Nanometer conductive composite material pipeline repair layer health monitoring system and method based on piezoresistive effect

Abstract

The invention belongs to the technical field of pipeline repair, and particularly relates to a nano conductive composite material pipeline repair layer health monitoring system and method based on a piezoresistive effect. The invention discloses a nano conductive composite material pipeline repair layer health monitoring system based on a piezoresistive effect. The system comprises a sensing layer, a data acquisition layer, a transmission layer, an analysis layer and an application layer, wherein the sensing layer is a graphene / carbon nanotube hybrid reinforced composite material pipeline repairing layer made of a self-sensing material; the data acquisition layer is used for acquiring signals and carrying out multi-channel acquisition, signal conditioning, A / D conversion and time synchronization; the transmission layer performs data communication through wired transmission / wireless transmission; the analysis layer performs data processing and algorithm processing including data preprocessing, trend analysis, damage identification and a positioning algorithm; and the application layer realizes user interaction and display, and performs visual interface, alarm push, operation and maintenance decision and report generation. The invention provides a nano conductive composite material pipeline repair layer health monitoring system and method based on a piezoresistive effect, and aims to solve the problems of no basis for conductive threshold design, insufficient monitoring sensitivity, incomplete system, indefinite alarm criterion, low positioning precision, low intelligent degree, high cost and the like in the prior art.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of pipeline repair, and particularly relates to a health monitoring system and method for a nanometer conductive composite material pipeline repair layer based on a piezoresistive effect. BACKGROUND

[0002] Underground pipe networks are the "lifeline" of urban infrastructure, bearing key functions such as sewage discharge, rainwater collection, and water supply transportation. According to statistics, the total length of urban underground drainage pipe networks in China exceeds 800,000 kilometers, of which more than 40% of the pipelines have been in service for more than 20 years. With the acceleration of urbanization and the increase in the service life of pipe networks, problems such as pipeline aging, corrosion, and rupture are becoming increasingly prominent, with direct economic losses caused by pipeline failure reaching tens of billions of yuan per year.

[0003] Trenchless repair technology has become the mainstream method for repairing underground pipe networks due to its small construction disturbance, short construction period, and low cost. UV-cured in-situ curing method (UV-CIPP) and inversion lining method are widely used at home and abroad. However, after the repair is completed, the pipeline enters a "blind pipe" state, and there is a lack of effective structural health monitoring means, which cannot timely detect secondary damage or failure risks, which may lead to sudden accidents.

[0004] Structural health monitoring (SHM) is a technology system that uses sensor networks to collect real-time structural response signals and uses signal processing and pattern recognition techniques to evaluate the health status of the structure. Traditional structural health monitoring methods include: Strain gauge monitoring method: metal foil strain gauges are attached to the surface of the structure to reflect the structural strain by measuring resistance changes. This method is mature and reliable, but has problems such as complex wiring, limited coverage of point measurement, and failure in humid environments.

[0005] Optical fiber sensing monitoring method: uses the optical characteristics of fiber Bragg gratings (FBG) or distributed optical fibers to sense strain. This method has the advantages of anti-electromagnetic interference and can achieve distributed measurement, but the optical fiber is brittle, has poor interface bonding with composite materials, and has high equipment costs.

[0006] Piezoelectric sensing monitoring method: uses the piezoelectric effect of piezoelectric materials to sense dynamic load. This method has fast response speed, but is not sensitive to static strain and has limitations in slow deformation monitoring of pipelines.

[0007] In recent years, the piezoresistive effect of nano-conductive composites provides a new idea for structural health monitoring. When carbon nanotubes (CNT), graphene and other nano-conductive fillers form a conductive network in the polymer matrix, the geometric configuration of the conductive path will change when the material deforms under stress, thereby causing a change in resistance. This piezoresistive effect can be used for strain sensing.

[0008] According to the percolation theory, there is a critical concentration of conductive fillers in the polymer matrix--the percolation threshold. When the concentration of fillers is below this threshold, the material behaves as an insulator; when the concentration of fillers exceeds this threshold, the conductive fillers form a continuous conductive path, and the material changes to a conductor. Near the percolation threshold, the resistance of the material is highly sensitive to strain, and has a high strain sensitivity coefficient (Gauge Factor, GF).

