Flexible sandwich intelligent column and preparation method and health monitoring method thereof

Abstract

The invention discloses a flexible sandwich intelligent column and a preparation method and a health monitoring method thereof, and relates to the technical field of structural engineering and intelligent monitoring, the flexible sandwich intelligent column comprises a core concrete column and a flexible sandwich FRP pipe which are coaxially arranged from inside to outside in sequence; wherein the flexible sandwich FRP pipe comprises an inner-layer FRP pipe, a flexible functional sandwich layer and an outer-layer FRP pipe which are sequentially coaxial from inside to outside; grooves distributed along a preset topological path are formed in the outer surface of the inner-layer FRP pipe; a distributed optical fiber sensing network is embedded in the groove, and the distributed optical fiber sensing network is fixed in the groove through a packaging material; and the space between the inner-layer FRP pipe and the outer-layer FRP pipe is filled with the flexible functional sandwich layer. Through the design of the flexible sandwich layer and the deep integration of the distributed sensing network, the pseudo-ductility performance is broken through, the structural toughness is improved, and intrinsic and full-scale monitoring of internal strain and damage initiation is realized.

Description

Technical Field

[0001] This invention relates to the fields of structural engineering and intelligent monitoring technology, and in particular to a flexible sandwich intelligent column, its preparation method, and its health monitoring method. Background Technology

[0002] Fiber-reinforced polymer / plastic (FRP) pipes are widely used as confinement and reinforcement materials for concrete columns due to their high strength, lightweight, and corrosion resistance, forming FRP pipe-concrete composite columns. However, traditional FRP pipe-concrete composite columns have two major drawbacks: firstly, in terms of mechanical properties, the failure mode often exhibits brittleness, lacking sufficient ductility and predictive behavior; secondly, in terms of health monitoring, the initiation and development of internal damage is difficult to detect effectively. Conventional point sensors have low survival rates and limited monitoring ranges, failing to achieve distributed, real-time, and accurate monitoring of the internal stress state, interface behavior, and damage evolution of the structure.

[0003] Distributed Optical Fiber Sensing (DOFS) technology, with its fully continuous, long-distance, and interference-resistant sensing capabilities, offers revolutionary prospects for solving the challenges of structural behavior sensing. While DOFS technology offers the possibility of solving full-scale structural sensing, current technologies often simply attach optical fibers to the structural surface. This makes the sensors susceptible to damage during construction and service, and the sensed information is disconnected from the critical mechanical paths within the structure. More importantly, current technologies fail to deeply integrate sensing functionality with the structural mechanical design itself. The monitoring system and the main structure exist in a disconnected state, neither helping to improve structural failure modes nor forming an intelligent closed loop from sensing to assessment. Summary of the Invention

[0004] The purpose of this invention is to provide a flexible sandwich smart column, its preparation method and health monitoring method. By designing a flexible sandwich layer and deeply integrating a distributed sensing network, it overcomes pseudo-ductility performance, improves structural toughness, and realizes intrinsic, full-scale monitoring of internal strain and damage initiation.

[0005] To achieve the above objectives, the present invention provides a flexible sandwich smart column, comprising a core concrete column and a flexible sandwich FRP pipe arranged coaxially from the inside to the outside; The flexible sandwich FRP tube includes an inner FRP tube, a flexible functional sandwich layer, and an outer FRP tube that are coaxial from the inside to the outside. The outer surface of the inner FRP tube is provided with grooves distributed along a predetermined topology path; a distributed optical fiber sensing network is embedded in the grooves, and the distributed optical fiber sensing network is fixed in the grooves by encapsulation material. The flexible functional sandwich layer is filled between the inner FRP tube and the outer FRP tube, and is made of flexible material. It is used to protect the distributed optical fiber sensing network, and to isolate the direct constraint force transmission between the inner FRP tube and the outer FRP tube during the normal load stage. At the same time, it provides a buffer and guides the load to the outer FRP tube when the inner FRP tube reaches the limit state. The outer FRP tube is used to protect the flexible functional sandwich layer and the distributed optical fiber sensing network during the normal load stage, and when the inner FRP tube enters a high stress state and is damaged, it participates in the cooperative stress through the deformation of the flexible functional sandwich layer, providing additional constraints.

[0006] Preferably, the inner FRP tube and the outer FRP tube are made by automatic fiber filament winding technology, and the thickness of each is not less than 2mm.

[0007] Preferably, the predetermined topology path includes: a path along the axial direction of the flexible sandwich smart column, a path along the circumferential direction of the inner FRP tube, and a monitoring loop located in the expected stress concentration and damage-prone area of ​​the flexible sandwich smart column; the monitoring loop is a loop path composed of optical fibers that is closed at least once.

