Quality detection and analysis monitoring system for concrete pouring

Abstract

The invention relates to the technical field of concrete pouring, in particular to a quality detection and analysis monitoring system for concrete pouring, and the system comprises a data collection module which is used for collecting monitoring data of concrete pouring; the data analysis module is used for receiving the monitoring data, inputting the monitoring data into a preset prediction algorithm and predicting the current performance of concrete pouring; and the control module is used for outputting a corresponding control strategy according to the current performance of concrete pouring. According to the invention, various key parameters in the concrete pouring process can be collected in real time, compared with traditional manual detection or single parameter detection, more comprehensive and more accurate monitoring data can be obtained, rich basic information is provided for subsequent accurate analysis, detection errors caused by data missing or inaccuracy are reduced, and the detection efficiency is improved. And furthermore, the current performance of concrete pouring can be predicted in real time, and once it is found that the performance index deviates from the normal range, the control module can immediately output a corresponding control strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of concrete pouring, and particularly relates to a quality detection, analysis and monitoring system for concrete pouring. Background Art

[0002] In the field of construction engineering, concrete pouring is a crucial construction link, and its quality directly affects the structural safety and service life of buildings. However, the traditional quality control of concrete pouring mainly relies on manual experience and post-detection, and there are many limitations. For example, it is difficult for manual detection to obtain various data during the concrete pouring process in real time and comprehensively, and it is easy to miss detection and misdetection; although post-detection can find problems, it is often too late, and the rectification cost is high.

[0003] With the continuous expansion of the scale of construction projects and the increasing complexity of structural forms, the requirements for the quality of concrete pouring are also getting higher and higher. With the rapid development of sensor technology, data communication technology and computer technology, it is possible to realize the real-time monitoring and intelligent control of concrete pouring quality. At present, most of the existing concrete pouring quality detection systems on the market have a single function and can only monitor some parameters, and cannot comprehensively evaluate and predict the overall performance of concrete pouring.

[0004] Therefore, how to improve the detection accuracy of the concrete pouring quality detection system is a current research direction. Summary of the Invention

[0005] (I) Object of the Invention

[0006] The object of the present invention is to provide a quality detection, analysis and monitoring system for concrete pouring with improved detection accuracy.

[0007] (II) Technical Solution

[0008] To solve the above problems, the present invention provides a quality detection, analysis and monitoring system for concrete pouring, including:

[0009] A data acquisition module, a data analysis module and a control module;

[0010] The data acquisition module is used to acquire the monitoring data of concrete pouring;

[0011] The data analysis module is used to receive the monitoring data, input the monitoring data into a preset prediction algorithm, and predict the current performance of concrete pouring;

[0012] The control module is used to output corresponding control strategies according to the current performance of concrete pouring.

[0013] On the other hand, preferably, of the present invention,

[0014] The properties of the concrete pouring include: the strength of the concrete pouring, the density of the concrete pouring, and the impermeability of the concrete pouring;

[0015] The preset prediction algorithms include a strength prediction algorithm, a density prediction algorithm, and an impermeability prediction algorithm.

[0016] On the other hand, preferably,

[0017] Using the density prediction algorithm, predicting the density of the concrete pouring includes:

[0018] Obtaining the original image of the target of the concrete pouring;

[0019] Using the original image and a preset pore recognition model to obtain the recognized pore image;

[0020] According to the pore image, obtaining the porosity of the target of the concrete pouring;

[0021] According to the porosity, predicting the density of the concrete pouring.

[0022] On the other hand, preferably,

[0023] The preset pore recognition model includes:

[0024] Calculating the similarity value between the original image and a preset color category in terms of pixels;

[0025] Clustering the pixels of the original image according to the similarity value;

[0026] The category is set according to a reference pore image.

[0027] On the other hand, preferably,

[0028] The similarity value between the original image and the clustering center is calculated using the following formula:

[0029]

[0030] where d ij represents the similarity value between the i-th pixel of the original image and the center of the j-th preset color category, (R i , G i , B i ) represents the i-th pixel of the original image, (R j , G j , B j ) represents the center of the j-th preset color category.

