Concrete chloride ion gradient monitoring and durability enhancing method and system based on MXene

Abstract

The invention relates to a concrete chloride ion gradient monitoring and durability enhancing method and system based on MXene, and solves the problems that a traditional drilling sampling method is high in operation process destructiveness, high in cost and low in detection efficiency, test results are remarkably influenced by environmental conditions, and field real-time performance and universal applicability are lacked. The method comprises the following steps: embedding a plurality of prefabricated sensing units containing MXene-Cl composite electrode layers and toughening protection layers into concrete at different depths according to a vertical gradient to form a monitoring network; potential signals are collected through a sensing unit, after correction, the potential signals are introduced into a Nernst equation for inversion to obtain the chloride ion concentration, and osmotic distribution imaging is achieved by combining a preset vertical and transverse gradient estimation method. The intercalation adsorption characteristic of MXene on chloride ions is utilized to capture and retard migration of the chloride ions to the reinforcing steel bar. The method has the advantages that chloride ions are monitored in real time, migration of the chloride ions to the steel bars is blocked, the concrete degradation state is evaluated in combination with multi-parameter data, and early warning and durability improvement are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of civil engineering material performance testing equipment, and in particular to a method and system for monitoring the chloride ion gradient and enhancing the durability of concrete based on MXene. Background Art

[0002] The durability of coastal concrete infrastructure is significantly impacted by chloride ion corrosion, and the resulting steel corrosion poses a serious threat to structural safety and service life. Chloride ion penetration, leading to concrete degradation and steel corrosion, is a key factor limiting project durability. Developing efficient and accurate chloride ion monitoring and durability enhancement technologies is crucial to ensuring coastal project safety, extending service life, and promoting sustainable development of the marine economy.

[0003] Currently, chloride ion monitoring relies primarily on chemical analysis of borehole sampling. While this method is highly accurate, it is a post-mortem method. Electrochemical sensing technology is increasingly being applied to chloride ion monitoring, particularly Ag / AgCl-based ion-selective electrodes, which are widely used due to their simple principle and ease of preparation.

[0004] Traditional borehole sampling methods are highly destructive, expensive, and inefficient. Their results are significantly affected by environmental conditions, lacking real-time field performance and universal applicability. Traditional Ag / AgCl electrodes, in highly alkaline, humid, and salt-rich concrete environments, face challenges such as dissolution and drift of the electrode material and unstable response potential. These electrodes' test results are significantly affected by pH and temperature, requiring frequent calibration and maintenance. Their lack of sensitivity makes it difficult to detect early corrosion signals. Furthermore, traditional liquid reference electrodes carry the risk of electrolyte leakage and environmental contamination, making them unsuitable for long-term embedded deployment. Summary of the Invention

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

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

[0007] A MXene-based method for monitoring chloride ion gradient and enhancing durability of concrete, comprising:

[0008] 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.

[0009] 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.

[0010] Utilizing the intercalation adsorption properties of MXene for chloride ions, the chloride ions are captured and blocked from migrating to the steel bars.

[0011] Multi-parameter data related to the concrete service environment and structural status are collected, and the multi-parameter data and chloride ion concentration data are summarized and transmitted to the cloud. The degradation status of the concrete structure is evaluated in combination with the preset corrosion life prediction model, and an early warning is triggered when the chloride ion concentration exceeds the preset critical threshold.

[0012] By adopting the above technical solution, this method can accurately monitor the distribution of chloride ions in concrete, use the characteristics of MXene to block the migration of chloride ions, and combine multi-parameter data with cloud analysis to provide early warning and assessment of structural degradation, effectively enhance the durability of concrete, and ensure project safety.

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

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

[0015] Figure 1 This is a flow chart of a method for monitoring chloride ion gradient and enhancing durability of concrete based on MXene in an embodiment of the present application.

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

[0017] Figure 3 This is a schematic diagram of the multi-gradient arrangement of a flat spray-type MXene-Cl sensing electrode and a ring-wrapped MXene-Cl sensing electrode in concrete according to another embodiment of the present application.

[0018] Figure 4 This is a partially enlarged schematic plan view of the multi-gradient arrangement of a planar spray-coated MXene-Cl sensing electrode and a ring-wrapped MXene-Cl sensing electrode in concrete according to another embodiment of the present application.

[0019] Figure 5Schematic diagram of the composition and preparation process of the MXene-Cl sensing electrode in the embodiment 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 method for monitoring chloride ion gradient and enhancing durability of concrete based on MXene disclosed in this application, comprising:

[0022] In step S100, a plurality of prefabricated sensing units containing a MXene-Cl composite electrode layer and a toughening protective layer are embedded into concrete at different depths according to a vertical gradient to form a monitoring network.

[0023] The sensor unit is prefabricated, consisting of a MXene-Cl composite electrode layer and a toughening protective layer. The MXene-Cl composite electrode layer utilizes a two-dimensional layered MXene material, intercalated with chloride ions through a specific process, resulting in selective adsorption and electrochemical response to chloride ions. The toughening protective layer primarily protects the electrode layer from mechanical damage and chemical corrosion in concrete, while maintaining its sensing performance. The sensor unit, comprised of these two layers, is the basic unit capable of sensing chloride ions. The sensor unit fabrication process is as follows: Based on the specific design requirements, the MXene-Cl composite electrode layer is prepared using a specific process. For example, the MXene material is reacted with a chloride compound to intercalate chloride ions into the MXene layer, forming an electrode layer with chloride ion sensing capabilities. Simultaneously, a suitable toughening protective layer material, such as a polymer or epoxy resin, is coated or wrapped around the electrode layer to form a complete sensor unit.

[0024] Vertical gradient embedding: Sensor units are arranged sequentially and regularly, vertically along the concrete structure, from the surface to different depths within it, creating a gradient distribution pattern to monitor chloride ion levels at different depths. Monitoring network: Multiple sensor units collaborate to cover the entire monitoring area, forming a network system that can fully perceive the penetration and distribution of chloride ions in concrete.

[0025] The specific execution process of step S100 may refer to steps S110 to S150 and will not be described in detail here.

[0026] Step S200 , collecting potential signals through the sensing unit, and then inverting the corrected signals into the Nernst equation to obtain the chloride ion concentration, and combining the preset vertical and horizontal gradient estimation methods to realize permeability distribution imaging.

[0027] Among them, the sensing unit collects the potential signal: a potential difference is formed between the working electrode and the reference electrode in the sensing unit. This potential difference is measured and recorded in real time through high-precision potential acquisition equipment, such as an electrochemical workstation or a data acquisition card, to obtain the original potential signal data.

[0028] Signal Correction: Because reference electrodes may drift and experience instability, errors may occur in the collected potential signal. Using specific algorithms or mathematical models, the raw potential signal is corrected to eliminate errors and improve data accuracy. For example, methods such as blank point pre-calibration, historical drift regression, or redundant channel averaging can be used to derive a potential correction term for the reference electrode, which is then used to correct the raw potential signal.

[0029] Nernst equation inversion: The Nernst equation is a fundamental equation in electrochemistry that describes the relationship between electrode potential and ion concentration in solution. By correcting the collected potential signal and substituting it into the inverse form of the Nernst equation, the corresponding chloride ion concentration is calculated.

[0030] Preset vertical and horizontal gradient estimation method: Based on a pre-set mathematical model or algorithm, the vertical concentration gradient and horizontal concentration change of chloride ions in concrete are calculated using the inverted chloride ion concentration data, thereby realizing the imaging of chloride ion penetration distribution.

[0031] The specific execution process of step S200 may refer to steps S210 to S260 and will not be described in detail here.

[0032] In step S300, the intercalation adsorption characteristics of MXene on chloride ions are utilized to capture and block the migration of chloride ions to the steel bars.

[0033] MXene's intercalation adsorption properties for chloride ions: MXene materials have a two-dimensional layered structure, allowing chloride ions to enter and intercalate between the layers. During this process, the chloride ions interact with the Cl⁻ terminal groups on the MXene surface, transferring some electrons from the chloride ions to the MXene surface. This enhances the covalent interaction and securely "locks" the chloride ions between the MXene layers, enabling adsorption.

[0034] Capture and block the migration of chloride ions to steel bars: Utilize the adsorption effect of MXene materials on chloride ions to build a "trap" for chloride ions in concrete, so that the migrating chloride ions are captured by the MXene material, reducing the concentration of freely migrating chloride ions in the concrete, thereby delaying their transmission to the surface of the steel bars and reducing the risk of steel bars rusting due to chloride ion erosion.

