Durability test regulation and control method and system for reinforced concrete structure

Abstract

The invention belongs to the technical field of durability detection regulation and control, and particularly relates to a durability test regulation and control method and system for a reinforced concrete structure. The test method comprises the following steps: setting a closed test area; arranging an environment monitoring module in the test area; arranging a structure state monitoring module; constructing a one-to-one connection relationship between the environment monitoring module and the intelligent control module and a one-to-one connection relationship between the structure state monitoring module and the intelligent control module; the test environment conditions are dynamically adjusted through the intelligent regulation and control system; and establishing a correlation model between the environmental factors and the structure durability by using the real-time environmental parameters and the real-time structure state data, and predicting the durability change trend of the structure under different environmental conditions by using a machine learning algorithm. According to the invention, the test environment condition is adjusted in real time according to the obtained environment parameters and structure state data through the intelligent regulation and control system, so that the test condition is closer to the actual environment change, and the accuracy of the test result is remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of durability detection and control, and in particular relates to a durability test control method and system for reinforced concrete structures. Background Art

[0002] During the use of reinforced concrete structures, due to the influence of the external environment, the internal steel bars are easily corroded by corrosive media such as moisture, salt, chemicals, etc., resulting in steel corrosion and concrete cracking, which in turn reduces the durability of the structure and even causes safety accidents such as building collapse, increasing people's safety risks.

[0003] To ensure the safety of buildings, existing technologies often use simulated environmental parameters to detect the durability of reinforced concrete structures. However, due to the variability and complexity of the actual environment, the simulation of environmental factors is relatively cumbersome and difficult to fully cover various environmental factors. At the same time, the existing technologies use environmental acidity, temperature and humidity, stretching, extrusion and other methods, and cannot adjust the simulation conditions in real time according to actual environmental changes, resulting in a large deviation between the test results and the structural performance under the actual environment, making the durability test of reinforced concrete structures in the simulated environment inaccurate.

[0004] Therefore, there is an urgent need for a test method and system that can monitor and control the simulated environment in real time, better adapt to actual environmental changes, and accurately evaluate the durability of reinforced concrete structures. Summary of the Invention

[0005] The purpose of the present invention is to overcome the defects in the prior art such as large deviation between the simulated environment and the actual environment, inaccurate durability assessment and inability to control the simulated environment in real time, and to provide a durability test control method and system for reinforced concrete structures that integrates real-time monitoring and real-time control of the simulated environment.

[0006] The technical solution adopted by the present invention to solve its technical problem is:

[0007] As a first aspect, a durability test control method for reinforced concrete structures includes the following contents:

[0008] Set up a closed test area;

[0009] Arrange an environmental monitoring module within the test area;

[0010] Setting a structural condition monitoring module, that is, setting a structural condition monitoring module on the reinforced concrete structure to be tested within the test area;

[0011] Establish a one-to-one connection relationship between the environmental monitoring module and the intelligent control module, and between the structural status monitoring module and the intelligent control module;

[0012] Dynamically adjusting the test environment conditions through the intelligent control system according to the real-time environmental parameters obtained by the environmental monitoring module and the real-time structural status data obtained by the structural status monitoring module;

[0013] The real-time environmental parameters and real-time structural status data are used to establish a correlation model between environmental factors and structural durability, and a machine learning algorithm is used to predict the durability change trend of the structure under different environmental conditions.

[0014] Specifically, the structural state data includes one or more of corrosion rate, crack data, and stress and strain data;

[0015] The environmental parameters include one or more of temperature, humidity, pH, salt concentration, and carbon dioxide concentration.

[0016] Specifically, the dynamic adjustment of the test environment conditions by the intelligent control system includes the following steps:

[0017] An environmental control module is configured in the test area; the environmental control module includes one or more of a temperature controller, a humidity controller, a salt spray generator, a pH regulator, and a wind controller;

[0018] Establishing a data analysis module for obtaining real-time parameter data of the environmental control module and performing data analysis and output;

[0019] Connecting the data analysis module to the intelligent control module through signals;

[0020] The intelligent control module controls the environmental control module to perform real-time environmental adjustment according to the analysis results of the data analysis module.

[0021] Specifically, the establishment of a correlation model between environmental factors and structural durability includes the following:

[0022] Acquiring real-time environmental parameters acquired by the environmental monitoring module and real-time structural status data acquired by the structural status monitoring module;

[0023] Performing big data analysis on the real-time environmental parameters and real-time structural status data to obtain a mapping relationship between environmental factors and structural durability;

[0024] The mapping relationship is purified to form a correlation model between environmental factors and structural durability.