[0009] Table 1 lists the disadvantages of the prior art.

[0010] SUMMARY

[0011] To solve the above-mentioned problems existing in the prior art, the purpose of the present application is to provide a nano-conductive composite pipeline repair layer health monitoring system and method based on the piezoresistive effect, to solve the problems of no basis for conductive threshold design, insufficient monitoring sensitivity, incomplete system, unclear alarm criterion, low positioning accuracy, low intelligent degree, high cost and other problems existing in the prior art.

[0012] The technical scheme adopted by the present application is: The nano-conductive composite pipeline repair layer health monitoring system based on the piezoresistive effect comprises a sensing layer, a data acquisition layer, a transmission layer, an analysis layer and an application layer. The sensing layer is a graphene / carbon nanotube hybrid reinforced composite pipeline repair layer using self-sensing material. The data acquisition layer is used for signal acquisition, multi-channel acquisition, signal conditioning, A / D conversion and time synchronization. The transmission layer communicates data through wired transmission / wireless transmission. The analysis layer performs data processing and algorithms, including data preprocessing, trend analysis, damage identification and positioning algorithm. The application layer realizes user interaction and display, visual interface, alarm push, operation and maintenance decision and report generation.

[0013] The nano-conductive composite pipeline repair layer health monitoring method based on the piezoresistive effect comprises the following steps: Conductive threshold optimization design; Strain sensitivity coefficient design; Sensor network arrangement; Damage identification and grading alarm.

[0014] As a preferred scheme of the present application, the conductive threshold value optimization design method specifically comprises the following steps: S1: determining a target sensitivity: determining a target strain sensitivity coefficient GF according to monitoring requirements, GF≥15; S2: calculating an optimal filler content interval: according to percolation theory, calculating an optimal filler content interval: φ opt =(1.2~2.0)×φ c ; For graphene / carbon nanotube hybrid fillers, the optimal filler content interval: φ opt =0.4~1.0wt%; S3: optimizing the ratio of graphene and carbon nanotube; S4: verifying performance indicators: preparing a sample and testing actual performance.

[0015] As a preferred scheme of the present application, the optimal mass ratio of graphene and carbon nanotube is 1.8:1.

[0016] As a preferred scheme of the present application, in the sensor network arrangement, the electrode arrangement follows the following principles: the electrode network should cover the entire repair area without monitoring blind area; the weak parts of the structure are arranged densely; the electrode lead should be convenient for construction and later maintenance; redundant electrodes are arranged at key positions to improve system reliability.

[0017] As a preferred scheme of the present application, the material of the electrode includes electrode sheet, conductive glue, lead, and junction box; the material of the electrode sheet is copper foil with a thickness of 0.05 mm and a size of 20*50 mm; the material of the conductive glue is silver-based conductive glue with a resistivity of ≤10 -5 Ω•cm; the material of the lead is shielded twisted pair wire with a cross section of 0.5 mm²; the material of the junction box meets the IP68 protection level.

[0018] As a preferred scheme of the present application, in the damage identification and grading alarm, the alarm levels include normal, pre-warning, and alarm; wherein, in the normal state, the resistance change rate threshold value is ΔR / R0<10%; in the pre-warning state, the resistance change rate threshold value is 10%≤ΔR / R0<30%; in the alarm state, the resistance change rate threshold value is ΔR / R0≥30%.

[0019] As a preferred scheme of the present application, in the damage identification and grading alarm, the pre-warning and damage positioning algorithm is: The grading alarm mechanism based on the resistance change rate ΔR / R0 is clear about the pre-warning threshold value and the alarm threshold value; the determination logic of continuous 3 times over threshold value is adopted; The segment positioning method is adopted to determine the damage position: for two adjacent electrodes Ei and E i+1 between the pipe segments, if the resistance change rate of the segment is significantly higher than that of the adjacent segments, it is determined that the damage is located in the segment; Damage axial position estimation: L damage = L i + η i × d / (η i + η i+1 ); wherein, L i is the axial position of the electrode E i , and d is the distance between adjacent electrodes.

