Concrete sandwich insulation board with self-monitoring function and monitoring method thereof

Abstract

The invention discloses a concrete sandwich insulation board with a self-monitoring function and a monitoring method, and belongs to the field of building structure health monitoring. The self-sensing concrete contains graphene as a conductive filler. The sandwich insulation board comprises an inner acanthus and an outer acanthus which are made of self-sensing concrete, and an insulation layer clamped between the inner acanthus and the outer acanthus, concrete short column connecting pieces distributed in an array mode are arranged in the insulation layer, and copper mesh electrodes are embedded in short columns. According to the invention, real-time and in-situ monitoring of structural strain, cracks and damage is realized by measuring the change of resistance between the copper mesh electrodes and combining signal processing and a damage identification algorithm. According to the invention, thermal insulation, load bearing and self-monitoring functions are integrated, and the problems of difficulty in sensor layout, high cost and difficulty in distributed measurement in a traditional monitoring method are solved.

Description

Technical Field

[0001] This invention relates to the field of building structural materials, and in particular to an intelligent concrete sandwich insulation board that integrates thermal insulation, load-bearing and structural health monitoring functions, and its monitoring method. Background Technology

[0002] Currently, building structural health monitoring mainly falls into several categories: traditional sensor sensing, fiber optic sensing, vibration modal identification (EMI), visual inspection, and wireless network monitoring. Traditional monitoring methods primarily utilize strain gauges, accelerometers, displacement gauges, and temperature sensors. These sensors are deployed at key structural locations to collect structural response signals in real time. While technically mature and highly accurate, this method has a limited number of sensors, making global monitoring difficult. Furthermore, complex wiring, high maintenance costs, and limited long-term stability make it unsuitable for long-term monitoring of large-volume or high-rise buildings. Fiber optic sensors, while highly accurate and offering advantages such as resistance to electromagnetic interference, long-distance transmission, and multi-point monitoring, are particularly suitable for complex environments like bridges and tunnels. However, their installation is complex and costly, they are sensitive to fiber optic bending and connection losses, and maintenance and data interpretation are challenging. Traditional and fiber optic sensors are only suitable for localized monitoring of bridges and buildings. Vibration modal identification (EMI), while offering good overall performance, is sensitive to environmental noise, complex inversion methods, and relies on finite element models and numerical algorithms (such as MCMC, EM, or KDE methods), making it more suitable for identifying the overall operational status of high-rise buildings and bridges. While visual monitoring can identify building cracks and exterior damage, it requires high-precision data acquisition equipment and is easily affected by lighting conditions. Wireless network monitoring, on the other hand, suffers from drawbacks such as power supply difficulties and unstable signals.

[0003] Improving current building structural health monitoring relies on addressing the sensitivity, cost, and distribution of sensors. Against this backdrop, the concept of self-sensing materials has emerged. These materials can directly reflect the stress, strain, and damage state of a structure through changes in their electrical, piezoelectric, or resistive properties under external loads or environmental changes, thus endowing the material itself with "sensing" capabilities. Self-sensing concrete, in particular, represented by conductive cement-based composite materials, forms a conductive network within the material by incorporating carbon-based nanofillers (such as graphene, carbon nanotubes, carbon fibers, and carbon black), achieving an organic integration of structural and sensing functions. For example, Chinese patent CN116143476A discloses a conductive concrete and its application and a self-detection system. Its technical solution adds conductive phase fillers to the concrete matrix material, endowing the conductive concrete with conductive properties and enabling it to self-sensitize changes in stress. While self-sensing concrete materials offer a new approach to solving these problems, how to effectively integrate them with building envelope components (such as thermal insulation wall panels) to form a stable, reliable, and practical integrated monitoring system remains a pressing technical challenge in this field. Summary of the Invention

[0004] The purpose of this invention is to address the problem that existing technologies cannot effectively combine self-sensing concrete materials with thermal insulation wall panels to form a stable and reliable self-monitoring system. This invention integrates thermal insulation, load-bearing, and self-monitoring functions into one, solving the problems of difficult sensor deployment, high cost, and difficulty in achieving distributed measurement in traditional monitoring methods.

