Crack self-monitoring concrete, preparation method and crack self-detection method

Abstract

The invention discloses crack self-monitoring concrete which comprises a concrete matrix, a differential electrode pair, an acoustic emission sensor array, an embedded temperature and humidity sensor and a multi-scale conductive network, the concrete matrix is concrete prepared from a cementing material, water, a fine aggregate, a water reducing agent, a conductive coarse aggregate, a hybrid fiber system, a nano conductive filler and a polymer interface reinforcing agent; the differential electrode pair comprises a tension area electrode and a compression area electrode; the embedded temperature and humidity sensor and the acoustic emission sensor array are arranged on the surface of concrete or in the concrete; the multi-scale conductive network is distributed in the concrete matrix; the invention further discloses a preparation method of the crack self-monitoring concrete and a crack self-monitoring method. The crack self-monitoring concrete is sensitive in resistance change response during stress, obvious in signal sudden change during fracture, sensitive and reliable in crack monitoring and low in false alarm rate, and can provide early warning time windows of different levels for structure maintenance and emergency decision.

Description

Technical Field

[0001] This invention belongs to the field of concrete, specifically relating to a self-monitoring concrete for cracks, its preparation method, and a self-detection method for cracks. Background Technology

[0002] Currently, crack monitoring in concrete structures mainly relies on external sensing devices, such as fiber optic sensors, piezoelectric sensors, or resistance strain gauges. These methods generally suffer from poor sensor compatibility with the concrete matrix, easy interface delamination, and signal attenuation. Furthermore, the deployment process is complex and can damage the structure; their monitoring functions are also relatively limited, making it difficult to comprehensively assess and warn of crack initiation, propagation, and failure modes, resulting in a high false alarm rate.

[0003] Furthermore, because the resistivity of concrete is significantly affected by ambient temperature and humidity, the resistance change signal caused by load in traditional resistance monitoring is easily drowned out by environmental noise, resulting in insufficient monitoring sensitivity and accuracy. Although some studies have attempted to add conductive materials such as carbon fiber, steel fiber, or carbon black to concrete to endow it with self-sensing capabilities, they usually only use one or two conductive phases. The conductive path is prone to breakage under stress, resulting in poor linearity and low repeatability of the resistance change response, and it is difficult to simultaneously achieve the mechanical and conductive properties of the material. Therefore, we present a novel crack self-monitoring concrete, its preparation method, and a crack self-detection method. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a self-monitoring concrete for cracks, its preparation method, and a self-detection method for cracks. The self-monitoring concrete for cracks, its preparation method, and its self-detection method of this invention require sensitive response to changes in resistance under stress and significant signal abrupt changes upon fracture. This improves the sensitivity and reliability of monitoring, enhances the toughness and crack resistance of the material, ensures the mechanical strength of the matrix, achieves synergistic improvement in function and performance, significantly reduces the false alarm rate, and provides different levels of early warning time windows for structural maintenance and emergency decision-making.

[0005] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0006] This invention first provides a self-monitoring concrete for cracks, comprising a concrete matrix, differential electrode pairs, an acoustic emission sensor array, an embedded temperature and humidity sensor, and a multi-scale conductive network, wherein:

[0007] The concrete matrix is ​​made of cementitious materials, water, fine aggregate, water-reducing agent, conductive coarse aggregate, mixed fiber system, nano-conductive filler and polymer interface reinforcing agent.

[0008] The differential electrode pair includes at least one pair, namely a tension zone electrode pre-embedded in the expected tension zone of the concrete and a compression zone electrode pre-embedded in the expected compression zone, wherein the electrode is a cement / CNT composite electrode;

[0009] The embedded temperature and humidity sensor and the acoustic emission sensor array are arranged on or inside the concrete surface and are linked with the resistance signal synchronous acquisition system.

[0010] The multi-scale conductive network is distributed in the concrete matrix. The multi-scale conductive network is a three-dimensional continuous conductive path formed by the conductive coarse aggregate, the mixed fiber system and the nano-conductive filler through the interface transition zone modification. The volume resistivity is ≤100Ω·m in the dry state.

[0011] In a self-monitoring concrete for cracks according to the present invention, the conductive coarse aggregate is a graphene / carbon nanotube composite coated aggregate with a continuous conductive layer on its surface, having a volume resistivity of 20-40 Ω·cm and a particle size range of 0.6-2.36 mm.

[0012] The hybrid fiber system includes carbon fiber and steel fiber, wherein: the volume fraction of carbon fiber is 0.1% to 0.3% of the volume of cementitious material, and the length is 6 to 10 mm; the volume fraction of steel fiber is 0.5% to 1.5% of the volume of cementitious material, the length is 12 to 18 mm, and the aspect ratio is 30 to 60.

[0013] The nano-conductive filler is a carbon nanotube / nano-carbon black composite filler, and its dosage is 0.5% to 1.5% of the mass of the cementitious material.

[0014] The polymer interface reinforcing agent is a polyacrylate-based dispersant, and its dosage is 0.1% to 0.3% of the mass of the cementitious material.