[0008] Preferably, the thickness of the base material layer on the outer wall of the inner FRP pipe is not less than 1 mm.

[0009] Preferably, the encapsulation material and the matrix material used for the inner FRP tube are at least one of epoxy resin, unsaturated polyester resin, vinyl ester resin, phenolic resin, polypropylene, polyurethane, and polyetheretherketone.

[0010] Preferably, the ratio of the thickness of the flexible functional sandwich layer to the wall thickness of the inner FRP tube is 0.1-2.0.

[0011] Preferably, the flexible material is one of polyvinyl alcohol, polyester, polyimide, polyethylene naphthalate, polyurethane foam, rubber particle composite material, and flexible epoxy resin-based composite material.

[0012] This invention provides a method for preparing a flexible sandwich smart column, comprising the following steps: S1. Prepare an inner FRP tube and process grooves distributed along a predetermined topological path on the outer surface of the inner FRP tube; S2. Deploy the distributed optical fiber sensor network in the groove and fix and encapsulate it using encapsulation material. S3. After fixed encapsulation, an outer FRP tube is set outside the inner FRP tube, and flexible material is poured into the annular gap between the inner FRP tube and the outer FRP tube to obtain a flexible functional sandwich layer. S4. Pour concrete into the cavity formed by the inner FRP pipe to obtain the core concrete column, thus completing the preparation of the flexible sandwich smart column.

[0013] This invention also provides a health monitoring method for a flexible sandwich smart column, used to monitor the health of the aforementioned flexible sandwich smart column, comprising the following steps: (1) Sensing steps: The distributed strain and temperature signals of the flexible sandwich smart column are continuously collected through a distributed optical fiber sensor network; (2) Demodulation and digitization steps: Demodulate the distributed strain and temperature signals acquired in the sensing steps to obtain digitized strain and temperature data distributed along the flexible sandwich smart column; (3) Data transmission steps: wirelessly transmit digital strain and temperature data to a remote processing platform; (4) Intelligent analysis steps: In the remote processing platform, based on digital strain and temperature data, feature parameters characterizing the mechanical state of the intelligent column are extracted, and the feature parameters are input into the machine learning model to identify the type, location and degree of structural damage, and obtain intelligent analysis results; (5) Early warning steps: Based on the intelligent analysis results, trigger the corresponding early warning information.

[0014] Preferably, the machine learning model is at least one of a supervised learning model, an unsupervised learning model, and a deep learning model that has been trained.

[0015] In summary, the flexible sandwich smart column, its preparation method, and its health monitoring method provided by this invention offer the following advantages compared to traditional technologies: Regarding structural safety mechanisms, by setting up a flexible sandwich FRP tube comprising an inner FRP tube, a flexible functional sandwich layer, and an outer FRP tube, the load transfer path is actively controlled, enabling the flexible sandwich smart column to exhibit a predictable pseudo-delay failure mode, significantly improving the structure's safety reserves and predictability. Regarding state perception capabilities, by embedding a distributed optical fiber sensor network into grooves distributed along a predetermined topological path on the outer surface of the inner FRP tube, an integrated implantation mode of sensors and key mechanical paths is created, achieving intrinsic, distributed, direct measurement of internal stress, damage initiation, and development processes, completely changing the previous reliance on indirect surface monitoring and poor data interpretability. The structural design of the flexible sandwich smart column promotes a paradigm shift in structural health monitoring from discrete point monitoring to full-field intelligent diagnosis, providing standardized, highly reliable prefabricated smart components, greatly simplifying the complexity of on-site sensor deployment, and providing plug-and-play key technology components for the digital construction and preventative maintenance of major infrastructure.

[0016] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of a flexible sandwich smart column in this invention; Figure 1 Part (a) in the diagram is a schematic diagram of the main structure; Figure 1 Part (b) is a schematic diagram of the vertical cross-section; Figure 1 Part (c) in the diagram is a cross-sectional view; Figure 2 This is a schematic diagram of the wall structure of a flexible sandwich FRP tube for a flexible sandwich smart column according to the present invention. Figure 2 Part (a) in the diagram is a schematic diagram of the cross-sectional structure; Figure 2 Part (b) is a schematic diagram of a vertical cross-section without an overall cross-section of a distributed optical fiber sensor network; Figure 2 Part (c) is a schematic diagram of the vertical cross-section of the overall cross-section of the distributed optical fiber sensor network; Figure 3 This is a flowchart of a method for preparing a flexible sandwich smart column according to the present invention; Figure 4 This is a diagram showing the deployment of the distributed fiber optic sensor network in the embodiment. Figure 4 Part (a) in the diagram is the regional layout map; Figure 4 Part (b) is the layer and column layout diagram; Figure 4 Part (c) is a schematic diagram of the intersection of the layers; Figure 4 Part (d) in the diagram is an expanded schematic of the deployment path after the optical fiber is embedded. Figure 5 This is a schematic diagram of a health monitoring method for a flexible sandwich smart column according to the present invention; Figure 6 This is a schematic diagram of a health monitoring system for a flexible sandwich smart column according to the present invention.

