Method and system for monitoring chloride ion gradient and durability enhancement of concrete based on mxene

Abstract

The application relates to a concrete chloride ion gradient monitoring and durability enhancement method and system based on MXene, solves the problems that a traditional drilling sampling method is high in destructiveness and cost, low in detection efficiency, and test results are significantly affected by environmental conditions, and lacks real-time and universal applicability, and the method comprises the following steps: embedding a plurality of prefabricated MXene-Cl composite electrode layers containing sensing units with toughening protective layers into different depths of concrete in a vertical gradient to form a monitoring network; collecting potential signals through the sensing units, inputting the signals into the Nernst equation after correction to obtain chloride ion concentration, combining preset vertical and horizontal gradient estimation methods to realize penetration distribution imaging; and capturing and blocking the migration of chloride ions to the steel bars by using the intercalation adsorption characteristics of MXene on chloride ions. The application has the following effects: the chloride ions are monitored in real time, the migration of the chloride ions to the steel bars is blocked, the deterioration state of the concrete is evaluated in combination with multi-parameter data, early warning and durability improvement are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of civil engineering material performance test equipment, in particular to a concrete chloride ion gradient monitoring and durability enhancement method and system based on MXene. BACKGROUND

[0002] The durability of coastal concrete infrastructure is significantly affected by chloride ion erosion, which causes serious problems of steel bar corrosion, thereby threatening the safety and service life of the structure. Concrete deterioration and steel corrosion caused by chloride ion penetration are key factors restricting the durability of engineering. Developing efficient and accurate chloride ion monitoring and durability enhancement technology is of great importance to ensuring the safety of coastal engineering, prolonging the service life and promoting the sustainable development of marine economy.

[0003] Currently, chloride ion monitoring mainly relies on the drilling sampling chemical analysis method, which has high accuracy but is a post-detection means. Electrochemical sensing technology has been gradually applied in the field of chloride ion monitoring, especially Ag / AgCl-based ion-selective electrodes, which are widely used due to their simple principle and easy preparation.

[0004] Traditional drilling sampling methods have strong destructive operation process, high cost, and low detection efficiency, and the test results are significantly affected by environmental conditions, lacking real-time and universal applicability in the field. Traditional Ag / AgCl electrodes face problems such as electrode material dissolution and drift, unstable response potential in strong alkali, humid, and salt-rich concrete environments. The detection results of such electrodes are significantly affected by pH value and temperature disturbances, requiring frequent calibration and maintenance, and have insufficient sensitivity, making it difficult to capture early corrosion signals in time. Moreover, traditional liquid reference electrodes have risks of electrolyte leakage and environmental pollution, which are not suitable for long-term embedded deployment. SUMMARY

[0005] In order to monitor chloride ions in real time and block their migration to steel bars, and to evaluate the deterioration state of concrete by combining multi-parameter data, early warning and durability improvement are achieved. The present application provides a concrete chloride ion gradient monitoring and durability enhancement method and system based on MXene.

[0006] In the first aspect, the present application provides a concrete chloride ion gradient monitoring and durability enhancement method based on MXene, which adopts the following technical solution:

[0007] A concrete chloride ion gradient monitoring and durability enhancement method based on MXene, comprising:

[0008] A plurality of prefabricated MXene-Cl composite electrode layers and toughening protective layer sensing units are embedded in different depths of concrete according to a vertical gradient to form a monitoring network;

[0009] The potential signal is collected by the sensing unit, and the chloride ion concentration is obtained by inversion after correction into the Nernst equation, and the vertical and horizontal gradient estimation methods are combined to realize the permeation distribution imaging;

[0010] MXene is used to capture and block the migration of chloride ions to the steel bar by its intercalation adsorption characteristics for chloride ions.

[0011] Collect multi-parameter data related to the service environment and structural state of concrete, and aggregate and transmit the multi-parameter data and chloride ion concentration data to the cloud, and evaluate the deterioration state of the concrete structure by combining the preset corrosion life prediction model. When the chloride ion concentration exceeds the preset critical threshold, a warning is triggered.

[0012] By using the above technical solution, the method can accurately monitor the chloride ion distribution of concrete, block the migration of chloride ions by using the characteristics of MXene, and evaluate the structural deterioration in advance by combining multi-parameter data and cloud analysis, effectively enhance the durability of concrete, and ensure the safety of the project.

[0013] In a second aspect, the application provides a MXene-based concrete chloride ion gradient monitoring and durability enhancement system, which adopts the following technical solution:

[0014] A MXene-based concrete chloride ion gradient monitoring and durability enhancement system, comprising a memory, a processor, and a program stored in the memory and executable on the processor. The program can be loaded and executed by the processor to implement the MXene-based concrete chloride ion gradient monitoring and durability enhancement method of the first aspect. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 is a flowchart of a MXene-based concrete chloride ion gradient monitoring and durability enhancement method according to an embodiment of the application.

[0016] Figure 2 is a schematic diagram of a ring-shaped wrapped Mxene-Cl sensing electrode wrapped around a steel bar in another embodiment of the application.

[0017] Figure 3 is a schematic diagram of a planar sprayed MXene-Cl sensing electrode and a ring-shaped wrapped MXene-Cl sensing electrode arranged in multiple gradients in concrete in another embodiment of the application.

[0018] Figure 4 is a planar local enlarged schematic diagram of a planar sprayed MXene-Cl sensing electrode and a ring-shaped wrapped MXene-Cl sensing electrode arranged in multiple gradients in concrete in another embodiment of the application.

[0019] Figure 5is a schematic diagram of the composition and preparation process of the MXene-Cl sensing electrode in the embodiments of the present application. DETAILED DESCRIPTION

[0020] The present application is further described in detail below with reference to the accompanying drawings.

[0021] Reference Figure 1 A MXene-based concrete chloride ion gradient monitoring and durability enhancement method disclosed in the present application comprises:

[0022] Step S100, embedding a plurality of prefabricated MXene-Cl-containing composite electrode layers and toughening protective layer sensing units into different depths of concrete in a vertical gradient to form a monitoring network.

[0023] Among them, the prefabricated MXene-Cl-containing composite electrode layer and the toughening protective layer sensing unit: the MXene-Cl composite electrode layer is an electrode layer with selective adsorption and electrochemical response capability to chloride ions, which is prepared by using MXene material with two-dimensional layered structure and making chloride ions intercalate into it through a specific process. The toughening protective layer mainly protects the electrode layer from mechanical damage and chemical corrosion in the concrete, while not affecting the sensing performance of the electrode. The sensing unit is a basic unit with the ability to sense chloride ions composed of the two layers. The process of preparing the sensing unit is as follows: according to the specific design requirements, the MXene-Cl composite electrode layer is prepared by using the corresponding process. For example, MXene material is reacted with chloride and other substances to make chloride ions intercalate into the layered structure of MXene, forming an electrode layer with chloride ion sensing performance. At the same time, select appropriate toughening protective layer materials such as polymers / epoxy resins, etc., and coat or wrap them outside the electrode layer to form a complete sensing unit.

[0024] Vertical gradient embedding: according to the vertical direction of the concrete structure, arrange the sensing units in different depth positions from the surface to the inside in order and regularly, forming a gradient distribution arrangement method to monitor the chloride ion situation at different depths. Monitoring network: a network system that can comprehensively perceive the chloride ion penetration and distribution in concrete formed by multiple sensing units cooperating with each other to cover the entire monitoring area.

[0025] The specific implementation process of step S100 can refer to steps S110 to S150, which will not be repeated here.

[0026] Step S200, collecting potential signals through the sensing unit, and after correction, substituting them into the Nernst equation to obtain the chloride ion concentration, and combining the preset vertical and horizontal gradient estimation method to realize the penetration distribution imaging.

[0027] Among them, the potential signal is collected by the sensing unit: the working electrode in the sensing unit and the reference electrode will form a potential difference, and through high-precision potential collection equipment such as electrochemical workstations or data acquisition cards, etc., the potential difference is measured and recorded in real time, and the original potential signal data is obtained.

[0028] Signal correction: due to the instability of the reference electrode, such as drift, the collected potential signal has errors. A certain algorithm or mathematical model is used to correct the original potential signal to eliminate errors and improve the accuracy of the data. For example, the potential correction term of the reference electrode is obtained by using the blank point pre-calibration, historical drift regression or redundant channel average, and then the original potential signal is corrected.

[0029] Nernst equation inversion: Nernst equation is a basic equation in electrochemistry, which describes the relationship between electrode potential and ion concentration in the solution. After the collected potential signal is corrected, it is substituted into the inverse form of the Nernst equation to calculate the corresponding chloride ion concentration.

[0030] Pre-set vertical and horizontal gradient estimation method: according to the pre-set mathematical model or algorithm, the inverse calculated chloride ion concentration data is used to calculate the vertical concentration gradient and horizontal concentration change of chloride ion in concrete respectively, so as to realize the imaging of chloride ion penetration distribution.

[0031] Among them, the specific execution process of step S200 can refer to steps S210 to S260, which will not be repeated here.

[0032] Step S300, using the intercalation adsorption characteristics of MXene to chloride ions, capturing and blocking the migration of chloride ions to the steel bar.

[0033] Among them, the intercalation adsorption characteristics of MXene to chloride ions: MXene material has a two-dimensional layered structure, and chloride ions can enter its interlayer and intercalate. In this process, there is interaction between chloride ions and Cl⁻ terminal groups on the surface of MXene, and part of the electrons from chloride ions transfer to the surface of MXene, enhancing the covalent interaction, so that chloride ions are firmly "locked" in the interlayer of MXene, thereby realizing the adsorption of chloride ions.

[0034] Capture and block the migration of chloride ions to the steel bar: using the adsorption of MXene material to chloride ions, a "trap" of chloride ions is constructed in the concrete, so that the migrating chloride ions are captured by MXene material, reducing the concentration of free migrating chloride ions in the concrete, thereby delaying the transmission of chloride ions to the surface of the steel bar and reducing the risk of corrosion of the steel bar due to chloride ion erosion.

[0035] The complete process is described as follows:

[0036] Preparation and optimization of MXene materials: Using appropriate preparation methods, such as mild alkali etching combined with Lewis acid post-treatment strategies, MXene materials rich in Cl⁻ terminal groups are prepared. By optimizing the preparation process parameters, such as reaction temperature, time, and raw material ratio, the interlayer spacing and surface activity of MXene materials are improved, and their intercalation adsorption capacity for chloride ions is enhanced. For example, during the preparation process, by adjusting the amount of LiF and hydrochloric acid and the reaction time, the etching degree of Ti3AlC2 powder is controlled to obtain MXene materials with appropriate interlayer spacing.