[0035] The complete process is described as follows:

[0036] Preparation and Optimization of MXene Materials: MXene materials rich in Cl⁻ terminal groups are prepared using appropriate preparation methods, such as mild alkaline etching combined with Lewis acid post-treatment. By optimizing preparation process parameters, such as reaction temperature, reaction time, and raw material ratios, the interlayer spacing and surface activity of the MXene material are increased, enhancing its intercalation and adsorption capacity for chloride ions. For example, during the preparation process, by adjusting the amounts of LiF and hydrochloric acid, as well as the reaction time, and controlling the degree of etching of the Ti⁃AlC⁂ powder, MXene materials with a suitable interlayer spacing are obtained.

[0037] Application and dispersion of MXene materials in concrete: Prepared MXene materials are introduced into concrete in an appropriate manner. They can be added directly to the concrete mix or combined with cement-based materials to create composite materials for localized repair or reinforcement of concrete structures. To ensure uniform dispersion and maximize the adsorption capacity of MXene materials in concrete, surface modification can be performed. For example, the surface of MXene flakes can be modified with didecyldimethylammonium bromide or water-soluble silk protein to increase interlamellar spacing, weaken van der Waals forces, and improve dispersion within the concrete matrix.

[0038] Implementation of a chloride ion capture and retardation mechanism: When concrete structures are exposed to chloride-containing environments, such as coastal areas, chloride ions gradually migrate into the concrete. Due to the presence of MXene materials in concrete, when chloride ions approach the interlayers of the MXene, intercalation occurs, causing the chloride ions to be adsorbed into the MXene interlayers and undergoing charge transfer, forming stable chemical bonds. This process not only secures the chloride ions between the MXene interlayers, reducing the concentration of freely migrating chloride ions in the concrete, but also, due to the distribution of the MXene materials in the concrete, creates multiple chloride ion adsorption sites, creating a "filtering" effect that effectively blocks further chloride ion migration toward the rebar. For example, in a concrete structure, the MXene material near the surface preferentially adsorbs the migrated chloride ions. As depth increases, subsequent MXene layers continue to capture the chloride ions, forming a gradient chloride ion adsorption barrier within the concrete, protecting the rebar from chloride ion attack.

[0039] In step S400, multi-parameter data related to the concrete service environment and structural status are collected, and the multi-parameter data and chloride ion concentration data are summarized and transmitted to the cloud. The degradation state of the concrete structure is evaluated in combination with a preset corrosion life prediction model, and an early warning is triggered when the chloride ion concentration exceeds a preset critical threshold.

[0040] Multi-parameter data acquisition collects various data related to the concrete's service environment and structural status, such as temperature, humidity, strain, and electrical resistance. These parameters reflect the concrete's environmental conditions and internal physical and chemical changes. Data fusion and transmission integrates different types of monitoring data and transmits them to a cloud platform via wireless communication technology, enabling centralized data storage and management.

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

[0042] The specific execution of S400 may refer to steps S410 to S440, which will not be described in detail here.

[0043] 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:

[0044] In step S110, the sensing unit includes a flat spray-coated MXene-Cl sensing electrode and a ring-wrapped MXene-Cl sensing electrode. The flat spray-coated MXene-Cl sensing electrode is arranged at the same depth of the concrete in a preset dot matrix with a preset dot spacing. The ring-wrapped MXene-Cl sensing electrode is placed in a ring-shaped manner around the steel bar. Figure 2 .

[0045] Among them, the flat spray-coated MXene-Cl sensing electrode: a sensing electrode made by evenly coating the MXene-Cl material on a substrate using a spraying process. It has good flat coverage and easy integration, and can effectively capture the chloride ion penetration on the surface and inside of the concrete. Its specific settings are as follows: 1. Substrate and conductive layer: The flexible substrate uses a polyimide (PI) film with a thickness of about 50μm. A PEDOT:PSS / carbon nanotube composite conductive film is spin-coated or sprayed on its surface. The film thickness is about 1μm and the surface resistance is ≤10Ω / m2 to ensure good conductivity and flexibility. 2. Preparation of MXene-Cl sensing film: Prepare Ti3C2T x-Cl water / ethanol (1:1) dispersion with a concentration of approximately 1 mg / mL. Air pressure spraying was used: 0.1 MPa, flow rate 0.4 mL / min, nozzle 10 cm from the substrate, and five parallel spraying passes. After each spraying pass, the film was allowed to stand for 3 minutes at room temperature. The total film thickness was approximately 10 μm (10–12 μm). After spraying, the film was dried in a 60°C oven for 2 hours to remove residual solvent and enhance film adhesion. 3. Reference and counter electrode arrangement: The Ag / AgCl reference electrode used a coated electrode wire with a diameter of 1 mm and a length of 15 mm; the graphite counter electrode used a carbon rod with a diameter of 0.5 mm and a length of 15 mm. Both electrodes were arranged coplanar with the MXene-Cl film layer, with a spacing of 5 mm between them. The leads were both silver-plated copper wires with a diameter of 0.15 mm. 4. Protective layer and packaging: The entire planar electrode surface is covered with a polyurethane (PU) protective film with a thickness of approximately 50 μm to prevent water seepage and mechanical wear; the electrode lead is covered with a polytetrafluoroethylene (PTFE) insulation layer, and a lead-out length of 200 mm is reserved to adapt to concrete formwork installation.

[0046] Ring-wrapped MXene-Cl sensing electrode: MXene-Cl material is formed into a ring structure and wrapped around the outer surface of the steel bar. The specific configuration is as follows: 1. Ring substrate and dimensions: The ring substrate is a polypropylene (PP) tube section with an inner diameter of 22mm, an outer diameter of 30mm, and a length of 100mm (compatible with HRB400 φ16–φ20 steel bars). A ring-shaped groove with a width of 2mm and a depth of 1mm is engraved along the inner wall to be filled with the sensing membrane. 2. MXene-Cl sensing membrane filling: 1mg / mL Ti3C2T3O3 is prepared as above. x-Cl dispersion; using a spray / injection method at an air pressure of 0.1 MPa and a flow rate of 0.3 mL / min, the dispersion was applied to the groove in three passes, with each pass achieving a film thickness of approximately 2 μm and a total film thickness of 5–6 μm. Drying conditions: 60°C for 2 h. 3. Counter and Reference Electrode Arrangement: A 2 mm diameter graphite rod was embedded in the inner side of the groove as the counter electrode, tightly fitting the sensing membrane. A 1 mm diameter, 20 mm long Ag / AgCl wire electrode was inserted into the corresponding position outside the groove, with a center-to-center distance of 10 mm. All electrode leads were 0.15 mm silver-plated copper wire, extending to the top and 200 mm in length. 4. External Protection and Installation: A 2 mm thick PVDF protective tube with a compressive strength of ≥12 MPa was sheathed around the outside of the pipe section to protect against impact from pouring. The end of the protective tube was equipped with a slot or thread for securement with the concrete formwork or rebar tie-downs, ensuring in-situ stability. 5. Pre-set Dot Matrix Planar Layout: Multiple flat, spray-coated MXene-Cl sensing electrodes are placed at the same depth in the concrete according to a pre-designed dot pattern and spacing. The dot spacing is a preset value, typically set at around 20mm, based on factors such as the concrete structure's dimensions, rebar density, and monitoring accuracy requirements. This layout ensures comprehensive coverage of the monitoring area, both on the concrete surface and within, improving monitoring accuracy and reliability.

[0047] Preset dot matrix layout: Based on the concrete structure's dimensions and monitoring requirements, determine the layout area for the flat spray-coated MXene-Cl sensing electrodes at the same depth. Using a precision measurement tool (such as a laser rangefinder or protractor), mark the electrode installation locations on the substrate using a pre-set dot matrix pattern (e.g., a 3×3 dot matrix). Ensure the dot spacing is within the preset value (e.g., 20 mm). Then, install the prepared flat spray-coated electrodes sequentially at the marked locations and seal them with epoxy adhesive to ensure bond strength and a tight seal between the electrodes and the substrate.

[0048] Ring-wrapped placement: During the rebar tying process, place the ring-wrapped MXene-Cl sensing electrode around the designated rebar, ensuring a tight fit without looseness or gaps. The size and shape of the ring substrate can be adjusted to accommodate rebars of varying diameters. During installation, ensure the electrode leads are protected from damage or short circuits. After installation, insulate the connection between the electrode and the rebar to prevent electrochemical corrosion.