[0015] In a self-monitoring crack concrete of the present invention, the component mass ratio of the concrete matrix includes:

[0016] Water: 0.38–0.42 parts;

[0017] Fine aggregate: 1.0–1.3 parts;

[0018] Conductive coarse aggregate: 2.5–3.2 parts;

[0019] Water-reducing agent: 0.008–0.015 parts;

[0020] Carbon fiber: 0.001 to 0.003 parts;

[0021] Steel fiber: 0.005–0.015 parts;

[0022] Nano-conductive filler: 0.005–0.015 parts;

[0023] Polymer interface enhancer: 0.001 to 0.003 parts.

[0024] In a self-monitoring crack concrete of the present invention, the concrete matrix is ​​further mixed with an alkali-activated cementitious system, including fly ash and slag, wherein the slag accounts for 70% to 85% of the total mass of the cementitious material; the alkali activator is a sodium silicate solution with a modulus of 1.2 to 1.8, a concentration of 30% to 40%, and a content of 5% to 7% of the mass of the cementitious material calculated as Na2O.

[0025] In a self-monitoring crack concrete of the present invention, the conductive coarse aggregate is further obtained by one of the following preparation methods:

[0026] Graphene layers were deposited on the surface of aggregates using chemical vapor deposition at a temperature of 1000–1100℃ and a deposition time of 30–60 minutes. The carrier gas was an argon / hydrogen mixture with a volume ratio of 9:1.

[0027] Alternatively, a CNT / polymer composite conductive layer can be formed on the aggregate surface using a vacuum impregnation-thermal reduction method. The impregnation solution is graphene oxide or CNT dispersion with a concentration of 3–5 mg / mL, and the dispersant is polyvinylpyrrolidone. The thermal reduction is carried out under nitrogen protection at a temperature of 400–500 °C for 1–2 hours.

[0028] In a crack self-monitoring concrete of the present invention, the conductive coarse aggregate is optimized through gradation to form a densely packed structure, and its gradation composition is as follows:

[0029] 2.36–1.18mm: 28%~32%;

[0030] 1.18–0.6mm: 23%~27%;

[0031] 0.6–0.315mm: 20%~23%;

[0032] 0.315–0.15mm: 15%~18%;

[0033] 0.15–0.075mm: 8%~12%.

[0034] In a self-monitoring concrete cracking method of the present invention, the spacing between the differential electrode pairs is 50-150 mm, and the electrode size is 10 mm × 20 mm × 1 mm.

[0035] In a self-monitoring concrete cracking method of the present invention, the acoustic emission sensor array includes at least three sensors arranged at the mid-span and supports of the concrete.

[0036] This invention also provides a method for preparing self-monitoring crack concrete, which includes the following steps:

[0037] The first step is to fix the cement / CNT composite electrode, acoustic emission sensor, and temperature and humidity sensor in the designed positions inside the mold and lead out their wires. The mold is made according to the shape and specifications of the concrete.

[0038] The second step is to put all the conductive coarse aggregate, nano-conductive filler, polymer interface reinforcing agent, water reducing agent and some mixing water into the mixer, stir at low speed for 1-2 minutes, and then disperse them together for 5-8 minutes under ultrasonic power of 500-800W and high shear rate of 5000-8000rpm.

[0039] The third step is to add the cementitious materials, fine aggregates, carbon fiber and steel fiber, and stir at low speed for 2-3 minutes;

[0040] Fourth step, add the remaining water and stir at high speed for 3-5 minutes until smooth;

[0041] The fifth step is to pour the mixed concrete mixture into the mold, and then pour and vibrate it to obtain concrete with self-monitoring function for cracks.

[0042] This invention also provides a method for self-monitoring concrete cracks, applied to the aforementioned self-monitoring concrete cracks. The self-monitoring method relies on a data processing system, which includes multi-channel synchronous data acquisition hardware and embedded signal processing software. The hardware circuitry includes: a resistance measurement bridge connecting differential electrode pairs, a preamplifier and filter circuit connecting an acoustic emission sensor, a digital interface circuit connecting an embedded temperature and humidity sensor, and a core microprocessor and data storage unit. The method includes the following steps:

[0043] Step 1: Data Synchronous Acquisition. Through the data acquisition system, the real-time resistance values ​​Rt and Rc of the tension zone measured by the differential electrode pair, the acoustic emission signals acquired by the acoustic emission sensor array, and the temperature and humidity data measured by the embedded temperature and humidity sensor are acquired synchronously. The acquired data includes resistance values, acoustic emission event parameter envelopes, temperature values, and humidity values ​​that vary with time series.

[0044] Step 2: Initial value calibration and data preprocessing. Under unloaded conditions in the concrete matrix, measure and record the initial resistance values ​​Rt0 of the tension zone electrode and Rc0 of the compression zone electrode. For the continuously collected real-time resistance values, based on the synchronously collected temperature and humidity data, and using the pre-established temperature-resistivity relationship model and humidity-resistivity relationship model, corrections are made to eliminate the influence of environmental fluctuations.