[0025] Specifically, the method of using a machine learning algorithm to predict the durability change trend of a structure includes the following:

[0026] The environmental parameters and structural state data are used as input features, the durability index of the structure is used as output features, and the input features are standardized;

[0027] Acquire historical test data, and divide the historical test data into a training set and a test set;

[0028] Use the random forest regression algorithm to build a random forest model;

[0029] Using the test data in the training set to train the random forest model, and optimizing the parameters of the random forest model;

[0030] The performance of the association model was evaluated using mean square error, mean absolute error, and coefficient of determination to obtain the final random forest model;

[0031] The real-time environmental parameters and structural status data are input into the final random forest model to output the durability change trend of the structure.

[0032] Specifically, the calculation formula of the association model is:

[0033] Y=β0+β1X1+β2X2+...+βnXn+ε;

[0034] Where Y represents the structural durability index, X1, X2, and Xn represent environmental parameters and structural status data, n is an integer, β0 represents the durability benchmark value when the environmental parameter is zero, β1, β2, and βn represent the regression coefficients of the influence of each environmental factor on durability, and ε represents the random error term.

[0035] The second aspect is a durability test control system for reinforced concrete structures, comprising:

[0036] Intelligent control module, used for outputting control instructions for durability experiments;

[0037] An environmental monitoring module, comprising a plurality of sensors for real-time monitoring of environmental parameters within the test area;

[0038] Structural status monitoring module, used to monitor the structural status data of reinforced concrete structures in real time;

[0039] A data processing module, configured to perform denoising processing on the environmental parameters and the performance parameters;

[0040] A data analysis module is used to store, analyze the mapping relationships of the environmental parameters and structural status data processed by the data processing module and form a correlation model; and use a machine learning algorithm to predict the durability change trend of the structure;

[0041] The environment control module is used to perform real-time dynamic environment control according to the instructions sent by the intelligent control module.

[0042] The user interaction module is used to provide a visual interface to view and adjust test data in real time and display durability evaluation results.

[0043] Specifically, the environment detection module includes at least one of a temperature sensor, a humidity sensor, a pH sensor, a salt concentration sensor, and a carbon dioxide sensor.

[0044] Specifically, the real-time structural status detection module includes at least one of an electrochemical sensor, an optical fiber sensor, an acoustic emission sensor, and an ultrasonic detector.

[0045] Specifically, the machine learning algorithm adopts a random forest learning algorithm, a neural network algorithm, or a gradient boosting tree algorithm.

[0046] The beneficial effects of the durability test control method and system of reinforced concrete structure of the present invention are:

[0047] The present invention sets an environmental monitoring module and a structural status monitoring module in the test area to monitor the test environment and the state of the reinforced slow-setting soil structure in real time. The intelligent control system obtains environmental parameters and structural status data to adjust the test environmental conditions in real time, so that the test conditions are closer to the actual environmental changes, and the accuracy of the test results is significantly improved. The coupling simulation of multiple environmental factors can cover the complex and changeable actual environment. Moreover, by using big data analysis and machine learning, it can predict the durability change trend of reinforced concrete structures under different environmental conditions, providing a scientific basis for engineering practice.

[0048] At the same time, the test system of the present invention is provided with a user interaction module, and the test personnel can view data information and adjust test parameters in real time, thereby ensuring the convenience and efficiency of the operation of the test system. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] Figure 1 It is a structural block diagram of the test system according to an embodiment of the present invention.

[0051] Figure 2 Schematic diagram of the installation positions of the environment monitoring module and the structural status monitoring module according to an embodiment of the present invention.

[0052] Figure 3 Schematic diagram of sensor connections within a test area according to an embodiment of the present invention.

[0053] Figure 44 is a flow chart of a test method according to an embodiment of the present invention.

[0054] Figure 5 This is a flow chart of dynamically adjusting test environment conditions according to an embodiment of the present invention.

[0055] In the figure: 1. Intelligent control module, 2. Environmental monitoring module, 3. Structural status monitoring module, 4. Data processing module, 5. Data analysis module, 6. Environmental control module, 7. User interaction module. DETAILED DESCRIPTION

[0056] The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic diagrams that illustrate the basic structure of the present invention in a schematic manner.