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

[0050] The synthesis steps of MXene-Cl materials are as follows:

[0051] The MXene precursor material was etched using an argon-protected mild alkaline etching method (ASA-MILD).

[0052] 1 g of LiF was mixed with 30 mL of 9 M hydrochloric acid and stirred for 10 min. Then, 1 g of Ti3AlC2 powder was slowly added in an ice-water bath. The mixture was placed in a sealed container with an argon inlet system and reacted with magnetic stirring at 45 °C for 24 h. Argon protection was continuously supplied during the reaction.

[0053] After the reaction, dilute acid and deionized water were used for alternating washing until the pH was close to neutral to obtain muddy Ti3C2T x Materials. The Ti3C2T x The material is placed in CuCl2 molten salt and heat treated at 750°C. CuCl2 acts as Lewis acid molten salt oxidant and reacts with Ti3C2T x The material undergoes an oxidation-reduction reaction, in which Cu²⁺ is reduced to Cu and Ti3C2T x Cl terminals were introduced on the surface.

[0054] The black product was washed once with a 5 mol / L FeCl solution to remove unreacted residues and metal ions. The product was then washed three or more times with deionized water and then anhydrous ethanol, alternating until the washings were neutral. The washed product was dried in a vacuum oven at 60°C for 12 hours to obtain a MXene material with Cl terminal groups on its surface, designated MX-Cl.

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

[0056] The MXene-Cl material mentioned in the present invention achieves interlayer selective exfoliation of the MAX phase through the ASA-MILD low-temperature etching method combined with the Lewis acid post-treatment strategy. The surface of the obtained MXene layer is rich in Cl⁻ terminals, and has excellent ion recognition, electrochemical response performance and dispersion stability. It is suitable for constructing chloride ion selective sensors, electrodes, electrocatalysis or flexible electronic device structures.

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

[0058] (1) The dried MXene-Cl material was dispersed in deionized water (concentration of 1–5 mg / mL) and ultrasonically treated for 30–60 min to form a stable dispersion.

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

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

[0061] (4) The composite solution is drop-coated, sprayed, or spin-coated onto the surface of any of the following substrate materials: conductive glass (ITO), metal electrodes (e.g., copper, nickel, platinum), or flexible conductive substrates (e.g., PET / Ag film). The coating thickness is 1–10 μm, and the solution is dried at 60–80°C for 2–4 h to form a dense, continuous sensing film layer.

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

[0063] These electrodes are installed at regular intervals within a single sensor module and tested using a potentiostat or electrochemical workstation. This three-electrode system is suitable for various electrochemical measurement methods, including potentiostat, electrochemical impedance spectroscopy (EIS), and cyclic voltammetry. It is particularly well-suited for laboratory calibration and in-situ precision monitoring on engineering sites.

[0064] Specifically, the MXene-Cl sensing electrode used in this application is a multilayer composite structure, and its composition and preparation process can be referred to Figure 5 The multi-layer composite structure includes: 1. A three-electrode system: A platinum counter electrode provides a current circuit, and an Ag / AgCl reference electrode stabilizes the potential baseline. 2. A flexible polymer protective substrate: This protects the structural stability of the MXene sensing and conductive film layers. 3. The conductive film layer: This layer generates sensing signals to adapt to micro-deformations in concrete. 4. The MXene sensing film layer: This core sensing layer detects Cl⁻ concentrations through potential changes.

[0065] The fabrication process (spray coating - lamination - packaging) is as follows: 1. Substrate preparation: Prepare a flat, uniformly conductive, flexible carbon-based membrane. 2. Sensing layer construction: Pneumatically spray a MXene-Cl dispersion to form a 1-3μm sensing film. 3. Protective layer lamination: Hot-press the PDMS substrate to protect it and ensure interlayer bonding. 4. Electrode integration: Assemble the counter and reference electrodes at the edge of the substrate and encapsulate with a PI film.

[0066] Step S120: Multiple monitoring layers are sequentially arranged at preset intervals along the thickness direction from the concrete surface to the concrete protective layer area on the outer surface of the steel bar. Only flat spray-coated MXene-Cl sensing electrodes are arranged in each monitoring layer to cover the concrete areas at different depths in the protective layer. When the monitoring layer reaches the location of the steel bar, only the ring-shaped wrapped MXene-Cl sensing electrodes are arranged at this location. For specific arrangement, please refer to Figure 3 and Figure 4 .in, Figure 3 Gray particles → concrete aggregate, forming the matrix; horizontal layered columnar structure → flat spray-type MXene-Cl sensing electrode (arranged in layers at preset intervals, with each layer forming a two-dimensional lattice, covering the concrete area at that depth); horizontal cylinder (with mesh wrapping) → steel bar, the mesh structure on its surface corresponds to the ring-wrapped MXene-Cl sensing electrode (arranged only in the monitoring layer where the steel bar is located). Figure 4 : Focus on the local working scene of the planar electrode: Red Cl⁻ diffuses from the concrete pores to the electrode (yellow active area), reflecting the planar electrode's ability to monitor the gradient of chloride ions in the protective layer.

[0067] The concrete cover is the concrete layer (typically 20-50mm thick) from the outer edge of the rebar to the concrete surface in structural design. Its integrity is crucial to the durability of the rebar. The pre-set concrete pouring formwork is a steel-wood composite formwork customized to the component dimensions. It has sensor mounting holes and rebar channels reserved inside. Once the formwork is removed, the sensors are permanently embedded in the concrete.

[0068] Complete process description:

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

[0070] Set preset intervals: Within the specified monitoring depth range, multiple monitoring layers are divided sequentially at preset intervals. Assuming the preset interval is 10mm, in the bridge pier example above, you can set monitoring layers at depths of 10mm, 20mm, 30mm, and 40mm. If the preset interval is 20mm, the monitoring layer depths are 20mm, 40mm, and so on.

[0071] The sensing electrodes are arranged in layers as follows:

[0072] Protective layer monitoring layer setting: Starting from the concrete surface, in the concrete protective layer area along the vertical thickness direction to the outer surface of the steel bar, multiple monitoring layers (such as 3-5 layers, the specific number of layers is determined by the protective layer thickness) are sequentially set at preset intervals of 10-25mm (preferably 15mm); each monitoring layer is only arranged with flat spray-type MXene-Cl sensing electrodes to form a two-dimensional dot matrix monitoring plane with a point spacing of 50-150mm (preferably 100mm), covering the entire protective layer cross section.

[0073] Rebar position monitoring layer setting: When the monitoring layer reaches the depth of the outer surface of the rebar, only ring-wrapped MXene-Cl sensing electrodes are arranged in this layer. Each electrode ring tightly wraps a single rebar to form a closed monitoring ring around the rebar; multiple rebars with the same cross-section are individually configured with ring electrodes, and each electrode ring is connected to the monitoring system in parallel through insulated wires.

[0074] Template collaborative pre-embedded process: Pre-open mounting holes with a diameter of 35-50mm (preferably 40mm) on the inner side of the template at the depth of each monitoring layer. The hole positions are consistent with the sensor layout matrix. The planar electrodes are fixed to the template through the epoxy resin layer (thickness 0.5-1mm) on the inside of the hole to ensure seamless integration of the electrode and the concrete interface after pouring. After the steel bars are inserted into the reserved channels of the template, the annular electrodes are slid into the designed position along the axial direction of the steel bars. 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 bars. At the same time, the edges of the template holes are sealed to prevent leakage during pouring.

[0075] At pre-set locations within each monitoring layer, flat spray-coated MXene-Cl sensing electrodes and ring-wrapped MXene-Cl sensing electrodes are placed. The flat spray-coated electrodes are arranged in a pre-set dot matrix at the same depth in the concrete, with a pre-set spacing (e.g., 20 mm). The ring-wrapped electrodes are placed in a ring-like pattern around the rebar. Ensure that the electrodes are firmly bonded to the concrete matrix, that the electrode leads are securely connected, and that protective measures such as waterproofing and insulation are in place. Forming a three-dimensional monitoring network: By sequentially placing multiple monitoring layers at varying depths, a three-dimensional monitoring network is formed, covering the concrete surface to the rebar.

