, defects should be distributed relatively regularly, such as only in small, localized areas. However, if vibration is chaotic, such as when the vibrator is moved haphazardly, defects will be randomly distributed, located at different depths, and involving different types of defects. For example, insufficient concrete slump during pouring can lead to inadequate vibration, forming continuous horizontal honeycomb bands, or leakage at formwork joints can cause dense air bubbles near horizontal joints. These defects disrupt the continuity of the concrete, creating localized stress concentrations, which in turn can lead to the propagation of wall cracks.

[0087] Therefore, at each monitoring point, the possible distribution and quantity of defects along the extension direction are considered, and a preliminary analysis of the degree of concern for defect hazards is conducted for each monitoring point. By analyzing the signal deviation and clustering distribution between key sections, the complexity of defects and the impact of location are reflected. Combined with defect assessment indicators, the degree of concern for defects is obtained, and the defect hazard risk of a single monitoring point is measured.

[0088] Preferably, in this embodiment of the invention, the method for obtaining defect attention level is described in [reference needed]. Figure 5 The diagram illustrates a flowchart of a method for obtaining defect concern according to an embodiment of the present invention, which includes the following steps:

[0089] S301: The initial attention level of the monitoring point is obtained based on the differences in ultrasonic signal sequences and distribution spacing between key segments at the monitoring point, as well as the distribution length of all key segments.

[0090] Considering that at each monitoring point, a higher proportion and more disordered distribution of defects have a greater impact on structural strength—for example, continuous voids in the thickness direction weaken shear resistance—we will focus our analysis on monitoring points with multiple critical sections, while monitoring points with only one critical section do not require attention analysis.

[0091] For any monitoring point with two or more critical sections, the similarity of the ultrasonic signal sequences between any two critical sections at that monitoring point is calculated. The mean of all similarities is negatively correlated and mapped as the defect complexity of that monitoring point. The degree of defect disorder is quantified by the signal differences between critical sections. The greater the defect complexity, the more irregular the signal distribution between sections, indicating a potentially more diverse range of defect types. This will affect the quality and long-term structural stability of the concrete wall, requiring greater attention.

[0092] Furthermore, the interval distance between every two adjacent key sections along the extension direction at the monitoring point is calculated. The mean of all interval distances is negatively correlated and mapped as the clustering distribution degree of the monitoring point. The clustering distribution degree reflects the concentration of defects. The smaller the interval distance, the more concentrated the defect distribution, the higher the risk of structural failure in the later stage, and the greater the attention required. It should be noted that the shortest distance between two key sections can be used as the interval distance to reflect the length of the distribution interval. The distance acquisition is a technical means well known to those skilled in the art and will not be elaborated here.

[0093] Furthermore, the ratio of the sum of the lengths of all key segments at the monitoring point to the total propagation length in the extension direction is used as the segment distribution degree of the monitoring point. The degree of harm is measured by the proportion of the total defect length at the monitoring point. The higher the overall defect proportion, the greater the risk of harm at the monitoring point and the higher the level of attention required.

[0094] Finally, by combining the defect complexity, clustering distribution degree, and segment distribution degree of the monitoring point, the initial attention degree of the monitoring point is obtained. In this embodiment of the invention, the product of defect complexity, clustering distribution degree, and segment distribution degree is normalized to obtain the initial attention degree of the monitoring point. The risk attention of the extended defense line at the monitoring point is initially measured by the spatial distribution of multiple attention segments.

[0095] S302: Based on the distribution distance between each critical section at the monitoring point and the center of the wall, and in conjunction with the defect assessment index, obtain the hazard assessment index for the critical section.

[0096] The center of a wall is the neutral axis of structural stress and the core area for stress transfer. Defects near the center directly weaken the wall's shear and bending resistance. For example, voids in the central area can lead to stress concentration under load, easily causing crack propagation. Therefore, considering the spatial distribution characteristics of critical sections, emphasizing the impact of defects on the wall's strength and load-bearing capacity, and combining the distribution of defects from the wall center with the potential significance of defects in the critical sections themselves, the hazard weight of defects in the central area is highlighted to accurately assess local risks.