[0018] Figure Labels 1. Core concrete column; 2. Flexible sandwich FRP pipe; 3. Inner FRP pipe; 4. Flexible functional sandwich layer; 5. Outer FRP pipe; 6. Distributed optical fiber sensor network; 7. Encapsulation material. Detailed Implementation

[0019] The technical method of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0020] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the scope of this application and its application or use.

[0021] In all the examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0022] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0023] like Figure 1 As shown, this invention provides a flexible sandwich smart column, employing a "sandwich" configuration, such as... Figure 1 As shown in part (a), the structure includes a core concrete column 1 and a flexible sandwich FRP pipe 2 arranged coaxially from the inside to the outside. In an exemplary embodiment of the present invention, the core concrete column 1 is made of self-compacting concrete with a strength grade of C60. After being poured in the prefabrication plant, it is cured for more than 14 days under standard conditions and mainly bears the vertical load transmitted from the superstructure.

[0024] Among them, such as Figure 1 Part (b) of the text (i.e.) Figure 1 (a) section AA) Figure 1 Part (c) of the middle (i.e. Figure 1 (a) BB section) Figure 2 Part (a) of the text Figure 2 Part (b) of the text (i.e.) Figure 2 (DD section in part (a)) Figure 2 Part (c) of the middle (i.e. Figure 2As shown in section (a) (CC section), the flexible sandwich FRP tube 2 includes an inner FRP tube 3, a flexible functional sandwich layer 4 and an outer FRP tube 5, which are coaxial from the inside to the outside.

[0025] The outer surface of the inner FRP tube 3 is provided with grooves distributed along a predetermined topology path. A distributed optical fiber sensing network 6 (using tight-buffered or slightly bent insensitive single-mode optical fiber) is embedded within these grooves. The distributed optical fiber sensing network 6 is fixed within the grooves by an encapsulation material 7, making it an integral intrinsic sensing layer of the tube wall. The predetermined topology path includes: a path along the axial direction of the flexible sandwich smart column, a path along the circumferential direction of the inner FRP tube, and a monitoring loop (a closed loop path composed of optical fibers) located in the expected stress concentration and damage-prone areas of the flexible sandwich smart column. Specifically, the expected stress concentration and damage-prone areas typically include, but are not limited to: the column base area (e.g., within 1.5 times the column cross-sectional height from the top surface of the foundation), the column middle area (e.g., the middle 1 / 3 of the column height), and the core area of ​​nodes connecting to beams, slabs, or other components. The monitoring loops deployed in these areas are preferably closed loops, but can also be deployed as multiple loops or grid-like loops as needed to achieve localized, intensified monitoring of the strain field.

[0026] A flexible functional sandwich layer 4 is filled between the inner FRP tube 3 and the outer FRP tube 5, and is made of flexible material. It protects the distributed optical fiber sensing network 6 and isolates the direct force transfer between the inner FRP tube 3 and the outer FRP tube 5 during normal load phases. Simultaneously, it provides buffering and guides the load transfer to the outer FRP tube 5 when the inner FRP tube 3 reaches its limit state. The flexible material is one of the following: polyvinyl alcohol, polyester, polyimide, polyethylene naphthalate, polyurethane foam, rubber particle composite material, and flexible epoxy resin-based composite material.

[0027] In an exemplary embodiment of the present invention, the flexible functional sandwich layer 4 is formed by infusing a low-modulus, high-toughness polyurethane foam material with a filling thickness of 10 mm. After curing, the flexible functional sandwich layer 4 provides flexible physical protection for the internal optical fiber network and can also coordinate the deformation of the inner and outer FRP tubes 5 under stress.