[0076] In step S130, a flat spray-coated MXene-Cl sensing electrode is attached to a hole reserved inside a preset concrete casting template and sealed with epoxy glue. After the steel bar passes through the template hole, a ring-shaped wrapped MXene-Cl sensing electrode is slid in, and the ring belt is tightly attached to the steel bar. The edge of the hole is then sealed with polyurethane grouting material, forming a three-dimensional monitoring network covering the concrete surface to the steel bar.

[0077] Pre-reserved holes inside the formwork: On the inside of the concrete pouring formwork, holes are pre-reserved based on the placement and size of the sensor units. These holes are used to install flat spray-on and ring-wrapped MXene-Cl sensing electrodes, ensuring they are accurately positioned during the concrete pouring process and tightly bonded to the concrete structure.

[0078] Epoxy Sealing: Epoxy resin adhesive is used to secure and encapsulate the flat, spray-coated MXene-Cl sensing electrodes. Epoxy adhesive has excellent adhesion, insulation, and chemical resistance, allowing the electrodes to be securely attached to the formwork. It also forms a good interface with the concrete after pouring, while protecting the electrodes from moisture and mechanical damage.

[0079] Polyurethane grouting: After installing the annular MXene-Cl sensing electrode, polyurethane grouting is used to seal the edges of the formwork holes. Polyurethane grouting, with its rapid curing, high expansion rate, and strong adhesion, effectively fills the pores, preventing grout leakage during concrete pouring, ensuring the stability of the sensing electrode installation, and providing waterproofing and corrosion resistance.

[0080] Three-dimensional monitoring network: This is achieved by embedding multiple sensor units at different depths and locations within the concrete, creating a three-dimensional spatial monitoring network. This network enables comprehensive, real-time monitoring of chloride ion penetration within concrete structures, enabling three-dimensional imaging of chloride ion concentration distribution and providing comprehensive data support for assessing the durability and corrosion risk of concrete structures.

[0081] The complete process is described as follows:

[0082] Pre-reserved holes in the formwork: Before pouring concrete, reserve holes on the inside of the formwork at the corresponding locations according to the sensor unit layout. The hole size should be slightly larger than the sensor unit's overall dimensions to ensure smooth electrode installation. For example, for a flat spray-on MXene-Cl sensor electrode, the reserved hole size can be 100mm × 100mm × 35mm (length × width × depth). For a ring-wrapped electrode, the reserved hole size can be determined based on the rebar diameter and electrode size, typically a circular hole with a diameter of 50mm and a depth of 40mm.

[0083] Flat-surface spray-coated electrode installation and packaging: Attach the flat-surface spray-coated MXene-Cl sensing electrode to the bottom of the pre-recorded hole in the template, ensuring the electrode surface is flat, free of bubbles and wrinkles. Then, apply epoxy glue evenly around the electrode and onto the surface to secure it to the template. The epoxy glue should be applied to a moderate thickness, typically 1-2 mm, to ensure a strong bond between the electrode and the template. Before the epoxy glue cures, avoid external forces on the electrode to ensure accurate positioning.

[0084] Ring-wrapped electrode installation: After inserting the rebar through the pre-reserved hole in the template, slide the ring-wrapped MXene-Cl sensing electrode, along with its protective layer and ring band, into the pre-reserved hole, ensuring a tight fit between the ring band and the rebar. During installation, be careful to protect the electrode leads to avoid damage or short circuits. Ensure good contact between the electrode and the rebar to ensure accurate measurements.

[0085] Polyurethane grouting: After the ring-wrapped electrode is installed, seal the edges of the template holes with polyurethane grouting. Inject the polyurethane grouting evenly into the pores, filling them tightly and ensuring no leakage. The injection pressure should be moderate, generally 0.2-0.3 MPa, to avoid damage to the electrode and template. After the grouting cures, inspect the sealing effect. Any leakage or looseness should be repaired promptly.

[0086] Forming a three-dimensional monitoring network: By sequentially installing flat spray-on and ring-wrapped MXene-Cl sensing electrodes in multiple pre-reserved holes in the formwork, encapsulating and sealing them, a three-dimensional monitoring network is formed, extending from the concrete surface to the rebar. This network monitors chloride ion penetration into concrete in real time, providing comprehensive data support for subsequent corrosion risk assessment and structural life prediction. During the concrete pouring process, the formwork must be stable to prevent electrode displacement or damage. Furthermore, during the concrete curing period, the sensing units must be protected from external interference or damage.

[0087] The three-dimensional monitoring network integrates redundant point layout strategies, as follows:

[0088] Step S140: In the constructed three-dimensional network, standby nodes are added according to a preset ratio, and the standby nodes are arranged at the same depth and with the same functions as the corresponding main nodes.

[0089] The three-dimensional monitoring network, formed by embedding multiple sensor units at different depths and locations within the concrete, can comprehensively and in real time monitor chloride ion penetration within the concrete structure, achieving three-dimensional imaging of chloride ion concentration distribution.

[0090] Backup nodes: In addition to the normally functioning primary nodes in a 3D monitoring network, backup monitoring points are set up for data verification. These backup nodes are in standby mode when the primary nodes are functioning normally. When the primary nodes experience performance degradation, they assist in reconstructing the chloride ion concentration field and verifying data accuracy.

[0091] The main node is the core sensing electrode, namely the flat spray-type MXene-Cl sensing electrode arranged in a matrix in each monitoring layer of the concrete protective layer and the ring-shaped wrapped MXene-Cl sensing electrode arranged in a ring at the steel bar.

[0092] The monitoring points mentioned in this application include a master node and a backup node. The master node is a core monitoring unit, and the backup node is a redundant protection unit. The three together constitute a complete monitoring network.

[0093] Preset Ratio: The ratio of backup nodes to primary nodes is pre-set based on monitoring accuracy requirements and project realities. This ratio is typically determined based on historical data, project experience, and reliability analysis.

[0094] Same-depth, same-function deployment: Backup nodes are deployed at the same depth as their corresponding primary nodes and have the same functions and performance parameters. This ensures that the backup nodes can replace the primary nodes for monitoring and are comparable during data verification.

[0095] The complete process is described as follows:

[0096] Determine the number of backup nodes: Based on the scale and reliability requirements of the monitoring network, calculate the number of backup nodes to be added according to the preset ratio. For example, if there are 100 active nodes in the monitoring network and the preset ratio is 20%, then 20 backup nodes will be required.

[0097] Deploy backup nodes: In a three-dimensional monitoring network, select appropriate locations for backup nodes. Backup nodes should be placed at the same depth and with the same functionality as their primary nodes, ensuring they can replace the primary nodes for monitoring when needed. For example, within each monitoring layer, select several locations adjacent to the primary nodes at regular intervals as backup nodes.

[0098] Install and debug the backup node: Install the backup node using the same installation and debugging methods as the primary node. Ensure that the backup node is tightly integrated with the concrete structure, that the electrode leads are securely connected, and that necessary protective measures are implemented.

[0099] Integration into the monitoring system: Connect the backup node to the monitoring system's data collection and transmission network to ensure real-time data upload to the cloud platform. In the monitoring system software, identify and manage the backup node so that it can be activated promptly if the primary node's performance degrades.

[0100] Step S150 , real-time monitoring of the potential signal, impedance value and chloride ion concentration data of each node. When the electrode potential drift exceeds a preset threshold, the impedance value change rate is greater than a 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.

[0101] Among them, potential signal drift refers to the deviation of the output potential of the electrode 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 that the electrode performance has degraded. Impedance value change rate: The impedance of the electrode at different frequencies will change with 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 output of a stable signal by the electrode. A prolonged response time usually indicates a decrease in electrode sensitivity, which may be due to contamination of the electrode surface, a reduction in active sites, or obstruction of ion transmission channels.

[0102] Preset thresholds and ranges: These are pre-determined criteria 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 issues with reasonable sensitivity and accuracy.

[0103] The complete process is described as follows:

[0104] Real-time data acquisition: The data acquisition system continuously collects the potential signal, impedance value, and chloride ion concentration data of each node. The acquisition frequency is determined by the monitoring requirements. Generally, the potential signal acquisition frequency is 1Hz, and the impedance value and chloride ion concentration data are collected once an hour.

[0105] Data Processing and Preliminary Analysis: The collected data is filtered, de-noised, and smoothed to remove outliers and interfering signals. Short-term and long-term drift of the potential signal are calculated. Short-term drift can be measured as the potential change per minute, while long-term drift can be measured as the potential change per hour or day. Impedance spectroscopy software is used to calculate the rate of change of the impedance value. The extension of the chloride ion response time is determined by comparing it with the change in chloride ion concentration in a standard solution.

