Concrete structure intelligent maintenance method and system based on multi-point impedance sensing

Abstract

The invention relates to the technical field of constructional engineering, and discloses a concrete structure intelligent maintenance method based on multi-point impedance sensing. The invention aims to solve the problems that the existing concrete curing technology depends on experience and cannot sense the spatial non-uniformity of the internal state of the structure in real time, so that the curing measures are blind and the structure quality homogeneity is poor. According to the invention, a plurality of impedance sensors integrated with temperature sensors are arranged in a concrete structure, and AC impedance spectrum data and temperature data at different positions of the structure are synchronously acquired; using a pre-constructed correlation model to solve the current performance index of each monitoring position in real time according to the electrical data subjected to temperature compensation correction; and comparing the calculated performance index with a preset maintenance target, and generating a control instruction to drive a maintenance execution mechanism to carry out partitioned and differentiated maintenance operation on the structure.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building engineering, in particular to a concrete structure intelligent curing method and system based on multi-point impedance sensing. BACKGROUND

[0002] In the construction of concrete structures, curing is a key link to ensure the final mechanical properties and long-term durability. In the prior art, methods such as watering, covering, and spraying curing agents are commonly used to cure the surface of the concrete. The purpose is to keep the surface of the concrete moist and at a suitable temperature within a certain period of time to ensure the smooth progress of the cement hydration reaction. The implementation of these methods usually relies on the fixed curing period given by the construction specification or is based on on-site experience, and the curing measures are often uniform and integral.

[0003] However, for the increasing number of large-volume, complex-geometry, or environmentally-varying concrete structures in modern engineering, the progress of the internal hydration reaction is not uniform in space. For example, the internal structure may cause high temperature due to hydration heat accumulation, while the surface may cause hydration delay due to rapid evaporation of water or low ambient temperature. The integral curing method used in the prior art cannot sense and respond to the spatial non-uniformity of the physical and chemical state of the structure, nor can it provide objective and real-time data to accurately judge the true strength development level of each part of the structure. Therefore, there is an urgent need in the art to solve a substantial technical problem: how to realize distributed and real-time quantitative monitoring of the performance evolution of the concrete structure, and based on the monitoring results, to carry out differential closed-loop regulation of the curing measures, in order to overcome the blindness of the traditional curing mode, and thus fundamentally improve the overall quality and homogeneity of the structure. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a concrete structure intelligent curing method based on multi-point impedance sensing, which solves the problem that the existing concrete curing technology relies on experience and cannot real-time sense the spatial non-uniformity of the internal state of the structure, thereby leading to blind curing measures and poor structure quality homogeneity. To solve the above technical problems, the present application provides the following technical solutions: The present application provides a concrete structure intelligent curing method based on multi-point impedance sensing, which includes the following steps, S1: obtaining alternating current impedance spectrum data and temperature data from a plurality of impedance sensors arranged inside the concrete structure; S2: based on the alternating current impedance spectrum data and the temperature data, and using a pre-constructed correlation model to calculate the current performance indicators of the concrete structure at multiple monitoring locations; S3: compare the calculated current performance indicator with a preset maintenance target, and generate a control instruction according to the comparison result; S4: drive the maintenance execution mechanism to perform partitioned maintenance operation on the concrete structure according to the control instruction.

[0006] As a preferred scheme of the intelligent maintenance method for concrete structure based on multi-point impedance sensing, the step of acquiring the AC impedance spectrum data and the temperature data is specifically that the impedance sensor integrated with the temperature sensor deployed inside the concrete structure synchronously acquires the AC impedance spectrum data and the temperature data at the same monitoring position.

[0007] As a preferred scheme of the intelligent maintenance method for concrete structure based on multi-point impedance sensing, the impedance sensor integrated with the temperature sensor adopts a four-electrode structure, which includes a pair of excitation electrodes for applying an AC excitation signal to the concrete structure, and a pair of measurement electrodes for acquiring the AC impedance spectrum data by measuring the voltage response generated by the excitation signal in the concrete structure.