[0057] Example 1

[0058] like Figure 1-Figure 3 The specific embodiment of the durability test and control system for reinforced concrete structures of the present invention shown in the figure includes: an intelligent control module 1, an environmental monitoring module 2, a structural state monitoring module 3, a data processing module 4, a data analysis module 5, an environmental control module 6, and a user interaction module 7. The intelligent control module 1 is used to output control instructions for the durability test, the environmental monitoring module 2 includes several sensors for real-time monitoring of environmental parameters within the test area, the structural state monitoring module 3 is used to monitor the structural state data of the reinforced concrete structure in real time, the data processing module 4 is used to perform denoising processing on environmental parameters and performance parameters, the data analysis module 5 is used to store, perform mapping analysis, and form a correlation model for the environmental parameters and structural state data processed by the data processing module 4; a machine learning algorithm is used to predict the durability change trend of the structure; the environmental control module 6 is used to perform real-time dynamic environmental control according to the instructions sent by the intelligent control module 1; and the user interaction module 7 is used to provide a visual interface for real-time viewing and adjustment of test data and display of durability evaluation results.

[0059] like Figure 2 and Figure 3 As shown, it is further explained that the environmental detection module in this embodiment includes at least one of a temperature sensor, a humidity sensor, a pH sensor, a salt concentration sensor, and a carbon dioxide sensor, and the real-time structural status detection module includes at least one of an electrochemical sensor, an optical fiber sensor, an acoustic emission sensor, and an ultrasonic detector.

[0060] In this embodiment, each sensor in the environmental detection module and the real-time structural status detection module is arranged within the test area, which can ensure that the data in the test area is accurately detected. By setting up the environmental monitoring module and the structural status monitoring module in the test area, the test environment and the state of the reinforced slow-setting soil structure are monitored in real time. The intelligent control system adjusts the test environment conditions in real time by obtaining environmental parameters and structural status data, making the test conditions closer to actual environmental changes, significantly improving the accuracy of the test results, and coupling simulation of multiple environmental factors to cover complex and changeable actual environments. In addition, by using big data analysis and machine learning, it is possible to predict the durability change trend of reinforced concrete structures under different environmental conditions.

[0061] like Figure 1 As shown, the test system of the present invention is provided with a user interaction module 7, which enables test personnel to view data information and adjust test parameters in real time, ensuring the convenience and efficiency of the test system. It should be understood that this embodiment also provides a user terminal, such as a mobile phone or computer, through which users can view and adjust data.

[0062] Example 2

[0063] like Figure 4 As shown, the test method based on the above test system includes the following contents:

[0064] S10: Set up a closed test area;

[0065] S20: Arrange environmental monitoring module 2 in the test area;

[0066] S30: Setting a structural state monitoring module 3; that is, setting a structural state monitoring module 3 on the reinforced concrete structure to be tested within the test area;

[0067] S40: Establish a one-to-one connection relationship between the environmental monitoring module 2 and the intelligent control module 1, and between the structural state monitoring module 3 and the intelligent control module 1;

[0068] S50: Dynamically adjust the test environment conditions through the intelligent control system according to the real-time environmental parameters obtained by the environmental monitoring module 2 and the real-time structural status data obtained by the structural status monitoring module 3;

[0069] S60: Using real-time environmental parameters and real-time structural status data, establish a correlation model between environmental factors and structural durability, and use machine learning algorithms to predict the durability change trend of the structure under different environmental conditions.

[0070] To be specific, the structural state data in step S50 of this embodiment includes one or more of corrosion rate, crack data, and stress-strain data, that is, the corrosion rate, crack development, and stress-strain of the reinforced concrete structure are monitored in real time by electrochemical sensors, optical fiber sensors, acoustic emission equipment, and ultrasonic detectors. The environmental parameters include one or more of temperature, humidity, pH, salt concentration, and carbon dioxide concentration, that is, multiple sensors are arranged in the test area to monitor the environmental parameters, including temperature, humidity, pH, salt concentration, and carbon dioxide concentration, in real time.

[0071] The present invention sets an environmental monitoring module 2 and a structural status monitoring module 3 in the test area to monitor the test environment and the state of the reinforced slow-setting soil structure in real time. The intelligent control system adjusts the test environment conditions in real time by obtaining environmental parameters and structural status data, so that the test conditions are closer to the actual environmental changes, and the accuracy of the test results is significantly improved. The coupling simulation of multiple environmental factors can cover the complex and changeable actual environment. Moreover, by using big data analysis and machine learning, the durability change trend of reinforced concrete structures under different environmental conditions can be predicted, providing a scientific basis for engineering practice.

[0072] like Figure 5 As shown, in step S50, the test environment conditions are dynamically adjusted by the intelligent control system, including the following steps:

[0073] S501: Configure an environmental control module 6 in the test area; the environmental control module 6 includes one or more of a temperature controller, a humidity controller, a salt spray generator, a pH regulator, and a wind controller; and monitor the corrosion rate, crack development, and stress and strain of the reinforced concrete structure in real time through electrochemical sensors, optical fiber sensors, acoustic emission equipment, and ultrasonic detectors.

