Sleeve grouting forming quality detection method and system

Abstract

The invention provides a sleeve grouting forming quality detection method and a sleeve grouting forming quality detection system, and relates to the field of sleeve grouting forming quality detection. The information acquisition module is used for acquiring structure information of a prefabricated concrete structure, material information of a grouting material, grouting operation information, grouting operation environment information, maintenance environment information and a concrete state; the defect prediction module is used for predicting sleeve grouting forming defect information based on the information; the track generation module is used for generating a quality detection track based on the sleeve grouting forming defect information; the quality detection module is used for performing sleeve grouting forming quality detection by using ultrasonic equipment based on the quality detection track; and the quality analysis module is used for generating a sleeve grouting forming quality analysis result based on the sleeve grouting forming quality detection data, and has the advantages that the destructiveness of sleeve grouting forming quality detection is reduced, and the efficiency and accuracy of sleeve grouting forming quality detection are improved.

Description

Technical Field

[0001] The invention relates to the field of sleeve grouting molding quality detection, and in particular to a sleeve grouting molding quality detection method and system. Background Art

[0002] As a structural form that conforms to the industrialized production mode, prefabricated assembled concrete structure has the advantages of fast construction speed, low labor intensity, less noise pollution and wet work, and easy control of product quality. It has become the mainstream direction of the development of the construction industry. In prefabricated building structures, especially in prefabricated concrete structures, component connection is a key node to ensure the overall quality of the structure. Rebar sleeve grouting connection is a commonly used form of steel bar connection in prefabricated concrete structures. It is a steel bar connection device composed of a specially designed columnar sleeve and a non-shrinkage grouting material as a binder. This connection technology embeds the grouting sleeve in the prefabricated component. After the component is installed on site, a special high-strength grouting material is poured into the gap between the sleeve and the steel bar. The steel bar is anchored in the sleeve through the grouting material. Therefore, the density and fullness of the grouting material in the sleeve directly affect the safety and reliability of the connection node and the overall structure. The inspection and supervision of its molding quality are also important measures to ensure the quality of prefabricated building projects. During the construction process, leakage, insufficient grouting, and blockage of the grouting sleeve often occur. There have also been reports of engineering problems where the connection quality of the grouting sleeve does not meet the requirements. During project acceptance, special attention is paid to the quality of the sleeve grouting. Since the steel sleeve grouting connection structure is complex and belongs to a hidden project, it is often affected by multiple factors such as steel bars, concrete, sleeves, grouting materials, and wall thickness. It is quite difficult to achieve non-destructive testing of the sleeve grouting quality.

[0003] Existing sleeve grouting molding quality inspection technologies include the industrial endoscope method, which inserts the endoscope probe into the interior of the sleeve and uses the electronic imaging of the probe to observe the defects of the sleeve. However, it is necessary to drill holes in the grouting area, which is a destructive inspection. Moreover, only the drilled area can be observed, and the grouting quality of the undrilled area cannot be judged.

[0004] Therefore, it is necessary to provide a sleeve grouting molding quality detection method and system for reducing the destructiveness of the sleeve grouting molding quality detection and improving the efficiency and accuracy of the sleeve grouting molding quality detection. Summary of the invention

[0005] The present invention provides a sleeve grouting molding quality detection system, comprising: an information acquisition module, including a structural information acquisition unit, a grouting material information acquisition unit, a construction information acquisition unit and a maintenance information acquisition unit, wherein the structural information acquisition unit is used to acquire structural information of a prefabricated assembled concrete structure, the grouting material information acquisition unit is used to acquire material information of the grouting material, the construction information acquisition unit is used to acquire grouting operation information and grouting operation environment information, and the maintenance information acquisition unit is used to acquire maintenance environment information and concrete status; a defect prediction module, used to predict sleeve grouting molding defect information based on the structural information of the prefabricated assembled concrete structure, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the maintenance environment information and the concrete status; a trajectory generation module, used to generate a quality detection trajectory based on the sleeve grouting molding defect information; a quality detection module, used to perform sleeve grouting molding quality detection using an ultrasonic device based on the quality detection trajectory to acquire sleeve grouting molding quality detection data; a quality analysis module, used to generate sleeve grouting molding quality analysis results based on the sleeve grouting molding quality detection data.

[0006] Furthermore, the structural information acquisition unit acquires structural information of the prefabricated and assembled concrete structure, including: acquiring design drawings and technical documents of the prefabricated and assembled concrete structure; extracting the structural information of the prefabricated and assembled concrete structure from the design drawings and technical documents of the prefabricated and assembled concrete structure through a structural information extraction model, wherein the structural information of the prefabricated and assembled concrete structure includes at least the sleeve type, sleeve specification, sleeve material, sleeve arrangement position and size information of the connecting steel bars of each sleeve.

[0007] Furthermore, the grouting material information acquisition unit includes a material composition acquisition unit and a material performance acquisition unit, wherein the material composition acquisition unit is used to acquire the composition information of the grouting material, and the material performance acquisition unit is used to acquire the performance information of the grouting material, wherein the material information of the grouting material at least includes the composition information and performance information of the grouting material; the material performance acquisition unit is also used to: acquire the composition information of multiple sample grouting materials; acquire the performance information of each of the sample grouting materials under multiple test environments; based on the composition information of the multiple sample grouting materials, test the multiple sample grouting materials; The method comprises the steps of: clustering the grouting materials to determine a plurality of sample grouting material clusters; for each of the sample grouting material clusters, determining a target test environment corresponding to the sample grouting material cluster based on performance information of each sample grouting material included in the sample grouting material cluster under a plurality of preset test environments; the material property acquisition unit acquires the performance information of the grouting material, including: determining a target sample grouting material cluster from the plurality of sample grouting material clusters based on the component information of the grouting material; acquiring a target test environment corresponding to the target sample grouting material cluster; and acquiring the performance information of the grouting material under the target test environment corresponding to the target sample grouting material cluster.