[0020] The beneficial effects of the present application are: 1. Conductive threshold optimization design method based on percolation theory: The present application first establishes a conductive threshold optimization design method for pipeline repair layer health monitoring. By analyzing the synergistic conductive mechanism of graphene and carbon nanotube hybrid fillers, the optimal filler content range (0.4-1.0wt%) is determined by using percolation theory, and the optimal GO:CNT ratio (optimal 1.8:1) is optimized by experiment, achieving the highest strain sensitivity (GF≥15) with the lowest filler dosage. This method provides a theoretical basis for the self-sensing function design of nanometer conductive composite materials, avoiding the problems of high cost and poor performance caused by traditional experience-based determination of filler dosage.

[0021] 2. Distributed strain sensing network construction method based on piezoresistive effect: The present application utilizes the inherent piezoresistive effect of graphene / carbon nanotube hybrid reinforced composite materials, and through reasonable arrangement of electrode network, the entire repair layer is converted into a distributed strain sensing network. This method does not need to embed additional sensors, realizes the integration of "structure-sensing" of repair materials, and greatly reduces the cost of monitoring system. The electrode arrangement scheme considers the principles of coverage, sensitivity, accessibility and redundancy, ensuring that there is no blind area in monitoring.

[0022] 3. Hierarchical alarm mechanism based on resistance change rate: The present application establishes a hierarchical alarm mechanism based on resistance change rate ΔR / R0, and clearly defines the early warning threshold (10%) and alarm threshold (30%). The determination logic of "continuous 3 times over threshold confirmation" effectively reduces the false positive rate. Combined with the segment positioning algorithm, the accurate identification of damage position is realized (positioning accuracy within ±0.5m). This mechanism provides a clear quantitative basis for pipeline operation and maintenance decision-making.

[0023] 4. Integrated intelligent monitoring platform: The application constructs a five-layer system architecture including a perception layer, a data acquisition layer, a transmission layer, an analysis layer and an application layer. The software platform integrates four function modules of conductive threshold design, health monitoring, data analysis and alarm management, realizes full-process coverage from material design to operation and management, and makes massive monitoring data effectively utilized through historical trend analysis and visual display functions. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 is a schematic diagram of the overall architecture of the system; Figure 2 is a schematic diagram of the principle of synergistically reducing the percolation threshold by hybrid fillers; Figure 3 is a schematic diagram of the piezoresistive effect; Figure 4 is a schematic diagram of electrode arrangement. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical scheme and advantages of the embodiments of the application clearer, the technical scheme in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only a part of the embodiments of the application, rather than all the embodiments of the application. The components of the embodiments of the application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0026] Therefore, the following detailed description of the embodiments of the application provided in the drawings is not intended to limit the scope of the claimed application, but only represents selected embodiments of the application. All other embodiments obtained by a person of ordinary skill in the art without creative labor based on the embodiments in the application belong to the scope of protection of the application. It should be noted that the embodiments in the application and the features in the embodiments can be combined with each other without conflict.

[0027] I. Overall architecture of the system The health monitoring system of the nanometer conductive composite pipeline repair layer based on the piezoresistive effect described in the application includes five levels of perception layer, data acquisition layer, transmission layer, analysis layer and application layer. Figure 1 is a schematic diagram of the overall architecture of the system.

[0028] II. Conductive threshold optimization design method 2.1 Percolation theory basis According to the percolation theory, the relationship between the electrical conductivity σ of the conductive filler in the polymer matrix and the volume fraction φ of the filler can be expressed as: Below the percolation threshold (φ<φc): σ=0 (insulation state); Above the percolation threshold (φ>φc): σ=σ0(φ-φc ) t ; Where: σ0 is the inherent conductivity of the conductive filler, φc is the percolation threshold, and t is the critical exponent (usually taken as 1.6 to 2.0).

[0029] 2.2 Synergistic effect of hybrid packing This invention employs a hybrid filler system of graphene (two-dimensional sheet structure) and carbon nanotubes (one-dimensional tubular structure). The geometric complementarity of the two nanomaterials significantly reduces the percolation threshold: graphene sheets provide a large-area conductive contact interface; carbon nanotubes act as "bridging" between graphene sheets, connecting conductive pathways; the covalent hybrid structure prevents graphene sheets from re-stacking, maintaining a high specific surface area. Figure 2 Schematic diagram illustrating the principle of synergistic reduction of percolation threshold using hybrid packing materials.