[0008] As a preferred scheme of the intelligent maintenance method for concrete structure based on multi-point impedance sensing, the step of calculating the current performance indicator includes temperature compensation correction of the AC impedance spectrum data by using the temperature data to obtain corrected AC impedance spectrum data.

[0009] As a preferred scheme of the intelligent maintenance method for concrete structure based on multi-point impedance sensing, the step of calculating the current performance indicator further includes extracting feature parameters capable of reflecting the frequency domain and complex impedance plane geometric characteristics from the corrected AC impedance spectrum data, and using the feature parameters as input to perform calculation through the correlation model.

[0010] As a preferred scheme of the intelligent maintenance method for concrete structure based on multi-point impedance sensing, the step of generating the control instruction further includes performing a cause determination process when the current performance indicator does not reach the preset maintenance target, and the cause determination process identifies the root cause of the slow development of the maintenance state of the monitoring position by comprehensively analyzing the time series of the AC impedance spectrum data and the temperature data.

[0011] As a preferred scheme of the concrete structure intelligent maintenance method based on multi-point impedance sensing provided by the application, wherein: the cause determination process specifically comprises: when the change trend of the alternating current impedance spectrum data analysis presents an abnormal sharp increase in impedance value, the root cause is determined as too fast water evaporation, and a control instruction for driving the maintenance execution mechanism to perform supplementary water operation is generated; when the temperature data continuously lower than the preset threshold value is analyzed, the root cause is determined as too low ambient temperature, and a control instruction for driving the maintenance execution mechanism to perform temperature increasing operation is generated.

[0012] The application further provides a concrete structure intelligent maintenance system based on multi-point impedance sensing, which is used to execute the above method, and specifically comprises: A data acquisition module configured to acquire alternating current impedance spectrum data and temperature data inside the concrete structure at the concrete structure; A processing and decision-making module in communication connection with the data acquisition module and configured to: Receive the alternating current impedance spectrum data and the temperature data; Calculate a performance index based on a pre-constructed correlation model; Generate a control instruction according to a comparison result of the performance index and a preset target; A partitioned maintenance module in communication connection with the processing and decision-making module and configured to receive the control instruction to perform partitioned maintenance operation on the concrete structure.

[0013] As a preferred scheme of the concrete structure intelligent maintenance system based on multi-point impedance sensing provided by the application, wherein: the step of calculating the performance index by the processing and decision-making module specifically comprises: correcting the received alternating current impedance spectrum data according to a pre-constructed temperature compensation algorithm and the received temperature data to obtain equivalent impedance spectrum data at a standard temperature; and calculating the performance index based on the equivalent impedance spectrum data.

[0014] As a preferred scheme of the concrete structure intelligent maintenance system based on multi-point impedance sensing provided by the application, wherein: the partitioned maintenance module comprises a spraying unit and a heating unit, and is configured with a control executor associated with the spraying unit and the heating unit; the control executor is used to receive instructions from the processing and decision-making module to independently drive the spraying unit and the heating unit located in a specific partition, so as to realize the partitioned maintenance operation.

[0015] The beneficial effects of this invention are: it can obtain the performance development status of different locations inside the concrete structure in real time and quantitatively, realizing closed-loop control of the curing process; by making precise control of curing measures in a zoned and differentiated manner, it can effectively deal with the internal state inhomogeneity caused by factors such as heat of hydration, water evaporation, and ambient temperature, thereby improving the overall quality and performance homogeneity of the final formed structure. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of an intelligent maintenance method for concrete structures based on multi-point impedance sensing.

[0018] Figure 2 This is a flowchart of data processing and calculation.

[0019] Figure 3 This is a logic diagram for intelligent decision-making.

[0020] Figure 4 This is a system architecture diagram for an intelligent maintenance method for concrete structures based on multi-point impedance sensing. Detailed Implementation

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0022] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0023] Secondly, the term "one embodiment" or "example" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the invention. The appearance of an embodiment in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that mutually excludes other embodiments.