[0008] Furthermore, the construction information acquisition unit includes a grouting operation monitoring component and a grouting environment monitoring component, wherein the grouting operation monitoring component is used to obtain the grouting operation information, and the grouting environment monitoring component is used to obtain the grouting operation environment information; the grouting operation monitoring component includes a grouting pressure monitoring device, a grouting flow rate monitoring device, a grouting material temperature monitoring device and a sleeve vibration monitoring device, wherein the grouting pressure monitoring device is used to collect the grouting pressure, the grouting flow rate monitoring device is used to collect the grouting flow rate, and the grouting material temperature monitoring device is used to collect the grouting material temperature. The sleeve vibration monitoring device is used to collect the vibration data of the sleeve during the grouting process, and the grouting operation information at least includes the grouting pressure, the grouting flow rate, the grouting material temperature and the vibration data of the sleeve during the grouting process; the grouting environment monitoring component includes a grouting environment temperature monitoring device and a grouting environment humidity monitoring device, wherein the grouting environment temperature monitoring device is used to collect the grouting environment temperature during the grouting process, and the grouting environment humidity monitoring device is used to collect the grouting environment humidity during the grouting process, and the grouting operation environment information at least includes the grouting environment temperature and the grouting environment humidity.

[0009] Furthermore, the maintenance information acquisition unit includes a maintenance environment monitoring component and a state monitoring component, wherein the maintenance environment monitoring component is used to obtain maintenance environment information, and the state monitoring component is used to obtain the concrete state; the maintenance environment monitoring component includes a maintenance environment temperature monitoring device and a maintenance environment humidity monitoring device, wherein the maintenance environment temperature monitoring device is used to collect the maintenance environment temperature, and the maintenance environment humidity monitoring device is used to collect the maintenance environment humidity, and the maintenance environment information at least includes the maintenance environment temperature and the maintenance environment humidity; the state monitoring component includes a temperature monitoring point determination component, an infrared temperature monitoring component, a vibration monitoring point determination component and a vibration monitoring component, and the temperature monitoring point determination component is used to determine the maintenance environment temperature based on the maintenance environment temperature. Based on the structural information of the prefabricated assembled concrete structure, the material information of the grouting material, the grouting operation information and the grouting operation environment information, a plurality of temperature monitoring points of the prefabricated assembled concrete structure are determined; the infrared temperature monitoring component is used to collect the temperature information of the plurality of temperature monitoring points; the vibration monitoring point determination component is used to determine the plurality of vibration monitoring points of the prefabricated assembled concrete structure based on the structural information of the prefabricated assembled concrete structure, the material information of the grouting material, the grouting operation information and the grouting operation environment information; the vibration monitoring component is used to collect the vibration information of the plurality of vibration monitoring points; the concrete state at least includes the temperature information of the plurality of temperature monitoring points and the vibration information of the plurality of vibration monitoring points.

[0010] Furthermore, the defect prediction module is also used to: obtain structural information of multiple sample prefabricated and assembled concrete structures, material information of grouting materials, grouting operation information, grouting operation environment information, maintenance environment information, concrete status and sleeve grouting molding quality; cluster multiple sample prefabricated and assembled concrete structures based on the structural information of multiple sample prefabricated and assembled concrete structures, material information of grouting materials, grouting operation information, grouting operation environment information, maintenance environment information and concrete status, and determine multiple sample prefabricated and assembled concrete structure clusters; the defect prediction module predicts sleeve grouting molding defect information based on the structural information of the prefabricated and assembled concrete structure, material information of the grouting materials, grouting operation information, grouting operation environment information, maintenance environment information and concrete status, including: predicting sleeve grouting molding defect information based on the structural information of the prefabricated and assembled concrete structure, material information of the grouting materials, grouting operation information, grouting operation environment information, maintenance environment information and concrete status and the multiple sample prefabricated and assembled concrete structure clusters.

[0011] Furthermore, the defect prediction module predicts sleeve grouting forming defect information based on the structural information of the prefabricated assembled concrete structure, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the curing environment information and the concrete state, including: determining a target sample prefabricated assembled concrete structure cluster based on the structural information of the prefabricated assembled concrete structure, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the curing environment information and the concrete state; predicting the sleeve grouting forming defect information based on the structural information of the prefabricated assembled concrete structure, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the curing environment information and the concrete state. state, determining a target sample prefabricated and assembled concrete structure from the target sample prefabricated and assembled concrete structure cluster; predicting sleeve grouting forming defect information through a defect prediction model based on structural information of the target sample prefabricated and assembled concrete structure, material information of the grouting material, grouting operation information, grouting operation environment information, maintenance environment information and concrete state and structural information of the prefabricated and assembled concrete structure, material information of the grouting material, grouting operation information, grouting operation environment information, maintenance environment information and concrete state, wherein the sleeve grouting forming defect information includes at least a plurality of defect risk positions and predicted defect information of each defect risk position.

[0012] Furthermore, the trajectory generation module generates a quality inspection trajectory based on the sleeve grouting forming defect information, including: for each of the defect risk positions, determining the ultrasonic detection parameters corresponding to the defect risk position based on the risk information of the defect risk position; generating multiple initial quality inspection trajectories based on multiple defect risk positions; and screening the multiple initial quality inspection trajectories based on the ultrasonic detection parameters corresponding to each of the defect risk positions to determine the quality inspection trajectory.