[0045] Step 3: Signal Analysis and Feature Extraction. The modified resistance signal is processed using a variational mode decomposition algorithm. The mode number K is set to 4-6, and the penalty parameter α = 2000. The resistance change components caused by the load and cracks are separated. Based on these components, the differential resistance change rate between the tension and compression zones is calculated: δ = (ΔRt / Rt0) - (ΔRc / Rc0). Simultaneously, the strain sensitivity factor is calculated using the formula GF = (ΔR / R0) / ε, where ε is the strain value calculated from the load or obtained from an external measurement system. Event identification is then performed on the acoustic emission signal.

[0046] Step 4: Status Judgment and Result Output. The data processing system automatically makes judgments based on the following logic and outputs the results to the human-machine interface or network communication interface:

[0047] When the δ value continuously exceeds the first preset threshold by 20% to 30% and the GF value shows non-linear growth, it is determined as a crack initiation warning, and a warning signal and corresponding data are output.

[0048] When the δ value continuously exceeds the second preset threshold and reaches the range of 70% to 90%, and a sudden signal with energy more than one order of magnitude higher than the average energy appears in the acoustic emission signal, it is determined to be a crack rapid development alarm, and an alarm signal is output and a high-energy event is marked.

[0049] When the δ value continuously exceeds the third preset threshold and reaches the range of 90% to 120%, and a series of high-energy events occur in the acoustic emission signal, it is determined as a warning of impending structural damage, and the highest level warning is output and a linkage alarm is triggered.

[0050] This invention relates to a self-monitoring concrete for cracks, its preparation method, and a self-detection method for cracks. Through practical application, it has achieved the following beneficial effects:

[0051] 1. The self-monitoring crack concrete of this invention achieves self-sensing of the concrete itself, improving the reliability and durability of monitoring. By employing multi-scale collaborative design of conductive coarse aggregates within a specific particle size range, hybrid fibers with specific volumetric admixtures, and nano-conductive fillers, a stable and sensitive three-dimensional continuous conductive network is constructed within the concrete. This network itself is a distributed sensor, enabling the concrete to possess self-sensing capabilities, eliminating interface problems caused by externally attached sensors, and achieving a monitoring system with the same lifespan as the structure.

[0052] 2. This invention significantly improves the accuracy and anti-interference capability of monitoring through differential design and advanced signal processing. By arranging differential electrode pairs in the tension and compression regions and combining them with variational mode decomposition algorithm to process the acquired resistance signal, it can effectively compensate for the influence of environmental fluctuations monitored in real time by embedded temperature and humidity sensors, accurately separate the intrinsic resistance change components caused by crack initiation and propagation, greatly reduce environmental noise interference, and improve the signal-to-noise ratio for microcrack identification.

[0053] 3. This invention establishes a quantitative, graded early warning mechanism, enabling full-process monitoring of cracks. It creatively integrates processed resistance characteristic parameters with acoustic emission physical signals for analysis, establishing a clear and quantitative three-level early warning threshold judgment logic. This method can distinguish different damage stages, such as crack initiation, rapid development, and near-collapse, providing different levels of early warning time windows for structural maintenance, significantly improving the accuracy of early warning and decision support capabilities.

[0054] 4. This invention balances mechanical properties and sensing functions, achieving an integration of material function and performance. The optimized conductive coarse aggregate forms the backbone and mechanical micro-skeleton of the conductive network; the hybrid fiber system enhances toughness and crack resistance while also improving the connectivity and stability of the conductive pathways. The ultrasonic-high shear synergistic dispersion process in the preparation method ensures the uniform dispersion of nanoscale conductive fillers, thereby endowing concrete with excellent inherent self-sensing functions without sacrificing its basic mechanical properties. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a schematic diagram of a self-monitoring concrete structure for cracks according to the present invention.

[0057] Figure 2 This is a hardware block diagram of a crack self-monitoring system according to the present invention.

[0058] Figure 3 This is a schematic flowchart of a method for preparing concrete with self-monitoring of cracks according to the present invention.

[0059] Figure 4 This is a flowchart illustrating a self-detection method for concrete cracks according to the present invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0061] like Figure 1 As shown, this invention first designs a novel self-monitoring concrete for cracks, which comprises a concrete matrix 1, differential electrode pairs, an acoustic emission sensor array 6, an embedded temperature and humidity sensor 9, and a multi-scale conductive network, wherein:

[0062] The concrete matrix 1 is a concrete made of cementitious materials, water, fine aggregate, water-reducing agent, conductive coarse aggregate 2, mixed fiber system, nano-conductive filler and polymer interface reinforcing agent.

[0063] The differential electrode pair includes at least one pair, namely a tension zone electrode 5 pre-embedded in the expected tension zone of the concrete and a compression zone electrode 4 pre-embedded in the expected compression zone. The compression zone 4 mainly bears the pressure from above, while the tension zone mainly bears the tensile force generated by pulling. The electrode is a cement / CNT composite electrode. The tension zone electrode 5 is placed in the expected crack initiation zone 10, that is, pre-embedded in the part expected to be under tensile force.