[0030] 2.3 Conductivity Threshold Optimization Design Process The conductivity threshold optimization design method provided by this invention includes the following steps: Step 1: Determine the target sensitivity: Determine the target strain sensitivity coefficient GF according to the monitoring requirements. Usually, GF is required to be ≥15.

[0031] Step 2: Calculate the optimal filler content range: According to percolation theory, the optimal operating range should be slightly higher than the percolation threshold. φ opt =(1.2~2.0)×φ c ; For graphene / carbon nanotube hybrid fillers (φc=0.3~0.5wt%): φ opt =0.4~1.0wt%.

[0032] Step 3: Optimize the GO:CNT ratio: Determine the optimal mass ratio of graphene to carbon nanotubes through experiments.

[0033] Table 2 is a comparison table of the optimal mass ratio of graphene to carbon nanotubes.

[0034]

[0035] Step 4: Verify performance indicators: Prepare samples and test actual performance to ensure that they meet design requirements.

[0036] III. Piezoresistive effect sensing principle 3.1 Piezoresistive effect mechanism When a composite material is subjected to external force and produces strain ε, the geometry of the conductive network changes: under tension, the spacing between conductive fillers increases, the number of contact points decreases, and the resistance increases; under compression, the spacing between conductive fillers decreases, the number of contact points increases, and the resistance decreases.

[0037] The relationship between resistance change and strain can be expressed as: ; Where: ΔR is the change in resistance, R0 is the initial resistance, GF is the strain sensitivity coefficient, and ε is the strain. Figure 3 This is a schematic diagram illustrating the piezoresistive effect.

[0038] 3.2 Design of Strain Sensitivity Coefficient Table 3 shows the relationship between the strain sensitivity coefficient GF and the filler content, which initially increases and then decreases.

[0039]

[0040] The preferred content of the hybrid filler in this invention is 0.8 wt%, at which point the GF value is in the optimal range of 16 to 22, which balances high sensitivity and good linearity.

[0041] IV. Sensor Network Deployment Scheme 4.1 Electrode Arrangement Principles The electrode arrangement should follow these principles: Coverage principle: The electrode network should cover the entire repair area with no monitoring blind spots; Sensitivity principle: Density arrangement should be placed in weak structural areas (interfaces, elbows, settlement zones); Accessibility principle: Electrode leads should be easy to install and maintain; Redundancy principle: Redundant electrodes are installed in critical parts to improve system reliability.

[0042] 4.2 Electrode Spacing Design Table 4 shows the electrode spacing determination based on pipe diameter and monitoring accuracy requirements.

[0043]

[0044] Figure 4 This is a schematic diagram of the electrode arrangement (pipe cross-section).

[0045] 4.3 Electrode Materials and Connections Table 4 shows the electrode materials and connections.

[0046]

[0047] V. Data Acquisition and Processing System 5.1 Hardware Architecture Table 5 shows the hardware of the data acquisition system.

[0048]

[0049] 5.2 Software Functional Modules Application layer interface: conductive prefabrication design module, health monitoring module, data analysis module, alarm management module; business logic layer: permeation curve calculation, real-time monitoring and acquisition, trend analysis and statistics, damage location algorithm, hierarchical alarm judgment; data layer: real-time database, historical database, configuration database.

[0050] VI. Damage Identification and Graded Alarm Methods 6.1 Calculation of Resistance Change Rate For the i-th sensing unit, the formula for calculating the rate of change of resistance is: ; Where: R i R is the current resistance value. 0i This is the reference resistance value (initial calibration value).

[0051] 6.2 Classified Alarm Thresholds This invention establishes a three-level alarm mechanism.

[0052] Table 6 shows the three-level alarm mechanism.

[0053]

[0054] 6.3 Early Warning and Damage Localization Algorithm A graded alarm mechanism based on the resistance change rate ΔR / R0 is established, defining the warning threshold (10%) and the alarm threshold (30%). The "three consecutive threshold exceedance confirmations" logic effectively reduces the false alarm rate.