[0013] Furthermore, the quality inspection module uses ultrasonic equipment to perform sleeve grouting quality inspection based on the quality inspection trajectory to obtain sleeve grouting quality inspection data, including: based on the quality inspection trajectory, using ultrasonic equipment to obtain ultrasonic inspection information of multiple defect risk positions; the quality analysis module generates sleeve grouting quality analysis results based on the sleeve grouting quality inspection data, including: for each of the defect risk positions, predicting the defect-free ultrasonic inspection information corresponding to the defect risk position based on the structural information of the prefabricated assembled concrete structure and the material information of the grouting material and the ultrasonic inspection parameters corresponding to the defect risk position through a detection prediction model; determining the defect information of the defect risk position based on the ultrasonic inspection information of the defect risk position and the defect-free ultrasonic inspection information corresponding to the defect risk position through a defect recognition model; generating sleeve grouting quality analysis results based on the defect information of each defect risk position.

[0014] The present invention provides a sleeve grouting molding quality detection method, comprising: obtaining structural information of a prefabricated assembled concrete structure; obtaining material information of a grouting material; obtaining grouting operation information and grouting operation environment information; obtaining maintenance environment information and concrete status; predicting sleeve grouting molding defect information based on the structural information of the prefabricated assembled concrete structure, material information of the grouting material, grouting operation information, grouting operation environment information, maintenance environment information and concrete status; generating a quality detection trajectory based on the sleeve grouting molding defect information; based on the quality detection trajectory, performing sleeve grouting molding quality detection using an ultrasonic device to obtain sleeve grouting molding quality detection data; generating sleeve grouting molding quality analysis results based on the sleeve grouting molding quality detection data.

[0015] Compared with the prior art, the sleeve grouting forming quality detection method and system provided by the present invention have at least the following beneficial effects:

[0016] 1. By acquiring information from multiple aspects such as structure, grouting materials, construction and maintenance, comprehensive monitoring of the sleeve grouting process is ensured, which helps to discover potential problems. The information obtained is detailed and accurate, providing a reliable data basis for subsequent quality prediction and testing. Based on the defect prediction results, targeted quality inspection tracks are generated to ensure the accuracy and efficiency of the inspection work. Automatic generation of inspection tracks reduces the tediousness of manual planning and improves the intelligence level of inspection work. Ultrasonic testing has high precision and high sensitivity, can detect tiny defects and hidden dangers, and improve the reliability of inspection results. In-depth analysis of quality inspection data reveals the nature and root causes of quality problems and provides a scientific basis for formulating improvement measures. Through the analysis results, the inspection process is continuously optimized, the construction process and maintenance measures are improved, and the continuous improvement of the quality of sleeve grouting is promoted.

[0017] 2. Different structural clusters are formed by clustering analysis of the structural information, grouting material information, construction information and maintenance information of multiple sample prefabricated and assembled concrete structures. This classification method based on data similarity enables the most similar sample cluster to be found when predicting the grouting defects of new structures, thereby improving the accuracy and pertinence of the prediction. After determining the target sample prefabricated and assembled concrete structure cluster, the sample most similar to the target structure is further selected from the cluster as a reference, further refining the prediction process and improving the prediction accuracy. Defect prediction not only considers basic factors such as structural information and grouting material information, but also incorporates multi-dimensional information such as grouting operation information, grouting operation environment information, maintenance environment information and concrete status. This comprehensive information input method enables the prediction model to more comprehensively reflect the actual situation and improve the comprehensiveness and reliability of the prediction. The invalid sleeve grouting forming quality inspection data obtained later is reduced.

[0018] 3. The trajectory generation module determines the corresponding ultrasonic detection parameters for the risk information of each defect risk position. This customized approach ensures that the detection process can be precisely adjusted for each potential problem area, improving the pertinence and accuracy of the detection. By generating and screening multiple initial quality detection trajectories, the optimal detection trajectory is finally determined. This reduces unnecessary detection paths, improves detection efficiency, and ensures comprehensive coverage of key areas. Based on the optimized quality detection trajectory, the quality detection module uses ultrasonic equipment to quickly obtain ultrasonic detection information of multiple defect risk positions. This efficient data collection method shortens the detection cycle and improves work efficiency. The quality analysis module predicts the defect-free ultrasonic detection information corresponding to each defect risk position through the detection prediction model and compares it with the actual detection information. This scientific prediction method improves the accuracy and reliability of defect identification. Based on the defect information of each defect risk position, the system is able to generate comprehensive sleeve grouting forming quality analysis results. This includes not only the specific location and type of the defect, but also the severity and potential impact of the defect, providing strong support for subsequent repair and improvement work. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0020] Figure 1 is a schematic diagram of a module of a sleeve grouting molding quality detection system according to some embodiments of this specification;

[0021] Figure 2 It is a flow chart of a sleeve grouting molding quality detection method according to some embodiments of this specification. DETAILED DESCRIPTION

[0022] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0023] Figure 1 is a schematic diagram of a module of a sleeve grouting molding quality detection system according to some embodiments of this specification, such as Figure 1As shown, the sleeve grouting molding quality detection system may include an information acquisition module, a defect prediction module, a trajectory generation module, a quality detection module and a quality analysis module.

[0024] The information acquisition module may include a structural information acquisition unit, a grouting material information acquisition unit, a construction information acquisition unit and a maintenance information acquisition unit, wherein the structural information acquisition unit is used to acquire structural information of the prefabricated assembled concrete structure, the grouting material information acquisition unit is used to acquire material information of the grouting material, the construction information acquisition unit is used to acquire grouting operation information and grouting operation environment information, and the maintenance information acquisition unit is used to acquire maintenance environment information and concrete status.

[0025] Preferably, the structural information acquisition unit acquires the structural information of the prefabricated assembled concrete structure, including:

[0026] Obtain design drawings and technical documents of prefabricated concrete structures. The design drawings of prefabricated concrete components will detail key information such as the location, specifications, quantity, and arrangement of the sleeves. Technical documents include construction plans, design instructions, calculations, etc. The technical documents will provide detailed descriptions of the technical requirements, construction methods, and acceptance criteria for sleeve grouting connections;

[0027] The structural information of the prefabricated concrete structure is extracted from the design drawings and technical documents of the prefabricated concrete structure through the structural information extraction model, wherein the structural information of the prefabricated concrete structure at least includes the sleeve type, sleeve specification, sleeve material, sleeve arrangement position and size information of the connecting steel bars of each sleeve.