[0028] The ratio of the thickness of the flexible functional sandwich layer 4 to the wall thickness of the inner FRP pipe 3 is 0.1-2.0. The thickness of the flexible functional sandwich layer 4 is determined according to the engineering design, but is usually not less than 2mm. Through extensive experiments, it has been found that controlling the thickness of the flexible functional sandwich layer 4 within the range of 0.1-2.0 times the wall thickness of the inner FRP pipe 3 can effectively isolate the constraint force under normal loads, while guiding the load to the outer layer most smoothly under extreme conditions, which is the optimal range for achieving the pseudo-ductile failure mode.

[0029] The outer FRP tube 5 does not directly bear axial load during the normal load stage, but protects the flexible functional sandwich layer 4 and the distributed optical fiber sensing network 6 from environmental factors erosion and interference. When the inner FRP tube 3 enters a high stress state and is damaged, it participates in the stress-bearing with the deformation of the flexible functional sandwich layer 4 in a delayed manner, providing additional circumferential constraints and radial support. This effectively delays the rapid degradation of structural stiffness, dissipates energy, and ultimately guides the flexible sandwich smart column to achieve a "pseudo-ductility" failure mode that develops from the inside out in stages.

[0030] The thickness of the substrate material layer on the outer wall of the inner FRP tube 3 is not less than 1 mm. To ensure that the groove processing does not damage the supporting fiber and to provide sufficient mechanical protection for the optical fiber, the inner FRP tube 3 is designed with a substrate material layer of sufficient thickness on its outer surface. When the distributed optical fiber sensing network 6 uses optical fiber with a cladding diameter of not less than 200 µm, and the thickness of the substrate material layer on the outer wall of the inner FRP tube 3 is not less than twice the cladding diameter of the optical fiber, the total thickness is not less than 1 mm.

[0031] The inner FRP tube 3 and the outer FRP tube 5 are manufactured using automatic fiber filament winding technology, and both have a thickness of not less than 2 mm. The cross-sectional shape of the inner FRP tube 3 and the outer FRP tube 5 is circular, square, rounded square, rectangular, rounded rectangular, or elliptical; the fiber type is at least one of carbon fiber, glass fiber, and basalt fiber, and the fiber winding angle ranges from 0° to 90°; the matrix material is at least one of epoxy resin, unsaturated polyester resin, vinyl ester resin, phenolic resin, polypropylene, polyurethane, and polyetheretherketone. Furthermore, the encapsulation material 7 is consistent with the matrix material used in the inner FRP tube 3, and is at least one of epoxy resin, unsaturated polyester resin, vinyl ester resin, phenolic resin, polypropylene, polyurethane, and polyetheretherketone.

[0032] In an exemplary embodiment of the present invention, the inner FRP tube 3 serves as the main load-bearing component. It is made of T700 grade carbon fiber and weather-resistant epoxy resin through a CNC winding process, with a fiber laying angle of ±85° (mainly circumferential), an inner diameter of 400mm, a wall thickness of 8mm, and a single section length of 3m. The outer FRP tube 5 uses the same material and the same fiber laying angle of ±85°, with an inner diameter of 410mm and a wall thickness of approximately 3mm. Its upper and lower end faces are 400mm shorter than the corresponding end faces of the inner FRP tube 3, mainly serving as a safety reserve and protecting the internal distributed optical fiber sensor network 6.

[0033] In this invention, the fiber type, matrix material type, fiber volume content and circumferential fiber strength of the outer FRP pipe 5 can be lower than, equal to or stronger than the relevant indicators of the inner FRP pipe 3. The specific parameters are determined according to the engineering design.

[0034] The flexible sandwich smart column of this invention consists of a coaxial core concrete column 1, an inner FRP tube 3, a flexible functional sandwich layer 4, and an outer FRP tube 5, from the inside out. A groove is pre-fabricated on the outer surface of the inner FRP tube 3 and a distributed optical fiber sensing network 6 is deployed. The outer FRP tube 5 is then wrapped around it, and finally, flexible material is injected to form the flexible functional sandwich layer 4. The flexible functional sandwich layer 4 protects the optical fiber sensors, cuts off the direct constraint force transmission between the inner and outer FRP tubes under conventional loads, and guides load transfer when the inner FRP tube 3 reaches its limit, causing the flexible sandwich smart column to exhibit a pseudo-ductile failure mode.

[0035] This invention provides a method for preparing a flexible sandwich smart column, used to prepare the aforementioned flexible sandwich smart column, such as... Figure 3 As shown, it includes the following steps: S1. Prepare the inner FRP tube 3 and process grooves distributed along a predetermined topological path on the outer surface of the inner FRP tube 3.

[0036] S2. The distributed optical fiber sensor network 6 is laid in the groove and fixed and encapsulated using encapsulation material 7.