[0037] Application and dispersion of MXene materials in concrete: The prepared MXene materials are introduced into concrete in an appropriate manner. It can be added directly to the concrete mixture, or used for local repair or reinforcement of concrete structures after being compounded with cement-based materials to form composite materials. In order to ensure that MXene materials can be uniformly dispersed in concrete and fully exert their adsorption effect, surface modification treatment can be performed on MXene materials. For example, using bisdecyldimethylammonium bromide, water-soluble silk fibroin, etc. to modify the surface of MXene layers, increase the interlayer spacing, weaken the van der Waals force, and improve the dispersibility in the concrete matrix.

[0038] Chloride ion capture and retardation mechanism: When concrete structures are exposed to chloride-containing environments such as coastal areas, chloride ions will gradually migrate to the interior of the concrete. Due to the presence of MXene materials in the concrete, when chloride ions approach the interlayer of MXene, intercalation occurs, and chloride ions are adsorbed into the interlayer of MXene and undergo charge transfer, forming stable chemical bonds. This process not only fixes chloride ions in the interlayer of MXene, reducing the concentration of freely mobile chloride ions in concrete, but also due to the distribution of MXene materials in concrete, multiple adsorption sites for chloride ions are constructed, forming a "filtration" effect, effectively retarding the further migration of chloride ions towards the steel. For example, in concrete structures, MXene materials near the surface preferentially adsorb migrating chloride ions, and as the depth increases, subsequent layers of MXene materials continue to capture chloride ions, forming a gradient chloride ion adsorption barrier in the interior of the concrete, protecting the internal steel from chloride ion corrosion.

[0039] Step S400, collect multi-parameter data related to the service environment and structure state of concrete, and aggregate and transmit the multi-parameter data and chloride ion concentration data to the cloud, and evaluate the deterioration state of the concrete structure by combining the preset corrosion life prediction model. When the chloride ion concentration exceeds the preset critical threshold, a warning is triggered.

[0040] Among them, multi-parameter data acquisition: collect various data related to concrete service environment and structure state, such as temperature, humidity, strain, resistance, etc. These parameters can reflect the environmental conditions and internal physical and chemical changes of concrete. Data fusion and transmission: integrate different types of monitoring data, and send them to the cloud platform through wireless communication technology, realize the centralized storage and management of data.

[0041] Corrosion life prediction model: a mathematical model established based on the material properties of concrete structure, environmental conditions and chloride ion erosion law, used to predict the corrosion life and remaining service life of the structure.

[0042] The execution of S400 can refer to steps S410 to S440, which will not be repeated here.

[0043] The multiple prefabricated MXene-Cl composite electrode layers and toughening protective layer sensing units are embedded in different depths of concrete in a vertical gradient to form a monitoring network, including:

[0044] Step S110, the sensing unit includes planar sprayed MXene-Cl sensing electrode and ring wrapped MXene-Cl sensing electrode, wherein the planar sprayed MXene-Cl sensing electrode is arranged at the same depth of the concrete with a preset dot matrix, and the dot spacing is a preset value, and the ring wrapped MXene-Cl sensing electrode is wrapped around the steel bar, which can be referred to in detail Figure 2 .

[0045] Among them, planar sprayed MXene-Cl sensing electrode: a sensing electrode made by uniformly coating MXene-Cl material on the substrate using spraying process, with good planar coverage and easy integration, which can effectively capture the chloride ion penetration of the concrete surface and internal. Its specific settings are as follows: 1, substrate and conductive layer: flexible substrate uses polyimide (PI) film with a thickness of about 50 μm. PEDOT:PSS / carbon nanotube composite conductive film is spin-coated or sprayed on its surface, with a film thickness of about 1 μm and a surface resistance of ≤10 Ω / m2, to ensure good conductivity and flexibility. 2, MXene-Cl sensing film preparation: prepare Ti3C2T X-Cl water / ethanol (1:1) dispersion, concentration of about 1 mg / mL. Air pressure spraying was used: air pressure 0.1 MPa, flow rate 0.4 mL / min, nozzle distance from the substrate 10 cm, parallel spraying for 5 times, 3 min of room temperature standing after each spraying, total film thickness about 10 pm (10-12 pm). After spraying, it was placed in a 60°C oven for 2 h to dry and remove residual solvent and enhance film adhesion. 3, Reference electrode and counter electrode arrangement: Ag / AgCl reference electrode uses a coated electrode wire with a diameter of 1 mm and a length of 15 mm; graphite counter electrode uses a carbon rod with a diameter of 0.5 mm and a length of 15 mm; the two electrodes are arranged in the same plane as the MXene-Cl film layer, with a distance of 5 mm between each other, and the leads are drawn out with silver-plated copper wires with a diameter of 0.15 mm. 4, Protective layer and packaging: cover the entire plane electrode surface with a polyurethane (PU) protective film with a thickness of about 50 pm to prevent water seepage and mechanical wear; the electrode leads are covered with a polytetrafluoroethylene (PTFE) insulation layer, with a reserved length of 200 mm to adapt to the concrete form installation.

[0046] Cyclic wrapped MXene-Cl sensing electrode: MXene-Cl material is made into a ring structure and wrapped around the sensing electrode on the outer surface of the steel bar. The specific settings are as follows: 1, Cyclic substrate and size: The cyclic substrate is a polypropylene (PP) tube segment with an inner diameter of 22 mm, an outer diameter of 30 mm, and a length of 100 mm (adapted to HRB400 φ16-φ20 steel bars). A cyclic groove is engraved along the inner wall, with a groove width of 2 mm and a groove depth of 1 mm, for filling the sensing film. 2, MXene-Cl sensing film filling: The same as above, 1 mg / mL Ti3C2T X-Cl dispersion; fill the dispersion into the groove in 3 passes with a spraying / injection method at a gas pressure of 0.1 MPa and a flow rate of 0.3 mL / min, with a film thickness of about 2 μm per pass, and a total film thickness of 5-6 μm; drying conditions: 60 °C x 2 h. 3, embed a graphite rod with a diameter of 2 mm as the counter electrode into the inner side of the groove and make it tightly adhere to the sensing film; insert an Ag / AgCl electrode wire with a diameter of 1 mm and a length of 20 mm as the reference electrode into the corresponding position on the outer side of the groove, with a center distance of 10 mm between the two; use 0.15 mm silver-plated copper wire for all electrode leads, which are drawn out to the top with a length of 200 mm. 4, external protection and installation: cover the PVDF protective tube with a wall thickness of 2 mm and a compressive strength ≥ 12 MPa on the outside of the pipe section to prevent pouring impact; the end of the protective tube is provided with a clamping groove or a thread, which can be fixed with the concrete formwork or the reinforcement binding piece to ensure stability in situ. 5, preset dot matrix planar arrangement: arrange multiple planar sprayed MXene-Cl sensing electrodes at the same depth of the concrete according to the pre-designed dot matrix pattern and spacing. The dot spacing is a preset value, which is usually determined according to the size of the concrete structure, the distribution density of the reinforcement, and the monitoring accuracy requirements, etc., and is generally set to about 20 mm. This arrangement can comprehensively cover the monitoring area on the surface and inside of the concrete, improving the accuracy and reliability of the monitoring.

[0047] Preset dot matrix planar arrangement: determine the arrangement area of the planar sprayed MXene-Cl sensing electrode at the same depth according to the size of the concrete structure and the monitoring requirements. Mark the installation position of each electrode on the substrate according to the preset dot matrix pattern (such as 3x3 dot matrix) using precision measuring tools (such as laser range finder, protractor, etc.). Ensure that the dot spacing is a preset value (such as 20 mm), then install the prepared planar sprayed electrode on the marked position in turn, and use epoxy glue for fixation to ensure the bonding strength and sealing between the electrode and the substrate.

[0048] Annular wrapping placement: during the reinforcement binding process, wrap the annular MXene-Cl sensing electrode around the specified reinforcement to ensure that the annular band tightly adheres to the reinforcement without looseness or gaps. Adjust the size and shape of the annular substrate to adapt to reinforcement of different diameters. During installation, pay attention to protect the electrode leads to avoid damage or short circuit. After installation, insulate the connection between the counter electrode and the reinforcement to prevent electrochemical corrosion between the electrode and the reinforcement.

[0049] In addition, the sensing electrode mentioned in the present application includes a sensing film composed of MXene-Cl and a conductive polymer, and a conductive substrate.

[0050] Among them, the synthesis steps of MXene-Cl material are as follows:

[0051] A mild etching method (ASA-MILD) under argon protection is used to etch the MXene precursor material.

[0052] 1 g of LiF is mixed with 30 mL of 9M hydrochloric acid, and after stirring for 10 min, 1 g of Ti3AlC2 powder is slowly added under an ice water bath, and the mixture is placed in a sealed container with an argon gas inlet system, and is magnetically stirred at 45°C for 24 h, with argon gas continuously introduced during the reaction.

[0053] After the reaction is completed, the product is washed with dilute acid and deionized water alternately until the pH approaches neutral, to obtain a muddy Ti3C2T X material. The above-obtained Ti3C2T X material is placed in a CuCl2 molten salt and is heat-treated at 750°C, and CuCl2 acts as a Lewis acid molten salt oxidant to undergo a redox reaction with Ti3C2T X material, in which Cu²⁺ is reduced to Cu element, and Ti3C2T X Cl terminal groups are introduced onto the surface.

[0054] The above black product is washed once with a 5 mol / L FeCl3 solution to remove unreacted residues and metal ions, and then is washed with deionized water and anhydrous ethanol alternately for more than three times until the washing liquid is neutral. The washed product is dried in a vacuum drying oven at 60°C for 12 h to obtain a MXene material with Cl terminal groups on the surface, denoted as MX-Cl.

[0055] The dried MX-Cl material is further dispersed in a 1 mol / L KOH solution, magnetically stirred for 30 min, and is subjected to ultrasonic treatment under an argon atmosphere for 30 min. After ultrasonic treatment, the mixture is centrifuged at 3500 rpm for 30 min to obtain a stable dispersed few-layer MXene colloidal liquid, which has good interlayer exfoliation and structural stability.

[0056] The MXene-Cl material mentioned in the present application is obtained by the ASA-MILD low-temperature etching method combined with the Lewis acid post-treatment strategy to realize the interlayer selective exfoliation of the MAX phase, and the surface of the obtained MXene layer is rich in Cl⁻ terminal groups, which has excellent ion recognition, electrochemical response performance and dispersion stability, and is suitable for constructing a chlorine ion selective sensor, electrode, electrocatalysis or flexible electronic device structure.

[0057] The preparation method of the sensing film composed of the MXene-Cl and the conductive polymer is as follows:

[0058] (1) The dried MXene-Cl material is dispersed in deionized water (concentration of 1-5 mg / mL), and is subjected to ultrasonic treatment for 30-60 min to form a stable dispersion liquid.

[0059] (2) Prepare a polypyrrole (PPy) or polyaniline (PANI) conductive polymer solution, respectively.

[0060] (3) Mix the two liquids in a mass ratio of 1:1 to 1:3 to form a MXene-Cl / polymer composite solution, and stir uniformly.