[0028] Specifically, use optical character recognition technology to convert text in technical documents into editable text format and annotate relevant structural information. Clean the design drawings and technical documents of prefabricated concrete structures to remove noise and incorrect annotations. Format the data into a format that can be processed by machine learning models, such as image data needs to be standardized in size and color space, and text data needs to be segmented and stop words removed.

[0029] The structural information extraction model may include a first structural information extraction sub-model, which is used to identify the sleeve in the design drawing, predict its bounding box, and determine the sleeve layout position. For text annotations in the design drawing, optical character recognition technology combined with natural language processing technology can be used to extract information such as sleeve type, sleeve specification, and sleeve material.

[0030] The structural information extraction model can also include a second structural information extraction sub-model, which uses a natural language processing model (such as BERT (Bidirectional Encoder Representations from Transformers), RoBERTa (Robustly Optimized BERTPretrainingApproach), etc.) to identify entities in technical documents (such as sleeve type, sleeve specification, sleeve material, and size information of connected steel bars) and the relationship between them.

[0031] Preferably, the grouting material information acquisition unit includes a material composition acquisition unit and a material performance acquisition unit, wherein the material composition acquisition unit is used to acquire the composition information of the grouting material, and the material performance acquisition unit is used to acquire the performance information of the grouting material, wherein the material information of the grouting material at least includes the composition information and performance information of the grouting material.

[0032] The composition information of the grouting material may include specific proportions of cementitious materials (for example, cement, fly ash, mineral powder, microsilica powder), expansive components (for example, gypsum, calcium sulfoaluminate, calcium oxide, calcium sulfoaluminate-calcium oxide), early strength components (for example, calcium formate, lithium chloride, lithium carbonate, etc.), water-reducing components (for example, melamine, melamine-based water-reducing agent, polycarboxylic acid water-reducing agent, etc.), thickening and water-retaining components (for example, medium and low viscosity cellulose ethers, polymer glue powder, etc.), aggregates (for example, quartz sand, high-strength river sand, etc.), and other additives (for example, waterproofing agents, reinforcing agents, etc.).

[0033] Preferably, the material property acquisition unit is further used for:

[0034] Obtain composition information of multiple sample grouting materials;

[0035] Obtain performance information for each sample grout under a variety of test environments;

[0036] Based on the component information of the multiple sample grouting materials, the multiple sample grouting materials are clustered to determine multiple sample grouting material clusters;

[0037] For each sample grouting material cluster, a target test environment corresponding to the sample grouting material cluster is determined based on performance information of each sample grouting material included in the sample grouting material cluster under a plurality of preset test environments.

[0038] Specifically, the composition similarity of the composition information of any two sample grouting materials may be determined, and a plurality of sample grouting materials may be clustered based on the composition similarity of the composition information of any two sample grouting materials using a K-means clustering algorithm to determine a plurality of sample grouting material clusters.

[0039] For each sample grouting material cluster, the differential parameters of each preset test environment corresponding to the sample grouting material cluster can be calculated based on the following formula:

[0040]

[0041] Among them, η (i,j) is the differentiation parameter of the jth sample grouting material cluster corresponding to the i-th preset test environment, K is the total number of sample grouting materials included in the j-th sample grouting material cluster, P ((i,j),k) It is the performance information of the kth sample grouting material included in the jth sample grouting material cluster under the ith preset test environment.

[0042] The preset test environment with a differentiation parameter greater than the differentiation parameter threshold is taken as the candidate test environment corresponding to the sample grouting material cluster.

[0043] The molding quality test data of the test blocks of each sample grouting material under the same curing environment are obtained. For each sample grouting material cluster, the correlation parameter between each candidate test environment and the sample grouting material cluster can be calculated based on the following formula:

[0044]

[0045] Among them, r (i,j) is the correlation parameter between the i-th candidate test environment of the j-th sample grouting material cluster and the j-th sample grouting material cluster, Q ((i,j),k) It is the molding quality inspection data of the kth sample grouting material included in the jth sample grouting material cluster.

[0046] The candidate test environment whose correlation parameter is greater than the correlation parameter threshold is used as the target test environment.

[0047] Preferably, the material performance acquisition unit acquires the performance information of the grouting material, including:

[0048] Based on the composition information of the grouting material, determining a target sample grouting material cluster from a plurality of sample grouting material clusters;

[0049] Obtain a target test environment corresponding to a target sample grouting material cluster;

[0050] The performance information of the grouting material under the target test environment corresponding to the target sample grouting material cluster is obtained, wherein the performance information may at least include fluidity, strength, setting time, water bleeding rate, expansion, etc.

[0051] Specifically, the composition similarity between the composition information of the grouting material and the composition information of the sample grouting material corresponding to the cluster center of each sample grouting material cluster may be calculated, and the sample grouting material cluster with composition similarity greater than the composition similarity threshold may be taken as the target sample grouting material cluster.

[0052] Preferably, the construction information acquisition unit includes a grouting operation monitoring component and a grouting environment monitoring component, wherein the grouting operation monitoring component is used to acquire grouting operation information, and the grouting environment monitoring component is used to acquire grouting operation environment information.

[0053] The grouting operation monitoring component includes a grouting pressure monitoring device, a grouting flow rate monitoring device, a grouting material temperature monitoring device and a sleeve vibration monitoring device, wherein the grouting pressure monitoring device is used to collect the grouting pressure, the grouting flow rate monitoring device is used to collect the grouting flow rate, the grouting material temperature monitoring device is used to collect the grouting material temperature, and the sleeve vibration monitoring device is used to collect the vibration data of the sleeve during the grouting process. The grouting operation information at least includes the grouting pressure, the grouting flow rate, the grouting material temperature and the vibration data of the sleeve during the grouting process.