[0024] Example 1 Reference Figures 1-3 This is one embodiment of the present invention, which provides a method for intelligent curing of concrete structures based on multi-point impedance sensing, including the following steps: S1. Acquire AC impedance spectrum data and temperature data from multiple impedance sensors installed inside the concrete structure; An impedance sensor, which integrates a temperature sensor and is deployed inside the concrete structure, is used to simultaneously collect AC impedance spectrum data and temperature data at the same monitoring location.

[0025] The impedance sensor integrating a temperature sensor adopts a four-electrode structure, which includes a pair of excitation electrodes for applying an AC excitation signal to the concrete structure, and a pair of measurement electrodes for obtaining AC impedance spectrum data by measuring the voltage response generated by the excitation signal in the concrete structure.

[0026] The sensing probe deployed inside the concrete structure is a composite sensing unit. Structurally, this unit comprises two functionally independent but physically integrated parts: an electrode system in direct contact with the concrete for impedance measurement; and a high-precision temperature sensing element, such as a PT100 platinum resistance thermometer, positioned at the center or adjacent to the electrode system. External analytical instruments are connected to both the electrode system and the temperature sensing element within the sensing unit. During data acquisition, the analytical instruments perform frequency sweeps through the electrode system to obtain the AC impedance spectrum and simultaneously read the signal from the temperature sensing element to calculate the temperature.

[0027] AC impedance spectroscopy data is a dataset that describes the impedance characteristics of concrete materials under AC excitation at different frequencies. It includes the impedance magnitude and phase angle information at each frequency, which is used to analyze the microstructure inside the concrete.

[0028] This method of simultaneously acquiring AC impedance spectrum data and temperature data at the same monitoring location by measuring two different physical quantities allows the acquired temperature data to accurately correspond to the impedance spectrum data.

[0029] In the field of impedance measurement, traditional two-electrode methods combine excitation and measurement functions, resulting in measurements that include the resistance of the conductor itself and the complex and unstable impedance of the contact interface formed between the electrode and the moist, porous concrete. This invention employs a four-electrode structure, which functionally separates the excitation and measurement circuits. The measurement focuses solely on the impedance of the concrete matrix itself, eliminating the influence of the contact interface impedance. During operation, the outer pair of excitation electrodes applies an alternating current signal of known frequency and amplitude to the concrete medium, forming a stable excitation electric field. The inner pair of measurement electrodes precisely measures the potential difference generated between them by the excitation current. By employing a high-input-impedance measurement circuit, the current flowing through the measurement electrodes is negligible, and the obtained voltage response directly reflects the potential difference of the concrete matrix itself, eliminating interference from conductor resistance and contact interface impedance, and reflecting only the impedance evolution of the concrete matrix itself.

[0030] S2. Based on AC impedance spectroscopy data and temperature data, and using a pre-built correlation model, the current performance indicators of the concrete structure at multiple monitoring locations are calculated. Temperature compensation correction is performed on the AC impedance spectrum data using temperature data to obtain corrected AC impedance spectrum data.

[0031] Feature parameters that reflect the frequency domain and complex impedance plane geometry are extracted from the corrected AC impedance spectrum data; and the feature parameters are used as input to solve the problem through an association model.

[0032] The electrical conductivity of concrete is extremely sensitive to temperature. Both the exothermic reaction itself and changes in the ambient temperature can cause temperature fluctuations at the measurement point, resulting in changes in the measured impedance value that are not caused by the hydration reaction itself. In this invention, the computing device performing data processing, upon receiving the raw data, first invokes a preset temperature compensation algorithm. This algorithm is an empirical model based on the theoretical form of the Arrhenius equation. The model is established based on the relationship between the electrical conductivity σ of concrete and the absolute temperature T.