[0061] (4) Drop coat, spray or spin coat the above composite solution on the surface of any of the following substrate materials: conductive glass (ITO), metal electrode (such as copper, nickel, platinum) or flexible conductive substrate (such as PET / Ag film). The coating thickness is 1-10 μm, and it is dried at 60-80°C for 2-4 hours to form a dense and continuous sensing film layer.

[0062] The MXene-Cl electrode structure in the present application can include but is not limited to the following three forms: 1. Single electrode structure: The single electrode structure is the simplest sensing unit, which includes a conductive substrate and a MXene-Cl / polymer composite sensing film deposited on the surface of the conductive substrate. The conductive substrate can be selected from glassy carbon electrode, copper electrode, ITO conductive glass, nickel foil or platinum sheet, preferably a Ni electrode or ITO conductive glass with high surface roughness and good adhesion. After drop coating or spin coating the MXene-Cl / polymer composite solution on the surface, it is dried at 60-80°C for 2-4 hours to form a dense and uniform film structure. This structure is suitable for basic potential response or conductivity change detection scenarios, and is simple to construct and easy to miniaturize and package. 2. Flexible electrode structure: To meet the monitoring needs of chloride ion erosion in engineering sites or irregularly shaped concrete members, the present application further provides a flexible sensing electrode structure. It uses a flexible conductive film (such as PET / Ag conductive film, flexible carbon cloth) as a substrate, sprays the MXene-Cl / polymer composite solution on the surface, and then performs low-temperature curing treatment (temperature not higher than 80°C) to obtain a flexible chloride ion sensing electrode film. This flexible structure can be attached to the surface of concrete or embedded in the member to realize in-situ monitoring, and has good bendability, crack resistance and interface adhesion, and is especially suitable for long-term monitoring systems in complex coastal environments. 3. Three-electrode system structure: Preferably, to realize higher sensitivity, more stable and standardized electrochemical testing, the present application provides a chloride ion sensing device of a three-electrode system. The device includes: a working electrode composed of a MXene-Cl / polymer composite film deposited on the surface of a metal substrate (such as Cu, Ni) or a glassy carbon electrode; a reference electrode selected from a standard Ag / AgCl electrode to ensure the stability of potential measurement; and a counter electrode preferably a Pt electrode as an electron conduction and compensation current channel.

[0063] The above electrodes are installed in the same sensing module at a certain interval, and are tested by a constant potential instrument or an electrochemical workstation. The three-electrode system is suitable for various electrochemical measurement methods such as constant potential difference method, electrochemical impedance spectroscopy (EIS), cyclic voltammetry, and is particularly suitable for laboratory calibration test and engineering field in-situ precision monitoring.

[0064] Specifically, the MXene-Cl sensing electrode used in the present application is a multilayer composite structure, the composition and preparation process of which can refer to Figure 5 The multilayer composite structure comprises: 1. Three-electrode system: platinum counter electrode provides current loop, Ag / AgCl reference electrode stabilizes potential reference. 2. Flexible polymer protective substrate: protects the structural stability of the MXene sensing film layer and the conductive film layer. 3. Conductive film layer: transmits sensing signals and adapts to concrete micro-deformation. 4. MXene sensing film layer: core sensing layer, detects Cl⁻concentration through potential change.

[0065] The preparation process (spraying-laminating-packaging) is as follows: 1. Substrate treatment: prepare a flat and conductive uniform flexible carbon-based film. 2. Sensing layer construction: pneumatically spray MXene-Cl dispersion liquid to form a 1-3 μm sensing film. 3. Protective layer lamination: hot-press PDMS protective substrate to ensure interlayer bonding. 4. Electrode integration: assemble counter / reference electrodes on the edge of the substrate and PI film packaging.

[0066] In step S120, a plurality of monitoring layers are arranged at a preset interval in the concrete cover region from the concrete surface to the outer surface of the steel bar in the thickness direction, and only planar spray type MXene-Cl sensing electrodes are arranged in each monitoring layer to cover the concrete region at different depths in the cover. When the monitoring layer reaches the position of the steel bar, only a ring-shaped wrapped MXene-Cl sensing electrode is arranged at the position. The arrangement can be specifically referred to Figure 3 and Figure 4 . Among them, Figure 3 : gray particles → concrete aggregate, constituting the matrix; horizontally layered columnar structure → planar spray type MXene-Cl sensing electrode (layered and arranged at a preset interval, each layer forming a two-dimensional lattice to cover the concrete region at that depth); horizontal cylinder (with mesh wrapping) → steel bar, and the mesh structure on its surface corresponds to the ring-shaped wrapped MXene-Cl sensing electrode (only arranged in the monitoring layer where the steel bar is located). Figure 4 : local working scene of the focusing planar electrode: red Cl⁻diffuses from the concrete pore to the electrode (yellow active area), which reflects the gradient monitoring capability of the planar electrode for Cl⁻in the cover.

[0067] Concrete cover: In structural design, the concrete layer from the outer edge of the steel bar to the surface of the concrete (usually 20-50mm thick), the integrity of which is crucial to the durability of the steel bar. Pre-set concrete pouring formwork: Steel-wood combined formwork customized according to the size of the component, with sensor installation holes and steel bar channels on the inside. After the formwork is removed, the sensors are permanently embedded in the concrete.

[0068] Complete process description:

[0069] Determine the monitoring depth range: According to the thickness of the concrete structure and the depth of the steel bar cover, determine the monitoring depth range from the surface of the concrete to the steel bar cover. For example, for a coastal bridge pier with a concrete cover thickness of 50mm, the monitoring depth range can be set from the surface to a depth of 50mm.

[0070] Set the preset interval: Within the determined monitoring depth range, divide multiple monitoring layers according to the preset interval. Assuming the preset interval is 10mm, in the above pier example, monitoring layers can be set at depths of 10mm, 20mm, 30mm, and 40mm. If the preset interval is 20mm, the monitoring layer depths are 20mm, 40mm, etc.

[0071] Arrange the sensing electrodes to implement layered arrangement as follows:

[0072] Cover monitoring layer setting: Starting from the surface of the concrete, along the vertical thickness direction to the concrete cover area of the outer surface of the steel bar, multiple monitoring layers (such as 3-5 layers, the specific number of layers is determined according to the thickness of the cover) are set at a preset interval of 10-25mm (preferably 15mm); each monitoring layer is only arranged with planar spray MXene-Cl sensing electrodes, forming a two-dimensional lattice monitoring plane, with a point spacing of 50-150mm (preferably 100mm), covering the entire cross-section of the cover.

[0073] Steel bar position monitoring layer setting: When the monitoring layer reaches the depth of the outer surface of the steel bar, only ring-shaped wrapped MXene-Cl sensing electrodes are arranged at this layer, each electrode ring tightly wraps a single steel bar, forming a closed monitoring ring around the steel bar; multiple steel bars in the same section are each independently configured with ring electrodes, and the electrode rings are connected in parallel through insulated wires and connected to the monitoring system.

[0074] Template coordination pre-embedding process: In the inner side of the template, corresponding to the depth of each monitoring layer, pre-drill installation holes with a diameter of 35-50mm (preferably 40mm), the hole position is consistent with the sensor layout point array; the planar electrode is fixed through the epoxy resin layer (thickness 0.5-1mm) on the inner side of the hole, ensuring that the electrode and the concrete interface are seamlessly combined after pouring; after the steel bar passes through the reserved channel of the template, the ring electrode slides along the steel bar axis to the designed position, and low-viscosity polyurethane grouting material (compressive strength ≥ 40MPa after curing) is used to fill the 0.5-2mm gap between the electrode and the steel bar, and at the same time, the edge of the template hole is sealed to prevent grouting leakage during pouring.

[0075] At the preset positions of each monitoring layer, planar spray type MXene-Cl sensing electrodes and ring-wrapped MXene-Cl sensing electrodes are arranged respectively. The planar spray type electrodes are arranged on the same depth of the concrete according to the preset point array, and the point spacing is a preset value (such as 20mm); the ring-wrapped electrodes are wrapped around the steel bar. Ensure that the electrode and the concrete matrix are tightly combined, the electrode lead connection is reliable, and protective measures such as waterproofing and insulation treatment are taken. Form a three-dimensional monitoring network: by arranging multiple monitoring layers at different depths in turn, a three-dimensional monitoring network covering the surface layer of the concrete to the steel bar is formed.

[0076] Step S130, by reserving hole positions in the inner side of the preset concrete pouring template, the planar spray type MXene-Cl sensing electrode is attached and fixed with epoxy glue, and after the steel bar passes through the template hole, the ring-wrapped MXene-Cl sensing electrode is slid into the ring and tightly attached to the steel bar, and then the polyurethane grouting material is used to seal the hole edge, forming a three-dimensional monitoring network covering the surface layer of the concrete to the steel bar.

[0077] Template inner side reserved hole position: On the inner side of the concrete pouring template, according to the arrangement position and size of the sensing unit, the corresponding hole is reserved in advance. These hole positions are used to install planar spray type and ring-wrapped MXene-Cl sensing electrodes, ensuring that the electrodes can be accurately positioned during the concrete pouring process and tightly combined with the concrete structure.

[0078] Epoxy glue fixation: use epoxy resin glue to fix and package the planar spray type MXene-Cl sensing electrode. Epoxy glue has good adhesion, insulation and chemical corrosion resistance, which can firmly paste the electrode on the template, and form a good interface combination with the concrete after the concrete pouring, while protecting the electrode from moisture and mechanical damage.

[0079] Polyurethane Grouting Seal: After the installation of the ring-wrapped MXene-Cl sensing electrode is completed, polyurethane grouting material is used to seal the edges of the template holes. Polyurethane grouting material has the characteristics of fast curing, high expansion rate, strong adhesion, etc., which can effectively fill the pores, prevent the leakage of slurry during concrete pouring, ensure the stability of the installation of the sensing electrode, and provide certain waterproof and corrosion-resistant functions.

[0080] Three-dimensional Monitoring Network: By embedding multiple sensing units at different depths and locations in the concrete, a three-dimensional spatial monitoring network is formed. This network can comprehensively and real-time monitor the penetration of chloride ions in the concrete structure, realize three-dimensional imaging of the distribution of chloride ion concentration, and provide comprehensive data support for evaluating the durability and corrosion risk of the concrete structure.

[0081] The complete process is described as follows:

[0082] Template Reserved Hole Position: Before pouring the concrete, according to the arrangement scheme of the sensing unit, the hole position corresponding to the inside of the template is reserved. The size of the hole position should be slightly larger than the external size of the sensing unit to ensure that the electrode can be smoothly installed. For example, for the planar sprayed MXene-Cl sensing electrode, the size of the reserved hole position can be 100mm×100mm×35mm (length×width×depth), and for the ring-wrapped electrode, the size of the reserved hole position can be determined according to the diameter of the steel bar and the size of the electrode, generally a circular hole with a diameter of 50mm and a depth of 40mm.