[0054] The grouting environment monitoring component includes a grouting environment temperature monitoring device and a grouting environment humidity monitoring device, wherein the grouting environment temperature monitoring device is used to collect the grouting environment temperature during the grouting process, and the grouting environment humidity monitoring device is used to collect the grouting environment humidity during the grouting process. The grouting operation environment information at least includes the grouting environment temperature and the grouting environment humidity.

[0055] Preferably, the maintenance information acquisition unit includes a maintenance environment monitoring component and a status monitoring component, wherein the maintenance environment monitoring component is used to acquire maintenance environment information, and the status monitoring component is used to acquire concrete status.

[0056] The maintenance environment monitoring component includes a maintenance environment temperature monitoring device and a maintenance environment humidity monitoring device, wherein the maintenance environment temperature monitoring device is used to collect the maintenance environment temperature, and the maintenance environment humidity monitoring device is used to collect the maintenance environment humidity. The maintenance environment information at least includes the maintenance environment temperature and the maintenance environment humidity.

[0057] The state monitoring component includes a temperature monitoring point determination component, an infrared temperature monitoring component, a vibration monitoring point determination component and a vibration monitoring component. The temperature monitoring point determination component is used to determine multiple temperature monitoring points of the prefabricated and assembled concrete structure based on the structural information of the prefabricated and assembled concrete structure, the material information of the grouting material, the grouting operation information and the grouting operation environment information. The infrared temperature monitoring component is used to collect temperature information of multiple temperature monitoring points. The vibration monitoring point determination component is used to determine multiple vibration monitoring points of the prefabricated and assembled concrete structure based on the structural information of the prefabricated and assembled concrete structure, the material information of the grouting material, the grouting operation information and the grouting operation environment information. The vibration monitoring component is used to collect vibration information of multiple vibration monitoring points. The concrete state at least includes temperature information of multiple temperature monitoring points and vibration information of multiple vibration monitoring points.

[0058] Specifically, a temperature detection point prediction model can be used to determine multiple temperature monitoring points of the prefabricated concrete structure based on the structural information of the prefabricated concrete structure, the material information of the grouting material, the grouting operation information, and the grouting operation environment information. The temperature detection point prediction model can be a convolutional neural network (CNN) model.

[0059] A vibration monitoring point prediction model can be used to determine multiple vibration monitoring points of the prefabricated concrete structure based on structural information of the prefabricated concrete structure, material information of the grouting material, grouting operation information, and grouting operation environment information, wherein the vibration monitoring point prediction model can be a convolutional neural network model.

[0060] The defect prediction module can be used to predict sleeve grouting forming defect information based on the structural information of the prefabricated assembled concrete structure, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the maintenance environment information and the concrete state.

[0061] Preferably, the defect prediction module is also used for:

[0062] Obtaining structural information of multiple sample prefabricated assembled concrete structures, material information of grouting materials, grouting operation information, grouting operation environment information, maintenance environment information, concrete status, and sleeve grouting molding quality;

[0063] Based on the structural information of multiple sample prefabricated and assembled concrete structures, the material information of grouting materials, the grouting operation information, the grouting operation environment information, the maintenance environment information and the concrete state, the multiple sample prefabricated and assembled concrete structures are clustered to determine the multiple sample prefabricated and assembled concrete structure clusters.

[0064] Specifically, the information similarity of any two sample prefabricated and assembled concrete structures can be determined through an information similarity determination model based on the structural information of any two sample prefabricated and assembled concrete structures, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the maintenance environment information and the concrete state, wherein the information similarity determination model can be a convolutional neural network model.

[0065] The K-means clustering algorithm is used to cluster multiple sample prefabricated and assembled concrete structures based on the information similarity of any two sample prefabricated and assembled concrete structures, and multiple sample prefabricated and assembled concrete structure clusters are determined.

[0066] Preferably, the defect prediction module predicts sleeve grouting forming defect information based on the structural information of the prefabricated assembled concrete structure, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the maintenance environment information and the concrete state, including:

[0067] Based on the structural information of prefabricated concrete structures, the material information of grouting materials, the grouting operation information, the grouting operation environment information, the maintenance environment information, the concrete state and multiple sample prefabricated concrete structure clusters, the sleeve grouting forming defect information is predicted, including:

[0068] Based on the structural information of the prefabricated concrete structure, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the maintenance environment information and the concrete state, the target sample prefabricated concrete structure cluster is determined;

[0069] Based on the structural information of the prefabricated concrete structure, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the curing environment information and the concrete state, the target sample prefabricated concrete structure is determined from the target sample prefabricated concrete structure cluster;

[0070] The defect prediction model is used to predict the sleeve grouting forming defect information based on the structural information of the target sample prefabricated and assembled concrete structure, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the maintenance environment information and the concrete state, and the structural information of the prefabricated and assembled concrete structure, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the maintenance environment information and the concrete state, wherein the sleeve grouting forming defect information includes at least multiple defect risk positions and the predicted defect information of each defect risk position, for example, the defect type (for example, voids, cracks, uncompacted areas, etc.), and the defect prediction model can be a convolutional neural network model.

[0071] Specifically, the information similarity determination model can be used to determine the information similarity between the prefabricated concrete structure and the sample prefabricated concrete structure corresponding to the cluster center of the sample prefabricated concrete structure cluster based on the structural information of the prefabricated concrete structure, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the maintenance environment information and the concrete state of the sample prefabricated concrete structure and the cluster center of the sample prefabricated concrete structure cluster, and the sample prefabricated concrete structure cluster to which the cluster center of the sample prefabricated concrete structure cluster belongs whose information similarity is greater than the first information similarity threshold is taken as the target sample prefabricated concrete structure cluster.