[0064] The multi-scale conductive network is distributed in the concrete matrix. The multi-scale conductive network is a three-dimensional continuous conductive network 3 formed by the conductive coarse aggregate, the mixed fiber system and the nano-conductive filler through the interface transition zone modification. The three-dimensional continuous conductive network 3 is a three-dimensional continuous conductive path with a volume resistivity ≤100Ω·m in the dry state.

[0065] The embedded temperature and humidity sensor 9 is a waterproof encapsulated digital temperature and humidity sensor, which is placed inside or on the surface of the concrete. Together with the acoustic emission sensor array 6, it is connected to the multi-channel data acquisition system 8 through shielded wires. The multi-channel data acquisition system 8 is configured to simultaneously acquire resistance signals, acoustic emission signals, and temperature and humidity data, which serve as the basis for subsequent self-monitoring of concrete cracks.

[0066] The above-mentioned component mass ratio of concrete matrix 1 includes:

[0067] Water: 0.38–0.42 parts;

[0068] Fine aggregate: 1.0–1.3 parts;

[0069] Conductive coarse aggregate: 2.5–3.2 parts;

[0070] Water-reducing agent: 0.008–0.015 parts;

[0071] Carbon fiber: 0.001 to 0.003 parts;

[0072] Steel fiber: 0.005–0.015 parts;

[0073] Nano-conductive filler: 0.005–0.015 parts;

[0074] Polymer interface enhancer: 0.001 to 0.003 parts.

[0075] The concrete matrix 1 also contains an alkali-activated cementitious system, including fly ash and slag, wherein the slag accounts for 70% to 85% of the total mass of the cementitious material; the alkali activator is a sodium silicate solution with a modulus of 1.2 to 1.8, a concentration of 30% to 40%, and a Na2O content of 5% to 7% of the mass of the cementitious material.

[0076] The aforementioned conductive coarse aggregate 2 is a graphene / carbon nanotube composite coated aggregate with a continuous conductive layer on its surface, exhibiting a volume resistivity of 20–40 Ω·cm and a particle size range of 0.6–2.36 mm. The conductive coarse aggregate 2 achieves a close-packed structure through gradation optimization, and its gradation composition is as follows:

[0077] 2.36–1.18mm: 28%~32%;

[0078] 1.18–0.6mm: 23%~27%;

[0079] 0.6–0.315mm: 20%~23%;

[0080] 0.315–0.15mm: 15%~18%;

[0081] 0.15–0.075mm: 8%~12%.

[0082] The hybrid fiber system includes carbon fiber and steel fiber 7, wherein: the volume fraction of carbon fiber is 0.1% to 0.3% of the volume of cementitious material, and the length is 6 to 10 mm; the volume fraction of steel fiber is 0.5% to 1.5% of the volume of cementitious material, the length is 12 to 18 mm, and the aspect ratio is 30 to 60.

[0083] The nano-conductive filler is a carbon nanotube / nano-carbon black composite filler, and its dosage is 0.5% to 1.5% of the mass of the cementitious material.

[0084] The polymer interface enhancer is a polyacrylate-based dispersant, and its dosage is 0.1% to 0.3% of the mass of the cementitious material.

[0085] The conductive coarse aggregate 2 is obtained by one of the following two preparation methods:

[0086] Graphene layers were deposited on the surface of aggregates using chemical vapor deposition at a temperature of 1000–1100℃ and a deposition time of 30–60 minutes. The carrier gas was an argon / hydrogen mixture with a volume ratio of 9:1.

[0087] Alternatively, a CNT / polymer composite conductive layer can be formed on the aggregate surface using a vacuum impregnation-thermal reduction method. The impregnation solution is graphene oxide or CNT dispersion with a concentration of 3–5 mg / mL, and the dispersant is polyvinylpyrrolidone. The thermal reduction is carried out under nitrogen protection at a temperature of 400–500 °C for 1–2 hours.

[0088] The spacing between the differential electrode pairs is 50-150 mm, and the electrode size is 10 mm × 20 mm × 1 mm.

[0089] The acoustic emission sensor array 6 includes at least three sensors, which are respectively arranged at the mid-span and the support.

[0090] The core innovation of this self-monitoring concrete invention lies in its systematic design of "conductive coarse aggregate - hybrid fiber - nanofiller," where each component works collaboratively at the macroscopic, mesoscopic, and microscopic scales to generate a synergistic conductivity effect. This network not only has low initial resistivity, but more importantly, it generates significant and stable resistance change signals under stress, laying a material foundation for high-sensitivity self-sensing that surpasses conventional methods.

[0091] like Figure 3 As shown, the present invention also provides a method for preparing self-monitoring crack concrete, which includes the following steps:

[0092] The first step is to pre-install sensing elements inside the mold, fix the cement / CNT composite electrode (compression zone electrode 4 and tension zone electrode 5), acoustic emission sensor and temperature and humidity sensor 9 in the designed positions inside the mold, and lead them out using wires. The mold is made according to the shape and specifications of the concrete.