[0005] The technical solution of this invention: A concrete sandwich insulation board with self-monitoring function includes an outer leaf plate 1 and an inner leaf plate 2. The feature is that there is an insulation layer between the outer leaf plate 1 and the inner leaf plate 2, and a plurality of concrete short column connectors 4 penetrate the insulation layer 3 and connect the outer leaf plate 1 and the inner leaf plate 2. The outer leaf plate 1, the inner leaf plate 2 and the concrete short column connectors 4 are all made of self-sensing concrete. The self-sensing concrete includes a cement matrix and conductive functional fillers dispersed in the cement matrix. Electrodes 5 are embedded in the concrete short column connectors 4.

[0006] The conductive functional filler is graphene nanosheets.

[0007] The amount of graphene nanosheets added is 0.1% to 0.5% of the weight of concrete cementitious materials.

[0008] The inner leaf plate 2 is incorporating microcapsule phase change material into its self-sensing concrete.

[0009] The surface of electrode 5 is coated with an anti-corrosion and insulating coating, which is made of epoxy resin or polymer cement-based coating.

[0010] Electrode 5 is connected to the signal acquisition device via a wire.

[0011] The concrete short column connectors 4 are arranged in an array with a spacing of 300mm to 600mm and a short column diameter of 30mm to 50mm.

[0012] The insulation layer 3 is an extruded polystyrene board with a thickness of 50mm to 100mm.

[0013] A monitoring method based on a concrete sandwich insulation board with self-monitoring function includes the following steps: A. Measuring the resistance value between each electrode 5; B. Performing noise reduction processing and temperature compensation on the collected resistance signal to obtain the compensated resistance change; C. Converting the resistance change into the strain state of the structure according to a pre-established resistance-strain relationship model; D. Judging the health status of the structure based on the strain state data through a damage identification algorithm and issuing an early warning when abnormalities occur.

[0014] The resistance-strain relationship model uses a signal decomposition method to extract damage features from the resistance signal and a clustering algorithm to automatically identify damage patterns.

[0015] The beneficial effects of this invention are: 1. Functional integration: It integrates the load-bearing structure, thermal insulation system and health monitoring function into one, realizing the intelligentization of the building envelope.

[0016] 2. Distributed monitoring: Shifting from point-based measurement with external sensors to an embedded, distributed, self-feedback monitoring system. Through an array of electrodes, distributed and full-coverage monitoring of stress and damage status across the entire plate surface can be achieved, rather than single-point measurement.

[0017] 3. Intrinsic Sensing: Utilizing the intrinsic properties of self-sensing concrete, the signal originates directly from within the structural material, avoiding issues such as debonding between the sensor and the structural interface. The signal is more realistic and reliable, the sensing signal is more direct, and the noise is lower, which can reduce signal loss and data uncertainty.

[0018] 4. Economical and durable: It saves a lot of the purchase and installation costs of external sensors, has a simple structure, is easy to manufacture in factories, and the copper mesh electrodes are protected by concrete, which makes them durable. Attached Figure Description

[0019] Figure 1 This is a three-dimensional structural diagram of the insulation board of the present invention.

[0020] Figure 2 This is a cross-sectional structural diagram of the insulation board.

[0021] Reference numerals: 1-Outer blade, 2-Inner blade, 3-Insulation layer, 4-Short column connector, 5-Electrode, 6-Reinforcing bar, 7-Wire. Detailed Implementation

[0022] Example 1: refer to Figure 1 In this embodiment, the sandwich insulation panel uses ultra-high performance concrete (UHPC) as the matrix for both the outer leaf plate 1 and the inner leaf plate 2, and incorporates graphene nanosheets at 0.3% of the weight of the cementitious material as a conductive filler. The inner leaf plate 2 also incorporates microcapsule phase change material to enhance its thermal regulation capabilities. The insulation layer 3 is made of 60mm thick XPS board. Concrete short column connectors 4 are arranged in an array at 400mm intervals, with a diameter of approximately 40mm. At the center of each short column, a copper mesh electrode 5 with an epoxy resin insulating layer is pre-embedded. All copper mesh electrodes 5 are led out via wires and connected to a data acquisition device.

[0023] The data acquisition device has a built-in high-precision resistance measurement circuit, a temperature sensor (for temperature compensation), and a transmission module, which can realize automatic data acquisition, processing, and transmission.