[0072] The information similarity determination model is based on the structural information of the prefabricated concrete structure, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the maintenance environment information and the concrete state of the sample prefabricated concrete structure included in the target sample prefabricated concrete structure cluster, and the information similarity between the prefabricated concrete structure and the sample prefabricated concrete structure included in the target sample prefabricated concrete structure cluster is determined, and the sample prefabricated concrete structure whose information similarity is greater than the second information similarity threshold is used as the target sample prefabricated concrete structure.

[0073] The input of the defect prediction model may include structural information of the target sample prefabricated and assembled concrete structure, material information of the grouting material, grouting operation information, grouting operation environment information, maintenance environment information and concrete status, structural information of the prefabricated and assembled concrete structure, material information of the grouting material, grouting operation information, grouting operation environment information, maintenance environment information and concrete status, and the information similarity between each target sample prefabricated and assembled concrete structure and the prefabricated and assembled concrete structure. The output of the defect prediction model may include sleeve grouting forming defect information.

[0074] The trajectory generation module can be used to generate quality inspection trajectories based on the sleeve grouting forming defect information.

[0075] Specifically include:

[0076] For each defect risk position, based on the risk information of the defect risk position, determine the ultrasonic detection parameters corresponding to the defect risk position. For example, the ultrasonic detection parameters corresponding to the defect risk position can be determined based on the risk information of the defect risk position by an ultrasonic parameter prediction model, wherein the ultrasonic parameter prediction model can be a Generative Adversarial Nets (GAN) model;

[0077] Based on multiple defect risk locations, multiple initial quality inspection trajectories are generated. For example, multiple initial quality inspection trajectories can be generated based on multiple defect risk locations through a trajectory generation model, wherein the trajectory generation model can be a generative adversarial network model;

[0078] Based on the ultrasonic detection parameters corresponding to each defect risk position, multiple initial quality detection trajectories are screened to determine the quality detection trajectory.

[0079] Specifically, for each initial quality inspection trajectory, the parameter smoothness of the initial quality inspection trajectory can be calculated based on the ultrasonic inspection parameters corresponding to each defect risk position:

[0080]

[0081] Among them, S g is the parameter smoothness of the g-th initial quality detection trajectory, P ((g,n),ultrasonic) is the ultrasonic testing parameter of the nth track point of the gth initial quality testing track, where one track point corresponds to one defect risk position, P ((g,n-1),ultrasonic) is the ultrasonic detection parameter of the n-1th trajectory point of the gth initial quality inspection trajectory, N is the total number of trajectory points, that is, the total number of defect risk positions, ψ is a preset parameter, and ψ is greater than 0.

[0082] For each initial quality inspection trajectory, the trajectory length of the initial quality inspection trajectory can be calculated, and the screening score of the initial quality inspection trajectory can be determined based on the trajectory length and parameter smoothness, and the initial quality inspection trajectory with the largest screening score can be used as the quality inspection trajectory.

[0083] For example, the screening score of the initial quality detection trajectory can be calculated based on the following formula:

[0084]

[0085] Among them, V g is the screening score of the g-th initial quality detection trajectory, a1 and a2 are weights, a1 and a2 are greater than 0, a1+a2=1, ε1 and ε2 are preset parameters, ε1 and ε2 are greater than 0, L g is the trajectory length of the g-th initial quality detection trajectory.

[0086] It is understandable that during the frequent adjustment process of ultrasonic equipment, its internal parameters and configuration may be difficult to maintain stability for a long time, resulting in fluctuations in equipment performance and affecting the use effect. Ultrasonic equipment often has strict requirements on parameters such as frequency and power. Frequent adjustments may cause these parameters to deviate from the optimal working point, thereby reducing the measurement or processing accuracy of the equipment. Each adjustment requires time to set and verify parameters. Frequent adjustments will significantly increase operation time and reduce work efficiency. During the adjustment process, the equipment may need to stop and wait, which will cause interruptions in the production or processing flow, further affecting overall efficiency. Therefore, the parameter smoothness of the initial quality detection trajectory is calculated. The greater the parameter smoothness, the greater the possibility that the initial quality detection trajectory will be screened as the quality detection trajectory, reducing the number of parameter adjustments of the ultrasonic equipment during the detection process and ensuring the effective implementation of ultrasonic detection.

[0087] The quality inspection module can be used to perform sleeve grouting molding quality inspection based on the quality inspection trajectory using ultrasonic equipment to obtain sleeve grouting molding quality inspection data.

[0088] Specifically, it includes: based on the quality inspection trajectory, using ultrasonic equipment to obtain ultrasonic inspection information of multiple defect risk locations.

[0089] The quality analysis module can be used to generate sleeve grouting quality analysis results based on sleeve grouting quality inspection data, including:

[0090] For each defect risk position, the defect-free ultrasonic detection information corresponding to the defect risk position is predicted by a detection prediction model based on the structural information of the prefabricated assembled concrete structure, the material information of the grouting material, and the ultrasonic detection parameters corresponding to the defect risk position, and the defect information of the defect risk position is determined by a defect recognition model based on the ultrasonic detection information of the defect risk position and the defect-free ultrasonic detection information corresponding to the defect risk position, wherein the detection prediction model may be a generative adversarial network model, and the defect recognition model may be a convolutional neural network model;

[0091] Based on the defect information of each defect risk location, the sleeve grouting forming quality analysis results are generated.