[0033] Where C is a material constant, Ea is the activation energy, and k is the Boltzmann constant. The specific establishment process involves measuring the volume resistivity of concrete specimens with the same mix proportions as those used in actual engineering projects at multiple different constant temperature points in the laboratory, obtaining a set of... The experimental data points are then fitted using mathematical tools such as the least squares method to calculate the empirical coefficients specific to this concrete mix proportion. and In field applications, this model can use synchronously acquired real-time temperature data to calculate and correct each data point on the original impedance spectrum, and uniformly convert all impedance values ​​measured at different real-time temperatures to equivalent impedance values ​​at a preset reference standard temperature (e.g., 20℃), thereby obtaining a corrected AC impedance spectrum data that can be compared under the same reference.

[0034] The corrected AC impedance spectrum data constitutes a high-dimensional dataset containing complex impedance values ​​at numerous scanning frequencies. At each frequency, this impedance characteristic is a complex number, which can be described in two ways: one is by the impedance magnitude and phase angle, and the other is by decomposing it into two mutually perpendicular components—a real part and an imaginary part. The real part mainly reflects the energy dissipation characteristics of the material, similar to those of a pure resistor, while the imaginary part mainly reflects the energy storage characteristics of the material, similar to those of a capacitor.

[0035] Before feeding the data into the association model, this invention first extracts feature parameters. This process does not directly employ general mathematical dimensionality reduction algorithms, but rather selectively calculates and extracts specific numerical values ​​with clear physical meaning from the dataset to construct a more refined feature vector. These extracted values ​​are essentially values ​​that reflect the geometric characteristics of the dataset in different coordinate systems.

[0036] For example, in a coordinate system with frequency as the x-axis and impedance magnitude or phase angle as the y-axis, the impedance magnitude and phase angle measured at a representative fixed frequency (such as 1 kHz) can be extracted. In a coordinate system with the real part of the complex impedance as the x-axis and the imaginary part as the y-axis, its geometry can be analyzed, and the radius or diameter representing the semi-circular geometry in the high-frequency band can be extracted. Ultimately, the feature vector composed of these feature parameters with clear physical meaning extracted from different dimensions is provided as input to the pre-built correlation model.

[0037] The core of the correlation model can be a neural network based on the backpropagation (BP) algorithm. The basic principle of this algorithm is: the extracted feature parameter vector is used as the input layer of the neural network, the predicted compressive strength is used as the output layer, and hidden layers are placed between the input and output layers. During the model training phase, after an input vector generates a predicted output through the network, the algorithm calculates the error between this predicted output and the actual compressive strength value. A common error function is the mean squared error: .

[0038] Then, the error propagates from the output layer backwards, updating the connection weights between neurons within the network according to the gradient descent principle. The basic principle of its weight update can be expressed as:

[0039] in, For learning rate, This is the partial derivative of the error with respect to the old weights. This process of "prediction-error calculation-backpropagation-weight update" is continuously performed through repeated iterations on a large number of training samples until the total error of the entire network is reached. It converges to an acceptable small value. In field applications, this trained neural network model, with its weights and biases fixed, can directly receive the feature vectors extracted on-site as input and output the corresponding current performance metrics.

[0040] Specifically, the construction and training process of the correlation model is based on physical experimental data with a specific mixing ratio, and the specific construction steps are as follows: 1. Sample preparation: Prepare several sets (e.g., no less than 30 sets) of standard concrete cube test blocks (e.g., 150mm×150mm×150mm) with the same mix proportion as the concrete structure to be monitored.

[0041] 2. Sensor deployment: During the casting process of each test block, a four-electrode impedance sensor with integrated temperature sensor of the same specifications as that used for on-site monitoring is pre-embedded to ensure good contact between the electrodes and the concrete substrate.

[0042] 3. Multi-environment curing: In order to cover different actual working conditions, the prepared test blocks are divided into multiple groups and placed in curing chambers with different temperature and humidity conditions to simulate the temperature fluctuations that may occur on site (e.g., covering the temperature range of 5℃ to 45℃).