[0083] Planar Sprayed Electrode Installation and Packaging: The planar sprayed MXene-Cl sensing electrode is attached to the bottom of the template reserved hole position, ensuring that the electrode surface is flat, bubble-free and wrinkle-free. Then, epoxy glue is evenly applied around the electrode and on the surface to fix the electrode on the template. The thickness of the epoxy glue should be moderate, generally 1-2mm, to ensure the bonding strength between the electrode and the template. Before the epoxy glue solidifies, the electrode should be protected from external force interference to ensure its accurate position.

[0084] Ring-wrapped Electrode Installation: After the steel bar is inserted through the template reserved hole, the ring-wrapped MXene-Cl sensing electrode is inserted into the reserved hole position together with its protective layer and ring belt, so that the ring belt is tightly attached to the steel bar. During the installation process, attention should be paid to protect the electrode lead to avoid damage or short circuit. At the same time, ensure good contact between the electrode and the steel bar to ensure the accuracy of the measurement.

[0085] Polyurethane grouting seal: After the installation of the ring-wrapped electrode is completed, polyurethane grouting material is used to seal the edges of the template holes. The polyurethane grouting material is uniformly injected into the pores, filling and compacting to ensure that there is no grouting leakage. The injection pressure of the polyurethane grouting material should be moderate, generally 0.2-0.3MPa, to avoid damage to the electrode and the template. After the grouting material solidifies, the sealing effect should be checked, and if there is leakage or non-compactness, it should be repaired in time.

[0086] Forming a three-dimensional monitoring network: By sequentially installing planar spray and ring-wrapped MXene-Cl sensing electrodes in multiple reserved hole positions of the template, and performing packaging and sealing, a three-dimensional monitoring network covering the concrete surface to the steel bar is finally formed. This network can monitor the penetration of chloride ions in concrete in real time, providing comprehensive data support for subsequent corrosion risk assessment and structure life prediction. During the concrete pouring process, the stability of the template should be ensured to avoid electrode position deviation or damage. At the same time, during the concrete curing period, attention should be paid to protect the sensing unit from external interference or damage.

[0087] The three-dimensional monitoring network integrates a redundant distribution strategy, as follows:

[0088] Step S140, in the three-dimensional network formed, the equipment nodes, standby nodes and corresponding main nodes are arranged at the same depth and function according to a preset ratio.

[0089] Among them, the three-dimensional monitoring network: through embedding multiple sensing units at different depths and positions of the concrete, a three-dimensional spatial monitoring network is formed. This network can comprehensively and real-time monitor the penetration of chloride ions in the concrete structure, realizing three-dimensional imaging of the chloride ion concentration distribution.

[0090] Standby node: In the three-dimensional monitoring network, in addition to the main nodes that work normally, standby monitoring points for data verification are additionally set. These standby points are in standby state when the main node performance is normal, and are used to assist in reconstructing the chloride ion concentration field and verifying data accuracy when the main node performance degrades.

[0091] The main node is the core sensing electrode, i.e. the planar spray MXene-Cl sensing electrode arranged in the point array of each monitoring layer of the concrete protective layer, and the ring-wrapped MXene-Cl sensing electrode arranged at the steel bar.

[0092] The monitoring points mentioned in this application include main nodes and standby nodes. The main node is the core monitoring unit, and the standby node is the redundant guarantee unit. The three together constitute a complete monitoring network.

[0093] Pre-set ratio: The ratio of the number of backup nodes to the number of main nodes pre-set according to the monitoring accuracy requirements and engineering actual situation. This ratio is usually determined based on historical data, engineering experience and reliability analysis.

[0094] Same depth and same function arrangement: Backup nodes and corresponding main nodes are arranged at the same depth and have the same function and performance parameters. This ensures that backup nodes can replace main nodes for monitoring and have comparability when data is checked.

[0095] The complete process is described as follows:

[0096] Determine the number of backup nodes: According to the size of the monitoring network and the reliability requirements, calculate the number of backup nodes that need to be added according to the pre-set ratio. For example, if there are 100 main nodes in the monitoring network, the pre-set ratio is 20%, then 20 backup nodes need to be added.

[0097] Arrange backup nodes: In the three-dimensional monitoring network, select appropriate positions to arrange backup nodes. Backup nodes should be arranged at the same depth and function as corresponding main nodes to ensure that they can replace main nodes for monitoring when needed. For example, in each monitoring layer, select positions next to a certain number of main nodes as the arrangement positions of backup nodes at certain intervals.

[0098] Install and debug backup nodes: Install backup nodes according to the same installation process and debugging method as main nodes. Ensure that backup nodes are tightly combined with concrete structures, electrode lead connections are reliable, and necessary protection treatment is carried out.

[0099] Integrate into the monitoring system: Connect backup nodes to the data acquisition and transmission network of the monitoring system to ensure that their data can be uploaded to the cloud platform in real time. In the monitoring system software, identify and manage backup nodes to enable them to be activated in time when main nodes degrade in performance.

[0100] Step S150, real-time monitoring of node potential signal, impedance value and chloride ion concentration data, when electrode potential drift exceeds the pre-set threshold, impedance value change rate is greater than the pre-set range or chloride ion response time is longer than the initial calibration value by more than the pre-set multiple, it is determined as a performance degradation node.

[0101] Among them, potential signal drift: refers to the deviation of the electrode output potential over time during operation. In a stable chloride ion concentration environment, the potential of a healthy electrode should remain relatively stable. If the potential fluctuation exceeds the normal range, it may indicate electrode performance degradation. Impedance value change rate: the impedance of the electrode at different frequencies will change over time, environment and its own state. The impedance value change rate reflects the stability of the electrode's ability to transport and respond to chloride ions. If the impedance value change rate is too large, it may mean that the internal structure or surface properties of the electrode have changed, affecting its normal function. Chloride ion response time: the time required from the change in chloride ion concentration to the stable output signal of the electrode. Prolonged response time usually indicates reduced electrode sensitivity, which may be due to electrode surface contamination, reduced active sites or blocked ion transport channels.

[0102] Pre-set threshold and range: decision criteria determined in advance based on laboratory calibration, historical data and engineering experience. These thresholds and ranges are used to distinguish between normal and degraded electrode performance, ensuring timely detection of potential problems with reasonable sensitivity and accuracy.

[0103] The complete process is described as follows:

[0104] Real-time data acquisition: use data acquisition system to continuously collect potential signal, impedance value and chloride ion concentration data of each node. The acquisition frequency is determined according to the monitoring requirements, generally the potential signal acquisition frequency is 1 Hz, and the impedance value and chloride ion concentration data acquisition frequency is once an hour.

[0105] Data processing and preliminary analysis: filter, denoise and smooth the collected data, remove outliers and interference signals. Calculate the short-term and long-term drift of the potential signal, the short-term drift can take the potential change per minute, and the long-term drift takes the potential change per hour or per day. Use impedance spectrum analysis software to calculate the change rate of impedance value, and determine the extension of chloride ion response time by comparing with the change of chloride ion concentration in standard solution.

[0106] Performance degradation determination: compare the processed data with the pre-set threshold and range. If the electrode potential drift exceeds the pre-set threshold (such as short-term drift exceeds ±5mV, long-term drift exceeds ±10mV), impedance value change rate is greater than the pre-set range (such as exceeds ±20%) or chloride ion response time is extended more than the initial calibration value by more than the pre-set multiple (such as extended more than 2 times), then the node is determined as a performance degradation node. At the same time, combined with multiple data and determination criteria for comprehensive evaluation, improve the determination accuracy and avoid misjudgment.

[0107] Recording and Alarm: Once the node performance degradation is determined, immediately record the location of the node, the degradation time and related data in the monitoring system. Trigger the alarm mechanism to inform the maintenance personnel to check and repair. According to the degree of degradation and the scope of influence, formulate the corresponding repair plan and priority to ensure the reliable operation of the monitoring system.

[0108] Step S160, for the performance degradation node, based on the transverse concentration gradient of the same layer non-degradation node and the longitudinal penetration model of the historical data of the upper and lower layers, the interpolation algorithm is used to reconstruct the chlorine ion concentration field in the region.

[0109] Among them, the performance degradation node: in the sensor network, the node whose monitoring performance is reduced due to various factors, cannot accurately reflect the chlorine ion concentration, needs to be identified and processed. Transverse concentration gradient: the rate of change of chlorine ion concentration with position in the same monitoring layer, reflects the diffusion trend of chlorine ion in the concrete surface layer. Longitudinal penetration model: a model describing the law of chlorine ion penetration at different depths of concrete, based on historical data, reflects the process of chlorine ion migration with time to depth. Interpolation algorithm: a mathematical method for estimating unknown point data based on existing data points, used to reconstruct the chlorine ion concentration at the degradation node.

[0110] The complete process is described as follows:

[0111] 1. Node state monitoring and identification: Real-time monitoring of all node potential signal, impedance value and chlorine ion concentration data, according to the preset performance degradation standard (such as potential drift threshold value, impedance change rate too large, response time extension, etc.), identify the performance degradation node.

[0112] 2. Calculation of transverse concentration gradient: Use the chlorine ion concentration data of the same layer non-degradation node to calculate the transverse concentration gradient of the layer by difference method.

[0113] 3. Construction of longitudinal penetration model: Based on historical monitoring data, construct a longitudinal penetration model to describe the variation law of chlorine ion concentration with depth and time.

[0114] 4. Application of interpolation algorithm: Combine the transverse concentration gradient and the longitudinal penetration model, select the appropriate interpolation algorithm (such as bilinear interpolation or Kriging interpolation), estimate the chlorine ion concentration at the degradation node, and realize the concentration field reconstruction.

[0115] 5. Concentration field reconstruction and data fusion: Integrate the concentration value estimated by interpolation into the overall concentration field data to generate a complete chlorine ion concentration distribution map, ensuring the continuity and accuracy of the data, and providing a reliable basis for subsequent corrosion risk assessment.

[0116] The method further comprises a step after reconstructing the chlorine ion concentration field in the region using the interpolation algorithm, specifically as follows:

[0117] Step S1A0, the proportion of performance degradation nodes to total nodes is calculated at a preset period. When the proportion exceeds a preset warning line, the multi-scale data fusion process is started.

[0118] Wherein, the preset period: the statistical period determined in advance according to monitoring requirements and system resource allocation. For example, every 24 hours or every week. Performance degradation node ratio: the ratio of the number of performance degradation nodes to the total number of nodes, used to quantify the degree of system performance degradation. The preset warning line: a threshold value set in advance based on system reliability analysis and historical data. When the performance degradation node ratio exceeds the threshold value, the multi-scale data fusion process is triggered.

[0119] The complete process is described as follows:

[0120] Periodic statistics and calculation: according to the preset period (such as 2 o'clock in the morning every day), the system automatically calculates the number of current performance degradation nodes and calculates the proportion of the number of performance degradation nodes to the total number of nodes.

[0121] Comparison and judgment: compare the calculated performance degradation node ratio with the preset warning line (such as 15%). For example, if the total number of nodes is 200 and the warning line is 15%, the warning is triggered when the number of performance degradation nodes exceeds 30.