[0074] S502: Establish a data analysis module 5 to obtain real-time parameter data of the environmental control module 6 and perform data analysis and output.

[0075] S503: Connect the data analysis module 5 to the intelligent control module 1 through signals.

[0076] S504: The intelligent control module 1 controls the environment control module 6 to perform real-time environment adjustment according to the analysis results of the data analysis module 5.

[0077] That is, based on the real-time monitored environmental parameters and structural status, the test environment conditions, including temperature and humidity, salt spray concentration, and chemical corrosion media, are dynamically adjusted through intelligent control devices.

[0078] The establishment of the correlation model between environmental factors and structural durability in step S60 includes the following contents:

[0079] A601: Acquire real-time environmental parameters acquired by the environmental monitoring module 2 and real-time structural status data acquired by the structural status monitoring module 3;

[0080] A602: Perform big data analysis on real-time environmental parameters and real-time structural status data to obtain the mapping relationship between environmental factors and structural durability;

[0081] A603: Purify the mapping relationship to form a correlation model between environmental factors and structural durability.

[0082] The calculation formula of the association model in this embodiment is:

[0083] Y=β0+β1X1+β2X2+...+βnXn+ε;

[0084] Where Y represents the structural durability index, X1, X2, and Xn represent environmental parameters and structural status data, n is an integer, β0 represents the durability benchmark value when the environmental parameter is zero, β1, β2, and βn represent the regression coefficients of the influence of each environmental factor on durability, and ε represents the random error term.

[0085] In actual calculations, the regression coefficients β1, β2, ..., βn are estimated by the least squares method so that the sum of squared errors between the model predicted values ​​and the actual values ​​is minimized.

[0086] As a specific implementation method, its environmental parameters and structural status data are as follows:

[0087] Temperature (X1) Humidity (X2) Salt concentration (X3) Corrosion rate (Y) 25 70 0.5 0.02 30 80 0.6 0.03 20 60 0.4 0.01

[0088] Build a multiple linear regression model:

[0089] Y=β0+β1X1+β2X2+β3X3+ε;

[0090] The regression coefficients β0, β1, β2, and β3 are solved by the least squares method to obtain the model formula:

[0091] Y=0.005+0.001X1+0.0005X2+β0.02X3+0.003.

[0092] The machine learning algorithm uses a random forest learning algorithm, a neural network algorithm, or a gradient boosting tree algorithm. This embodiment uses a random forest learning algorithm. Specifically, the machine learning algorithm is used to predict the durability change trend of the structure, including the following:

[0093] B601: Take environmental parameters and structural status data as input features, structural durability index as output features, and perform standardization on the input features;

[0094] B602: Obtain historical test data and divide the historical test data into training sets and test sets;

[0095] B603: Use the random forest regression algorithm to build a random forest model;

[0096] B604: Use the experimental data in the training set to train the random forest model and optimize the random forest model parameters;

[0097] B605: Use mean square error, mean absolute error, and coefficient of determination to evaluate the performance of the association model and obtain the final random forest model;

[0098] B606: Input the real-time environmental parameters and structural status data into the final random forest model to output the durability change trend of the structure.

[0099] The use of random forests can handle nonlinear relationships and high-dimensional data, has high prediction accuracy, is insensitive to noise data and missing values, and is suitable for data characteristics in actual engineering. Through feature importance analysis, we can understand which environmental factors have the greatest impact on structural durability.

[0100] This embodiment deploys sensors from the environmental monitoring module 2 and the structural monitoring module within the test area, connecting them to the data processing module 4. The environmental control module 6 is equipped with equipment and connected to the intelligent control system. A user interaction module 7 is deployed, providing a visual interface for test personnel. Big data analysis techniques are used to establish a correlation model between environmental factors and structural durability. Machine learning algorithms are employed to predict durability trends under different environmental conditions.

[0101] When in use, start the system to monitor environmental parameters and structural status in real time. Based on the monitoring data, dynamically adjust the simulated environmental conditions, analyze the collected data, and evaluate the durability of the reinforced concrete structure.

[0102] It should be understood that the specific embodiments described above are only used to explain the present invention and are not intended to limit the present invention. Obvious changes or modifications derived from the spirit of the present invention are still within the scope of protection of the present invention.