[0043] 4. Data Acquisition: Synchronous data acquisition is performed at multiple preset key age points (e.g., 12 hours, 1 day, 3 days, 7 days, 14 days, and 28 days). First, AC impedance spectrum data and temperature data are acquired at that moment using an impedance sensor; then, a standard destructive compressive strength test (e.g., using a pressure testing machine) is conducted on the specimen to obtain the actual compressive strength value at that moment.

[0044] 5. Training Set Construction: After the collected AC impedance spectroscopy data undergoes the aforementioned temperature compensation correction and feature parameter extraction, a feature vector is formed. Use the corresponding actual compressive strength value as the target label. All of them ( , The data pairs are aggregated to form a training dataset for training the association model.

[0045] S3. Compare the calculated current performance indicators with the preset maintenance targets, and generate control commands based on the comparison results; S4: Drive the maintenance actuator to perform zoned maintenance operations on the concrete structure according to control commands.

[0046] When the current performance indicators fail to meet the preset maintenance targets, a cause determination process is executed. The cause determination process identifies the root cause of the slow development of the maintenance status at the monitoring location by comprehensively analyzing the time series of AC impedance spectroscopy data and temperature data.

[0047] When the analysis of AC impedance spectrum data shows an abnormally sharp increase in impedance value, the root cause is determined to be excessively rapid water evaporation, and a control command is generated to drive the maintenance actuator to perform water replenishment operations; when the analysis of temperature data is consistently below the preset threshold, the root cause is determined to be excessively low ambient temperature, and a control command is generated to drive the maintenance actuator to perform heating operations.

[0048] In this implementation, the computing device executing the judgment logic compares the current performance indicators calculated from all monitoring points with the preset curing targets in real time. Here, the current performance indicator in this embodiment is specifically represented as the equivalent compressive strength. This value is not directly measured through physical loading, but rather calculated using a constructed correlation model based on measured electrical characteristic parameters. It is numerically equivalent to the compressive strength value that the concrete can achieve at that moment through a standard destructive strength test. The preset curing target is a benchmark set according to specific needs. It can be a standard strength development curve pre-drawn based on the strength development law of the concrete mix under standard curing conditions, or a strength threshold required for a specific key construction node. Concrete structure design codes typically specify corresponding minimum strengths for key construction nodes such as formwork removal and prestressing application. This invention can use the strength values ​​in these codes as thresholds. (For example, setting the minimum strength required for formwork removal to 75% of the design strength, or setting the minimum strength required for prestressing application to 85% of the design strength). Once the performance indicators of a monitoring location are found to be lagging behind the target, the device will not blindly execute uniform maintenance actions, but will activate its built-in cause-determination program. This program will automatically retrieve historical data for that monitoring location over a period of time, generating time series of AC impedance spectroscopy and temperature data. By comprehensively analyzing these two independent but related time series data, the system can delve into the physical causes leading to the slow performance development.

[0049] The cause determination process distinguishes different failure modes by analyzing different data sources.

[0050] When the cause is determined to be excessively rapid water evaporation, the computing device analyzes the AC impedance spectrum time series at the monitoring location, particularly the rate of change of a characteristic parameter sensitive to moisture content, such as the impedance modulus at 100 Hz. If the device calculates that the slope of this parameter's change far exceeds the range expected by normal hydration reactions within a short period, exhibiting a non-linear and abnormally steep growth trend, it determines the root cause as "excessively rapid water evaporation." This is because the significant loss of moisture, acting as a conductive medium in the pores, is the only physical mechanism that can cause a sharp increase in impedance without a significant increase in strength. Based on this determination, the system generates a clear water replenishment command.

[0051] When the cause is determined to be low ambient temperature, the computing device analyzes the time series of temperature data at the monitored location. If it finds that the temperature reading at that location remains below a preset threshold that is unfavorable to the effective hydration reaction for a continuous period of time (typically more than 6 hours), typically set at 15 degrees Celsius, it determines the root cause to be "low ambient temperature." Based on this determination, the system will generate a completely different temperature increase command.