[0037] S3. After fixed encapsulation, an outer FRP tube 5 is set outside the inner FRP tube 3, and flexible material is poured into the annular gap between the inner FRP tube 3 and the outer FRP tube 5 to obtain a flexible functional sandwich layer 4.

[0038] S4. Pour concrete into the cavity formed by the inner FRP pipe 3 to obtain the core concrete column 1, thus completing the preparation of the flexible sandwich smart column and obtaining the flexible sandwich smart column.

[0039] In an exemplary embodiment of the present invention, such as Figure 4 As shown, a distributed fiber optic sensor network 6 is deployed, such as... Figure 4 Part (a) of the text Figure 4 Part (b) and Figure 4 As shown in section (c), when the inner FRP pipe 3 is prefabricated in the factory, its outer surface is processed by a CNC machine tool according to... Figure 4The preset topology path is precisely cut to create an axial and circumferential groove network with a depth of 1mm and a width of 0.5mm. This network is divided into node regions and intelligent detection regions, resulting in mesh layers L1, L2, L3, L4, L5, columns A, B, C, and D. The intersection points A1, B1, C1, and D1 of layer L1 with each column; A2, B2, C2, and D2 of layer L2 with each column; A3, B3, C3, and D3 of layer L3 with each column; A4, B4, C4, and D4 of layer L4 with each column; and A5 of layer L5 with each column. B5, C5, D5, and then a 250μm diameter tight-buffered single-mode optical fiber is embedded into the groove according to a preset path: the main path is a spiral upward traversal of 5 circumferential paths through the mid-span of the load-bearing column, used to accurately capture circumferential strain to assess constraint stress; the axial path is traversed vertically at 90° intervals to monitor axial strain and bending; the L1 and L5 circumferential paths are located at the junction of the node reinforcement area and the central monitoring area, the L3 circumferential path is located at the high section of the column, and the L2 and L4 circumferential paths are located on the golden section lines of the L1-L3 and L5-L3 intervals, respectively. Figure 4 As shown in section (d), after the optical fiber is embedded, it is filled and fixed with epoxy resin adhesive of the same origin as the FRP tube to ensure its integration with the inner FRP tube 3 wall structure. After the optical fiber is led out from the outer FRP tube 5, it is inserted into the stainless steel protective box attached to the outer wall of the outer FRP tube 5 and connected to the signal demodulation equipment inside the protective box. The spatial resolution of demodulation is set to 0.1m, and the strain measurement accuracy is ±2με.

[0040] This invention provides a health monitoring method for a flexible sandwich smart column, used for health monitoring of the aforementioned flexible sandwich smart column, such as... Figure 5 As shown, it includes the following steps: (1) Sensing steps: The distributed strain and temperature signals of the flexible sandwich smart column are continuously collected through a distributed optical fiber sensor network.

[0041] (2) Demodulation and digitization steps: Demodulate the distributed strain and temperature signals acquired in the sensing steps to obtain digitized strain and temperature data distributed along the flexible sandwich smart column.

[0042] (3) Data transmission steps: wirelessly transmit digital strain and temperature data to a remote processing platform.

[0043] (4) Intelligent analysis steps: In the remote processing platform, based on digital strain and temperature data, feature parameters characterizing the mechanical state of the flexible sandwich smart column are extracted, and the feature parameters are input into the machine learning model to identify the type, location, and degree of structural damage, thereby obtaining intelligent analysis results. Among them, the machine learning model is at least one of the trained supervised learning model, unsupervised learning model, and deep learning model.

[0044] (5) Early warning steps: Based on the intelligent analysis results, trigger the corresponding early warning information.

[0045] This invention provides a health monitoring method for a flexible sandwich smart column, implemented based on a health monitoring system for the flexible sandwich smart column, such as... Figure 6 As shown, the health monitoring system includes a sensing layer, a signal demodulation layer, and a cloud-based intelligent processing layer, as detailed below: The sensing process is implemented through a sensing layer. This sensing layer consists of a distributed optical fiber sensing network 6, used to continuously acquire distributed strain and temperature signals from the flexible sandwich smart column. The optical fibers are deployed in a specific topology on the outer surface of the inner FRP tube 3. The main paths include: a linear path along the axis of the flexible sandwich smart column, used to monitor axial strain distribution and bending deformation; a circumferential path along the circumference of the inner FRP tube 3, used to accurately assess the constraint stress of the FRP tube on the core concrete column 1; and a denser loop path in critical stress or potentially weak areas (such as at 1 / 2H of the flexible sandwich smart column, where H is the height of the flexible sandwich smart column), used for early damage location and refined monitoring. The distributed optical fiber sensing network 6, pre-embedded within the flexible functional sandwich layer 2, is directly coupled to the critical stress points of the structure, allowing for deep integration of the distributed optical fiber sensing network 6 with the key mechanical paths of the structure, directly acquiring strain and temperature information from the most sensitive locations.