[0055] The location of the damage was determined using the segmental localization method: For a pipe segment between two adjacent electrodes Ei and Ei+1, if the resistance change rate of this segment is significantly higher than that of the adjacent segment, the damage is determined to be located within this segment.

[0056] Damage axial location estimation: ; Where: Li is the axial position of electrode Ei, and d is the distance between adjacent electrodes.

[0057] VII. Examples 7.1 Example 1: Monitoring of the Repair Layer of DN600 Sewage Pipe Project Overview: Pipe type: Reinforced concrete sewage drainage pipe; Nominal diameter: DN600; Repair length: 50m; Repair material: Graphene / carbon nanotube hybrid reinforced epoxy composite material.

[0058] Table 7 is the monitoring system configuration table.

[0059]

[0060] Table 8 shows the monitoring results.

[0061]

[0062] 7.2 Example 2: Monitoring of DN1000 rainwater pipe repair layer Project Overview: Pipe type: Reinforced concrete rainwater drainage pipe; Nominal diameter: DN1000; Repair length: 80m; Repair material: Graphene / carbon nanotube hybrid reinforced epoxy composite material (erosion resistant formula).

[0063] Table 9 is the monitoring system configuration table.

[0064] Monitoring results: The system ran continuously for 180 days and successfully issued warnings of three potential damages. Positioning accuracy: Measured ±0.35m, meeting design requirements; False alarm rate: <2%.

[0065] VIII. Key technical points that bring about the beneficial effects of this invention: Key Point 1: Conductivity threshold optimization design method based on percolation theory.

[0066] This invention establishes for the first time a conductive threshold optimization design method for health monitoring of pipeline repair layers. By analyzing the synergistic conductivity mechanism of graphene-carbon nanotube hybrid fillers, the optimal filler content range (0.4–1.0 wt%) was determined using percolation theory, and the GO:CNT ratio was experimentally optimized (optimal 1.8:1), achieving the highest strain sensitivity (GF≥15) with the lowest filler content. This method provides a theoretical basis for the self-sensing function design of nano-conductive composite materials, avoiding the problems of high cost and poor performance caused by traditional empirical determination of filler content.

[0067] Key Point 2: Construction Method of Distributed Strain Sensing Network Based on Piezoresistive Effect.

[0068] This invention utilizes the inherent piezoresistive effect of graphene / carbon nanotube hybrid reinforced composite materials, transforming the entire repair layer into a distributed strain sensing network through a rationally arranged electrode network. This method eliminates the need for additional embedded sensors, achieving integrated "structure-sensing" of the repair material and significantly reducing the cost of the monitoring system. The electrode arrangement scheme comprehensively considers the principles of coverage, sensitivity, accessibility, and redundancy, ensuring no blind spots in monitoring.

[0069] Key Point 3: A graded alarm mechanism based on the rate of change of resistance.

[0070] This invention establishes a tiered alarm mechanism based on the resistance change rate ΔR / R0, clearly defining the warning threshold (10%) and the alarm threshold (30%). The "three consecutive threshold exceedance confirmations" judgment logic effectively reduces the false alarm rate. Combined with a section location algorithm, it achieves accurate identification of damage locations (location accuracy within ±0.5m). This mechanism provides clear quantitative basis for pipeline network operation and maintenance decisions.

[0071] Key Point 4: Integrated Intelligent Monitoring Platform.

[0072] This invention constructs a five-layer system architecture comprising a sensing layer, a data acquisition layer, a transmission layer, an analysis layer, and an application layer. The software platform integrates four major functional modules: conductivity threshold design, health monitoring, data analysis, and alarm management, achieving full-process coverage from material design to operation and maintenance management. Historical trend analysis and visualization capabilities enable the effective utilization of massive amounts of monitoring data.

[0073] Table 10 shows the expected technical effects.

[0074]

[0075] This invention is not limited to the above-described optional embodiments. Anyone can derive other various forms of products under the guidance of this invention. However, regardless of any changes made in their shape or structure, any technical solution that falls within the scope of the claims of this invention shall be protected by this invention.