[0097] Furthermore, the distance between each critical section at the monitoring point and the center of the wall is calculated and negatively correlated to determine the center threat level of each critical section; the closer the section is to the center, the higher the threat level. Combining the center threat level and defect assessment index of each critical section, a hazard assessment index for each critical section is obtained. In this embodiment of the invention, the product of the center threat level and the defect assessment index is used as the hazard assessment index for each critical section; the higher the hazard assessment index, the greater the potential hazard posed by the critical section.

[0098] S303: Based on the hazard assessment indicators and initial concern level of the key sections at the monitoring point, obtain the defect concern level of the monitoring point.

[0099] By adjusting the attention level based on the spatial distribution of defect risk impact, and combining the hazard assessment indicators of all key sections at the monitoring point with the initial attention level, the defect attention level of the monitoring point is obtained. In this embodiment of the invention, the mean value of the hazard assessment indicators of all key sections at the monitoring point is normalized, and the normalized value is multiplied by the initial attention level to obtain the defect attention level of the monitoring point.

[0100] Because defects in concrete walls, such as honeycomb, voids, or slag inclusions, are not limited to a single area, they can extend vertically. For example, when the wall is poured in layers and vibrated in layers (each layer is about 30-50cm high), if a vibration step is missed (e.g., the vibrator is not inserted into the layer), the concrete density of that layer will be low. If multiple layers are missed, the defects will be continuously distributed vertically (e.g., honeycomb can be present from 10cm to 30cm in height).

[0101] Once a connection defect is formed, it will threaten the overall structural strength and stability of the entire wall. This vertical connection defect will form a weak zone that runs through the wall. If the vertical connection is severe, the void will prevent the upper load from being transferred to the foundation, causing local collapse.

[0102] Therefore, this analysis examines the potential connectivity of key segments along the same vertical line and assesses the risk impact of connectivity distribution along the vertical direction. First, the connected segments are identified. Due to their consistent extension direction, when the propagation distance of the key segments is consistent, connectivity effects may occur at the same vertical level.

[0103] Therefore, in this embodiment of the invention, for any vertical line, when there is a critical segment whose distribution position overlaps with that of each monitoring point and each adjacent monitoring point on the vertical line, the overlapping critical segments are merged into one overlapping segment. Please refer to [link to relevant documentation]. Figure 6 The diagram illustrates the distribution of key sections at monitoring points along the same vertical line according to an embodiment of the present invention. Monitoring point x, monitoring point x-1 and monitoring point x-2 are adjacent monitoring points along the same vertical line. The dashed line indicates that adjacent monitoring points x and x-1 have overlapping positions. Key section 1 and key section 2 are an overlapping section.

[0104] After traversing the critical sections of all monitoring points along the vertical line, each overlapping section is treated as a connected segment. Further analysis of the spatial continuity of the defect is conducted based on connectivity, providing a basis for assessing systemic risk. The location distribution and proportion of connected segments reflect the impact of the defect on the load-bearing area and the scale of the defect. Combined with the level of attention given to defects at individual monitoring points, the comprehensive risk is determined by reflecting local risks, and the overall impact of defects in different vertical directions is systematically assessed.

[0105] Therefore, by comprehensively considering the scale, location, and defect characteristics of the connected sections, their impact on compactness is quantified to determine the risk situation of the wall after comprehensive analysis. Preferably, in this embodiment of the invention, the method for obtaining the comprehensive risk is described in [reference needed]. Figure 7 The diagram illustrates a flowchart of a method for obtaining comprehensive risk according to an embodiment of the present invention, the method comprising the following steps:

[0106] S311: Based on the distribution of each connected segment on each vertical line in the extension direction and vertical direction, as well as the degree of attention to defects including monitoring points, obtain the density influence factor of each connected segment.

[0107] First, we analyze the impact of a single connected section, reflecting the extent of defect expansion by analyzing the proportion of connectivity in the horizontal and vertical directions. Then, we combine this with the wind direction of the defect at the monitoring point itself to quantify the impact of a single connected section on the overall compaction, thus intuitively reflecting the comprehensive risk of the connected section.