[0046] The demodulation and digitization steps, as well as the data transmission steps, are implemented through the signal demodulation layer. The signal demodulation layer includes: a power supply, a local data storage module, an optical signal demodulation module, a wireless signal transmission module, a command receiving module, and an optical signal excitation module. The signal demodulation layer is a field-deployable hardware unit responsible for signal excitation, demodulation, storage, and remote transmission. The device interfaces with a pre-embedded distributed optical fiber sensor network 6 via a pluggable fiber optic connector, injects probe light pulses into the optical fiber, receives the backscattered light signal, and transmits it in real time to a remote server via a built-in wireless transmission module (such as 4G / 5G or LPWAN), ensuring data continuity and timeliness.

[0047] The power supply is used to power all power-consuming modules in the signal demodulation layer.

[0048] The local data storage module is used to back up and store monitoring digital data.

[0049] The optical signal demodulation module is connected to the distributed optical fiber sensor network 6. Based on Brillouin optical time-domain analysis technology or phase-sensitive optical time-domain reflectometry technology, it is used to demodulate the distributed strain and temperature signals to obtain digital strain and temperature data.

[0050] The wireless signal transmission module is used to transmit digital strain and temperature data to a remote processing platform.

[0051] The instruction receiving module is used to receive instructions from the remote processing platform.

[0052] The optical signal excitation module is used to excite optical signals at a corresponding frequency according to the instructions received by the instruction receiving module.

[0053] The intelligent analysis and early warning processes are implemented through a cloud-based intelligent processing layer. Deployed on a remote server, this layer receives and processes digital strain and temperature data, and includes: a wireless signal receiving module, a data fusion and feature extraction module, a mechanical state inversion and model update module, a visualization and data management module, a damage identification and safety early warning module, and a command sending module. The cloud-based intelligent processing layer is the intelligent core of the system, containing dedicated data processing algorithms, a mechanical-damage model, and a visual human-machine interface.

[0054] The wireless signal receiving module is used to receive digital strain and temperature data sent from the wireless signal transmitting module in the signal demodulation layer.

[0055] The data fusion and feature extraction module is used to process digital strain and temperature data and extract structural mechanical feature parameters. Specifically, it is based on Brillouin optical time-domain analysis or phase-sensitive optical time-domain reflectometry to preprocess massive spatiotemporal data and convert it into digital strain and temperature data distributed along the fiber length, and extract key mechanical feature parameters such as overall deformation curvature, strain cloud map of key sections, and constraint stress distribution.

[0056] The mechanical state inversion and model update module is used to invert the internal stress state and interface properties of the structure based on characteristic parameters. Based on the extracted characteristic parameters, the system uses a flexible sandwich smart column multiphysics coupling analysis and state assessment model to invert the true stress state of the core concrete column 1 and the development process of interfacial bond-slip between the FRP pipe and concrete. The system can utilize long-term monitoring data to self-calibrate and update the inversion model, improving the accuracy of the state assessment.

[0057] The visualization and data management module is used to display the structural health status and store data.

[0058] The damage identification and safety early warning module is used to identify damage and trigger early warnings based on machine learning algorithms. It integrates a pre-trained machine learning model to identify and classify abnormal patterns in feature parameters or inversion results. The machine learning model is at least one of the following: supervised learning model, unsupervised learning model, and deep learning model. The machine learning model includes, but is not limited to: convolutional neural networks for spatial pattern recognition, recurrent neural networks or long short-term memory networks for time series analysis, support vector machines for classification, and at least one of random forest, gradient boosting decision tree ensemble learning models. The machine learning model can classify and identify at least one damage type and its development degree, including concrete crushing, FRP pipe fiber fracture, delamination, or interface debonding, based on the input strain distribution, strain history, or inverted stress data. When monitoring indicators exceed preset safety thresholds or specific damage patterns are identified, the system automatically triggers graded early warning information (such as attention, warning, alarm, etc.).

[0059] The instruction sending module is used to send corresponding instructions to the instruction receiving module of the signal demodulation layer based on the damage level determination obtained by the damage identification and safety early warning module.

[0060] The cloud-based intelligent processing layer also includes a full lifecycle visualization and data management module. Through the visualization platform, it displays the structure's "health cloud map," historical trend curves, and early warning status in real time, and stores all raw data and analysis results to form a complete digital health record of the structure, providing data support for operation and maintenance decisions.