Claims

1. A health monitoring system for pipeline repair layers based on piezoresistive effect nano-conductive composite materials, characterized in that: It includes a perception layer, a data acquisition layer, a transmission layer, an analysis layer, and an application layer; The system comprises the following components: a sensing layer consisting of a graphene / carbon nanotube hybrid reinforced composite material pipe repair layer; a data acquisition layer for signal acquisition, including multi-channel acquisition, signal conditioning, A / D conversion, and time synchronization; a transmission layer for data communication via wired / wireless transmission; an analysis layer for data processing and algorithms, including data preprocessing, trend analysis, damage identification, and location algorithms; and an application layer for user interaction and display, providing a visual interface, alarm push notifications, maintenance decision-making, and report generation.

2. A method for monitoring the health of a pipe repair layer based on piezoresistive effect using a nano-conductive composite material, comprising the pipe repair layer health monitoring system based on piezoresistive effect as described in claim 1, characterized in that: Includes the following steps: Conductivity threshold optimization design; Strain sensitivity coefficient design; Sensor network deployment; Damage identification and graded alarm.

3. The method for health monitoring of pipeline repair layers based on piezoresistive effect of nano-conductive composite materials according to claim 2, characterized in that: The conductivity threshold optimization design method specifically includes the following steps: S1: Determine target sensitivity: Determine the target strain sensitivity coefficient GF according to monitoring requirements, where GF ≥ 15; S2: Calculate the optimal filler content range: Based on percolation theory, calculate the optimal filler content range: f opt =(1.2~2.0)×φ c ; For graphene / carbon nanotube hybrid fillers, the optimal filler content range is: φ opt =0.4~1.0wt%; S3: Optimize the ratio of graphene to carbon nanotubes; S4: Verify performance indicators: Prepare samples and test actual performance.

4. The method for health monitoring of pipeline repair layers based on piezoresistive effect of nano-conductive composite materials according to claim 3, characterized in that: The optimal mass ratio of graphene to carbon nanotubes is 1.8:

1.

5. The method for health monitoring of pipeline repair layers based on piezoresistive effect of nano-conductive composite materials according to claim 2, characterized in that: In sensor network deployment, electrode placement should follow these principles: the electrode network should cover the entire repair area with no blind spots; the electrode network should be densely distributed in structurally weak areas; electrode leads should facilitate construction and subsequent maintenance; and redundant electrodes should be installed in critical areas to improve system reliability.

6. The method for health monitoring of pipeline repair layers based on piezoresistive effect of nano-conductive composite materials according to claim 5, characterized in that: The electrode materials include electrode sheets, conductive adhesive, leads, and junction boxes; the electrode sheets are made of copper foil, 0.05 mm thick, and 20 × 50 mm in size; the conductive adhesive is made of silver-based conductive adhesive with a resistivity ≤ 10. -5 Ω•cm; the lead wire is made of shielded twisted pair with a cross-section of 0.5mm²; the junction box material meets the IP68 protection rating.

7. The method for health monitoring of pipeline repair layers based on piezoresistive effect of nano-conductive composite materials according to claim 2, characterized in that: In damage identification and graded alarm, the alarm levels include normal, warning, and alarm. In the normal state, the resistance change rate threshold is ΔR / R0 < 10%; in the warning state, the resistance change rate threshold is 10% ≤ ΔR / R0 < 30%; and in the alarm state, the resistance change rate threshold is ΔR / R0 ≥ 30%.

8. The method for health monitoring of pipeline repair layers based on piezoresistive effect of nano-conductive composite materials according to claim 2, characterized in that: In damage identification and graded alarm systems, the algorithms for early warning and damage localization are as follows: A graded alarm mechanism based on the resistance change rate ΔR / R0 is established, clearly defining the warning threshold and the alarm threshold; a judgment logic of confirming three consecutive exceedances of the threshold is adopted. Damage location is determined using the segmented localization method: for two adjacent electrodes E i and E i+1 If the resistance change rate of a pipe segment is significantly higher than that of the adjacent segment, the damage is determined to be located within that segment. Damage axial location estimation: L damage =L i +n i ×d / (η i +n i+1 ); Among them, L i Electrode E i The axial position of , d is the distance between adjacent electrodes.

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