[0093] The second step involves adding all the conductive coarse aggregate 2, nano-conductive filler, polymer interface reinforcing agent, water-reducing agent, and some mixing water into a mixer. First, stir at low speed for 1-2 minutes, then disperse synergistically for 5-8 minutes under ultrasonic power of 500-800W and high shear rate of 5000-8000rpm to achieve conductive synergistic dispersion.

[0094] The third step is to add the cementitious material, fine aggregate, carbon fiber and steel fiber 7 to the mixer and mix at low speed for 2-3 minutes.

[0095] Fourth step, add the remaining water and stir at high speed for 3-5 minutes until homogeneous.

[0096] The fifth step involves pouring the mixed concrete mixture into the mold from the first step, then pouring and vibrating it, followed by curing. The final product is a concrete component with self-monitoring crack detection capabilities.

[0097] like Figure 2 and Figure 4 As shown, this invention also provides a method for self-monitoring concrete cracks, applied to the aforementioned self-monitoring concrete cracks. The self-monitoring method relies on a data processing system, which is a multi-channel data acquisition and processing system. This system includes multi-channel synchronous data acquisition hardware and embedded signal processing software. The circuit configuration of the multi-channel synchronous data acquisition hardware includes: a resistance measurement bridge connecting differential electrode pairs, a preamplifier and filter circuit connecting an acoustic emission sensor array, and a digital interface circuit connecting an embedded temperature and humidity sensor. The resistance measurement bridge, preamplifier and filter circuit, and digital interface circuit are respectively connected to a core processing unit, which includes a core microprocessor and a data storage unit. The self-monitoring method includes the following steps:

[0098] Step 1: Data Synchronous Acquisition. Through the data acquisition system, the real-time resistance values ​​Rt and Rc of the tension zone measured by the differential electrode pair, the acoustic emission signals collected by the acoustic emission sensor array, and the temperature and humidity data measured by the embedded temperature and humidity sensor are acquired synchronously. The acquired data includes resistance values, acoustic emission event parameter envelopes, temperature values, and humidity values ​​that change over time.

[0099] Step 2: Initial value calibration and data preprocessing. Under unloaded conditions in the concrete matrix, the initial resistance values ​​Rt0 of the tension zone electrode and Rc0 of the compression zone electrode are measured and recorded. The continuously collected real-time resistance values ​​are corrected based on the synchronously collected temperature and humidity data using pre-established temperature-resistivity relationship models and humidity-resistivity relationship models to eliminate the influence of environmental fluctuations and achieve environmental compensation correction.

[0100] Step 3: Signal Analysis and Feature Extraction. The modified resistance signal is processed using a variational mode decomposition algorithm to extract variational mode decomposition features. The mode number K is set to 4-6, and the penalty parameter α = 2000. The resistance change components caused by load and cracks are separated. Based on these resistance change components, the differential resistance change rate δ between the tension and compression zones is calculated.

[0101] δ=(ΔRt / Rt0)-(ΔRc / Rc0);

[0102] Calculate the strain sensitivity factor GF:

[0103] GF = (ΔR / R0) / ε, where ε is the strain value calculated by load or obtained by an external measurement system; event identification of acoustic emission signals;

[0104] Step 4: Status Judgment and Result Output. The data processing system automatically judges the status and outputs the results to the human-machine interface or network communication interface according to the following logic:

[0105] When the δ value continuously exceeds the first preset threshold by 20% to 30% and the GF value shows non-linear growth, it is determined as a crack initiation warning, and a warning signal and corresponding data are output.

[0106] When the δ value continuously exceeds the second preset threshold and reaches the range of 70% to 90%, and a sudden signal with energy more than one order of magnitude higher than the average energy appears in the acoustic emission signal, it is determined to be a crack rapid development alarm, and an alarm signal is output and a high-energy event is marked.

[0107] When the δ value continuously exceeds the third preset threshold and reaches the range of 90% to 120%, and a series of high-energy events occur in the acoustic emission signal, it is determined as a warning of impending structural damage, and the highest level warning is output and a linkage alarm is triggered.

[0108] The core diagnostic technology of the self-monitoring method for concrete cracks in this invention is a signal processing strategy that combines differential electrodes with variational mode decomposition (VMD) algorithm. This method can effectively eliminate interference from environmental temperature and humidity, and accurately separate the resistance change components caused by load, strain, and crack events, thereby calculating the characteristic parameters (δ and GF) that truly characterize the damage, achieving a leap from monitoring to diagnosis.

[0109] Furthermore, this invention establishes a comprehensive early warning mechanism based on "multi-signal fusion." Specifically, by cross-validating the resistance parameters (δ and GF) with acoustic emission physical signals, a three-level alarm system is established: the first level uses the nonlinear correlation between δ and GF to warn of microcrack initiation; the second level uses a sharp increase in δ and high-energy acoustic emission signals to warn of crack propagation; and the third level uses the δ limit value and continuous acoustic emission events to warn of structural failure. The establishment of this three-level early warning mechanism significantly improves the accuracy and reliability of the early warning system.