[0061] In this invention, after the flexible sandwich smart column is hoisted into place on site, the signal demodulation layer transmits the digital strain and temperature data to the cloud intelligent processing layer through the wireless signal transmission module. First, temperature compensation and data fusion are performed to reconstruct the two-dimensional strain field on the column surface and calculate the real-time constraint stress distribution. Second, a comparative analysis is performed with the benchmark finite element model established based on the design load. The system's built-in damage identification algorithm runs continuously, while generating a visual user interface and storing the data.

[0062] The damage identification algorithm's early warning system includes: Warning Logic 1: If the circumferential strain values ​​at more than three consecutive measuring points in the circumferential monitoring area of ​​the high-level flexible sandwich smart column exceed 2000 με and the strain growth rate remains positive, it is determined that "Core concrete column 1 has entered a highly nonlinear compressive state," triggering a Level 1 warning. Warning Logic 2: If, in the same area, the circumferential strain difference between adjacent measuring points continuously exceeds 500 με / m (indicating a drastic gradient in the strain field), and the axial strain data at the corresponding location exhibits unloading or abrupt change characteristics, the evaluation model will determine that "Local debonding or concrete crushing may occur at the FRP pipe-concrete interface," automatically triggering a Level 2 warning. Alarm information and damage location diagrams will be pushed through the full lifecycle visualization and data management module to guide maintenance.

[0063] An exemplary embodiment of the present invention applies the flexible sandwich smart column, its preparation method and health monitoring system to the reinforcement and intelligent upgrading of existing bridge steel-concrete composite piers.

[0064] Taking bridge steel-concrete composite piers as the reinforcement target, a flexible sandwich smart column, its preparation method, and a health monitoring system are integrated into a solution for the reinforcement of bridge steel-concrete composite piers. Specifically, this includes: First, carbon fiber-epoxy resin composite material is directly molded onto the surface of the bridge steel-concrete pier using a wet winding process to obtain the inner FRP tube 3. Simultaneously, a distributed optical fiber sensor network 6 is embedded into the outer resin layer according to a designed path while winding the inner FRP tube 3. After the inner FRP tube 3 is formed, it is wound and bonded using a rollable flexible material to form the outer FRP tube 5, which provides protection and safety reserves. The distributed optical fiber sensor network adopts a topology of "axial array as the main component, with circumferential closure at key locations": four axial optical fibers are evenly distributed along the circumference to monitor the overall bending strain distribution; three continuous circumferential optical fiber paths are set at the top, bottom, and middle of the reinforced area to form a "monitoring hoop." This ultimately achieves the monitoring of the collaborative working performance of the interface between the reinforced system and the existing structure.

[0065] The evaluation model of the flexible sandwich smart column health monitoring system has been specially enhanced with interface analysis algorithms for real-world scenarios. It diagnoses the problem by comparing the circumferential strain response of "monitoring hoops" at different heights in real time: Under axial load, if the strain growth of each hoop is continuous and coordinated, it indicates good interface bonding and effective load transfer. If the circumferential strain value at a certain height is found to be significantly lower than that of adjacent areas (e.g., more than 30% lower than the average), and this anomaly persists after temperature compensation, the evaluation model initially determines that interface delamination has occurred between the reinforcement layer and the original column in that area. If, under continuous load, the strain difference between adjacent "monitoring hoops" shows discontinuous jumps or an abnormally large growth rate (e.g., the difference increases by more than 50µε within 10 minutes), the evaluation model determines that the interface bonding is deteriorating or local slippage is occurring, and triggers a corresponding early warning based on a preset threshold.

[0066] Therefore, the flexible sandwich smart column, its preparation method, and health monitoring system of the present invention can not only serve as intelligent components for new structures, but also as a standardized solution integrating high-performance reinforcement and full life-cycle intelligent sensing. Through adaptive design, it can be widely applied to the transformation and upgrading of existing structures, endowing traditional structures with "pseudo-ductility" mechanical properties and real-time self-sensing capabilities.

[0067] This invention, through the innovative design of the flexible functional sandwich layer 4, achieves a deep unification of mechanical performance enhancement (pseudo-ductility) and intelligent sensing capabilities (full life cycle monitoring) at the structural body level. It provides a reliable technical solution for the intelligent construction and operation and maintenance of major infrastructure, and establishes a complete system integrating embedded sensing, wireless transmission, and cloud-based intelligent assessment. It realizes the automatic conversion and early warning from continuous data flow to real-time safety status, and promotes the paradigm upgrade of structural health monitoring from "discrete point monitoring" to "full-field intelligent diagnosis".