[0108] In this embodiment of the invention, for any connected segment on any vertical line, the ratio of the length of the connected segment in the extension direction to the total propagation length in the extension direction is used as the horizontal distribution index of each connected segment. The larger the horizontal distribution index, the greater the proportion of defects in the wall, resulting in more air pores, honeycomb-like or void defects, and thus a greater impact on the wall's density. The length of the connected segment in the extension direction is the length between the minimum propagation position and the maximum propagation position of the connected segment containing the critical section.

[0109] Furthermore, the number of monitoring points contained in the connected section is used as the vertical distribution index of each connected section. When the vertical distribution index is high, it indicates that the more monitoring points a connected section contains on the vertical line, the greater the extent of the defect expansion in the vertical space, and the more severely the internal density of the wall is affected.

[0110] Furthermore, the average defect attention of each connected segment containing monitoring points is taken as the defect impact of each connected segment. The greater the overall defect attention of the connected segment containing monitoring points, the more complex the defects in the connected segment are, the wider the scope of influence, and the greater the impact on the compactness of the concrete wall.

[0111] Finally, by combining the horizontal distribution index, vertical distribution index, and defect impact degree of each connected segment, the density impact factor of each connected segment is obtained. In this embodiment of the invention, the product of the horizontal distribution index, vertical distribution index, and defect impact degree of each connected segment is used as the density impact factor of each connected segment.

[0112] S312: Based on the clustering distribution of connected sections on each vertical line at a distance from the wall and the ground, and combined with the density influence factor, obtain the risk coefficient of each connected section.

[0113] Different connected sections are located in different parts of the wall and have varying degrees of importance. For example, the bottom or supporting area of ​​the wall may bear greater external forces, and the closer a connected section is to the ground, the greater its impact on the wall's load-bearing capacity. Therefore, connected sections that are closer to the load-bearing parts and have a higher concentration of loads require more attention.

[0114] In this embodiment of the invention, the minimum distance between each connected segment and each other connected segment on the vertical line is calculated, and the average of all minimum distances is used as the distribution spacing value of each connected segment. The distribution spacing value reflects the distribution distance between connected segments. The smaller the distribution spacing value, the more concentrated the multiple connected segments are, and the higher the risk.

[0115] Furthermore, the product of the distance from each connected section to the wall and the ground and the distribution spacing value is negatively correlated and normalized to obtain the ground distribution cluster index of each connected section. The larger the ground distribution cluster index of the connected section, the closer the connected section is to the load-bearing area and the more concentrated the distribution is, and the higher the risk of the connected section.

[0116] Finally, by combining the density influence factor and the distance-from-ground distribution clustering index of each connected segment, the risk coefficient of each connected segment is obtained. By integrating the risk impact of the connected segment's inherent characteristics and the risk impact of its spatial distribution, a more comprehensive risk assessment is achieved. In this embodiment of the invention, the product of the density influence factor and the distance-from-ground distribution clustering index of each connected segment is used as the risk coefficient of each connected segment.

[0117] S313: Combine the risk coefficients of all connected sections on the vertical lines to obtain the overall risk of the wall.

[0118] By synthesizing the risk assessment results of all connected sections along the vertical lines, the overall risk of the wall is assessed. In this embodiment of the invention, the sum of the risk coefficients of all connected sections along each vertical line is taken as the vertical risk level of each vertical line, which is then used as the risk assessment result for that vertical line. Finally, the average of the vertical risk levels of all vertical lines is taken as the overall risk level of the wall.

[0119] Thus, the overall risk of the wall is obtained by combining the spatial distribution characteristics of the defects.

[0120] S4: Based on the density and distance from the ground of the monitoring points in key sections of the wall, the influence concentration of the wall is obtained; combined with the comprehensive risk and influence concentration of the wall, the compactness assessment index is obtained; and the compactness assessment is carried out based on the compactness assessment index.

[0121] For a full-wall concrete structure, when monitoring points potentially prone to defects are concentrated in a specific local area, the density within that area will be lower, potentially leading to a sharp stress concentration and directly causing cracks, thus affecting the safety and durability of the building. Therefore, by analyzing the clustered distribution of monitoring points in critical sections, preferably, in this embodiment of the invention, the method for obtaining the concentration of influence includes:

[0122] Each monitoring point with a critical section is designated as a critical point. The ratio between the total number of critical points and the total number of all monitoring points is taken as the critical percentage. The higher the percentage, the more widespread and common the defects in the wall, and the worse the compactness.