[0031] On the other hand, preferably,

[0032] The porosity is calculated using the following formula:

[0033]

[0034] where u 1 represents the porosity, S 孔 represents the number of pores, and S_total represents the total number;

[0035] The compactness is predicted using the following formula:

[0036] u 2 = 100% - u 1

[0037] where u 2 represents the compactness.

[0038] On the other hand, preferably,

[0039] The strength of the concrete pouring is predicted using the strength prediction algorithm and the compactness:

[0040]

[0041] where N represents the predicted strength of the concrete pouring, N 基 represents the reference strength of the concrete pouring, u 2 represents the compactness, I 1 represents the sand aggregate ratio, I 2 represents the water-cement ratio, γ represents a constant, C represents the cement strength grade, E represents the environmental parameter, and T represents the time parameter.

[0042] On the other hand, preferably,

[0043] The impermeability of the concrete pouring is predicted using the impermeability prediction algorithm and the compactness:

[0044]

[0045] where M represents the predicted impermeability of the concrete pouring, M 基 represents the reference impermeability of the concrete pouring, u 2 represents the compactness, α represents the cement mass percentage, I 1 represents the sand aggregate ratio, I 2 represents the water-cement ratio.

[0046] On the other hand, preferably,

[0047] The control module is used to output corresponding control strategies according to the performance of the concrete pouring, including:

[0048] Obtain the historical performance of concrete pouring under different historical target parameters;

[0049] Obtain the current performance and current target parameters of concrete pouring;

[0050] Match the current target parameters with the historical target parameters to obtain historical samples;

[0051] In the historical samples, compare the current performance with the historical performance, and obtain a control strategy according to the comparison result.

[0052] On the other hand, preferably, the current target parameters and the historical target parameters are matched using the following formula:

[0053]

[0054] Where Q represents the matching value; n represents the total number of features, represents the average value of each feature of the current target parameters, represents the average value of each feature of the historical target parameters; X k represents the value of feature k in the current target parameters, Y k represents the value of feature k in the historical target parameters.

[0055] (III) Beneficial Effects

[0056] The above technical solutions of the present invention have the following beneficial technical effects:

[0057] The present invention can collect various key parameters in the process of concrete pouring in real time. Compared with traditional manual detection or single-parameter detection, it can obtain more comprehensive and accurate monitoring data, providing rich basic information for subsequent precise analysis, reducing detection errors caused by data missing or inaccurate, improving detection accuracy, and further, being able to predict the current performance of concrete pouring in real time. Once it is found that the performance index deviates from the normal range, the control module can immediately output the corresponding control strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a schematic diagram of the overall structure of an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0059] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with the specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present invention.

[0060] Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0061] In the description of the present invention, it should be noted that the terms "first", "second", and "third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0062] In addition, the technical features involved in different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0063] The present invention will be described in more detail below with reference to the accompanying drawings. In the respective drawings, like elements are denoted by like reference numerals. For the sake of clarity, the various parts in the drawings are not drawn to scale.

[0064] Embodiment 1

[0065] A quality inspection, analysis and monitoring system for concrete pouring Figure 1 shows an overall flowchart of an embodiment of the present invention, as Figure 1 shown, including:

[0066] A data acquisition module, a data analysis module and a control module;

[0067] The data acquisition module is used to acquire the monitoring data of concrete pouring; the acquisition method of the data acquisition module is not limited here. Optionally, corresponding sensors can be set for data acquisition, such as using a temperature sensor to acquire relevant temperature and a camera to acquire images, etc.; according to the technological characteristics and quality control requirements of concrete pouring, a suitable data acquisition frequency is determined. The acquired data can be transmitted to the data analysis module through wired or wireless communication means.

[0068] The data analysis module is used to receive the monitoring data and input the monitoring data into a preset prediction algorithm to predict the current performance of concrete pouring; the specific content of the current performance of concrete pouring is not limited here. Optionally, in this embodiment, the performance of concrete pouring includes: the strength of concrete pouring, the density of concrete pouring, and the impermeability of concrete pouring; the specific content of the preset prediction algorithm is also not limited here. Optionally, in this embodiment, the preset prediction algorithms include a strength prediction algorithm, a density prediction algorithm, and an impermeability prediction algorithm.