[0106] Performance Degradation Determination: Processed data is compared against preset thresholds and ranges. If the electrode potential drift exceeds a preset threshold (e.g., short-term drift exceeds ±5mV, long-term drift exceeds ±10mV), the impedance change rate exceeds a preset range (e.g., exceeds ±20%), or the chloride ion response time exceeds a preset multiple (e.g., more than 2 times) of the initial calibration value, the node is identified as a performance degraded node. A comprehensive assessment combining multiple data and criteria is performed to improve determination accuracy and avoid misjudgments.

[0107] Recording and Alarming: Once a node's performance is determined to be degraded, the monitoring system immediately records the node's location, degradation time, and related data. This triggers an alarm mechanism, notifying maintenance personnel to conduct inspections and repairs. Based on the degree of degradation and the scope of impact, a maintenance plan and priority are developed to ensure reliable operation of the monitoring system.

[0108] Step S160 , for the 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 the non-degraded nodes in the same layer and the longitudinal permeation model of the historical data of the upper and lower layers.

[0109] Among them, performance-degraded nodes: In the sensor network, nodes whose monitoring performance has degraded due to various factors cannot accurately reflect the chloride ion concentration and need to be identified and processed. Horizontal concentration gradient: In the same monitoring layer, the rate of change of chloride ion concentration with position reflects the diffusion trend of chloride ions in the surface layer of concrete. Longitudinal penetration model: A model that describes the regularity of chloride ion penetration at different depths in concrete. It is constructed based on historical data and reflects the process of chloride ions migrating deeper over time. Interpolation algorithm: A mathematical method for estimating unknown point data based on existing data points, used to reconstruct the chloride ion concentration at degraded nodes.

[0110] The complete process is described as follows:

[0111] 1. Node status monitoring and identification: Real-time monitoring of the potential signals, impedance values, and chloride ion concentration data of all nodes. Based on preset performance degradation standards (such as potential drift exceeding the threshold, excessive impedance change rate, prolonged response time, etc.), nodes with performance degradation are identified.

[0112] 2. Calculation of transverse concentration gradient: Using the chloride ion concentration data of the non-degraded nodes in the same layer, the transverse concentration gradient of the layer is calculated by the difference method.

[0113] 3. Construction of vertical permeability model: Based on historical monitoring data, a vertical permeability model is constructed to describe the variation of chloride ion concentration with depth and time.

[0114] 4. Application of interpolation algorithm: Combine the lateral concentration gradient and longitudinal permeability model, select a suitable interpolation algorithm (such as bilinear interpolation or Kriging interpolation) to estimate the chloride ion concentration at the degraded node and realize concentration field reconstruction.

[0115] 5. Concentration field reconstruction and data fusion: The interpolated estimated concentration values ​​are integrated into the overall concentration field data to generate a complete chloride ion concentration distribution map, ensuring the continuity and accuracy of the data and providing a reliable basis for subsequent corrosion risk assessment.

[0116] A MXene-based method for monitoring chloride ion gradients and enhancing durability of concrete also includes steps following the use of an interpolation algorithm to reconstruct the chloride ion concentration field in the region, specifically as follows:

[0117] Step S1A0: Count the proportion of performance-degraded nodes to the total nodes according to a preset period. When the proportion exceeds a preset warning line, start the multi-scale data fusion process.

[0118] The preset period is a statistical period predetermined based on monitoring requirements and system resource allocation, for example, every 24 hours or every week. The percentage of degraded nodes is the ratio of the number of degraded nodes to the total number of nodes, used to quantify the degree of system performance degradation. The preset warning line is a threshold set based on system reliability analysis and historical data. When the percentage of degraded nodes exceeds this threshold, the multi-scale data fusion process is triggered.

[0119] The complete process is described as follows:

[0120] Regular statistics and calculations: At a preset period (such as 2:00 a.m. every day), the system automatically counts the number of nodes with current performance degradation and calculates their proportion to the total number of nodes.

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

[0122] Triggering the multi-scale data fusion process: When the proportion of performance-degraded nodes 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] In step S1B0, the vertical gradients of different depth layers and the lateral distribution data of the same layer are called, the spatial weight is calculated by the preset Gaussian kernel function, a three-dimensional spatial correlation matrix is ​​constructed, and the preset historical period data is extracted simultaneously. A time attenuation coefficient model is established in combination with environmental variables, and the time weight of the data of each time period is calculated through the model.

[0124] The vertical gradient is the rate of change of chloride ion concentration along the depth of the concrete, reflecting the depth dependence of chloride ion penetration. The Gaussian kernel function is a distance-based weight calculation method that assigns high weights to adjacent nodes and decreases exponentially with increasing distance. The three-dimensional spatial correlation matrix describes the spatial correlation between monitoring nodes and is constructed by combining vertical gradient and lateral distribution data. The time decay coefficient model is a model that characterizes how data weights change over time, based on environmental variables and time series data. The weights decay over time according to preset rules.

[0125] The complete process is described as follows:

[0126] 1. Data retrieval and preprocessing: Extract the vertical gradient and lateral distribution data of each depth layer at the current moment from the database and organize them into a matrix. Normalize the data to eliminate dimensional differences.

[0127] 2. Spatial weight calculation: Use the Gaussian kernel function to calculate the spatial weight between nodes. The formula is:

[0128] ;

[0129] in, is the weight coefficient between node i and node j, is the spatial distance between node i and node j, is the kernel function bandwidth parameter, which controls the speed at which the weight decays with distance.

[0130] Construction of three-dimensional spatial correlation matrix: The calculated spatial weights are filled into the matrix, with rows and columns corresponding to different nodes, and the matrix elements represent the spatial correlation strength between the corresponding two nodes.

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

[0132] Data fusion preparation: Integrate the constructed 3D spatial correlation matrix and the calculated time weight data to prepare for subsequent Kalman filter fusion. Ensure data format consistency and time correspondence, verify matrix dimension matching and weight rationality, and eliminate outliers.

[0133] In step S1C0, 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.

[0134] Among them, the Kalman filter algorithm is a recursive algorithm used to estimate the state of dynamic systems. It can integrate multi-source data, reduce the impact of noise, and improve monitoring accuracy. Monte Carlo simulation is a numerical calculation method using random sampling to assess data uncertainty and generate reconstruction results with confidence intervals. Confidence interval: In statistics, it represents the credible range of parameter estimates, determined based on the degree of dispersion of the data and the sample size.

[0135] The complete process is described as follows:

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

[0137] Results generation and verification: Monte Carlo simulations (e.g., 10,000 random sampling runs) are used to generate reconstructions with confidence intervals to assess the reliability of the results. The reconstructions are compared with the actual chloride ion concentration field data to ensure that the monitoring error is within a preset range (e.g., ±5%), verifying the effectiveness of the fusion process.

[0138] Feedback optimization: If the monitoring error exceeds the preset range, analyze the cause (such as model parameters, data quality issues), adjust the Kalman filter parameters or rerun the simulation to optimize the fusion process and improve the reconstruction accuracy.

[0139] The 3D monitoring network achieves project adaptation through a flexible combination of preset parameters, as follows:

[0140] Step S1a0: Dynamically adjust the number of monitoring layers within a preset range of 2 to 6 layers based on the designed thickness of the concrete component. The spacing between each layer is set in equal proportion or gradient to ensure that the full depth range from the concrete surface to the steel bar protective layer is covered.

[0141] Dynamically adjust the number of monitoring layers: Based on the design thickness of different concrete components, the number of monitoring layers can be flexibly increased or decreased within a preset range. This change affects the vertical resolution and accuracy of the data. Proportional or gradient settings are available: Proportional settings ensure equal spacing between monitoring layers; gradient settings vary the spacing between layers based on chloride ion penetration rate or structural characteristics, such as smaller spacing between surface layers and larger spacing between deep layers.

[0142] The complete process is described as follows:

[0143] Determine the thickness of concrete components: Obtain the design drawings or actual dimensions of the concrete components to be monitored and determine their design thickness.