[0122] Triggering the multi-scale data fusion process: when the performance degradation node ratio exceeds the preset warning line, the system automatically starts the multi-scale data fusion process to improve the reliability and accuracy of the data.

[0123] Step S1B0, call the vertical gradient of different depth layers and the horizontal distribution data of the same layer, calculate the spatial weight through the preset Gaussian kernel function, construct the three-dimensional space correlation matrix, and simultaneously extract the preset historical period data, establish the time decay coefficient model combined with the environmental variables, and calculate the time weight of each period data through the model.

[0124] Wherein, vertical gradient: the rate of change of chloride ion concentration in the depth direction of concrete, reflecting the depth dependence of chloride ion penetration. Gaussian kernel function: a distance-based weight calculation method, the weight of adjacent nodes is high, and the weight decreases exponentially with the increase of distance. Three-dimensional space correlation matrix: a matrix describing the spatial correlation between monitoring nodes, constructed by integrating vertical gradient and horizontal distribution data. Time decay coefficient model: a model that characterizes the law of data weight change with time according to environmental variables and time series data, the weight decays according to a preset rule over time.

[0125] The complete process is described as follows:

[0126] 1. Data calling and preprocessing: Extract the vertical gradient and horizontal distribution data of each depth layer at the current time from the database, and arrange them into a matrix form. Normalize the data to eliminate the dimensional differences.

[0127] 2. Spatial weight calculation: Calculate the spatial weight between nodes using the Gaussian kernel function, formula:

[0128] ;

[0129] where, is the weight coefficient between node i and node j, is the spatial distance between node i and node j, is the kernel bandwidth parameter, which controls the speed of weight decay with distance.

[0130] Three-dimensional spatial correlation matrix construction: Fill the calculated spatial weight into the matrix, with rows and columns corresponding to different nodes, and matrix elements representing the spatial correlation strength between the corresponding two nodes.

[0131] Historical data extraction and time decay coefficient calculation: Extract the monitoring data of the preset historical period (such as the past 7 days, 30 days) from the database, combine with environmental variables, and calculate the data weight of each period according to the preset time decay model (such as exponential decay model: , is the initial weight, λ is the decay constant, and t is the time).

[0132] Data fusion preparation: Integrate the constructed three-dimensional spatial correlation matrix and the calculated time weight data to prepare for subsequent Kalman filter fusion. Ensure data format uniformity, time correspondence, verify matrix dimension matching and weight rationality, and eliminate outliers.

[0133] Step S1C0, fuse the spatial correlation matrix and time weight data through the preset Kalman filter algorithm, generate the reconstruction result containing the preset confidence interval through the preset number of Monte Carlo simulation, and control the monitoring error within the preset range.

[0134] where, Kalman filter algorithm: a recursive algorithm for estimating the state of a dynamic system, which can fuse multiple data sources, reduce noise influence, and improve monitoring accuracy. Monte Carlo simulation: a numerical calculation method through random sampling, which can evaluate the uncertainty of data and generate reconstruction results containing confidence intervals. Confidence interval: in statistics, it represents the confidence range of parameter estimation, which is determined based on the dispersion of data and sample size.

[0135] The complete process is described as follows:

[0136] Data fusion: Input spatial correlation matrix (describing spatial correlation between nodes) and time weight data (reflecting the importance of data changes over time) into Kalman filter algorithm, fuse multi-source data, dynamically update state estimation, and reduce noise impact.

[0137] Result generation and verification: Generate reconstruction results with confidence intervals through Monte Carlo simulation (such as 10000 random samplings), evaluate result reliability. Compare reconstruction with true chloride concentration field data, ensure monitoring error within preset range (such as ±5%), verify fusion process effectiveness.

[0138] Feedback optimization: If monitoring error exceeds preset range, analyze reasons (such as model parameters, data quality issues), adjust Kalman filter parameters or re-run simulation, optimize fusion process, improve reconstruction accuracy.

[0139] Three-dimensional monitoring network realizes engineering adaptation through flexible combination of preset parameters, as follows:

[0140] Step S1a0, according to the design thickness of concrete members, dynamically adjust the number of monitoring layers within the preset range of 2 to 6 layers, set the interval between each layer in proportion or gradient manner, ensure complete depth interval coverage from concrete surface to steel reinforcement protection layer.

[0141] Among them, dynamically adjusting the number of monitoring layers: according to the design thickness of different concrete members, flexibly increase or decrease the number of monitoring layers within the preset range. The increase or decrease of monitoring layers will affect the longitudinal resolution and monitoring accuracy of data. Proportional or gradient manner: equal interval refers to equal interval between each monitoring layer; gradient manner refers to setting different layer intervals according to chloride ion penetration rate or structural characteristics, such as small surface interval and large deep interval.

[0142] The complete process is described as follows:

[0143] Determine the thickness of concrete members: Obtain the design drawings or actual size of the concrete members to be monitored, and determine the design thickness.

[0144] Preset monitoring layer number range and initial interval: According to the thickness of the member, initially determine the number of monitoring layers within the preset range of 2 to 6 layers. For example, for a member with a thickness of 300mm, 4 monitoring layers can be initially determined. The initial equal interval is calculated as total thickness divided by (number of layers -1), i.e. for 4 monitoring layers, the initial equal interval is 300mm / (4-1)=100mm.

[0145] Dynamically adjust the number and spacing of monitoring layers: If component thickness changes or monitoring requirements adjust, the number of monitoring layers can be dynamically increased or decreased. When the number of layers is adjusted, the spacing between layers is recalculated using a proportional or gradient method. For example, if the number of monitoring layers is adjusted to 5 and a proportional setting is used, the new spacing is 300mm / (5-1) = 75mm. If a gradient method is used, based on the chloride ion penetration model, the surface layer spacing can be set to 50mm, the middle layer spacing to 75mm, and the deep layer spacing to 100mm.

[0146] Verify the monitoring network coverage: Use 3D modeling software to construct a monitoring network model and verify that each monitoring layer fully covers the depth range from the concrete surface to the steel cover. If there is insufficient coverage or excessive overlap, further optimize the number of monitoring layers and spacing.

[0147] In step S1b0, for different monitoring accuracy requirements, the horizontal dot matrix supports selection from a preset 3×3 to 5×5 discrete dot array. The spacing between each dot matrix unit is kept evenly distributed to form a regular grid structure, achieving a balance between monitoring density and data volume.

[0148] Discrete Point Array: Within the same monitoring layer of concrete, flat, spray-coated MXene-Cl sensing electrodes are discretely arranged according to a pre-set pattern to form an array. The more discrete points, the higher the monitoring accuracy and the greater the data volume. Uniform Distribution: Each dot array element is equally spaced, forming a regular grid. Uniform distribution ensures uniform coverage of the monitoring area and avoids blind spots.

[0149] The complete process is described as follows:

[0150] Determine monitoring accuracy requirements: Based on the project's chloride ion monitoring accuracy requirements, determine the required discrete point array density. For example, for critical components requiring high accuracy, choose a 5×5 point array; for areas requiring general accuracy, choose a 3×3 point array.

[0151] Design the dot matrix layout: On a concrete floor plan, plan the layout of the discrete dot array. Ensure that the spacing between each dot matrix unit is evenly distributed, forming a regular grid. For example, if a 5×5 dot matrix is ​​selected for a 1m square monitoring area, the spacing between each dot matrix unit is 20cm.

[0152] Mark the dot positions: Use a measuring tool (such as a laser rangefinder, tape measure, etc.) to mark the positions of each dot unit on the actual concrete component template. Ensure the marking is accurate to achieve uniform distribution.

[0153] Install the sensing electrodes: Install the flat, spray-applied MXene-Cl sensing electrodes at the marked locations. Ensure the electrodes are tightly bonded to the formwork and securely installed. After the concrete is poured, the electrodes will be distributed according to the pre-set discrete point array, forming a uniform monitoring network.

[0154] Step S1c0, according to the diameter of the monitored reinforcement and the corrosion protection level, select the appropriate number of preset 3 to 6 ring electrodes, each ring sleeve is equidistantly distributed along the reinforcement axis, and the single sleeve width is a preset value, ensuring full circumferential chloride ion penetration monitoring of the reinforcement.

[0155] Wherein, the corrosion protection level: refers to the standard for preventing reinforcement corrosion according to the corrosiveness of the environment of the concrete structure and the design service life. Different levels have different requirements for the accuracy and coverage of chloride ion monitoring. Equidistant distribution: each ring sleeve is uniformly distributed along the reinforcement axis, ensuring continuity and uniformity of monitoring. Equidistant distribution can avoid monitoring blind area and ensure uniform monitoring of chloride ion penetration around the reinforcement.

[0156] The complete process is described as follows:

[0157] Determine the reinforcement parameters: obtain the diameter and corrosion protection level of the monitored reinforcement. For example, the reinforcement diameter is 20mm, and the corrosion protection level is II level.

[0158] Select the number of ring electrodes: according to the diameter of the reinforcement and the corrosion protection level, select the appropriate number of preset 3 to 6 ring electrodes. For example, for a reinforcement with a diameter of 20mm and a protection level of II, select 4 ring electrodes.

[0159] Equidistant distribution setting: determine the equidistant distribution of each ring sleeve along the reinforcement axis. For example, if the reinforcement length is 1m, 4 ring electrodes can be arranged at a distance of 100mm, 300mm, 500mm and 700mm from the end of the reinforcement.

[0160] Install the ring electrodes: install the ring electrodes on the reinforcement according to the set position, ensure that the width of each ring sleeve is a preset value, and the ring sleeves are equidistantly distributed. For example, the ring sleeve width is 20mm, and the distance between adjacent ring sleeves is 200mm. Ensure that the ring electrodes are tightly attached to the reinforcement, firmly installed, and good protection measures are taken.

[0161] Step S1d0, the distance between adjacent monitoring modules is dynamically adjusted within the preset range of 15 to 25mm.

[0162] Wherein, the distance between adjacent monitoring modules refers to the distance between two adjacent monitoring modules when arranging the sensing unit. Dynamic adjustment within the preset range of 15 to 25mm can flexibly set the appropriate distance according to the specific monitoring requirements and the characteristics of the concrete structure.

[0163] The penetration distribution imaging is realized by combining the preset vertical and horizontal gradient estimation methods, including:

[0164] Step S210, the sensing unit is composed of a MXene selective electrode with Cl-terminated surface and a reference electrode. The original potential difference of different depths and different positions of the concrete is synchronously collected by using the specific intercalation and charge transfer characteristics of MXene to Cl⁻, and a multi-dimensional potential data matrix is formed.

[0165] Among them, the MXene selective electrode: the electrode made of MXene material with Cl-terminated surface has specific intercalation ability to Cl⁻, and detects the concentration of Cl⁻ by using the charge transfer caused by intercalation.

[0166] Reference electrode: an electrode used as a reference potential in electrochemical measurement, which usually has a stable potential and cooperates with the working electrode to measure the potential difference.