[0110] Example 1

[0111] This embodiment demonstrates the preparation of a self-monitoring crack concrete beam and its application in monitoring the entire process from loading to failure in a four-point bending test.

[0112] (1) Raw materials and proportions

[0113] 1.1 Raw materials: PO 42.5 grade ordinary silicate cement; natural river sand (fineness modulus 2.6); graphene-coated basalt aggregate prepared according to the method of this invention (particle size range 0.6-2.36 mm, preparation method is as follows); PAN-based carbon fiber (length 8 mm); hooked steel fiber (length 15 mm, aspect ratio 55); multi-walled carbon nanotube and nano-carbon black composite filler (mass ratio 1:2); polycarboxylate superplasticizer; polyacrylate-based dispersant.

[0114] 1.2 Mixing ratio

[0115] In this mix design, a higher amount of conductive coarse aggregate is intentionally included to improve the connectivity of the conductive network. To ensure the cohesiveness and workability of the mixture, the combination of water-reducing agent and dispersant is optimized, and the ultrasonic-high shear synergistic dispersion process described in claim 9 is employed to ensure good formability of the concrete.

[0116]

[0117] (2) Preparation of conductive coarse aggregate

[0118] Clean basalt aggregate with a particle size of 0.6-2.36 mm was mixed according to the gradation of claim 4. It was then immersed in an aqueous dispersion of graphene oxide (GO) with a concentration of 4 mg / mL (containing 0.1% polyvinylpyrrolidone) under a vacuum of -0.095 MPa for 30 minutes, followed by soaking at normal pressure for 2 hours. After removal, it was dried at 105 °C and then heat-treated at 450 °C for 2 hours under nitrogen protection to thermally reduce the GO. The resulting conductive coarse aggregate had a volume resistivity of approximately 0.35 Ω·m.

[0119] (3) Concrete beam fabrication and sensor placement

[0120] The mixture was stirred and molded according to the preparation method described in claim 9. A beam specimen with dimensions of 100mm × 100mm × 400mm was cast. Sensors were arranged according to claims 1 and 8: a cement / CNT composite electrode (100mm spacing) was pre-embedded at the bottom (tension zone) and top (compression zone) mid-span of the beam; three acoustic emission sensors were attached at the mid-span of the beam and at both supports; and a temperature and humidity sensor was embedded inside the beam. Standard curing was performed for 28 days.

[0121] (4) Performance testing and monitoring

[0122] 4.1 Basic Performance

[0123] The dry volume resistivity is 68 Ω·m (meets the requirement of ≤100 Ω·m); the 28-day compressive strength is 48.5 MPa.

[0124] 4.2 Four-point bending test and real-time monitoring

[0125] A four-point bending load was applied, with a span of 300 mm and a spacing of 100 mm between the loading points. All signals were acquired synchronously and processed strictly according to the method described in claim 10.

[0126] Environmental correction: Based on real-time temperature and humidity data, a model established in advance through temperature and humidity calibration experiments on test blocks with the same ratio is used to correct the resistance measurement value.

[0127] Signal decomposition: The VMD algorithm (with K=5 and α=2000) is used to process the corrected resistance signal and separate the target component.

[0128] Feature calculation and judgment: Based on the separated components, the δ and GF values ​​are calculated, and combined with the acoustic emission energy signal, the logic of step four of claim 10 is applied to make an automatic judgment.

[0129] 4.3 Verification of Monitoring Results and Tiered Early Warning

[0130] Phase 1 (Elastic Loading): δ and GF grow slowly and linearly.

[0131] Phase Two (Crack Initiation Warning): When the load reaches approximately 55% of the ultimate load, the δ value enters the 20%–30% range (approximately 25%), and simultaneously, the GF value exhibits a significant nonlinear jump, successfully triggering the "Crack Initiation Warning." At this point, no cracks are visible to the naked eye.

[0132] Phase 3 (Rapid Development Alarm): When the load reaches 85% of the ultimate load, the δ value rapidly increases to 82% (>70% threshold), and the acoustic emission system captures multiple sudden high-energy events with energies more than 10 times higher than the background, successfully triggering the "rapid crack development alarm". At this point, crack propagation is visible.

[0133] Phase 4 (Imminent Failure Warning): When approaching the peak load, the δ value soars to 110% (>90% threshold), and the acoustic emission event exhibits continuous high energy. The system successfully triggers the "Structural Imminent Failure Warning," and the beam fractures approximately 3 seconds later.

[0134] This embodiment fully verifies the effectiveness of the self-sensing of the present invention and the accuracy of the three-level early warning of the monitoring method.

[0135] Comparative Example 1

[0136] (1) Proportional preparation

[0137] Only steel fibers with a volume content of 1.0% were used as the conductive phase, without the addition of carbon fibers or nano-conductive fillers, and ordinary basalt aggregate with no conductive layer but identical particle size distribution to that of Example 1 was used for equal-mass replacement. The remaining components and conventional mixing process remained basically the same as in Example 1.