[0069] Further, in this embodiment, using the density prediction algorithm, predicting the density of concrete pouring includes:

[0070] Obtain the original image of the target of concrete pouring; for a large-area concrete pouring surface, multiple industrial cameras can be installed at different positions and angles at the construction site to ensure full coverage of the pouring area; for concrete specimens that need to observe the microscopic pore structure, high-definition industrial cameras are used.

[0071] Use the original image and a preset pore recognition model to obtain the recognized pore image; the specific content of the pore recognition model is not limited here. Optionally, a deep learning algorithm such as a convolutional neural network (CNN) can be used to construct the pore recognition model. In the training stage, a large number of concrete images containing different types and sizes of pores are collected as the training data set, and the images are labeled with information such as the position, shape, and size of the pores. In this embodiment, the preset pore recognition model includes:

[0072] Calculate the similarity value between the original image and a preset color category in terms of pixels; according to the color characteristics of the reference pore image, several color category ranges representing pores are set in the color space. For example, through the analysis of the reference pore image, the color values of the pore area are concentrated in a certain interval, such as [50, 150], then this interval is divided into a color category. At the same time, since the pore color may have a certain range of variation, several adjacent intervals can be set as auxiliary color categories to improve the recognition ability of pores with different color performances.

[0073] Cluster the pixels of the original image according to the similarity value; in this embodiment, the similarity value between the original image and the cluster center is calculated using the following formula:

[0074]

[0075] where, d ij represents the similarity value between the i-th pixel of the original image and the center of the j-th preset color category, (R i , G i , B i ) represents the i-th pixel of the original image, (R j , G j , B j ) represents the center of the j-th preset color category.

[0076] The category is set according to the reference pore image.

[0077] Obtain the porosity of the target of concrete pouring according to the pore image; in this embodiment, the porosity is calculated using the following formula:

[0078]

[0079] where, u 1denotes the porosity, S 孔 denotes the number of pores, S_total denotes the total number; the porosity is obtained by calculating the ratio of the total number of pixels of the pores in the pore image to the total number of pixels of the original image. To improve the accuracy of the porosity, multiple images of the same target area can be collected for calculation, and the average value is taken as the final porosity result. For cases with complex pore structures or uneven pore distributions, methods such as zonal calculation or stratified calculation can also be used to obtain the porosity of different regions or different layers respectively.

[0080] According to the porosity, predict the compactness of the concrete pouring. In this embodiment, the compactness is predicted using the following formula:

[0081] u 2 = 100% - u 1

[0082] where u 2 denotes the compactness.

[0083] Furthermore, in this embodiment,

[0084] the strength of the concrete pouring is predicted using the strength prediction algorithm and the compactness:

[0085]

[0086] where N denotes the predicted strength of the concrete pouring, N 基 denotes the reference strength of the benchmark concrete pouring, u 2 denotes the compactness, I 1 denotes the sand-aggregate ratio, I 2 denotes the water-cement ratio, γ denotes a constant, C denotes the cement strength grade, E denotes the environmental parameter, and T denotes the time parameter. It covers various factors affecting the concrete strength, and the comprehensive consideration makes the prediction result closer to the actual situation, avoiding prediction deviations caused by ignoring important factors. In different construction projects, the mix ratio of concrete, construction environment, and time arrangement often vary. It can adapt to this diversity, and through specific values of each parameter, strength prediction can be carried out for specific engineering conditions. The compactness, as a key parameter, can accurately reflect the number and distribution of pores inside the concrete. The lower the porosity, the higher the compactness, the denser the internal structure of the concrete, and the higher the strength is often. By incorporating the compactness into the strength prediction formula, the influence of the internal structure on the strength can be directly quantified. Compared with the traditional prediction methods that only consider factors such as mix ratio, the accuracy of the prediction is further improved. The reference strength of the benchmark concrete pouring can be empirical data or a reference value of a large number of test results.