[0024] The preparation method of this sandwich insulation board includes: Step 1: In the factory prefabrication mold, first place XPS insulation board 3 and drill through holes according to the design spacing.

[0025] Step 2: Position and install the copper mesh electrode 5 with the insulating coating in the through hole.

[0026] Step 3: Pour the self-sensing concrete for the inner and outer blades separately, and ensure that the concrete fills the mold and penetrates the through hole at the same time by vibration, forming the connecting short column 4, which is cast into a whole with the inner and outer blades in one go.

[0027] Step 4: Perform standard curing. After the concrete has hardened, connect the electrode wire 7 to the data acquisition device.

[0028] The monitoring method includes the following steps: A. Measure the resistance value between each electrode (5); B. Perform noise reduction and temperature compensation on the collected resistance signal to obtain the resistance change after compensation; C. Convert the resistance change into the strain state of the structure according to the pre-established resistance-strain relationship model; D. Based on the strain state data, determine the health status of the structure through the damage identification algorithm and issue an early warning when abnormal.

[0029] During monitoring, the system continuously measures the resistance between each electrode. Noise is reduced from the original signal using wavelet transform, and a temperature and humidity compensation function f(T,H) is established using a support vector regression (SVR) model to calculate the accurate resistance change ΔR_corrected = ΔR_measured - f(T,H). Subsequently, based on the resistance-strain relationship obtained from laboratory calibration, the resistance change is converted into strain. Finally, a damage identification algorithm based on variational mode decomposition and Gaussian mixture model is used to extract features and perform cluster analysis on the strain data to achieve damage localization and assessment.

[0030] Example 2: I. Sandwich Panel Structure Design A novel sandwich concrete wall panel integrating thermal insulation, heat storage, and self-sensing functions aims to achieve functional integration and intelligent information management of building envelope structures. The wall panel consists of inner and outer leaf panels, a sandwich insulation layer, and a conductive connection system. Through multifunctional material composites and structural synergistic design, a composite system possessing thermal performance, mechanical properties, and self-sensing capabilities is constructed.

[0031] The inner blade 2 utilizes graphene nanosheets (GNP) reinforced microencapsulated ultra-high performance concrete (MPCM-UHPC), employing the latent heat absorption and release process of the phase change material to achieve energy storage and temperature regulation. When the ambient temperature rises to the phase change temperature range, the MPCM absorbs heat and undergoes a phase change, thereby weakening the peak indoor temperature; when the temperature decreases, it releases the stored heat, slowing down the drop in room temperature and improving the building's thermal inertia and thermal comfort. Simultaneously, the incorporated GNP forms a stable conductive network within the cement matrix, causing measurable resistivity changes under strain, cracking, and temperature and humidity disturbances, thus endowing the concrete with a self-sensing function. The outer blade 1 uses a composite system of ultra-high performance concrete (UHPC) and GNP, possessing high strength, high density, and excellent durability, effectively resisting external environmental erosion and providing structural and environmental protection for the functional layers. The inner and outer blades of the sandwich panel are equipped with single-layer bidirectional distributed reinforcement.

[0032] An extruded polystyrene (XPS) core layer is placed between the two leaf plates as insulation layer 3, forming an efficient thermal barrier to reduce heat transfer and enhance the overall insulation performance of the wall. To achieve structural integrity and mechanical transfer, several short concrete column connectors are arranged within insulation layer 3, and copper mesh is embedded in the connectors as electrodes 5. The copper mesh electrodes 5 serve as both an interface for electrical signal conduction, enabling stable access and monitoring of the conductive network of the wall panel, and a durable electrode structure that can be used for long-term service within the concrete, thereby constructing a stable electrical measurement system with low contact resistance. This design not only ensures the shear resistance and overall load-bearing capacity of the sandwich wall panel, but also effectively reduces the thermal bridging effect caused by traditional metal connectors.

[0033] This sandwich wall panel design achieves a synergistic integration of materials, structure, and information: the UHPC blades provide mechanical support and durability protection, the MPCM layer stores and releases thermal energy, and the GNP conductive network and copper mesh electrodes constitute a self-sensing system. This configuration enables real-time monitoring of structural strain, cracks, and temperature changes, and achieves adaptive thermal regulation and status awareness through energy conversion and information feedback. Overall, this wall panel possesses high thermal efficiency, excellent durability, and intelligent monitoring capabilities, providing key technological support for near-zero energy consumption and smart buildings.