[0138] (2) Performance Comparison

[0139] The dry volume resistivity is as high as 2500 Ω·m, and the conductivity is poor.

[0140] In the four-point bending test, the resistance signal exhibited high noise and a sluggish response. When the visible crack width reached 0.2 mm, the δ value was only 40%, far below the warning threshold, and the GF value showed no significant change. The acoustic emission signal was chaotic, making it difficult to identify valid events.

[0141] (3) Comparison of conclusions

[0142] The comparative example, lacking the multi-scale conductive network described in claim 1 of this invention, cannot construct a low-resistance, highly sensitive continuous conductive path, and its self-sensing function is essentially ineffective. This further confirms the inventiveness of the material system co-design in claim 1 of this invention, and its necessity for realizing the precise monitoring method described in claim 10.

[0143] This invention innovates a dedicated fabrication process and an intelligent monitoring method integrating environmental correction, signal decomposition, and multi-source fusion. These three elements constitute an inseparable technical whole, which is key to achieving intelligent structural sensing and safety assurance.

[0144] The above are merely a limited number of implementations of this invention. Based on the same concept, other similar methods and structural substitutions are possible, not limited to the steps and structural components already described. In summary, the scope of protection of this invention also includes other variations and substitutions that are obvious to those skilled in the art.

Claims

1. A type of self-monitoring concrete for cracks, characterized in that, Its components include a concrete matrix, differential electrode pairs, an acoustic emission sensor array, an embedded temperature and humidity sensor, and a multi-scale conductive network, wherein: The concrete matrix is ​​made of cementitious materials, water, fine aggregate, water-reducing agent, conductive coarse aggregate, mixed fiber system, nano-conductive filler and polymer interface reinforcing agent. The conductive coarse aggregate, mixed fiber system and nano-conductive filler are modified through the interface transition zone to form a three-dimensional continuous conductive path. The volume resistivity is ≤100Ω·m in the dry state. The differential electrode pair includes at least one pair, namely a tension zone electrode pre-embedded in the expected tension zone of the concrete and a compression zone electrode pre-embedded in the expected compression zone, wherein the electrode is a cement / CNT composite electrode; The embedded temperature and humidity sensor is a waterproof encapsulated digital temperature and humidity sensor, which is placed inside or on the surface of concrete. It is connected to a multi-channel data acquisition system together with the acoustic emission sensor array through shielded wires. The multi-channel data acquisition system is configured to simultaneously acquire resistance signals, acoustic emission signals and temperature and humidity data.

2. The self-monitoring concrete for cracks according to claim 1, characterized in that, The conductive coarse aggregate specifically refers to graphene / carbon nanotube composite coated aggregate with a particle size range of 0.6 to 2.36 mm and a continuous conductive layer on the surface, and a volume resistivity of 0.20 to 0.4 Ω·m; The hybrid fiber system includes carbon fiber and steel fiber, wherein: the volume fraction of carbon fiber is 0.1% to 0.3% of the total concrete volume, and the length is 6 to 10 mm; the volume fraction of steel fiber is 0.5% to 1.5% of the total concrete volume, the length is 12 to 18 mm, and the aspect ratio is 30 to 60. The nano-conductive filler is a carbon nanotube / nano-carbon black composite filler, and its dosage is 0.5% to 1.5% of the mass of the cementitious material. The polymer interface reinforcing agent is a polyacrylate-based dispersant, and its dosage is 0.1% to 0.3% of the mass of the cementitious material.

3. The self-monitoring concrete for cracks according to claim 2, characterized in that, The conductive coarse aggregate is obtained by one of the following preparation methods: Graphene layers were deposited on the surface of aggregates using chemical vapor deposition at a temperature of 1000-1100℃ for 30-60 minutes. The carrier gas was an argon / hydrogen mixture with a volume ratio of 9:

1. Alternatively, a CNT / polymer composite conductive layer can be formed on the aggregate surface using a vacuum impregnation-thermal reduction method. The impregnation solution is graphene oxide or CNT dispersion with a concentration of 3-5 mg / mL, and the dispersant is polyvinylpyrrolidone. The thermal reduction is carried out under nitrogen protection at a temperature of 400-500℃ for 1-2 hours.

4. The self-monitoring concrete for cracks according to claim 2, characterized in that, The conductive coarse aggregate is optimized to form a close-packed structure, and its gradation composition is as follows: 2.36--1.18mm: 28%~32%; 1.18--0.6mm: 23%~27%; 0.6--0.315mm: 20%~23%; 0.315--0.15mm: 15%~18%; 0.15--0.075mm: 8%~12%.

5. The self-monitoring concrete for cracks according to claim 1, characterized in that, The concrete matrix also contains an alkali-activated cementitious system, including fly ash and slag, wherein the slag accounts for 70% to 85% of the total mass of the cementitious material; the alkali activator is a sodium silicate solution with a modulus of 1.2 to 1.8, a concentration of 30% to 40%, and a Na2O content of 5% to 7% of the mass of the cementitious material.