[0087] On the other hand, preferably, of the present invention,

[0088] The impermeability of the concrete placement is predicted using the impermeability prediction algorithm and the compactness:

[0089]

[0090] where M represents the predicted impermeability of the concrete placement, M 基 represents the reference impermeability of the concrete placement, u 2 represents the compactness, α represents the percentage of cement quality, I 1 represents the sand aggregate ratio, I 2 represents the water-cement ratio. The compactness of concrete is closely related to its impermeability. The higher the compactness, the fewer and less connected the internal pores of the concrete are, and the fewer the channels for water molecules to penetrate, thus effectively preventing water intrusion and improving the impermeability. Cement is an important component of concrete, and its quality and dosage have an important impact on the impermeability of concrete. Sufficient cement dosage can ensure good gelling properties of concrete and improve the impermeability. Different varieties and qualities of cement have different hydration products, particle sizes and distributions, etc., and these factors will affect the impermeability of concrete. The reference impermeability of the reference concrete placement can be an empirical data or a reference value of a large number of test results.

[0091] The control module is used to output corresponding control strategies according to the current performance of the concrete placement. The specific content of the control strategy is not limited here. In this embodiment, the control module is used to output corresponding control strategies according to the performance of the concrete placement, including:

[0092] Obtain the historical performance of the concrete placement under different historical target parameters; the historical target parameters are the design parameters of the concrete placement object;

[0093] Obtain the current performance and current target parameters of the concrete placement; the current target parameters can be obtained by connecting to the design system;

[0094] Match the current target parameters with the historical target parameters to obtain historical samples; use an intelligent matching algorithm to search for the historical target parameter records that are closest or similar to the current target parameters in the historical database according to each dimension of the current target parameters (such as strength grade, work performance requirements, durability indicators, etc.).

[0095] In the historical samples, the current performance is compared with the historical performance, and control strategies are obtained according to the comparison results. For the selected historical samples, methods such as time series analysis and difference analysis are used to compare the performance data of the current concrete pouring with the historical performance data in detail. For example, compare the strength growth curves of the current concrete at the same pouring time point or the same age with those of the historical samples, and analyze the slope differences and numerical deviations; compare the temperature change trends of the current concrete with the historical data to determine whether there are abnormal temperature rises or drops; observe the equality of the slump loss rates of the current concrete and the historical experience data. Through these comparison analyses, the system can accurately identify possible performance anomalies or situations deviating from the normal development trajectory during the current concrete pouring process; according to the performance comparison results, combined with the pre-set control strategy knowledge base and expert experience rules, targeted control strategies are automatically generated.

[0096] Furthermore, in this embodiment, the current target parameters and the historical target parameters are matched using the following formula:

[0097]

[0098] where Q represents the matching value; n represents the total number of features, represents the average value of each feature of the current target parameters, represents the average value of each feature of the historical target parameters; X k represents the value of feature k in the current target parameters, and Y k represents the value of feature k in the historical target parameters.

[0099] Provides an objective and accurate basis for the comparison between the current target parameters and the historical target parameters. Compared with subjective judgments or qualitative comparison methods, the quantitative matching value can more clearly indicate the similarity between the two, avoiding the subjectivity and uncertainty of human judgment. Engineering personnel can directly sort and screen the historical samples according to the size of the matching value, quickly determine the historical cases closest to the current situation, and improve work efficiency and the scientific nature of decision-making.

[0100] The present invention can collect various key parameters during the concrete pouring process in real time. Compared with traditional manual detection or single-parameter detection, it can obtain more comprehensive and accurate monitoring data, providing rich basic information for subsequent precise analysis, reducing detection errors caused by data missing or inaccurate, improving detection accuracy. Furthermore, it can predict the current performance of the concrete pouring in real time. Once it is found that the performance indicators deviate from the normal range, the control module can immediately output corresponding control strategies.

[0101] It should be understood that the above specific embodiments of the present invention are only for illustrative explanation or interpretation of the principles of the present invention, and do not constitute a limitation on the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present invention shall be included within the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all changes and modifications that fall within the scope and boundaries of the appended claims, or equivalent forms of such scope and boundaries.

[0102] The present invention has been described above with reference to the embodiments of the present invention. However, these embodiments are only for illustrative purposes and not for limiting the scope of the present invention. The scope of the present invention is defined by the appended claims and their equivalents. Without departing from the scope of the present invention, those skilled in the art can make various substitutions and modifications, and these substitutions and modifications should all fall within the scope of the present invention.

[0103] Although the embodiments of the present invention have been described in detail, it should be understood that various changes, substitutions, and alterations can be made to the embodiments of the present invention without departing from the spirit and scope of the present invention.