[0144] Preset monitoring layer range and initial spacing: Based on the component thickness, initially set the number of monitoring layers within a preset range of 2 to 6 layers. For example, for a 300mm thick component, you can initially set 4 monitoring layers. Calculate the initial equal spacing by dividing the total thickness by (number of layers - 1). For example, for 4 monitoring layers, the initial equal spacing 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] In step S1c0, an appropriate number of ring electrodes is selected from the preset 3 to 6 according to the diameter and corrosion protection level of the monitored steel bar. The rings are evenly distributed along the axial direction of the steel bar, and the width of a single ring is a preset value to ensure full circumferential chloride ion penetration monitoring of the steel bar.

[0155] The Corrosion Protection Level (CPL) refers to the standard for preventing steel bar corrosion, determined based on the corrosive environment and the designed service life of the concrete structure. Different CPL levels require different accuracy and coverage for chloride ion monitoring. Equidistant Distribution: Rings are evenly spaced along the rebar axis to ensure continuous and uniform monitoring. This equidistant distribution avoids blind spots and ensures uniform monitoring of chloride ion penetration around the entire circumference of the rebar.

[0156] The complete process is described as follows:

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

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

[0159] Equidistant Distribution: This setting determines how the rings are evenly spaced along the rebar axis. For example, if the rebar is 1m long, the four ring electrodes can be placed at 100mm, 300mm, 500mm, and 700mm from the rebar end, respectively.

[0160] Installing Ring Electrodes: Install the ring electrodes at the designated locations on the rebar, ensuring that each ring is the desired width and that the rings are evenly spaced. For example, a ring width of 20 mm should be spaced 200 mm apart. Ensure the ring electrodes fit snugly against the rebar, are securely installed, and take protective measures.

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

[0162] The spacing between adjacent monitoring modules refers to the distance between two adjacent monitoring modules when the sensor units are arranged. This spacing is dynamically adjusted within a preset range of 15 to 25 mm, allowing for flexible setting of the appropriate spacing based on specific monitoring needs and the characteristics of the concrete structure.

[0163] Combining the preset vertical and lateral gradient estimation methods to achieve permeability distribution imaging includes:

[0164] In step S210, the sensing unit consists of a MXene selective electrode with a Cl⁻ terminal on the surface and a reference electrode. Utilizing the specific intercalation and charge transfer properties of MXene for Cl⁻, the original potential differences at different depths and locations in the concrete are synchronously collected to form a multi-dimensional potential data matrix.

[0165] Among them, MXene selective electrode: an electrode made of MXene material with Cl terminals on the surface, which has specific intercalation ability for Cl⁻, and uses the charge transfer caused by intercalation to detect Cl⁻ concentration.

[0166] Reference electrode: An electrode used as a reference potential in electrochemical measurements. It usually has a stable potential and is used in conjunction with the working electrode to measure potential differences.

[0167] Original potential difference: The instantaneous potential difference between the MXene selective electrode (working electrode) and the reference electrode, expressed as: ;in, No. Tier Point in time The original potential difference, represents the open circuit potential of the working electrode, Indicates the instantaneous potential value of the reference electrode.

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

[0169] Step S220 , obtaining a reference electrode potential correction term through a reference electrode potential correction algorithm, correcting the original signal, and obtaining corrected potential data.

[0170] The formulas involved in the specific corrections are as follows: ;in, For the Column reference electrode at time The potential correction term on the CMOS can be obtained through blank point pre-calibration, historical drift regression or redundant channel averaging.

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

[0172] Blank Point Precalibration: Calibrate the reference electrode in a chloride-free solution to obtain its ideal potential value, which serves as a benchmark for subsequent potential corrections. Historical Drift Regression: Analyzes the potential drift data of the reference electrode in historical measurements to establish a regression model and predict the current drift. Redundant Channel Averaging: Calculates the average value using data collected from multiple redundant channels to reduce random errors and improve measurement accuracy. Reference Electrode Potential Correction: Corrects the offset of the original potential signal to ensure the accuracy of the measured potential.

[0173] In step S230, based on the potential response law of Cl⁻ intercalation between MXene layers, the corrected potential is substituted into the Nernst equation for inversion, and the chloride ion concentration at each monitoring point is calculated to form a concentration distribution matrix.

[0174] The inversion formula is as follows: ;in, is the reference potential value at standard concentration (1 mol / L), is the electrode sensitivity constant, with a theoretical value of 59.16 mV / decade (25°C, applicable to monovalent ions Cl⁻).

[0175] Step S240 , calculating the vertical interlayer concentration gradient and the concentration change between adjacent points in the horizontal direction according to a preset algorithm, constructing a gradient characteristic matrix, and quantifying the migration trend of chloride ions.

[0176] The vertical interlayer concentration gradient (the rate of change of chloride ion concentration across the depth of the concrete) reflects the depth dependence of chloride ion penetration. The horizontal concentration variation (the difference in chloride ion concentration between adjacent monitoring points within the same monitoring layer) reflects the diffusion trend of chloride ions in the concrete surface. The gradient characteristic matrix (the matrix constructed by combining the vertical interlayer concentration gradient and the horizontal concentration variation between adjacent monitoring points) is used to quantify chloride ion migration trends.

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

[0178] In step S250, based on the gradient feature matrix, concentration contour lines and front evolution curves are drawn in the depth-time dimension, and a three-dimensional isosurface map is generated by combining the horizontal dot matrix data to intuitively present the spatial distribution morphology and penetration path of chloride ions inside the concrete.

[0179] Depth-time two-dimensional concentration contour lines and front evolution curves: In the plane formed by depth and time, by connecting the contour lines formed by points with the same concentration, the dynamic process of chloride ion penetration into the deep concrete over time is intuitively presented.

[0180] Stereoscopic 3D isosurface map: Integrates 3D information of depth, time, and lateral position to construct an isosurface of chloride ion concentration, presenting the penetration path and spatial distribution of chloride ions in concrete in a comprehensive and three-dimensional manner.

[0181] The complete process is described as follows:

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

[0183] Developing 2D depth-time concentration contours and front evolution curves: Based on the depth-time-concentration database, selected plotting software is used to generate 2D depth-time concentration contour maps and chloride ion penetration front evolution curves. In the contour map, the horizontal axis represents time and the vertical axis represents depth. Contour lines connect points of equal concentration, clearly outlining the trajectory of chloride ion penetration over time. The front evolution curve precisely locates the chloride ion penetration front, showing its speed and depth.

[0184] Combine transverse lattice data with 3D contour surface plotting: Integrate transverse lattice data with 2D depth-time data, and use 3D plotting capabilities to generate a 3D contour surface plot. In a 3D contour surface plot, the horizontal and vertical axes represent the lateral position and depth of the concrete, respectively, while the third dimension represents time. The contour surface is composed of points with the same concentration. Observing the contour surface from different angles provides a comprehensive understanding of chloride ion penetration details, including the main penetration path and localized areas of abnormal penetration.

[0185] Visualization and Analysis of Chloride Ion Permeation Paths: 3D isosurface plots provide a visual representation of the complex chloride ion penetration paths within concrete. By observing the shape, density, and orientation of the isosurfaces, in-depth analysis is provided of the prevailing direction of chloride ion penetration, as well as areas of localized acceleration or obstruction. This provides key insights for durability assessment and protection strategy development for concrete structures. For example, densely populated areas of the isosurface indicate drastic changes in chloride ion concentration, potentially indicating weak links in the penetration path and requiring specific attention and protection.

[0186] In step S260 , the chloride ion critical threshold is determined according to the engineering scenario, the concentration distribution matrix is ​​compared with the critical threshold, the corrosion risk area is located in combination with the gradient feature matrix, and the risk marker is superimposed in the three-dimensional imaging.

[0187] Chloride ion critical threshold: The chloride ion concentration value determined based on engineering experience and specifications. When the measured concentration exceeds this value, corrosion of steel reinforcement in concrete may occur. Risk identification: A visual element used to mark corrosion risk areas in 3D imaging, such as color change, text labeling, or special symbols, to intuitively display the location of potential corrosion risks.

[0188] The complete process is described as follows:

[0189] Determine the critical chloride ion threshold: This threshold is determined experimentally or by reference to relevant standards, based on the project scenario, concrete mix, and environmental conditions. For example, for the concrete structure of a coastal bridge, the critical threshold is determined to be 0.03 mol / L, based on the "Design Code for Marine Concrete Structures" and local environmental corrosion data.

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

[0191] Analyze risk areas using the gradient feature matrix: Using the gradient feature matrix constructed in step S240, analyze chloride ion migration trends to assist in identifying corrosion risk areas. For example, if a region in the gradient feature matrix exhibits a large vertical gradient and abnormal lateral concentration variations, this indicates rapid deep penetration of chloride ions and possible localized diffusion anomalies. This region is identified as a high-risk area.

