A method for predicting the long-term service performance of expansion joint devices based on IoT smart sensing

Abstract

This invention relates to the field of road and bridge engineering technology, and in particular to a method for predicting the long-term service performance of expansion joint devices based on IoT smart sensing. The method includes the following steps: deploying an IoT smart sensing system on the expansion joint device and using this system to collect response and environmental data during service; constructing a long-term service performance prediction model for the expansion joint device, which includes a remaining life prediction model and a typical defect prediction model; and using the long-term service performance prediction model in conjunction with the response and environmental data to predict the long-term service performance of the expansion joint device. This method, through IoT smart sensing technology, can meet the long-term performance prediction needs of expansion joint devices, aiding in the planning of maintenance and replacement schedules, and contributing to improved maintainability and safety of bridge engineering.

Description

Technical Field

[0001] This invention relates to the field of road and bridge engineering technology, and in particular to a method for predicting the long-term service performance of expansion joint devices based on IoT smart sensing. Background Technology

[0002] Because bridges are affected by temperature changes, concrete beams undergo thermal expansion and contraction deformation. To ensure that the concrete beams can expand and contract freely and to avoid stress caused by the obstruction of deformation due to temperature changes, which could lead to structural damage, deformable bridge deck expansion joints are generally installed at adjacent ends or between the beam and the abutment back wall.

[0003] In the field of road and bridge engineering technology, bridge expansion joint devices are key components that ensure the operational safety and service performance of bridges. Their main function is to allow the bridge superstructure to expand and contract freely under the influence of factors such as temperature changes and material deformation, thus ensuring driving safety and comfort. However, existing bridge expansion joint devices suffer from problems such as easy aging and damage, loud vehicle bouncing noise, and difficulty in subsequent maintenance, which affect road traffic efficiency and easily lead to traffic accidents.

[0004] Traditional bridge expansion joint devices typically use rubber as the sealing material. Due to prolonged use, environmental factors, and vehicle impacts, rubber is prone to aging and hardening, leading to decreased sealing effectiveness and damage to the expansion joint device. This, in turn, affects the safety of the bridge structure and the comfort of drivers and passengers. Furthermore, expansion joint devices are usually inspected and maintained manually on a regular basis, which is time-consuming, labor-intensive, and makes it difficult to detect problems promptly. Once a failure occurs, replacement is difficult and very costly. Summary of the Invention

[0005] To address the needs of practical applications, this invention provides a method for predicting the long-term service performance of expansion joint devices based on IoT smart sensing. The aim is to improve the safety and maintainability of bridge operation by predicting the long-term service performance of expansion joint devices without affecting their normal use.

[0006] The present invention provides a method for predicting the long-term service performance of expansion joint devices based on IoT smart sensing, comprising the following steps: deploying an IoT smart sensing system on the expansion joint device, and using the IoT smart sensing system to collect response data and environmental data of the expansion joint device during service, wherein the response data includes strain data, displacement data, and audio data, and the environmental data includes temperature data and humidity data; constructing a long-term service performance prediction model for the expansion joint device, wherein the long-term service performance prediction model includes a remaining service life prediction model and a typical defect prediction model; and using the long-term service performance prediction model in combination with the response data and the environmental data to predict the long-term service performance of the expansion joint device. The long-term service performance prediction method for expansion joint devices based on IoT smart sensing provided by the present invention, through IoT smart sensing technology, can meet the long-term performance prediction needs of expansion joint devices, help plan maintenance and replacement schedules, and contribute to improving the maintainability and safety of bridge engineering.

[0007] Optionally, the expansion joint device includes an elastic expansion component and an edge fixing component; wherein, the elastic expansion component is formed by casting spring steel from a high-polymer elastic material, one end of the elastic expansion component is fixedly connected to one end of the bridge joint through the edge fixing component, and the other end of the elastic expansion component is fixedly connected to the other end of the bridge joint through the edge fixing component. The expansion joint device provided by this option is highly maintainable, has a wide range of applications, and can better meet the implementation requirements of the aforementioned method for predicting the long-term service performance of expansion joint devices based on IoT smart sensing.

[0008] Optionally, the IoT smart sensing system is deployed along the driving direction on the side of the elastic telescopic component facing away from the driving surface.

[0009] Optionally, the IoT smart sensing system includes a strain sensor, a displacement sensor, an audio sensor, a temperature sensor, and a humidity sensor; wherein, the strain sensor is used to collect real-time strain data of the expansion joint device during service; the displacement sensor is used to collect real-time displacement data of the expansion joint device during service; the audio sensor is used to collect real-time sound wave data of the expansion joint device during service; the temperature sensor is used to collect ambient temperature data of the expansion joint device during service; and the humidity sensor is used to collect ambient humidity data of the expansion joint device during service. This optional IoT smart sensing system can provide comprehensive information to meet the data requirements for long-term performance prediction of the expansion joint device.

[0010] Optionally, the remaining life prediction model of the expansion joint device satisfies the following formula: ,in, Indicates the expansion joint device Remaining life coefficient during the second expansion / contraction. This represents the temperature correction factor. This represents the humidity correction factor. This represents the lateral strain correction factor. This represents the longitudinal strain correction factor. Indicates the expansion joint device The actual amount of expansion and contraction during each expansion and contraction. This indicates the cumulative expansion and contraction amount of the expansion joint device, representing its fatigue limit. The remaining life prediction model for expansion joint devices provided in this option estimates the remaining life of the device by monitoring and calculating the cumulative expansion and contraction amount, and considering multiple correction factors such as temperature, humidity, and strain. The application of this remaining life prediction model in practical engineering helps identify potential fatigue problems in expansion joint devices, thus providing important information for the maintenance and replacement of bridge facilities, ensuring the maintainability and safety of road and bridge engineering.