[0052] These control commands with clear objectives will be sent to the corresponding zone maintenance modules to execute precise and differentiated physical interventions.

[0053] Example 2 Reference Figures 1-3 This is the second embodiment of the present invention. In order to further clarify the present invention, a specific segment of a large prestressed concrete continuous box girder bridge will be used as the maintenance object to describe the technical solution of the present invention in more detail below.

[0054] A typical segment of the box girder was selected, with a top slab thickness of 0.5 meters and a web thickness of 0.8 meters. Within this segment, 20 four-electrode impedance sensors integrating PT100 temperature sensors were deployed in a three-dimensional mesh configuration. Some sensors were placed in a shallow region 5 cm from the top slab surface to monitor surface water loss; some were placed in the geometrically abrupt junction between the web and bottom slab to monitor the strength development in stress concentration areas; and some were placed in a deep region at the center of the web (40 cm from the surface) to monitor the internal hydration heat and strength development of the large-volume concrete. The sensors were installed after the formwork was erected and the reinforcing steel reinforcement was tied, using high-strength nylon cable ties to securely fasten them to the reinforcement.

[0055] The on-site data acquisition equipment is configured to synchronously acquire data from all sensors every 30 minutes and transmit the data in real time via a communication network to a computing device (e.g., a cloud server) responsible for processing and decision-making. Upon receiving the data, the computing device performs the following series of continuous processing steps: First, the device invokes a preset temperature compensation algorithm to correct the received raw AC impedance spectrum data to an equivalent value at a standard temperature (e.g., 20 degrees Celsius) using the real-time temperature data in the data packet, thereby obtaining a corrected AC impedance spectrum.

[0056] Secondly, the device extracts a set of preset feature parameters from this calibrated data, which can reflect the geometric characteristics of the spectrum. In this embodiment, the extracted parameters include the impedance modulus at 1 kHz, the corresponding phase angle, and the radius of the semi-circular geometry presented by the high-frequency band of the complex impedance plane spectrum.

[0057] Finally, the device takes the extracted feature parameters as an input vector and provides it to the pre-trained correlation model based on a backpropagation neural network. Through a forward propagation operation, it calculates the current equivalent compressive strength of the monitoring location.

[0058] At the 48th hour of maintenance, the computing device monitored the following two typical situations: Scenario 1 (Diagnosis and Rehydration of Dehydration): The sensor located on the top surface (e.g., number T-01) has an equivalent compressive strength of 18.5 MPa, lower than the target value of 22 MPa. The computing device, by analyzing its historical impedance data (the impedance modulus at 100 Hz increased sharply from 500 ohms to 1500 ohms within 6 hours), determined the cause to be "excessive moisture evaporation" and immediately generated a water replenishment command, driving the corresponding spray unit to perform a 5-minute spraying operation.

[0059] Scenario 2 (Hypothermia Diagnosis and Warming): The sensor located at the center of the web (e.g., number C-05) has an equivalent compressive strength of 19.2 MPa, which is also lower than the target value. The computing device analyzes its historical data (the strength value hardly increased within 12 hours, while the temperature remained around 14 degrees Celsius) and determines that the cause is "the ambient temperature is too low". It then generates a heating command to drive the corresponding heating unit to start a low-power heating program.

[0060] After the intervention was performed, the system confirmed that the steep increase in impedance value at sensor point T-01 was curbed, the temperature at point C-05 rose back to 18 degrees Celsius, and the rate of intensity increase at both points began to return to normal.

[0061] Example 3 Reference Figure 4This is the third embodiment of the present invention, which provides an intelligent curing system for concrete structures based on multi-point impedance sensing, for performing the method steps in embodiment 1.

[0062] The system's software architecture consists of a set of highly collaborative functional modules, specifically including: The data acquisition module is configured to collect AC impedance spectrum data and temperature data inside the concrete structure. The processing and decision-making module communicates with the data acquisition module and is configured to: receive AC impedance spectral data and temperature data; calculate performance indicators based on a pre-built correlation model; and generate control commands based on the comparison results between the performance indicators and preset targets. The zoned curing module communicates with the processing and decision-making module and is configured to receive control commands to perform zoned curing operations on the concrete structure.