[0123] Then, the distance from each key point to the wall and ground is calculated and averaged to obtain the key load-bearing influence degree, which characterizes the degree to which defects are close to the ground load-bearing area. The smaller the key load-bearing influence degree, the closer the overall key points are to the ground area, and the greater the load-bearing pressure. Then, the distance between each pair of key points is calculated and averaged to obtain the key distance distribution degree, which characterizes the degree of defect concentration. The smaller the key distance distribution degree, the more concentrated the overall distribution of key points.

[0124] By negatively mapping the product of critical load-bearing influence and critical spacing distribution, the concentration distribution influence of critical points is obtained. The smaller both critical load-bearing influence and critical spacing distribution are, the more concentrated the critical points are in the part close to the ground, which may affect the foundation or structural stability of the wall and result in poor compactness.

[0125] Finally, by combining the degree of concentrated distribution influence and the critical proportion, the influence concentration of the wall is obtained. In this embodiment of the invention, the product of the degree of concentrated distribution influence and the critical proportion is used as the influence concentration of the wall to more comprehensively assess the spatial clustering risk of defects. The greater the influence concentration, the higher the risk.

[0126] Therefore, by combining the comprehensive risk and the concentration of influence of the wall, a density assessment index is obtained. The smaller the comprehensive risk and the concentration of influence of the wall, the lower the defect risk faced by the wall and the higher the stability of the wall. Therefore, in this embodiment of the invention, the product of the comprehensive risk and the concentration of influence of the wall is negatively correlated and normalized to obtain the density assessment index. The larger the density assessment index, the better the density of the wall.

[0127] Thus, by analyzing the spatial distribution of defects and their harmful impact on the wall's density, the density assessment result of the concrete wall is determined. Based on the density assessment index, a threshold judgment method can be used to conduct density assessment and detection. In this embodiment of the invention, a preset first detection threshold of 0.8 and a preset second detection threshold of 0.5 are set. While ensuring that the preset first detection threshold is greater than the preset second detection threshold, the specific values ​​can be adjusted by the implementer.

[0128] When the density assessment index is greater than or equal to the preset first detection threshold, the density of the concrete wall is considered to meet the design requirements, the defect risk is low, and the density assessment result is recorded as qualified.

[0129] When the density assessment index is less than the preset first detection threshold and greater than the preset second detection threshold, it is considered that the concrete wall has a small number of defects such as honeycomb and pitting that do not affect structural safety. The density assessment result is recorded as good, and more detailed inspection and repair can be carried out in the future.

[0130] When the density assessment index is less than or equal to the preset second detection threshold, the concrete wall is considered to have significant defects such as through cracks or large-area voids, which makes the strength significantly substandard and affects the structural bearing capacity. The density assessment result is recorded as unqualified, and the wall can be repaired on a large scale or demolished and recast.

[0131] In summary, this invention uses ultrasonic signals to capture the internal structural features of a concrete wall along its thickness at multiple monitoring points, extending in a direction perpendicular to the wall surface. Along the same vertical line, abnormal sections are identified based on the deviation of ultrasonic signal data points between monitoring points. The degree of mismatch between abnormal sections and other monitoring points is analyzed to obtain defect assessment indicators, identifying potential latent defects within the same vertical line, quantifying the defect probability of abnormal sections, and screening key sections. High-risk sections selected in the initial screening are then used for subsequent hazard risk assessment, improving the comprehensive analysis of latent defects. At each monitoring point, signal deviation and clustering distribution between key sections are analyzed to reflect defect complexity and locational impact. Defect attention is obtained by combining defect assessment indicators, measuring the defect risk of a single monitoring point. Considering the spatial distribution of defect associations, connected sections are determined based on the distribution of key sections. The location distribution and proportion of connected sections reflect the impact of defects on the load-bearing area and the scale of the defects. Combined with the defect attention at a single point, local risks are reflected to determine the overall risk, and the overall impact of defects in different vertical directions is systematically assessed. By analyzing the density and distance from the ground of monitoring points in key areas, the invention reflects the clustering of defects and their impact on load-bearing zones, obtaining the concentration of impact. This allows for further assessment of the spatial clustering risk of defects, avoiding the overlooking of potential hazards from concentrated defects. Combining comprehensive risk and impact concentration, a density assessment index is derived and evaluated, providing a more comprehensive and accurate reflection of the overall wall density. This invention identifies associated defects and quantifies risks through multiple monitoring points along the wall thickness direction. By combining the spatial distribution of associated defects with quantified risks, a comprehensive assessment of wall density is achieved, making the test results more accurate and reliable.