Claims

1. A durability test control method for reinforced concrete structures, characterized in that: Includes the following: Set up a closed test area; Arrange an environmental monitoring module within the test area; Setting a structural condition monitoring module, that is, setting a structural condition monitoring module on the reinforced concrete structure to be tested within the test area; Establish a one-to-one connection relationship between the environmental monitoring module and the intelligent control module, and between the structural status monitoring module and the intelligent control module; Dynamically adjusting the test environment conditions through the intelligent control system according to the real-time environmental parameters obtained by the environmental monitoring module and the real-time structural status data obtained by the structural status monitoring module; The real-time environmental parameters and real-time structural status data are used to establish a correlation model between environmental factors and structural durability, and a machine learning algorithm is used to predict the durability change trend of the structure under different environmental conditions.

2. The durability test control method for reinforced concrete structure according to claim 1, characterized in that: The structural state data includes one or more of corrosion rate, crack data, and stress-strain data; The environmental parameters include one or more of temperature, humidity, pH, salt concentration, and carbon dioxide concentration.

3. The durability test control method for reinforced concrete structure according to claim 1, characterized in that: The method of dynamically adjusting the test environment conditions by the intelligent control system includes the following steps: An environmental control module is configured in the test area; the environmental control module includes one or more of a temperature controller, a humidity controller, a salt spray generator, a pH regulator, and a wind controller; Establishing a data analysis module for obtaining real-time parameter data of the environmental control module and performing data analysis and output; Connecting the data analysis module to the intelligent control module through signals; The intelligent control module controls the environmental control module to perform real-time environmental adjustment according to the analysis results of the data analysis module.

4. The durability test control method for reinforced concrete structure according to claim 1, characterized in that: The establishment of the correlation model between environmental factors and structural durability includes the following contents: Acquiring real-time environmental parameters acquired by the environmental monitoring module and real-time structural status data acquired by the structural status monitoring module; Performing big data analysis on the real-time environmental parameters and real-time structural status data to obtain a mapping relationship between environmental factors and structural durability; The mapping relationship is purified to form a correlation model between environmental factors and structural durability.

5. The durability test control method for reinforced concrete structure according to claim 4, characterized in that: The calculation formula of the association model is: Y=β0+β1X1+β2X2+...+βnXn+ε; Where Y represents the structural durability index, X1, X2, and Xn represent environmental parameters and structural status data, n is an integer, β0 represents the durability benchmark value when the environmental parameter is zero, β1, β2, and βn represent the regression coefficients of the influence of each environmental factor on durability, and ε represents the random error term.

6. The durability test control method for reinforced concrete structure according to claim 1, characterized in that: The method of using a machine learning algorithm to predict the durability change trend of a structure includes the following: The environmental parameters and structural state data are used as input features, the durability index of the structure is used as output features, and the input features are standardized; Acquire historical test data, and divide the historical test data into a training set and a test set; Use the random forest regression algorithm to build a random forest model; Using the test data in the training set to train the random forest model, and optimizing the parameters of the random forest model; The performance of the association model was evaluated using mean square error, mean absolute error, and coefficient of determination to obtain the final random forest model; The real-time environmental parameters and structural status data are input into the final random forest model to output the durability change trend of the structure.

7. A durability test control system for reinforced concrete structures, characterized in that: include: Intelligent control module, used for outputting control instructions for durability experiments; An environmental monitoring module, comprising a plurality of sensors for real-time monitoring of environmental parameters within the test area; Structural status monitoring module, used to monitor the structural status data of reinforced concrete structures in real time; A data processing module, configured to perform denoising processing on the environmental parameters and the performance parameters; A data analysis module is used to store, analyze the mapping relationships of the environmental parameters and structural status data processed by the data processing module and form a correlation model; and use a machine learning algorithm to predict the durability change trend of the structure; An environmental control module, configured to perform real-time dynamic environmental control according to instructions sent by the intelligent control module; The user interaction module is used to provide a visual interface to view and adjust test data in real time and display durability evaluation results.

8. The durability test control system for reinforced concrete structures according to claim 7, characterized in that: The environment detection module includes at least one of a temperature sensor, a humidity sensor, a pH sensor, a salt concentration sensor, and a carbon dioxide sensor.

9. The durability test control system for reinforced concrete structures according to claim 7, characterized in that: The structural status real-time detection module includes at least one of an electrochemical sensor, an optical fiber sensor, an acoustic emission sensor, and an ultrasonic detector.

10. The durability test control system for reinforced concrete structures according to claim 7, characterized in that: The machine learning algorithm adopts a random forest learning algorithm, a neural network algorithm, or a gradient boosting tree algorithm.

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