[0104] Obviously, the above embodiments are only examples given for clear illustration and not limitations on the embodiments. For those of ordinary skill in the art, other different forms of changes or variations can be made on the basis of the above description. It is not necessary and impossible to enumerate all the embodiments here. And the obvious changes or variations derived therefrom are still within the protection scope of the present invention.

Claims

1. A quality detection and analysis monitoring system for concrete pouring, characterized in that: include: Data collection module, data analysis module and control module; The data acquisition module is used to collect monitoring data of concrete pouring; The data analysis module is used to receive the monitoring data, input the monitoring data into a preset prediction algorithm, and predict the current performance of concrete pouring; The control module is used to output a corresponding control strategy according to the current performance of the concrete pouring.

2. The quality detection and analysis monitoring system for concrete pouring according to claim 1 is characterized in that: The performance of the concrete pouring includes: the strength of the concrete pouring, the density of the concrete pouring and the impermeability of the concrete pouring; The preset prediction algorithms include a strength prediction algorithm, a density prediction algorithm and a permeability resistance prediction algorithm.

3. The quality detection and analysis monitoring system for concrete pouring according to claim 2 is characterized in that: Using the density prediction algorithm, the density of concrete pouring is predicted including: Acquire the original image of the concrete pouring target; Using the original image and a preset pore recognition model, obtaining a recognized pore image; According to the pore image, obtaining the porosity of the target of the concrete pouring; Based on the porosity, the density of concrete pouring is predicted.

4. The quality detection and analysis monitoring system for concrete pouring according to claim 3 is characterized in that: The preset pore identification model includes: Calculate the similarity between the original image and the preset color category in pixels; Clustering the original image pixels according to the similarity value; The categories are set based on a reference pore image.

5. The quality detection and analysis monitoring system for concrete pouring according to claim 4 is characterized in that: The similarity value between the original image and the cluster center is calculated using the following formula: Among them, d ij Represents the similarity value between the i-th pixel of the original image and the center of the j-th preset color category, (R i ,G i ,B i ) represents the i-th pixel of the original image, (R j ,G j ,B j ) represents the center of the j-th preset color category.

6. The quality detection and analysis monitoring system for concrete pouring according to claim 3 is characterized in that: The porosity was calculated using the following formula: Where u1 represents the porosity, S 孔 represents the number of pores, and Stotal represents the total number; The compactness is predicted using the following formula: u2=100%-u1 Among them, u2 represents the density.

7. The quality detection and analysis monitoring system for concrete pouring according to claim 3 is characterized in that: The strength of the concrete pouring is predicted using the strength prediction algorithm and the density: Where N represents the predicted strength of concrete pouring, N 基 It represents the strength of the benchmark concrete pouring, u2 represents the density, I1 represents the sand-aggregate ratio, I2 represents the water-cement ratio, γ represents a constant, C represents the cement strength grade, E represents the environmental parameter, and T represents the time parameter.

8. The quality detection and analysis monitoring system for concrete pouring according to claim 3 is characterized in that: The impermeability of the concrete pouring is predicted using the impermeability prediction algorithm and the density: Where M represents the predicted impermeability of concrete pouring, M 基 It represents the impermeability of the benchmark concrete pouring, u2 represents the density, α represents the mass percentage of cement, I1 represents the sand-aggregate ratio, and I2 represents the water-cement ratio.

9. The quality detection and analysis monitoring system for concrete pouring according to claim 1, characterized in that: The control module is used to output corresponding control strategies according to the performance of the concrete pouring, including: Obtain the historical performance of concrete pouring under different historical target parameters; Obtain the current performance and current target parameters of concrete pouring; Matching the current target parameters with historical target parameters to obtain historical samples; In the historical samples, the current performance is compared with the historical performance, and a management and control strategy is obtained according to the comparison result.

10. The quality detection and analysis monitoring system for concrete pouring according to claim 9, characterized in that: The current target parameters and the historical target parameters are matched using the following formula: Among them, Q represents the matching value; n represents the total number of features, Represents the average value of each feature of the current target parameter, represents the average value of each characteristic of historical target parameter; X k Indicates the value of feature k in the current target parameter, Y k Represents the value of feature k in the historical target parameters.

Images