[0132] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0133] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for testing the density of concrete during pouring construction, characterized in that, The method includes: At each monitoring point in the concrete wall, the direction perpendicular to the wall surface is taken as the extension direction; the ultrasonic signal sequence in the extension direction is acquired. On the same vertical line, based on the signal value deviation of data points in the ultrasonic signal sequence between each monitoring point and other monitoring points, the abnormal segments of the monitoring points in the extension direction are determined; the degree of matching deviation between each abnormal segment of each monitoring point and the ultrasonic signal sequence of other monitoring points is analyzed to obtain the defect assessment index of the abnormal segments; based on the defect assessment index, key segments are screened from the abnormal segments. At each monitoring point, the defect attention level of each monitoring point is obtained based on the degree of deviation of the ultrasonic signal sequence between key sections, the distribution and clustering of key sections from the center of the wall, and defect assessment indicators. On the same vertical line, connected sections are determined based on the distribution of key sections of all monitoring points in the extension direction. The overall risk of the wall is obtained based on the distribution and clustering of connected sections from the ground on each vertical line, the proportion of each connected section, and the defect attention level of the corresponding monitoring point. The concentration of the wall's impact is obtained by analyzing the density and distance from the ground of monitoring points in key sections of the wall; and by combining the wall's overall risk and the concentration of its impact, a density assessment index is obtained. Density assessment is conducted based on density assessment indicators.

2. The method for detecting the density of concrete during pouring construction according to claim 1, characterized in that, The method for obtaining the abnormal segment includes: For any given monitoring point, each other monitoring point on the same perpendicular line as the monitoring point is successively used as the analysis monitoring point; The DTW algorithm was used to obtain matching pairs of each data point in the ultrasound signal sequence of the monitoring point and the analysis monitoring point. The distance between data points in each matching pair was calculated and negative correlation mapping was performed to obtain the matching value of each data point to the analysis monitoring point. When the average matching value between the data point of the monitoring point and all other monitoring points on the same vertical line is less than the preset abnormal threshold, the corresponding data point is recorded as an abnormal data point. If there are two or more consecutive abnormal data points in the ultrasound signal sequence of the monitoring point, they are merged into one abnormal time period; the propagation length position in the extension direction corresponding to each abnormal time period of the ultrasound signal sequence is taken as each abnormal segment.

3. The method for detecting the density of concrete during pouring construction according to claim 1, characterized in that, The methods for obtaining the defect assessment indicators include: For any abnormal segment at any monitoring point, calculate the similarity between the ultrasound signal sequence and the ultrasound signal sequences of other monitoring points on the same vertical line during the time period corresponding to the abnormal segment, and use it as the matching factor between the abnormal segment and each other monitoring point. Other monitoring points with matching factors less than the preset matching threshold are used as difference monitoring points for this abnormal segment; The ratio of the total number of differential monitoring points in the abnormal section to the total number of all other monitoring points on the same perpendicular line is taken as the matching deviation degree of the abnormal section. The abnormal segment is negatively correlated with the mean of the matching factors of all difference monitoring points to obtain the matching abnormality degree of the abnormal segment; By combining the matching deviation and matching anomaly of the abnormal section, the defect assessment index of the abnormal section is obtained.