[0092] Specifically, the ultrasonic detection information of the defect risk position can be subjected to variational modal decomposition to generate multiple modal components of the ultrasonic detection information of the defect risk position. The defect-free ultrasonic detection information corresponding to the defect risk position can be subjected to variational modal decomposition to generate multiple modal components of the defect-free ultrasonic detection information. The time domain characteristics and frequency domain characteristics of each modal component of the ultrasonic detection information of the defect risk position and the time domain characteristics and frequency domain characteristics of each modal component of the defect-free ultrasonic detection information are extracted, and the defect information of the defect risk position, such as defect type, defect size, etc., is determined based on the time domain characteristics and frequency domain characteristics of each modal component of the ultrasonic detection information of the defect risk position and the time domain characteristics and frequency domain characteristics of each modal component of the defect-free ultrasonic detection information through the defect recognition model.

[0093] Figure 2 is a flow chart of a sleeve grouting molding quality detection method according to some embodiments of this specification, such as Figure 2 As shown, the sleeve grouting molding quality inspection method may include the following steps.

[0094] Step 210, obtaining structural information of the prefabricated assembled concrete structure;

[0095] Step 220, obtaining material information of the grouting material;

[0096] Step 230, obtaining grouting operation information and grouting operation environment information;

[0097] Step 240, obtaining maintenance environment information and concrete status;

[0098] Step 250, predicting sleeve grouting forming defect information based on the structural information of the prefabricated assembled concrete structure, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the maintenance environment information and the concrete state;

[0099] Step 260, generating a quality inspection track based on the sleeve grouting forming defect information;

[0100] Step 270, based on the quality inspection trajectory, use ultrasonic equipment to perform sleeve grouting molding quality inspection to obtain sleeve grouting molding quality inspection data;

[0101] Step 280, generating sleeve grouting molding quality analysis results based on the sleeve grouting molding quality detection data.

[0102] The sleeve grouting forming quality detection system can be used to execute the sleeve grouting forming quality detection method. For more descriptions of the sleeve grouting forming quality detection method, please refer to the relevant description of the sleeve grouting forming quality detection system, which will not be repeated here.

[0103] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. Sleeve grouting molding quality inspection system, characterized in that: include: The information acquisition module includes a structure information acquisition unit, a grouting material information acquisition unit, a construction information acquisition unit and a maintenance information acquisition unit, wherein the structure information acquisition unit is used to acquire the structure information of the prefabricated assembled concrete structure, the grouting material information acquisition unit is used to acquire the material information of the grouting material, the construction information acquisition unit is used to acquire the grouting operation information and the grouting operation environment information, and the maintenance information acquisition unit is used to acquire the maintenance environment information and the concrete state; A defect prediction module, used to predict sleeve grouting forming defect information based on the structural information of the prefabricated assembled concrete structure, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the maintenance environment information and the concrete state; A trajectory generation module, used to generate a quality inspection trajectory based on the sleeve grouting forming defect information; A quality inspection module, used to perform sleeve grouting quality inspection using ultrasonic equipment based on the quality inspection trajectory to obtain sleeve grouting quality inspection data; The quality analysis module is used to generate sleeve grouting forming quality analysis results based on the sleeve grouting forming quality detection data.

2. The sleeve grouting molding quality detection system according to claim 1 is characterized in that: The structural information acquisition unit acquires structural information of the prefabricated assembled concrete structure, including: Obtaining design drawings and technical documents of the prefabricated concrete structure; The structural information of the prefabricated and assembled concrete structure is extracted from the design drawings and technical documents of the prefabricated and assembled concrete structure through a structural information extraction model, wherein the structural information of the prefabricated and assembled concrete structure includes at least the sleeve type, sleeve specification, sleeve material, sleeve arrangement position and size information of the connecting steel bars of each sleeve.

3. The sleeve grouting molding quality detection system according to claim 1 is characterized in that: The grouting material information acquisition unit includes a material composition acquisition unit and a material performance acquisition unit, wherein the material composition acquisition unit is used to acquire the composition information of the grouting material, and the material performance acquisition unit is used to acquire the performance information of the grouting material, wherein the material information of the grouting material at least includes the composition information and performance information of the grouting material; The material property acquisition unit is also used for: Obtain composition information of multiple sample grouting materials; Obtaining performance information of each sample grouting material under multiple test environments; Based on the component information of the multiple sample grouting materials, clustering the multiple sample grouting materials to determine multiple sample grouting material clusters; For each of the sample grouting material clusters, based on the performance information of each sample grouting material included in the sample grouting material cluster under a plurality of preset test environments, determining a target test environment corresponding to the sample grouting material cluster; The material performance acquisition unit acquires the performance information of the grouting material, including: Based on the composition information of the grouting material, determining a target sample grouting material cluster from the plurality of sample grouting material clusters; Obtaining a target test environment corresponding to the target sample grouting material cluster; The performance information of the grouting material under the target test environment corresponding to the target sample grouting material cluster is obtained.

4. The sleeve grouting molding quality detection system according to claim 1 is characterized in that: The construction information acquisition unit includes a grouting operation monitoring component and a grouting environment monitoring component, wherein the grouting operation monitoring component is used to acquire the grouting operation information, and the grouting environment monitoring component is used to acquire the grouting operation environment information; The grouting operation monitoring component includes a grouting pressure monitoring device, a grouting flow rate monitoring device, a grouting material temperature monitoring device and a sleeve vibration monitoring device, wherein the grouting pressure monitoring device is used to collect the grouting pressure, the grouting flow rate monitoring device is used to collect the grouting flow rate, the grouting material temperature monitoring device is used to collect the grouting material temperature, and the sleeve vibration monitoring device is used to collect the vibration data of the sleeve during the grouting process, and the grouting operation information at least includes the grouting pressure, the grouting flow rate, the grouting material temperature and the vibration data of the sleeve during the grouting process; The grouting environment monitoring component includes a grouting environment temperature monitoring device and a grouting environment humidity monitoring device, wherein the grouting environment temperature monitoring device is used to collect the grouting environment temperature during the grouting process, and the grouting environment humidity monitoring device is used to collect the grouting environment humidity during the grouting process, and the grouting operation environment information at least includes the grouting environment temperature and the grouting environment humidity.