[0167] Original potential difference: the instantaneous potential difference between the MXene selective electrode (working electrode) and the reference electrode, the expression is: ; wherein, The first layer of the first point in time , the original potential difference, represents the open circuit potential of the working electrode, represents the instantaneous potential value of the reference electrode.

[0168] Multi-dimensional potential data matrix: a matrix formed by arranging the original potential difference data of different depths and positions according to certain rules, which is used for subsequent data processing and analysis.

[0169] Step S220, the reference electrode potential correction term is obtained by the reference electrode potential correction algorithm, the original signal is corrected, and the corrected potential data is obtained.

[0170] The specific correction formula is as follows: ; wherein, is the column reference electrode potential correction term at time , which can be obtained by blank point pre-calibration, historical drift regression or redundant channel average.

[0171] Among them, the reference electrode potential correction algorithm includes three processing algorithms of blank point pre-calibration, historical drift regression and redundant channel average.

[0172] Blank point pre-calibration: Calibrate the reference electrode in a solution without chloride ions to obtain its potential value in an ideal state, which serves as the basis for subsequent potential correction. Historical drift regression: By analyzing the potential drift data of the reference electrode in historical measurements, a regression model is established to predict the current drift amount. Redundant channel averaging: Use the data collected by multiple redundant channels to calculate the average value to reduce random errors and improve measurement accuracy. Reference electrode potential correction term: used to correct the offset of the original potential signal to ensure the accuracy of the measured potential.

[0173] Step S230, based on the potential response law of MXene interlayer Cl⁻ intercalation, the corrected potential is substituted into the Nernst equation to calculate the chloride ion concentration of each monitoring point, forming a concentration distribution matrix.

[0174] Wherein, the inversion formula is as follows: ; wherein, is the reference potential value under the standard concentration (1 mol / L), is the electrode sensitivity constant, the theoretical value is 59.16 mV / decade (25°C, applicable to monovalent ion Cl⁻).

[0175] Step S240, calculate the vertical interlayer concentration gradient and the concentration change of the adjacent points in the horizontal direction according to the preset algorithm, construct a gradient feature matrix, and quantify the migration trend of chloride ions.

[0176] Wherein, the vertical interlayer concentration gradient: the rate of change of chloride ion concentration in the depth direction of concrete, reflecting the depth dependence of chloride ion penetration. The concentration change of the adjacent points in the horizontal direction: the difference of chloride ion concentration between adjacent monitoring points in the same monitoring layer, reflecting the diffusion trend of chloride ions in the surface layer of concrete. Gradient feature matrix: a matrix constructed by combining the vertical interlayer concentration gradient and the concentration change of the adjacent points in the horizontal direction, used to quantify the migration trend of chloride ions.

[0177] The specific complete process can be referred to steps S241 to S243, which will not be repeated here.

[0178] Step S250, based on the gradient feature matrix, draw the concentration contour and front evolution curve in the depth-time dimension, and generate a three-dimensional contour surface graph combining the horizontal point array data, to intuitively present the spatial distribution form and penetration path of chloride ions in the concrete.

[0179] Depth-time two-dimensional concentration contour and front evolution curve: in the plane formed by depth and time, the contour line formed by connecting points of the same concentration intuitively presents the dynamic process of chloride ions penetrating into the depth of concrete over time.

[0180] Stereoscopic three-dimensional isosurface map: Fusing three-dimensional information of depth, time, and lateral position, constructing chloride concentration isosurface, and comprehensively and stereoscopically presenting the penetration path and spatial distribution pattern of chloride ions in the concrete.

[0181] The complete process is described as follows:

[0182] Constructing depth-time-concentration database: Integrating the concentration distribution matrix obtained in step S230 and the gradient feature matrix constructed in step S240, a three-dimensional database is constructed with depth, time, and chloride concentration as the core fields. For example, for a project with a depth interval of 10 mm and a monitoring time of 30 days, the concentration values of each depth layer at different time points are recorded.

[0183] Drawing depth-time two-dimensional concentration contour and front evolution curve: Based on the depth-time-concentration database, using selected drawing software to generate depth-time two-dimensional concentration contour map and chloride ion penetration front evolution curve. In the contour map, the horizontal axis represents time, the vertical axis represents depth, and the contour line connects the same concentration points, clearly outlining the trajectory of chloride ions penetrating deep over time; the front evolution curve accurately locates the chloride ion penetration front and presents its advancing speed and depth change.

[0184] Combining lateral point array data to draw three-dimensional isosurface map: Integrate the lateral point array data into the depth-time two-dimensional data, and use three-dimensional drawing function to generate stereoscopic three-dimensional isosurface map. In the three-dimensional isosurface map, the horizontal and vertical axes represent the lateral position and depth of the concrete, respectively, and the third dimension is time. The isosurface is composed of points with the same concentration. By observing the isosurface from different angles, the details of chloride ion penetration can be fully understood, including the main penetration path and local abnormal penetration area.

[0185] Chloride ion penetration path visualization and analysis: With the help of three-dimensional isosurface map, the complex penetration path of chloride ions in the concrete is intuitively presented. By observing the shape, density, and trend of the isosurface, the main direction of chloride ion penetration, local acceleration or resistance area can be analyzed in depth, providing key basis for durability evaluation and protection strategy of concrete structure. For example, the dense area of isosurface indicates that the chloride ion concentration changes dramatically, which may be a weak link in the penetration path and needs to be focused on and protected.

[0186] Step S260, according to the engineering scene to determine the critical threshold of chloride ion, compare the concentration distribution matrix with the critical threshold, combine the gradient feature matrix to locate the corrosion risk area, and superimpose the risk identification in three-dimensional imaging.

[0187] Chloride critical threshold: The chloride ion concentration value determined according to engineering experience and specifications. When the measured concentration exceeds this value, the steel bars in the concrete may corrode. Risk identification: Visual elements used to mark corrosion risk areas in three-dimensional imaging, such as color changes, text annotations, or special symbols, to visually display potential corrosion risk locations.

[0188] The complete process is described as follows:

[0189] Determine the chloride critical threshold: According to the engineering scene, concrete mix and environmental conditions, determine the chloride critical threshold through experiments or reference specifications. For example, for the concrete structure of a coastal bridge, reference the "Design Code for Marine Concrete Structures" and combine local environmental corrosion data to determine the critical threshold of 0.03 mol / L.

[0190] Compare the concentration distribution matrix with the critical threshold: Compare the concentration value of each monitoring point in the concentration distribution matrix obtained in step S230 with the critical threshold. For example, in the concentration distribution matrix, if the concentration of a certain monitoring point is 0.035 mol / L, which exceeds the critical threshold of 0.03 mol / L, then this point is identified as a potential corrosion risk point.

[0191] Analyze the risk area in combination with the gradient feature matrix: Use the gradient feature matrix constructed in step S240 to analyze the migration trend of chloride ions to assist in determining the corrosion risk area. For example, in the gradient feature matrix, if the vertical gradient of a certain area is large and the horizontal concentration changes abnormally, it indicates that chloride ions penetrate rapidly into the deep and there may be local diffusion anomalies, and this area is determined as a high-risk area.

[0192] Overlay risk identification in three-dimensional imaging: In the three-dimensional contour map generated in step S250, overlay risk identification according to the location and degree of corrosion risk area. For example, use red to identify high-risk areas, orange to identify medium-risk areas, and yellow to identify low-risk areas, and mark the specific location and concentration value of the risk area in the graph. Through color changes and location annotations, the distribution of potential corrosion risks in the concrete structure is visually presented, providing a basis for maintenance decisions.

[0193] Calculate the vertical interlayer concentration gradient and horizontal adjacent point concentration change according to the preset algorithm to construct the gradient feature matrix and quantify the migration trend of chloride ions, including:

[0194] Step S241, based on the concentration distribution matrix, using the interlayer concentration difference quotient algorithm, calculating the vertical gradient value of each monitoring point according to the ratio of the concentration difference value of adjacent depth layers to the layer spacing, quantifying the penetration rate and depth distribution trend of chloride ions along the thickness direction of the concrete.

[0195] The concentration distribution matrix is obtained by extracting the concentration data of each monitoring point from the concentration inversion result obtained in step S230 to form the concentration distribution matrix.

[0196] The interlayer concentration difference quotient algorithm is applied: the vertical gradient value of each monitoring point is calculated using the following formula:

[0197] , wherein, is the chloride ion concentration at depth i+1 layer and horizontal j, is the chloride ion concentration at depth i layer and horizontal j, is the depth layer spacing, is the vertical concentration gradient.

[0198] Quantify the vertical penetration rate and depth distribution trend: the size and sign of the vertical gradient value reflect the penetration rate and depth distribution trend of chloride ions along the thickness direction of the concrete. A positive value indicates that the chloride ion concentration increases with depth, and a negative value is the opposite. By calculating the vertical gradient value of all monitoring points, the vertical penetration of chloride ions in the concrete can be comprehensively understood.

[0199] Step S242, for the same depth layer of horizontal point array data, the horizontal gradient value is calculated by the ratio of the concentration difference value of adjacent monitoring points and the horizontal spacing, and the concentration abnormal area in the horizontal direction is identified.

[0200] The complete process is described as follows:

[0201] Extract the horizontal point array data: extract the horizontal data of the same depth layer from the concentration distribution matrix.

[0202] Apply the adjacent concentration difference quotient algorithm: the horizontal gradient value is calculated using the following formula:

[0203] , is the spacing of the adjacent monitoring points in the horizontal direction.

[0204] Identify the concentration abnormal area in the horizontal direction: the size and sign of the horizontal gradient value reflect the concentration change trend of chloride ions in the horizontal direction. A positive value indicates that the chloride ion concentration increases in the horizontal direction, and a negative value is the opposite. By calculating the gradient value of all horizontal monitoring points, the concentration abnormal area in the horizontal direction can be identified, which may indicate local chloride ion penetration anomalies or the presence of cracks.

[0205] Step S243, the vertical gradient value and the horizontal gradient value are associated according to the spatial coordinates to form a three-dimensional feature matrix. The area where the gradient absolute value is greater than the preset threshold value in the matrix is marked as the active penetration area. Through the gradient vector direction of the matrix elements, the three-dimensional migration path of chloride ions is intuitively mapped. Combined with time series data, the gradient amplitude change is dynamically tracked, and the acceleration / decay trend of the penetration rate is quantified.

[0206] wherein, three-dimensional gradient feature matrix: a spatial matrix integrating vertical and horizontal gradient information, used to comprehensively describe the three-dimensional characteristics of chloride ion migration. Active permeation area: an area with gradient absolute value exceeding a preset threshold, indicating high chloride ion permeation rate or significant concentration change. Gradient vector direction: the direction composed of vertical and horizontal gradients, indicating the chloride ion migration path. Time series data: data collected in chronological order, used to analyze the dynamic changes of permeation trends.