[0192] Overlaying risk markers on the 3D image: On the 3D contour map generated in step S250, risk markers are overlaid based on the location and severity of corrosion risk areas. For example, high-risk areas are marked in red, medium-risk areas in orange, and low-risk areas in yellow. The specific locations and concentration values ​​of the risk areas are annotated on the map. This color change and location annotation visually demonstrates the distribution of potential corrosion risks within the concrete structure, providing a basis for maintenance decisions.

[0193] 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:

[0194] In step S241, 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 to the inter-layer distance, and quantify the penetration rate and depth distribution trend of chloride ions along the thickness direction of the concrete.

[0195] Wherein, obtaining the concentration distribution matrix: extracting the concentration data of each monitoring point from the concentration inversion result obtained in step S230 to form a concentration distribution matrix.

[0196] Apply the inter-layer concentration difference quotient algorithm: use the following formula to calculate the vertical gradient value of each monitoring point:

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

[0198] Quantifying vertical penetration rate and depth distribution trends: The magnitude and sign of the vertical gradient reflect the chloride ion penetration rate and depth distribution trends along the thickness of the concrete. Positive values ​​indicate an increase in chloride ion concentration with depth, while negative values ​​indicate the opposite. By calculating the vertical gradient values ​​for all monitoring points, a comprehensive understanding of the vertical penetration of chloride ions in concrete can be obtained.

[0199] In step S242 , 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 abnormality area in the horizontal direction is identified.

[0200] The complete process is described as follows:

[0201] Extract lateral dot matrix data: Extract lateral data of the same depth layer from the concentration distribution matrix.

[0202] Apply the adjacent concentration difference quotient algorithm: calculate the lateral gradient value using the following formula:

[0203] , is the distance between adjacent monitoring points in the horizontal direction.

[0204] Identifying Horizontal Concentration Anomalies: The magnitude and sign of the lateral gradient reflect the horizontal trend of chloride ion concentration. Positive values ​​indicate an increase in chloride ion concentration along the horizontal axis, while negative values ​​indicate the opposite. By calculating the gradient values ​​at all lateral monitoring points, it is possible to identify areas of horizontal concentration anomalies, which may indicate localized chloride ion infiltration anomalies or the presence of cracks.

[0205] In step S243, 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 the time series data, the gradient amplitude change is dynamically tracked to quantify the acceleration / attenuation trend of the infiltration rate.

[0206] The three-dimensional gradient feature matrix (3DG) integrates vertical and lateral gradient information to comprehensively describe the three-dimensional characteristics of chloride ion migration. Active infiltration zones (APIs) are areas where the absolute value of the gradient exceeds a preset threshold, indicating a high chloride ion infiltration rate or significant concentration changes. Gradient vector direction (the direction derived from the composite of vertical and lateral gradients) indicates the chloride ion migration path. Time series data (TCDs) are data collected in chronological order and used to analyze the dynamic changes in infiltration trends.

[0207] The complete process is described as follows:

[0208] Integrate gradient data: The vertical gradient obtained in step S241 and the lateral gradient obtained in step S242 are integrated into a three-dimensional gradient feature matrix according to spatial coordinates. The matrix elements contain the vertical gradient, lateral gradient, and their composite gradient vector for each monitoring point.

[0209] Marking Active Infiltration Areas: Compare the absolute value of the gradient of the matrix element to a preset threshold. If the absolute value of the vertical or horizontal gradient exceeds the threshold, it is marked as an active infiltration area.

[0210] Mapping 3D migration paths: Chloride ion migration paths are plotted in the matrix along the gradient vector direction. Specifically, starting from each active permeation zone, migration lines are drawn along the gradient vector direction.

[0211] Dynamic Trend Tracking: Analyze changes in gradient amplitude based on time series data. A continuous increase in gradient amplitude indicates an acceleration in the permeation rate; a decrease indicates a decrease in the permeation rate. For example, comparing 30 consecutive days of gradient data, if the gradient amplitude increases from 0.001 mol / (L・mm) to 0.0015 mol / (L・mm) from day 15 to day 30, the permeation rate is considered to be accelerating.

[0212] 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:

[0213] Step S410 involves targeted collection of environmental parameters, structural parameters, and MXene parameters. Core influencing factors are identified through cause-and-effect diagram analysis. Chloride ion concentration and MXene intercalation amount are identified as direct driving factors, while temperature, humidity, and strain are identified as indirect regulating factors. A multidimensional correlation dataset is constructed, including potential signals corrected for reference electrode drift. Environmental parameters include temperature, humidity, and corrosive medium type; structural parameters include strain and crack width; and MXene parameters include intercalation amount and potential signals.

[0214] Among them, environmental parameters: refer to the conditions of the environment in which the concrete structure is located, such as temperature, humidity and the type of corrosive medium. These factors 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. These parameters reflect the working state of the MXene sensor and its response to chloride ions. Causal graph analysis: an analytical method used to identify causal relationships between variables, helping to determine which factors are the core influencing factors. Multidimensional correlation data set: integrates different types of parameter data to form a comprehensive data set for comprehensive analysis of the deterioration state of concrete structures.

[0215] The complete process is described below: 1. Targeted parameter data collection: According to the pre-set monitoring plan, appropriate sensors are used to collect environmental parameters (temperature, humidity, and corrosive medium type), structural parameters (strain and crack width), and MXene parameters (intercalation amount and potential signal). Sensor accuracy and reliability are ensured, with regular calibration and maintenance. 2. Causal diagram analysis identifies core influencing factors: A causal diagram is constructed, identifying chloride ion concentration and MXene intercalation amount as direct driving factors, and temperature, humidity, and strain as indirect moderating factors. By analyzing the causal relationships between variables, the core factors most significantly impacting concrete structural degradation are identified. For example, if a significant increase in chloride ion concentration is found in high humidity environments, accompanied by significant strain, humidity is identified as one of the key factors accelerating structural degradation. 3. Constructing a multidimensional correlation dataset: The collected parameter data are integrated to construct a multidimensional correlation dataset. The dataset should include potential signals corrected for reference electrode drift to ensure data accuracy. Data preprocessing, including data cleaning and normalization, is performed to improve data quality and usability. 4. Data Integration and Transmission: The processed multi-dimensional correlation dataset and chloride ion concentration data are aggregated and transmitted to the cloud via wireless communication modules. In the cloud, the data is further analyzed using a pre-defined corrosion life prediction model to assess the degradation state of the concrete structure. The stability and security of data transmission are regularly checked to ensure data integrity and timeliness.

[0216] In step S420, a causal inference algorithm based on the electrochemical properties of MXene is used to process the data. Noise is eliminated through blank calibration, parameter weights are assigned according to a dynamic weight formula, and the concentration is inverted using the potential response characteristics of the reversible intercalation of MXene to generate a characteristic vector.

[0217] Among them, the causal inference algorithm: an algorithm developed based on the electrochemical properties of MXene, used to analyze causal relationships between data and extract key features. The general processing logic is as follows: Based on the characteristics of MXene, the algorithm distinguishes the true causal relationship of "chloride ion concentration → MXene signal" from multi-dimensional data (excluding surface correlations caused by interfering factors such as temperature and humidity). The core logic of this algorithm is as follows: 1. Determine the core causal relationship: lock on the "Cl⁻ concentration → MXene potential" (because Cl⁻ is embedded in the MXene interlayer and directly changes the potential, this is an inevitable relationship); 2. Identify interfering factors: identify temperature, humidity, etc. (only indirectly, with no direct effect on MXene, and are surface-related); 3. Remove interference: Use blank calibration (samples without Cl⁻) to eliminate potential noise caused by temperature and other factors, retaining the pure Cl⁻ response signal; 4. Extract features: Ultimately, extract only features reflecting the "true correlation between Cl⁻ and MXene" for subsequent analysis.

[0218] Blank calibration: Use blank samples to calibrate the measurement system to eliminate system noise and offset. Dynamic weight formula: A formula that dynamically adjusts weights based on the importance and relevance of parameters, ensuring that key parameters receive greater weight in the analysis. Eigenvector: A eigenvector extracted from processed data that represents the system's state and is input into the corrosion life prediction model.