[0063] Based on a pre-built temperature compensation algorithm and the received temperature data, the received AC impedance spectrum data is corrected to obtain equivalent impedance spectrum data at standard temperature; and performance indicators are calculated based on the equivalent impedance spectrum data. The zoned maintenance module includes a spray unit and a heating unit, and is equipped with control actuators associated with the spray unit and heating unit; the control actuators receive instructions from the processing and decision-making module to independently drive the spray unit and heating unit located in a specific zone, thereby realizing zoned maintenance operations.

[0064] The data acquisition module includes multiple four-electrode impedance sensors embedded in the concrete structure, each integrating a PT100 temperature sensing element, and a multi-channel impedance analyzer placed on-site and connected to all sensors. Following a preset program, such as every 30 minutes, the analyzer automatically cycles through all sensors, synchronously acquiring AC impedance spectrum data and real-time temperature data from each sensor at the same monitoring location, and then packages the acquired data packets.

[0065] In this embodiment, the processing and decision-making module consists of a remote server deployed in the cloud. It communicates in real time with the on-site multi-channel impedance analyzer via a 5G wireless network to receive packaged data. After receiving the data, the server executes a series of preset processing and decision-making procedures.

[0066] The server calculates performance indicators by: correcting the received AC impedance spectrum data according to a preset temperature compensation algorithm and the received temperature data to obtain equivalent impedance spectrum data at standard temperature; and calculating performance indicators based on the equivalent impedance spectrum data. In specific operation, the server first calls an empirical model based on the Arrhenius equation for temperature compensation, then extracts feature parameters from the corrected spectrum, and provides these parameters as input to a pre-trained neural network correlation model. The model then calculates the current equivalent compressive strength as the performance indicator.

[0067] The server also generates control commands based on the comparison between the calculated performance indicators and the preset maintenance targets. For example, when the intensity value of a certain monitoring point is found to be lower than the preset target, the server will automatically retrieve historical data, determine the cause of the slow maintenance, and generate a water replenishment command or a temperature increase command with a clear objective.

[0068] The zoned maintenance module consists of an automatic sprinkler system for replenishing water and an electric heating system for increasing temperature.

[0069] The automatic sprinkler system consists of multiple physically separate sprinkler units, each responsible for a specific maintenance zone. Each sprinkler unit has a control actuator, such as a solenoid valve, connected in series on its water supply line.

[0070] The electric heating system consists of multiple physically separate heating cables, each corresponding to a specific curing zone. Each heating cable's power supply circuit is connected in series with a control actuator, such as a solid-state relay, that can also be controlled by an electrical signal.

[0071] The control actuators (whether solenoid valves or solid-state relays) all integrate network communication interfaces, enabling them to independently receive and respond to control commands from the cloud server (processing and decision-making module). When the cloud server generates a water replenishment command for a specific zone, it sends the command precisely via the network to the solenoid valve of the specific spray unit responsible for that zone, causing it to open, while all other actuators remain unchanged. When a heating command is generated, the command is also sent only to the solid-state relay of the specific heating cable responsible for that zone, causing it to close.

[0072] In summary, it can obtain the performance development status of different locations inside the concrete structure in real time and quantitatively, realizing closed-loop control of the curing process; by making precise control of curing measures in different zones and with differentiation, it can effectively deal with the non-uniformity of internal state caused by factors such as heat of hydration, water evaporation, and ambient temperature, thereby improving the overall quality and performance homogeneity of the final formed structure.