[0034] II. Sandwich Panel Manufacturing Scheme The self-sensing concrete sandwich insulation wall panel includes: an outer leaf plate 1 and an inner leaf plate 2 made of self-sensing concrete material; a sandwich insulation layer 3 made of XPS insulation board; a concrete short column connector 4 penetrating the inner and outer leaf plates and the sandwich insulation layer 3; and a copper mesh electrode 5 embedded in the short column as a sensing unit. The copper mesh electrode 5 is connected to the data acquisition system to form a complete structural health monitoring system.

[0035] The outer leaf plate 1 and inner leaf plate 2 of the self-sensing concrete sandwich insulation wall panel are composed of a cement matrix and conductive functional filler. The conductive functional filler is graphene, and the dosage is 0.1%-0.5% of the weight of the concrete cementitious material. The concrete short column connectors 4 are distributed in an array with a spacing of 300-600mm and a short column diameter range of 30-50mm. The XPS sandwich insulation layer 3 has a thickness of 50-100mm and a thermal conductivity of no more than 0.035W / (m·K).

[0036] The surface of the copper mesh electrode 5 is coated with an anti-corrosion and insulating coating, the coating material being epoxy resin or polymer cement-based coating. The data acquisition device includes a resistance measurement module, a temperature compensation module, and a wireless transmission module, capable of acquiring, processing, and transmitting sensor data in real time.

[0037] The manufacturing steps for sandwich insulated wall panels are as follows: Step 1: Install the XPS insulation board and make through holes in the preset positions; Step 2: Install the copper mesh electrode template inside the through hole; Step 3: Pour self-sensing concrete to form inner and outer leaf plates and connecting short columns; Step 4: Connect the data acquisition system after curing and hardening.

[0038] III. In-situ monitoring plan The monitoring method for self-sensing concrete sandwich insulation boards includes the following steps: A. Measure the resistance change between the copper mesh electrodes using a data acquisition system; B. Use a temperature compensation model to eliminate the influence of ambient temperature; C. Calculate the strain state of the structure based on the resistance-strain relationship model; D. Use data analysis to determine the structural health status and issue early warnings.

[0039] IV. Wall Resistance Mapping Damage Identification (1) Signal preprocessing and environmental compensation The original resistance signal is very weak and susceptible to interference, so it must be preprocessed.

[0040] Noise reduction: Wavelet transform and other algorithms are used to denoise the acquired raw resistance signal in order to eliminate electrical noise and random fluctuations.

[0041] Environmental Compensation: Fluctuations in ambient temperature and humidity are the main noise sources affecting the stability of resistance signals. Establishing a reliable compensation model is crucial; for example, support vector regression algorithms can be used to establish a nonlinear mapping relationship between resistance change and temperature and humidity. The compensated resistance change rate can be expressed as: ΔR_corrected = ΔR_measured - f(T,H), where f(T,H) is the temperature and humidity compensation function.

[0042] f(T,H)=f0+αT+βH+γTH in: f(T,H) is the output signal after temperature and humidity compensation; T is temperature (usually measured in degrees Celsius or Fahrenheit); H stands for humidity (usually expressed as a percentage, in the unit of relative humidity). f0 is the reference output, the output under standard conditions (such as a specific temperature and humidity condition); α, β, γ are empirical constants used to describe the effects of temperature, humidity, and their interactions on the sensor output. (2) Feature extraction and damage decoupling In practical engineering, the resistance signal of concrete is often the result of the combined effects of multiple factors such as load, damage, and temperature. This step aims to extract features purely caused by damage from the mixed signal.

[0043] Signal decomposition: Using signal processing methods such as variational mode decomposition helps to separate different influencing factors mixed in the data.

[0044] Feature clustering identification: Unsupervised learning algorithms are used to cluster extracted features to automatically identify different damage patterns. For example, Gaussian mixture models (GMMs) can be used for clustering, and the clustering effect can be evaluated by indicators such as the profile coefficient. The larger the coefficient, the closer the data points within a cluster and the farther the data points between clusters, indicating a better clustering effect. By constructing an adaptive damage identification method based on Grid Search, the types of concrete samples that can be selected are not limited, effectively improving the adaptability of existing methods to damage identification of different concrete samples.