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A flexible sandwich smart column, characterized in that, This includes a core concrete column and a flexible sandwich FRP pipe arranged coaxially from the inside out; The flexible sandwich FRP tube includes an inner FRP tube, a flexible functional sandwich layer, and an outer FRP tube that are coaxial from the inside to the outside. The outer surface of the inner FRP tube is provided with grooves distributed along a predetermined topology path; a distributed optical fiber sensing network is embedded in the grooves, and the distributed optical fiber sensing network is fixed in the grooves by encapsulation material. The flexible functional sandwich layer is filled between the inner FRP tube and the outer FRP tube, and is made of flexible material. It is used to protect the distributed optical fiber sensing network, and to isolate the direct constraint force transmission between the inner FRP tube and the outer FRP tube during the normal load stage. At the same time, it provides a buffer and guides the load to the outer FRP tube when the inner FRP tube reaches the limit state. The outer FRP tube is used to protect the flexible functional sandwich layer and the distributed optical fiber sensing network during the normal load stage, and when the inner FRP tube enters a high stress state and is damaged, it participates in the cooperative stress through the deformation of the flexible functional sandwich layer, providing additional constraints.

2. The flexible sandwich smart column according to claim 1, characterized in that, The inner FRP tube and the outer FRP tube are made by automatic winding technology of fiber filaments, and the thickness of each is not less than 2mm.

3. A flexible sandwich smart column according to claim 1, characterized in that, The predetermined topology path includes: a path along the axial direction of the flexible sandwich smart column, a path along the circumferential direction of the inner FRP tube, and a monitoring loop located in the expected stress concentration and damage-prone area of ​​the flexible sandwich smart column; the monitoring loop is a loop path composed of optical fibers that is closed at least once.

4. A flexible sandwich smart column according to claim 1, characterized in that, The thickness of the base material layer on the outer wall of the inner FRP pipe is not less than 1 mm.

5. A flexible sandwich smart column according to claim 1, characterized in that, The encapsulation material and the matrix material used for the inner FRP tube are both at least one of epoxy resin, unsaturated polyester resin, vinyl ester resin, phenolic resin, polypropylene, polyurethane, and polyetheretherketone.

6. A flexible sandwich smart column according to claim 1, characterized in that, The ratio of the thickness of the flexible functional sandwich layer to the wall thickness of the inner FRP pipe is 0.1-2.

0.

7. A flexible sandwich smart column according to claim 1, characterized in that, The flexible material is one of polyvinyl alcohol, polyester, polyimide, polyethylene naphthalate, polyurethane foam, rubber particle composite material, and flexible epoxy resin-based composite material.

8. A method for preparing a flexible sandwich smart column, characterized in that, The method for preparing a flexible sandwich smart column as described in any one of claims 1-7 includes the following steps: S1. Prepare an inner FRP tube and process grooves distributed along a predetermined topological path on the outer surface of the inner FRP tube; S2. Deploy the distributed optical fiber sensor network in the groove and fix and encapsulate it using encapsulation material. S3. After fixed encapsulation, an outer FRP tube is set outside the inner FRP tube, and flexible material is poured into the annular gap between the inner FRP tube and the outer FRP tube to obtain a flexible functional sandwich layer. S4. Pour concrete into the cavity formed by the inner FRP pipe to obtain the core concrete column, thus completing the preparation of the flexible sandwich smart column.

9. A health monitoring method for a flexible sandwich smart column, characterized in that, For health monitoring of a flexible sandwich smart column according to any one of claims 1-7, the following steps are included: (1) Sensing steps: The distributed strain and temperature signals of the flexible sandwich smart column are continuously collected through a distributed optical fiber sensor network; (2) Demodulation and digitization steps: Demodulate the distributed strain and temperature signals acquired in the sensing steps to obtain digitized strain and temperature data distributed along the flexible sandwich smart column; (3) Data transmission steps: wirelessly transmit digital strain and temperature data to a remote processing platform; (4) Intelligent analysis steps: In the remote processing platform, based on digital strain and temperature data, the characteristic parameters representing the mechanical state of the intelligent column are extracted, and the characteristic parameters are input into the machine learning model to identify the type, location and degree of structural damage, and obtain the intelligent analysis results; (5) Early warning steps: Based on the intelligent analysis results, trigger the corresponding early warning information.

10. The health monitoring method for a flexible sandwich smart column according to claim 9, characterized in that, The machine learning model is at least one of the trained supervised learning model, unsupervised learning model, and deep learning model.

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