[0073] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for intelligent maintenance of concrete structures based on multi-point impedance sensing, characterized in that, The method comprises the following steps: S1: acquiring AC impedance spectrum data and temperature data from a plurality of impedance sensors arranged inside a concrete structure; S2: calculating the current performance indicators of the concrete structure at a plurality of monitoring locations based on the AC impedance spectrum data and the temperature data and using a pre-constructed correlation model; S3: comparing the calculated current performance indicators with preset maintenance targets and generating control instructions according to the comparison results; S4: driving a maintenance execution mechanism to perform zoned maintenance work on the concrete structure according to the control instructions.

2. The multi-point impedance sensing based intelligent concrete structure maintenance method of claim 1, wherein, The step of acquiring AC impedance spectrum data and temperature data specifically involves synchronously collecting the AC impedance spectrum data and the temperature data at the same monitoring location point by an impedance sensor integrated with a temperature sensor and arranged inside the concrete structure.

3. The multi-point impedance sensing based intelligent concrete structure maintenance method of claim 2, wherein, The impedance sensor integrated with a temperature sensor adopts a four-electrode structure, which includes a pair of excitation electrodes for applying an AC excitation signal to the concrete structure and a pair of measurement electrodes for acquiring the AC impedance spectrum data by measuring the voltage response generated by the excitation signal in the concrete structure.

4. The multi-point impedance sensing based intelligent concrete structure maintenance method of claim 1, wherein, The step of calculating the current performance indicators involves temperature compensation correction of the AC impedance spectrum data using the temperature data to obtain corrected AC impedance spectrum data.

5. The multi-point impedance sensing based intelligent concrete structure maintenance method of claim 4, wherein, The step of calculating the current performance indicators further includes extracting feature parameters capable of reflecting the frequency domain and complex impedance plane geometric characteristics from the corrected AC impedance spectrum data and using the feature parameters as input to perform calculation through the correlation model.

6. The multi-point impedance sensing based intelligent concrete structure maintenance method of claim 1, wherein, The step of generating control instructions further includes performing a cause determination process when the current performance indicators do not meet the preset maintenance targets; the cause determination process identifies the root cause of the slow development of the maintenance state of the monitoring location by comprehensively analyzing the time series of the AC impedance spectrum data and the temperature data.

7. The multi-point impedance sensing based intelligent concrete structure maintenance method of claim 6, wherein, The cause determination process specifically includes determining the root cause as excessive water evaporation and generating control instructions for driving the maintenance execution mechanism to perform supplementary water work when the change trend of the AC impedance spectrum data shows an abnormal sharp increase in impedance value; and determining the root cause as excessively low ambient temperature and generating control instructions for driving the maintenance execution mechanism to perform warming work when the temperature data continuously falls below a preset threshold.

8. A multi-point impedance sensing based intelligent maintenance system for concrete structures, based on the multi-point impedance sensing based intelligent maintenance method for concrete structures according to any one of claims 1 to 7, characterized in that, The method comprises: a data acquisition module configured to collect AC impedance spectrum data and temperature data inside a concrete structure at the structure; a processing and decision-making module in communication connection with the data acquisition module and configured to: receive the AC impedance spectrum data and the temperature data; calculate performance indicators based on a pre-constructed correlation model; and generate control instructions according to the comparison results of the performance indicators and preset targets; a zoned maintenance module in communication connection with the processing and decision-making module and configured to receive the control instructions to perform zoned maintenance work on the concrete structure.

9. The multi-point impedance sensing based intelligent concrete structure maintenance system of claim 8, wherein, The step of calculating the performance index by the processing and decision module specifically comprises: correcting the received AC impedance spectrum data according to a pre-constructed temperature compensation algorithm and the received temperature data to obtain equivalent impedance spectrum data at a standard temperature; and calculating the performance index based on the equivalent impedance spectrum data.

10. The multi-point impedance sensing based intelligent concrete structure maintenance system of claim 8, wherein, The sub-area maintenance module comprises a spraying unit and a heating unit, and is configured with a control executor associated with the spraying unit and the heating unit; the control executor is used to receive instructions from the processing and decision module to independently drive the spraying unit and the heating unit located in a specific sub-area, so as to realize the sub-area maintenance operation.

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