5. The sleeve grouting molding quality detection system according to claim 1 is characterized in that: The maintenance information acquisition unit includes a maintenance environment monitoring component and a state monitoring component, wherein the maintenance environment monitoring component is used to acquire maintenance environment information, and the state monitoring component is used to acquire concrete state; The maintenance environment monitoring component includes a maintenance environment temperature monitoring device and a maintenance environment humidity monitoring device, wherein the maintenance environment temperature monitoring device is used to collect the maintenance environment temperature, and the maintenance environment humidity monitoring device is used to collect the maintenance environment humidity, and the maintenance environment information at least includes the maintenance environment temperature and the maintenance environment humidity; The state monitoring component includes a temperature monitoring point determination component, an infrared temperature monitoring component, a vibration monitoring point determination component and a vibration monitoring component. The temperature monitoring point determination component is used to determine multiple temperature monitoring points of the prefabricated and assembled concrete structure based on the structural information of the prefabricated and assembled concrete structure, the material information of the grouting material, the grouting operation information and the grouting operation environment information. The infrared temperature monitoring component is used to collect the temperature information of the multiple temperature monitoring points. The vibration monitoring point determination component is used to determine multiple vibration monitoring points of the prefabricated and assembled concrete structure based on the structural information of the prefabricated and assembled concrete structure, the material information of the grouting material, the grouting operation information and the grouting operation environment information. The vibration monitoring component is used to collect the vibration information of the multiple vibration monitoring points. The concrete state at least includes the temperature information of the multiple temperature monitoring points and the vibration information of the multiple vibration monitoring points.

6. The sleeve grouting molding quality detection system according to any one of claims 1 to 5, characterized in that: The defect prediction module is also used for: Obtaining structural information of multiple sample prefabricated assembled concrete structures, material information of grouting materials, grouting operation information, grouting operation environment information, maintenance environment information, concrete status, and sleeve grouting molding quality; Based on the structural information of the multiple sample prefabricated and assembled concrete structures, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the curing environment information and the concrete state, the multiple sample prefabricated and assembled concrete structures are clustered to determine the multiple sample prefabricated and assembled concrete structure clusters; The defect prediction module predicts sleeve grouting forming defect information based on the structural information of the prefabricated assembled concrete structure, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the curing environment information and the concrete state, including: Based on the structural information of the prefabricated and assembled concrete structure, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the maintenance environment information and the concrete state and the multiple sample prefabricated and assembled concrete structure clusters, the sleeve grouting forming defect information is predicted.

7. The sleeve grouting molding quality detection system according to claim 6 is characterized in that: The defect prediction module predicts sleeve grouting forming defect information based on the structural information of the prefabricated assembled concrete structure, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the curing environment information and the concrete state, including: Determine a target sample prefabricated and assembled concrete structure cluster based on the structural information of the prefabricated and assembled concrete structure, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the curing environment information and the concrete state; Determine a target sample prefabricated and assembled concrete structure from the target sample prefabricated and assembled concrete structure cluster based on the structural information of the prefabricated and assembled concrete structure, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the curing environment information and the concrete state; The sleeve grouting forming defect information is predicted through a defect prediction model based on the structural information of the target sample prefabricated and assembled concrete structure, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the maintenance environment information and the concrete state, and the structural information of the prefabricated and assembled concrete structure, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the maintenance environment information and the concrete state, wherein the sleeve grouting forming defect information includes at least a plurality of defect risk positions and the predicted defect information of each defect risk position.

8. The sleeve grouting molding quality detection system according to claim 7 is characterized in that: The trajectory generation module generates a quality inspection trajectory based on the sleeve grouting forming defect information, including: For each of the defect risk positions, determining the ultrasonic detection parameters corresponding to the defect risk position based on the risk information of the defect risk position; Generate multiple initial quality inspection trajectories based on multiple defect risk locations; Based on the ultrasonic detection parameters corresponding to each of the defect risk positions, the multiple initial quality detection trajectories are screened to determine the quality detection trajectory.

9. The sleeve grouting molding quality detection system according to claim 8 is characterized in that: The quality detection module uses ultrasonic equipment to perform sleeve grouting quality detection based on the quality detection track to obtain sleeve grouting quality detection data, including: Based on the quality inspection trajectory, using ultrasonic equipment, obtaining ultrasonic inspection information of multiple defect risk locations; The quality analysis module generates sleeve grouting forming quality analysis results based on the sleeve grouting forming quality detection data, including: For each defect risk position, predict the defect-free ultrasonic detection information corresponding to the defect risk position based on the structural information of the prefabricated assembled concrete structure and the material information of the grouting material and the ultrasonic detection parameters corresponding to the defect risk position through a detection prediction model, and determine the defect information of the defect risk position based on the ultrasonic detection information of the defect risk position and the defect-free ultrasonic detection information corresponding to the defect risk position through a defect recognition model; Based on the defect information of each defect risk position, a sleeve grouting forming quality analysis result is generated.

10. A sleeve grouting molding quality detection method, characterized in that: include: Obtain structural information of prefabricated concrete structures; Get the material information of grouting material; Obtain grouting operation information and grouting operation environment information; Obtain maintenance environment information and concrete status; Based on the structural information of the prefabricated assembled concrete structure, the material information of the grouting material, the grouting operation information, the grouting operation environment information, the maintenance environment information and the concrete state, the sleeve grouting forming defect information is predicted; Based on the sleeve grouting forming defect information, a quality inspection trajectory is generated; Based on the quality inspection trajectory, an ultrasonic device is used to inspect the sleeve grouting quality to obtain sleeve grouting quality inspection data; Based on the sleeve grouting forming quality detection data, a sleeve grouting forming quality analysis result is generated.

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