[0207] The complete process is described as follows:

[0208] Integrate gradient data: integrate the vertical gradient obtained in step S241 and the horizontal gradient obtained in step S242 into a three-dimensional gradient feature matrix according to spatial coordinates. The matrix elements include the vertical gradient, horizontal gradient and their synthesized gradient vector of each monitoring point.

[0209] Label active permeation area: compare the gradient absolute value of the matrix elements with the preset threshold. If the vertical or horizontal gradient absolute value exceeds the threshold, it is labeled as an active permeation area.

[0210] Map three-dimensional migration path: draw the chloride ion migration path in the matrix according to the gradient vector direction. Specifically, from each active permeation area, draw a migration line along the gradient vector direction.

[0211] Dynamic tracking trend: combine time series data to analyze gradient amplitude changes. If the gradient amplitude continuously increases, it indicates that the permeation rate is accelerating; if it decreases, it indicates that the permeation rate is decaying. For example, by comparing the gradient data of 30 consecutive days, if the gradient amplitude increases from 0.001 mol / (L·mm) to 0.0015 mol / (L·mm) from the 15th day to the 30th day, it is determined that the permeation rate is accelerating.

[0212] Collect multi-parameter data related to concrete service environment and structure state, and aggregate multi-parameter data and chloride ion concentration data and transmit to the cloud, combined with the preset corrosion life prediction model to evaluate the concrete structure deterioration state, including:

[0213] Step S410, directional collection of environmental parameters, structural parameters and MXene parameters, identification of core influencing factors through causal diagram analysis, determination of chloride ion concentration and MXene intercalation amount as direct driving factors, determination of temperature and humidity and strain as indirect adjusting factors, construction of multi-dimensional correlation data set, including potential signal corrected by reference electrode drift, wherein, environmental parameters include temperature and humidity and type of corrosive medium, structural parameters include strain and crack width, MXene parameters include intercalation amount and potential signal.

[0214] Among them, environmental parameters refer to the conditions of the environment where the concrete structure is located, such as temperature and humidity and the type of corrosive medium, which will affect the penetration rate of chloride ions and the deterioration process of concrete. Structural parameters refer to the physical state parameters of the concrete structure, such as strain and crack width, which reflect the stress and damage of the structure during use. MXene parameters refer to parameters related to MXene materials, such as intercalation amount and potential signal, which reflect the working state of MXene sensors and the response to chloride ions. Causal diagram analysis: an analysis method for identifying the causal relationship between variables, which helps to determine which factors are the core influencing factors. Multi-dimensional correlation dataset: integrating different types of parameter data together to form a comprehensive dataset for comprehensive analysis of the deterioration state of concrete structures.

[0215] The complete process is as follows: 1. Directional collection of parameter data: according to the preset monitoring plan, use the corresponding sensors to collect environmental parameters (temperature and humidity and the type of corrosive medium), structural parameters (strain and crack width) and MXene parameters (intercalation amount and potential signal). Ensure the accuracy and reliability of the sensors, and calibrate and maintain them regularly. 2. Causal diagram analysis to identify core influencing factors: draw a causal diagram, take chloride ion concentration and MXene intercalation amount as direct driving factors, and take temperature and humidity and strain as indirect adjusting factors. By analyzing the causal relationship between variables, identify the core factors that have the greatest impact on the deterioration of concrete structures. For example, if it is found that the concentration of chloride ions increases significantly in a high humidity environment, and is accompanied by a larger strain, then humidity is determined to be one of the key factors that accelerate the deterioration of the structure. 3. Build a multi-dimensional correlation dataset: integrate the collected parameter data together to build a multi-dimensional correlation dataset. The dataset should include the potential signal corrected by the reference electrode drift to ensure the accuracy of the data. Preprocess the data, including data cleaning, normalization and other operations, to improve data quality and usability. 4. Data integration and transmission: integrate the processed multi-dimensional correlation dataset with the chloride ion concentration data and transmit it to the cloud through the wireless communication module. In the cloud, use the preset corrosion life prediction model to further analyze the data and evaluate the deterioration state of the concrete structure. Regularly check the stability and security of data transmission to ensure the integrity and timeliness of the data.

[0216] Step S420, use the causal inference algorithm based on the electrochemical characteristics of MXene to process the data, eliminate noise by blank calibration, assign parameter weights according to the dynamic weight formula, use the potential response characteristics of MXene reversible intercalation to invert the concentration, and generate a feature vector.

[0217] Among them, the causal inference algorithm: an algorithm developed based on the electrochemical properties of MXene, used to analyze the causal relationship between data and extract key features, the general processing logic is as follows: Based on the characteristics of MXene, distinguish the real causal relationship of "chloride ion concentration→MXene signal" from multi-dimensional data (exclude surface correlation of interference factors such as temperature and humidity) Algorithm. The core logic of this algorithm is as follows: 1. Define the core cause: lock "Cl⁻ concentration→MXene potential" (because Cl⁻ embedded in MXene interlayer directly changes the potential, it is a necessary correlation); 2. Identify interference factors: identify temperature, humidity, etc. (only indirectly affect, and have no direct effect on MXene, belong to surface correlation); 3. Remove interference: use blank calibration (samples containing no Cl⁻) to eliminate potential noise caused by temperature, etc. and retain pure Cl⁻ response signal; 4. Feature extraction: Finally extract the features that only reflect the "real correlation between Cl⁻ and MXene" for subsequent analysis.

[0218] Blank calibration: Use a blank sample to calibrate the measurement system to eliminate system noise and offset. Dynamic weight formula: a formula that dynamically adjusts the weight according to the importance and correlation of parameters, ensuring that key parameters occupy a more important position in analysis. Feature vector: a feature vector extracted from the processed data, used to represent the state of the system and input into the corrosion life prediction model.

[0219] The complete process is described as follows:

[0220] Data acquisition and preprocessing: Obtain the multi-dimensional correlation data set from step S410, including environmental parameters, structural parameters and MXene parameters. Preprocess the data, including removing outliers and filling missing values.

[0221] Blank calibration noise removal: Use a blank sample to calibrate the measurement system, calculate the noise level and offset. Correct the original data, the formula is as follows:

[0222] ;

[0223] Where, is the corrected data, is the original data, is the blank sample data.

[0224] Dynamic weight allocation: According to the dynamic weight formula, consider the correlation and importance of each parameter, and allocate weights to different parameters. The formula is as follows:

[0225] ;

[0226] Where, is the dynamic weight of the ith parameter, is the initial weight, R is the correlation coefficient, and n is the total number of parameters.

[0227] Potential response characteristic inversion concentration: Using the reversible intercalation potential response characteristic of MXene, the corrected potential signal is inverted into the chloride ion concentration. The formula is as follows:

[0228] ; wherein, is the chloride ion concentration, is the reference concentration, is the potential signal, is the reference potential, is the electrochemical constant.

[0229] Feature vector generation: The processed data is integrated into a feature vector, including chloride ion concentration, MXene intercalation amount, temperature and humidity, and strain information. The formula is as follows:

[0230] ;

[0231] wherein, is the feature vector, is the chloride ion concentration, is the MXene intercalation amount, is the humidity, is the temperature, is the strain.

[0232] Step S430, a dynamic model integrating MXene function is constructed, the correlation between intercalation amount and retardation efficiency is embodied when correcting the diffusion coefficient, the two-way action of crack propagation and MXene interlayer peeling is coupled to derive the corrosion rate, and the model adjusts the parameters through the trend of MXene potential drift.

[0233] wherein, dynamic model: a model that can update and reflect the state change of the system in real time, used to simulate the deterioration process of concrete structure. Diffusion coefficient: a parameter representing the diffusion rate of chloride ions in concrete, affected by MXene intercalation amount and retardation efficiency. Two-way action: crack propagation affects MXene interlayer peeling, and vice versa. Self-adaptive parameter adjustment: model parameters can be automatically adjusted according to the trend of MXene potential drift to improve the accuracy and adaptability of the model.

[0234] For complete steps, refer to steps S431 to S434.

[0235] Step S440, introduce transfer learning to optimize the model, call the historical case library of different service environments to optimize the model, combine the preset corrosion life prediction model to output the causal contribution degree distribution of the deterioration state and the health trend of the whole life cycle, and the causal contribution degree includes the proportion of MXene retardation effect.

[0236] Among them, transfer learning: a machine learning method that uses existing knowledge (historical case data) to accelerate new model training and improve its performance. Causal contribution distribution: the proportion of each factor's contribution to concrete deterioration, including MXene blocking effect proportion. Life cycle health trend: the health state change trend of concrete structure from service to scrap.

[0237] The complete process is as follows:

[0238] Introduce transfer learning optimization model: introduce transfer learning in model training, pre-train the model on general data set, and then fine-tune with target environment data to accelerate training and improve generalization ability.

[0239] Call historical case library optimization model: incorporate historical case data from different environments into training to make the model adaptable to multiple scenarios and improve prediction accuracy.

[0240] Combine model output evaluation results: combine the transfer learning optimized model with the preset corrosion life prediction model, input multi-parameter data, and output causal contribution distribution and life cycle health trend. Among them, the causal contribution includes MXene blocking effect proportion, which can intuitively present the influence of each factor.

[0241] Provide decision support: based on the evaluation results, provide decision support for concrete structure maintenance and repair. For example, if the MXene blocking effect proportion is low, consider increasing the MXene content or replacing the sensor.

[0242] Among them, a dynamic model incorporating MXene function is constructed, the correlation between intercalation amount and blocking efficiency is reflected when modifying the diffusion coefficient, the bidirectional action of crack propagation and MXene interlayer peeling is coupled to derive the corrosion rate, and the model adjusts parameters through MXene potential drift trend as follows:

[0243] Step S431, dynamic diffusion coefficient correction: collect MXene intercalation amount data, construct a time-varying correction model of chloride ion diffusion coefficient containing environmental factors, and the core formula is:

[0244] ;

[0245] Among them, is the corrected diffusion coefficient, is the baseline diffusion coefficient, is the MXene intercalation amount at time t, is the intercalation amount-blocking efficiency correlation function (calibrated through experiments, the higher the intercalation amount, the stronger the blocking effect), is the temperature and humidity correction factor (quantifying the influence of environment on MXene function).

[0246] Step S432, bidirectional coupling derivation: identify the interaction between concrete crack propagation and MXene interlayer peeling, establish the coupling relationship between the two: crack width increase accelerates MXene interlayer peeling, and MXene interlayer peeling reacts on the crack, making its propagation rate increase; combine the modified diffusion coefficient in step 1 to derive the steel corrosion rate, and realize the coupling of the "crack-MXene deterioration-corrosion" bidirectional mechanism.

[0247] Among them, the coupling relationship is established: based on the understanding of the above interaction, the coupling relationship between the two is established. Specifically, the mutual influence of crack width and MXene interlayer peeling rate, and the reaction of MXene interlayer peeling degree on crack propagation rate. This coupling relationship can be described by a series of mathematical formulas and models, for example, a coupling coefficient is introduced to quantitatively represent the interaction strength between the two.