[0045] (3) Quantitative assessment and visualization of damage The processed resistance characteristics are mapped to specific damage indicators.

[0046] Damage variable calculation: In mechanics, the damage variable (D) is often used to quantify the degree of degradation of material properties. A classic definition is based on the decrease in elastic modulus: D = 1 - E_s / E_0, where E_0 is the initial elastic modulus and E_s is the current secant modulus.

[0047] Intelligent recognition and visualization: Convolutional neural networks are used to perform deep learning and pattern recognition on processed signal features, thereby enabling automatic classification of damage types, prediction of damage severity, and forecasting of future states. Some research also explores the application of computer vision technology to concrete structure damage detection, using deep learning algorithms to identify apparent damage images of structures.

[0048] (4) Model training and validation To ensure the accuracy and reliability of the algorithm, a large amount of sample data is needed to train and validate the established mapping model.

[0049] Training and Testing: The dataset is divided into training and testing sets. The training set is used to learn the model parameters, and the testing set is used to evaluate its generalization ability.

[0050] Performance optimization: A multi-objective optimization method is adopted, considering both feature importance and feature similarity. This retains important indicators while effectively removing redundancy, improving the computational efficiency and accuracy of cluster analysis. Model parameters are optimized using methods such as cross-validation to prevent overfitting.

Claims

1. A concrete sandwich insulation board with self-monitoring function, comprising an outer leaf plate (1) and an inner leaf plate (2), characterized in that: There is an insulation layer (3) between the outer leaf plate (1) and the inner leaf plate (2). Several concrete short column connectors (4) penetrate the insulation layer (3) and connect the outer leaf plate (1) and the inner leaf plate (2). The outer leaf plate (1), the inner leaf plate (2) and the concrete short column connectors (4) are all made of self-sensing concrete. The self-sensing concrete includes a cement matrix and conductive functional fillers dispersed in the cement matrix. Electrodes (5) are embedded in the concrete short column connectors (4).

2. The concrete sandwich insulation board with self-monitoring function according to claim 1, characterized in that: The conductive functional filler is graphene nanosheets.

3. The concrete sandwich insulation board with self-monitoring function according to claim 2, characterized in that: The amount of graphene nanosheets added is 0.1% to 0.5% of the weight of concrete cementitious materials.

4. The concrete sandwich insulation board with self-monitoring function according to claim 2, characterized in that: The inner leaf plate (2) is filled with microcapsule phase change material in the self-sensing concrete.

5. The concrete sandwich insulation board with self-monitoring function according to claim 4, characterized in that: The electrode (5) surface is coated with an anti-corrosion and insulating coating, the coating material being epoxy resin or polymer cement-based coating.

6. The concrete sandwich insulation board with self-monitoring function according to claim 4, characterized in that: Electrode (5) is connected to signal acquisition device via wire (7).

7. The concrete sandwich insulation board with self-monitoring function according to claim 4, characterized in that: The concrete short column connectors (4) are arranged in an array with a spacing of 300mm to 600mm and a short column diameter of 30mm to 50mm.

8. The concrete sandwich insulation board with self-monitoring function according to claim 4, characterized in that: The insulation layer (3) is an extruded polystyrene board with a thickness of 50mm to 100mm.

9. A monitoring method based on a concrete sandwich insulation board with self-monitoring function as described in any one of claims 1 to 8, characterized in that... Includes the following steps: A. Measure the resistance between each electrode (5); B. Perform noise reduction and temperature compensation on the collected resistance signal to obtain the resistance change after compensation; C. Based on the pre-established resistance-strain relationship model, convert the resistance change into the strain state of the structure; D. Based on the strain state data, determine the health status of the structure through the damage identification algorithm and issue an early warning when abnormal.

10. The monitoring method for a concrete sandwich insulation board with self-monitoring function according to claim 9, characterized in that, The resistance-strain relationship model uses a signal decomposition method to extract damage features from the resistance signal and a clustering algorithm to automatically identify damage patterns.

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