[0219] The complete process is described as follows:

[0220] Data acquisition and preprocessing: Obtain a multi-dimensional correlation dataset 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 rejection: Use blank samples to calibrate the measurement system and calculate the noise level and offset. Correct the raw data using the following formula:

[0222] ;

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

[0224] Dynamic weight allocation: According to the dynamic weight formula, the relevance and importance of each parameter are taken into account to assign weights to different parameters. The formula is as follows:

[0225] ;

[0226] in, is the dynamic weight of the i-th parameter, is the initial weight, is the correlation coefficient, and n is the total number of parameters.

[0227] Potential response characteristics to invert concentration: Utilizing the reversible intercalation potential response characteristics of MXene, the corrected potential signal is inverted into chloride ion concentration. The formula is as follows:

[0228] ;in, 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, which contains information such as chloride ion concentration, MXene intercalation amount, temperature and humidity, and strain. The formula is as follows:

[0230] ;

[0231] in, is the eigenvector, is the chloride ion concentration, is the MXene intercalation amount, For humidity, is the temperature, For strain.

[0232] In step S430, a dynamic model incorporating MXene functionality is constructed. The correlation between the amount of intercalation and the blocking efficiency is reflected when correcting the diffusion coefficient. The corrosion rate is derived by coupling the bidirectional effects of crack propagation and MXene interlayer delamination. The model adaptively adjusts parameters based on the MXene potential drift trend.

[0233] The dynamic model, which can update and reflect changes in system state in real time, is used to simulate the degradation process of concrete structures. The diffusion coefficient, a parameter that characterizes the diffusion rate of chloride ions in concrete and is affected by the amount of MXene intercalation and its retardation efficiency, is used to simulate the degradation process of concrete structures. The bidirectional effect is that crack propagation affects MXene interlayer delamination, and conversely, MXene interlayer delamination inhibits crack propagation. Adaptive parameter adjustment is used to automatically adjust model parameters based on MXene potential drift trends to improve model accuracy and adaptability.

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

[0235] In step S440, a transfer learning optimization model is introduced, and the optimization model of the historical case library of different service environments is called. The preset corrosion life prediction model is combined to output the causal contribution distribution of the degradation state and the health trend of the entire life cycle. The causal contribution includes the proportion of MXene blocking effect.

[0236] Transfer learning: A machine learning method that leverages existing knowledge (historical case data) to accelerate new model training and improve its performance. Causal contribution distribution: The proportion of each factor contributing to concrete degradation, including the proportion of MXene's retardation effect. Lifecycle health trends: The changing health status of concrete structures from service to retirement.

[0237] The complete process is described as follows:

[0238] Introducing transfer learning to optimize the model: Introducing transfer learning in model training, first pre-training the model on a general dataset, and then fine-tuning it using target environment data, accelerates training and improves generalization capabilities.

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

[0240] Combined model output evaluation results: The transfer learning optimized model is combined with a pre-set corrosion life prediction model. Multi-parameter data is input to output the causal contribution distribution and lifecycle health trends. The causal contribution includes the proportion of MXene retardation, which can intuitively demonstrate the influence of various factors.

[0241] Decision support: Based on the assessment results, support can be provided for the maintenance and repair of concrete structures. For example, if the MXene blocking effect is low, consideration can be given to increasing the MXene content or replacing the sensor.

[0242] Among them, a dynamic model incorporating MXene functionality is constructed, the relationship between intercalation amount and blocking efficiency is reflected when correcting the diffusion coefficient, and the corrosion rate is derived by coupling the two-way effect of crack extension and MXene interlayer delamination. The model adaptively adjusts parameters based on the MXene potential drift trend, including the following steps:

[0243] Step S431, dynamic diffusion coefficient correction: collect MXene intercalation data and construct a time-varying correction model for chloride ion diffusion coefficient including environmental factors. The core formula is:

[0244] ;

[0245] in, is the corrected diffusion coefficient, is the base diffusion coefficient, is the amount of MXene intercalation at time t, is the intercalation amount-blocking efficiency correlation function (through experimental calibration, the higher the intercalation amount, the stronger the blocking effect), is the temperature and humidity correction factor (quantifying the impact of the environment on MXene functionality).

[0246] Step S432, bidirectional coupling derivation: Identify the interaction between concrete crack expansion and MXene interlayer delamination, and establish a coupling relationship between the two: the increase in crack width accelerates MXene interlayer delamination, while MXene interlayer delamination reacts on the crack, increasing its expansion rate; combined with the corrected diffusion coefficient in step 1, deduce the steel corrosion rate, realizing the bidirectional mechanism coupling of "crack-MXene degradation-corrosion".

[0247] The coupling relationship is established based on an understanding of the aforementioned interactions. Specifically, this is manifested in the mutual influence between crack width and the rate of MXene interlayer exfoliation, and the reverse effect of the degree of MXene interlayer exfoliation on the crack propagation rate. This coupling relationship can be described using a series of mathematical formulas and models. For example, a coupling coefficient is introduced to quantitatively represent the strength of the interaction between the two.

[0248] Derivation of Rebar Corrosion Rate: The modified diffusion coefficient (accounting for the influence of MXene intercalation and environmental factors) is combined with a two-way coupling relationship to further derive the rebar corrosion rate. The interaction between cracks and MXene delamination affects the diffusion path and rate of chloride ions, which in turn influences the corrosion process. By establishing a model that correlates the corrosion rate with multiple factors, including crack width, MXene delamination, and chloride ion concentration, more accurate predictions of rebar corrosion are possible.

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

[0250] ;in, is the model parameter set (including diffusion coefficient correction parameters, coupling coefficients, etc.), is the potential drift of MXene. The parameters are adjusted in real time through this formula to make the model adapt to the material performance degradation. is the learning rate (controls the parameter update step size, using the Adagrad algorithm for adaptive adjustment), is the log-likelihood gradient (a measure of how well the current parameters match the observed data).

[0251] Step S434, full-process dynamic model construction and output: Construct a spatiotemporal coupled concrete degradation dynamic model, the core control equation is:

[0252] ,in, is the time-varying corrected diffusion coefficient (related to time t and MXene intercalation amount , interlayer peeling degree Deep coupling), is the chloride ion concentration gradient (the driving force for diffusion), is the divergence operator (describing the spatial divergence / convergence characteristics of the diffusion effect).

[0253] The finite volume method is used to handle the diffusion process, integrate multi-source monitoring data, quantify prediction uncertainty, and output the corrosion rate and structural degradation trend including the MXene influence mechanism.

[0254] Based on the same inventive concept, an embodiment of the present invention provides a MXene-based concrete chloride ion gradient monitoring and durability enhancement system, including a memory and a processor, wherein the memory stores data that can be executed on the processor to implement the following Figure 1 Procedure of the method shown.

[0255] The embodiments of this specific implementation method are all preferred embodiments of the present application and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present 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. Multi-parameter data related to the concrete service environment and structural status are collected, and the multi-parameter data and chloride ion concentration data are summarized and transmitted to the cloud. The degradation status of the concrete structure is evaluated in combination with the preset corrosion life prediction model, and an early warning is triggered when the chloride ion concentration exceeds the preset critical threshold.

2. A method for monitoring chloride ion gradient and enhancing durability of concrete based on MXene according to claim 1, characterized in that: 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. By reserving holes on the inside of the preset concrete pouring template, the flat spray-type MXene-Cl sensing electrode is attached and sealed with epoxy glue. After the steel bar passes through the template hole, the ring-shaped wrapped MXene-Cl sensing electrode is slid in and the ring belt is tightly fitted to the steel bar. The edge of the hole is then sealed with polyurethane grouting material to form a three-dimensional monitoring network covering the concrete surface to the steel bar.

3. A method for monitoring chloride ion gradient and enhancing durability of concrete based on MXene according to claim 2, 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.

4. A method for monitoring chloride ion gradient and enhancing durability of concrete based on MXene according to claim 3, 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.

5. A method for monitoring chloride ion gradient and enhancing durability of concrete based on MXene according to any one of claims 1 to 4, characterized in that: 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, locate the corrosion risk area based on the gradient feature matrix, and superimpose the risk markers in the three-dimensional imaging.

6. A method for monitoring chloride ion gradient and enhancing durability of concrete based on MXene according to claim 5, 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.

7. A method for monitoring chloride ion gradient and enhancing durability of concrete based on MXene according to claim 1, characterized in that: 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.

8. 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 claimed in any one of claims 1 to 7.

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