4. The method for detecting the density of concrete during pouring construction according to claim 1, characterized in that, The method for obtaining the defect attention level includes: For any monitoring point with two or more key segments, calculate the similarity of the ultrasonic signal sequences between every two key segments at that monitoring point, and perform a negative correlation mapping on the mean of all similarities to obtain the defect complexity of that monitoring point. Calculate the interval distance between every two adjacent key segments in the extended direction at the monitoring point, and perform a negative correlation mapping on the mean of all interval distances to obtain the clustering distribution degree of the monitoring point. The ratio of the sum of the lengths of all key segments at the monitoring point to the total propagation length in the extended direction is taken as the segment distribution degree of the monitoring point. The initial level of attention for the monitoring point is obtained by combining the defect complexity, clustering distribution, and segment distribution of the monitoring point. Calculate the distance between each critical section at the monitoring point and the center of the wall and perform a negative correlation mapping to obtain the center threat level of each critical section; combine the center threat level of each critical section with the defect assessment index to obtain the hazard assessment index of each critical section. By combining the hazard assessment indicators and initial attention levels of all key sections at the monitoring point, the defect attention level of the monitoring point is obtained.

5. The method for detecting the density of concrete during pouring construction according to claim 1, characterized in that, The method for obtaining the connected segment includes: For any vertical line, when there is a critical segment that overlaps in the distribution of the critical segment along the extension direction between each monitoring point and each adjacent monitoring point on the vertical line, the overlapping critical segments are merged into one overlapping segment. After traversing the critical segments of all monitoring points along the vertical line, each overlapping segment is treated as a connected segment.

6. The method for detecting the density of concrete during pouring construction according to claim 1, characterized in that, The methods for obtaining the overall risk profile include: For any connected segment on any vertical line, the ratio of the length of the connected segment in the extension direction to the total propagation length in the extension direction is used as the horizontal distribution index of each connected segment; the number of monitoring points contained in the connected segment is used as the vertical distribution index of each connected segment; the average defect attention of each connected segment containing monitoring points is used as the defect impact of each connected segment; combining the horizontal distribution index, vertical distribution index and defect impact of each connected segment, the compactness impact factor of each connected segment is obtained. Calculate the minimum distance between each connected segment and every other connected segment on the vertical line, and use the mean of all minimum distances as the distribution spacing value of each connected segment; perform negative correlation mapping on the product of the distance of each connected segment from the wall and the ground and the distribution spacing value and normalize it to obtain the ground-distance distribution clustering index of each connected segment; combine the density influence factor and the ground-distance distribution clustering index of each connected segment to obtain the risk coefficient of each connected segment; By combining the risk coefficients of all connected sections on the vertical lines, the overall risk of the wall is obtained.

7. The method for detecting the density of concrete during pouring construction according to claim 1, characterized in that, The methods for obtaining the concentration of influence include: Each monitoring point with a critical segment is designated as a critical point; the ratio between the total number of critical points and the total number of all monitoring points is taken as the critical percentage. After calculating the distance of each key point from the wall and the ground, the average value is obtained to obtain the key load-bearing influence degree; after calculating the distance between each pair of key points, the average distance is obtained to obtain the key distance distribution degree; the product of the key load-bearing influence degree and the key distance distribution degree is negatively correlated to obtain the concentrated distribution influence degree of the key points. By combining the degree of concentrated distribution of influence and the key proportion, the concentration of the wall's influence can be obtained.

8. The method for detecting the density of concrete during pouring construction according to claim 1, characterized in that, The method for obtaining the density assessment index includes: The product of the wall's overall risk and the concentration of its impact is negatively correlated and normalized to obtain a density assessment index.

9. The method for detecting the density of concrete during pouring construction according to claim 1, characterized in that, The density assessment based on the density assessment index includes: When the density assessment index is greater than or equal to the preset first detection threshold, the density assessment result is recorded as qualified. When the density assessment index is less than the preset first detection threshold and greater than the preset second detection threshold, the density assessment result is recorded as good. When the density assessment index is less than or equal to the preset second detection threshold, the density assessment result is recorded as unqualified; the preset first detection threshold is greater than the preset second detection threshold.

10. The method for detecting the density of concrete during pouring construction according to claim 1, characterized in that, The method for obtaining the key segment includes: Abnormal sections where the defect assessment index exceeds the preset defect threshold are designated as critical sections.

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