[0248] Derivation of steel corrosion rate: combine the modified diffusion coefficient (considering the influence of MXene intercalation amount and environmental factors) with the bidirectional coupling relationship to further derive the steel corrosion rate. Since the interaction between cracks and MXene interlayer peeling affects the diffusion path and rate of chloride ions, and thus affects the corrosion process of steel bars. By establishing a model that relates corrosion rate to crack width, MXene interlayer peeling degree, and chloride ion concentration, etc., the corrosion of steel bars can be more accurately predicted.

[0249] Step S433, adaptive adjustment of model parameters: extract MXene potential drift trend data, construct the mapping relationship between potential drift and model parameters, the core formula is as follows:

[0250] ; wherein, is the set of model parameters (including diffusion coefficient correction parameters, coupling coefficients, etc.), is the MXene potential drift, which adjusts the parameters in real time through the formula to adapt the model to material performance degradation, is the learning rate (controls the parameter update step, uses the Adagrad algorithm for adaptive adjustment), is the log-likelihood gradient (measures the matching degree of the current parameters and the observed data).

[0251] Step S434, construction and output of full-process dynamic model: construct a time and space coupled concrete deterioration dynamic model, the core control equation is:

[0252] , wherein, is the time-varying modified diffusion coefficient (coupled with time t, MXene intercalation amount , interlayer peeling degree ), and for the chloride ion concentration gradient (the driving force for diffusion), for the divergence operator (describing the spatial divergence / convergence property of diffusion).

[0253] The diffusion process is handled by the finite volume method, multi-source monitoring data is fused, uncertainty is quantified, and the corrosion rate and structure deterioration trend containing the MXene influence mechanism are output.

[0254] Based on the same inventive concept, the embodiment of the application provides a concrete chloride ion gradient monitoring and durability enhancement system based on MXene, which comprises a memory and a processor. Figure 1 The memory has a program capable of running on the processor to realize the method as shown in

[0255] The embodiments of the specific implementation are the preferred embodiments of the application, not limited to the protection scope of the application, so: any equivalent changes made according to the structure, shape, principle of the application should be covered within the protection scope of the application.

Claims

1. A method for monitoring chloride ion gradient and enhancing durability of concrete based on MXene, characterized in that: include: Multiple prefabricated sensing units containing MXene-Cl composite electrode layers and toughened protective layers are embedded in the concrete at different depths according to a vertical gradient to form a monitoring network. The potential signal is collected by the sensor unit, and after correction, it is inverted into the Nernst equation to obtain the chloride ion concentration. The permeability distribution imaging is achieved by combining the preset vertical and horizontal gradient estimation methods. Utilizing the intercalation adsorption properties of MXene for chloride ions, the chloride ions are captured and blocked from migrating to the steel bars. Collect multi-parameter data related to the concrete service environment and structural status, aggregate the multi-parameter data and chloride ion concentration data, and transmit them to the cloud. Combined with the preset corrosion life prediction model, the degradation status of the concrete structure is evaluated and an early warning is triggered when the chloride ion concentration exceeds the preset critical threshold. Multiple prefabricated sensing units containing MXene-Cl composite electrode layers and toughened protective layers are embedded in the concrete at different depths according to a vertical gradient to form a monitoring network including: The sensing unit includes a flat spray-type MXene-Cl sensing electrode and a ring-wrapped MXene-Cl sensing electrode. The flat spray-type MXene-Cl sensing electrode is arranged in a preset dot matrix at the same depth in the concrete, with a preset dot spacing. The ring-wrapped MXene-Cl sensing electrode is placed in a ring-shaped manner around the steel bar. Multiple monitoring layers are set up at preset intervals along the concrete cover from the concrete surface to the outer surface of the steel bar along the thickness direction. Only flat spray-type MXene-Cl sensing electrodes are arranged in each monitoring layer to cover the concrete areas at different depths within the cover. When the monitoring layer reaches the location of the steel bar, only the ring-shaped wrapped MXene-Cl sensing electrode is arranged at this location. Holes are reserved inside the pre-set concrete pouring formwork, where a flat spray-coated MXene-Cl sensing electrode is attached and sealed with epoxy glue. After the steel bars pass through the formwork holes, a ring-shaped wrapped MXene-Cl sensing electrode is slid in, tightly fitting the ring to the steel bars. The hole edges are then sealed with polyurethane grouting, forming a three-dimensional monitoring network covering the concrete surface to the steel bars. The potential signal is collected by the sensor unit, and after correction, it is inverted into the Nernst equation to obtain the chloride ion concentration. The permeability distribution imaging is achieved by combining the preset vertical and horizontal gradient estimation methods, including: The sensing unit consists of a MXene selective electrode with a Cl⁻ terminal on its surface and a reference electrode. Utilizing the specific intercalation and charge transfer properties of MXene for Cl⁻, the original potential difference at different depths and locations in the concrete is simultaneously collected to form a multi-dimensional potential data matrix. Obtain the reference electrode potential correction term through the reference electrode potential correction algorithm, correct the original signal, and obtain the corrected potential data; Based on the potential response law of Cl⁻ intercalation between MXene layers, the corrected potential is substituted into the Nernst equation for inversion to calculate the chloride ion concentration at each monitoring point and form a concentration distribution matrix; Calculate the vertical interlayer concentration gradient and the concentration change of adjacent points in the horizontal direction according to the preset algorithm, construct the gradient characteristic matrix, and quantify the migration trend of chloride ions; Based on the gradient characteristic matrix, concentration contour lines and front evolution curves are drawn in the depth-time dimension. Combined with the horizontal dot matrix data, a three-dimensional isosurface map is generated to intuitively present the spatial distribution and penetration path of chloride ions in concrete. Determine the critical threshold of chloride ions based on the engineering scenario, compare the concentration distribution matrix with the critical threshold, and locate the corrosion risk area in combination with the gradient feature matrix, and superimpose the risk markers in the 3D imaging; Collect multi-parameter data related to the concrete service environment and structural status, aggregate the multi-parameter data and chloride ion concentration data, and transmit them to the cloud. Combined with the preset corrosion life prediction model, the degradation status of the concrete structure is evaluated, including: Environmental parameters, structural parameters, and MXene parameters were collected in a targeted manner. Cause-and-effect diagram analysis was used to identify core influencing factors. Chloride ion concentration and MXene intercalation amount were identified as direct driving factors, while temperature, humidity, and strain were identified as indirect regulating factors. A multi-dimensional correlation dataset was constructed, including potential signals corrected for reference electrode drift. Environmental parameters included temperature, humidity, and corrosive medium type; structural parameters included strain and crack width; and MXene parameters included intercalation amount and potential signals. A causal inference algorithm based on the electrochemical properties of MXene was used to process the data. Noise was removed through blank calibration, parameter weights were assigned according to a dynamic weight formula, and the potential response characteristics of MXene reversible intercalation were used to invert the concentration and generate a characteristic vector. A dynamic model incorporating MXene functionality was constructed to reflect the relationship between intercalation amount and retardation efficiency when modifying the diffusion coefficient. The corrosion rate was derived by coupling the bidirectional effects of crack propagation and MXene interlayer delamination. The model adaptively adjusted parameters based on the MXene potential drift trend. A transfer learning optimization model is introduced, and the historical case library optimization model of different service environments is called. Combined with the preset corrosion life prediction model, the causal contribution distribution of the degradation state and the health trend of the entire life cycle are output. The causal contribution includes the proportion of MXene blocking effect.

2. The method for monitoring chloride ion gradient and enhancing durability of concrete based on MXene according to claim 1, characterized in that: The three-dimensional monitoring network integrates redundant point layout strategies, as follows: In the constructed three-dimensional network, standby nodes are added according to a preset ratio. The standby nodes are arranged at the same depth and function as the corresponding main nodes. The main nodes are the core sensing electrodes, namely the flat spray-type MXene-Cl sensing electrodes arranged in a matrix within each monitoring layer of the concrete cover and the ring-shaped wrapped MXene-Cl sensing electrodes arranged in a ring shape at the steel bars. Real-time monitoring of the potential signal, impedance value and chloride ion concentration data of each node. When the electrode potential drift exceeds the preset threshold, the impedance value change rate is greater than the preset range, or the chloride ion response time is extended by more than a preset multiple compared to the initial calibration value, it is determined to be a performance-degraded node; For performance-degraded nodes, an interpolation algorithm is used to reconstruct the chloride ion concentration field in the area based on the lateral concentration gradient of non-degraded nodes in the same layer and the longitudinal penetration model of historical data of the upper and lower layers.

3. The method for monitoring chloride ion gradient and enhancing durability of concrete based on MXene according to claim 2, characterized in that: The following steps are included after reconstructing the chloride ion concentration field in the region using an interpolation algorithm: The ratio of performance-degraded nodes to total nodes is counted at a preset period. When the ratio exceeds the preset warning line, the multi-scale data fusion process is initiated. The vertical gradients of different depth layers and the lateral distribution data of the same layer are called, and the spatial weight is calculated by the preset Gaussian kernel function to construct a three-dimensional spatial correlation matrix. The preset historical period data is simultaneously extracted, and a time attenuation coefficient model is established in combination with environmental variables. The time weight of the data of each period is calculated through this model; The spatial correlation matrix and the time weight data are fused through a preset Kalman filter algorithm, and a reconstruction result containing a preset confidence interval is generated through a preset number of Monte Carlo simulations, so that the monitoring error is controlled within a preset range.

4. The method for monitoring chloride ion gradient and enhancing durability of concrete based on MXene according to claim 1, characterized in that: The vertical interlayer concentration gradient and the concentration change of adjacent points in the horizontal direction are calculated according to the preset algorithm, and the gradient characteristic matrix is ​​constructed to quantify the migration trend of chloride ions, including: Based on the concentration distribution matrix, the inter-layer concentration difference quotient algorithm is used to calculate the vertical gradient value of each monitoring point according to the ratio of the concentration difference between adjacent depth layers and the inter-layer distance, and quantify the penetration rate and depth distribution trend of chloride ions along the thickness direction of concrete; For the horizontal dot matrix data of the same depth layer, the horizontal gradient value is calculated by the ratio of the concentration difference between adjacent monitoring points to the horizontal spacing, and the concentration anomaly area in the horizontal direction is identified; The vertical gradient value and the lateral gradient value are associated according to the spatial coordinates to form a three-dimensional feature matrix. The areas in the matrix where the absolute value of the gradient is greater than the preset threshold are marked as active infiltration areas. The three-dimensional migration path of chloride ions is intuitively mapped through the gradient vector direction of the matrix elements. Combined with time series data, the gradient amplitude changes are dynamically tracked and the acceleration / attenuation trend of the infiltration rate is quantified.

5. A MXene-based concrete chloride ion gradient monitoring and durability enhancement system, characterized in that: The invention comprises a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program can be loaded and executed by the processor to implement a MXene-based concrete chloride ion gradient monitoring and durability enhancement method as described in any one of claims 1 to 4.

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