6. The self-monitoring concrete for cracks according to claim 1, characterized in that, The mass ratio of the components in the concrete matrix includes: Water: 0.38–0.42 parts; Fine aggregate: 1.0–1.3 parts; Conductive coarse aggregate: 2.5–3.2 parts; Water-reducing agent: 0.008–0.015 parts; Carbon fiber: 0.001 to 0.003 parts; Steel fiber: 0.005–0.015 parts; Nano-conductive filler: 0.005–0.015 parts; Polymer interface enhancer: 0.001 to 0.003 parts.

7. The self-monitoring concrete for cracks according to claim 1, characterized in that, The spacing between the differential electrode pairs is 50-150 mm, and the electrode size is 10 mm × 20 mm × 1 mm.

8. The self-monitoring concrete for cracks according to claim 1, characterized in that, The acoustic emission sensor array includes at least three sensors arranged at the mid-span and supports of the concrete structure.

9. A method for preparing self-monitoring crack concrete, used to prepare the self-monitoring crack concrete according to any one of claims 1 to 8, characterized in that, The first step is to fix the cement / CNT composite electrode, acoustic emission sensor, and temperature and humidity sensor in the designed positions inside the mold and lead out their wires. The mold is made according to the shape and specifications of the concrete. The second step is to put all the conductive coarse aggregate, nano-conductive filler, polymer interface reinforcing agent, water reducing agent and some mixing water into the mixer, stir at low speed for 1-2 minutes, and then disperse them together for 5-8 minutes under ultrasonic power of 500-800W and high shear rate of 5000-8000rpm. The third step is to add the cementitious materials, fine aggregates, carbon fiber and steel fiber, and stir at low speed for 2-3 minutes; Fourth step, add the remaining water and stir at high speed for 3-5 minutes until smooth; The fifth step is to pour the mixed concrete mixture into the mold, and then pour and vibrate it to obtain concrete with self-monitoring function for cracks.

10. A method for self-monitoring concrete cracks, characterized in that, The crack self-monitoring method is applied to concrete with crack self-monitoring as described in any one of claims 1-8, wherein the crack self-monitoring method is executed by a data processing system, which includes multi-channel synchronous data acquisition hardware and embedded signal processing software. The circuit configuration of the multi-channel synchronous data acquisition hardware includes: a resistance measurement bridge connecting the differential electrode pairs, a preamplifier and filter circuit connecting the acoustic emission sensor, a digital interface circuit connecting the embedded temperature and humidity sensor, and a core microprocessor and data storage unit; the crack self-monitoring method includes the following steps: Step 1: Data Synchronous Acquisition. Through the data acquisition system, the real-time resistance values ​​Rt and Rc of the tension zone measured by the differential electrode pair, the acoustic emission signals acquired by the acoustic emission sensor array, and the temperature and humidity data measured by the embedded temperature and humidity sensor are acquired synchronously. The acquired data includes resistance values, acoustic emission event parameter envelopes, temperature values, and humidity values ​​that vary with time series. Step 2: Initial value calibration and data preprocessing. Under unloaded conditions in the concrete matrix, measure and record the initial resistance values ​​Rt0 of the tension zone electrode and Rc0 of the compression zone electrode. For the continuously collected real-time resistance values, based on the synchronously collected temperature and humidity data, and using the pre-established temperature-resistivity relationship model and humidity-resistivity relationship model, corrections are made to eliminate the influence of environmental fluctuations. Step 3: Signal Analysis and Feature Extraction. The modified resistance signal is processed using a variational mode decomposition algorithm. The mode number K is set to 4-6, and the penalty parameter α = 2000. The resistance change components caused by the load and cracks are separated. Based on these components, the differential resistance change rate between the tension and compression zones is calculated: δ = (ΔRt / Rt0) - (ΔRc / Rc0). Simultaneously, the strain sensitivity factor is calculated using the formula GF = (ΔR / R0) / ε, where ε is the strain value calculated from the load or obtained from an external measurement system. Event identification is then performed on the acoustic emission signal. Step 4: Status Judgment and Result Output. The data processing system automatically makes judgments based on the following logic and outputs the results to the human-machine interface or network communication interface: When the δ value continuously exceeds the first preset threshold by 20% to 30% and the GF value shows non-linear growth, it is determined as a crack initiation warning, and a warning signal and corresponding data are output. When the δ value continuously exceeds the second preset threshold and reaches the range of 70% to 90%, and a sudden signal with energy more than one order of magnitude higher than the average energy appears in the acoustic emission signal, it is determined to be a crack rapid development alarm, and an alarm signal is output and a high-energy event is marked. When the δ value continuously exceeds the third preset threshold and reaches the range of 90% to 120%, and a series of high-energy events occur in the acoustic emission signal, it is determined as a warning of impending structural damage, and the highest level warning is output and a linkage alarm is triggered.

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