[0011] Optionally, the remaining lifespan of the expansion joint device is positively correlated with the magnitude of the remaining lifespan coefficient.

[0012] Optionally, the feature is that: when the temperature hour, The value is 1, otherwise, The value is 1.5; when the humidity... hour, The value is 1, otherwise, The value is 1.2; when the longitudinal strain hour, The value is 1, otherwise, The value is 1.5; when the transverse strain hour, The value is 1, otherwise, The value is 1.3. The correction factor provided by this option is adjusted according to different environmental and strain conditions to more accurately reflect the actual situation of the expansion joint device, which helps to improve the accuracy of the remaining life factor prediction.

[0013] Optionally, the typical disease prediction model satisfies the following formula: ,in, Indicates the expansion joint device for the first Early warning assessment coefficients for typical diseases This indicates the number of times the expansion joint device was tested for typical defects. Indicates the first The test results identified it as the first The number of short-term energy characteristics of a typical disease. Representing the characterization of the first The number of short-time energy characteristics of various typical defects. The typical defect prediction model provided in this option assesses the probability of occurrence of different typical defects by statistically analyzing the number of short-time energy characteristics under different typical defects. This allows for the early detection and prediction of potential problems, enabling maintenance measures to be taken to ensure the maintainability and safety of road and bridge engineering.

[0014] Optionally, the expansion joint device suffers from the first The likelihood of a typical disease and the first The magnitudes of the early warning assessment coefficients for typical diseases are positively correlated.

[0015] Optionally, the present invention also provides an IoT-based smart sensing-based long-term service performance prediction system for expansion joint devices, applicable to the IoT-based smart sensing method for predicting long-term service performance of expansion joint devices. The IoT-based smart sensing-based long-term service performance prediction system includes an input device, a processor, a memory, and an output device, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to call the program instructions to execute the IoT-based smart sensing-based long-term service performance prediction method for expansion joint devices. The IoT-based smart sensing-based long-term service performance prediction system for expansion joint devices provided by the present invention combines IoT smart sensing technology, allowing for long-term performance prediction and typical defect prediction, providing important information and decision support for maintenance and management personnel. Attached Figure Description

[0016] Figure 1 This is a flowchart of the long-term service performance prediction method for expansion joint devices based on IoT smart sensing provided in an embodiment of the present invention.

[0017] Figure 2 This is a schematic diagram of the expansion joint device provided in an embodiment of the present invention;

[0018] Figure 3 This is a schematic diagram of the deployment of the IoT smart sensing system provided in an embodiment of the present invention;

[0019] Figure 4 This is a schematic diagram of the long-term service performance prediction system for expansion joint devices based on IoT smart sensing, provided in an embodiment of the present invention. Detailed Implementation

[0020] Specific embodiments of the present invention will now be described in detail. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been specifically described to avoid obscuring the invention.

[0021] Throughout this specification, references to "an embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "in an embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale.

[0022] In an optional embodiment, please refer to Figure 1 , Figure 1 This is a flowchart illustrating the long-term service performance prediction method for expansion joint devices based on IoT smart sensing, as provided in an embodiment of the present invention. Figure 1 As shown, the long-term service performance prediction method for expansion joint devices based on IoT smart sensing includes the following steps:

[0023] S01. An IoT smart sensing system is installed on the expansion joint device, and the IoT smart sensing system is used to collect response data and environmental data of the expansion joint device during service. The response data includes strain data, displacement data and audio data, and the environmental data includes temperature data and humidity data.

[0024] The expansion joint device described in this invention is an important structure installed between the two adjacent ends of the bridge deck or between the beam and the abutment back wall to ensure that the bridge structure can freely expand and contract under various conditions. The specific design and specifications of the expansion joint device will vary depending on the project requirements and the conditions of the region; different types of expansion joint devices will use different materials to adapt to different application requirements, such as rubber expansion joints.

[0025] In an optional embodiment, please refer to Figure 2 , Figure 2 This is a schematic diagram of the expansion joint device provided in an embodiment of the present invention. Figure 2In this diagram, 11 represents a high-polymer elastic polymer material, 12 represents spring steel, and 2 represents an IoT smart sensing system. For example... Figure 2 As shown, the expansion joint device includes an elastic expansion component and an edge fixing component; wherein, the elastic expansion component is formed by casting spring steel from a high-molecular elastic polymer material, one end of the elastic expansion component is fixedly connected to one end of the bridge joint through the edge fixing component, and the other end of the elastic expansion component is fixedly connected to the other end of the bridge joint through the edge fixing component.

[0026] Among them, elastic polymer materials are a special type of material synthesized in chemical laboratories, whose main components are long-chain structures composed of polymer molecules. These polymer chains have high flexibility, similar to rubber, but with higher elasticity and durability. In this embodiment, the physical and chemical properties of the elastic polymer material can be adjusted according to specific synthesis methods and compositions to meet its application requirements in the field of road and bridge engineering.

[0027] Furthermore, the elastic telescopic component includes multiple spring steels arranged in parallel along the driving direction, with the gaps between any two adjacent spring steels staggered. The staggered ratio of the gaps between any two adjacent spring steels can be determined according to actual engineering requirements. By staggering the adjacent spring steels, the stress on a single spring steel under vehicle load is reduced, extending the service life of the spring steel.

[0028] The expansion joint device provided in this embodiment replaces the rubber material in the original expansion joint structure with a combination of high-polymer elastic polymer material and spring steel. This achieves a seamless and invisible bridge expansion joint without altering the original stress system of the bridge superstructure. Compared to traditional expansion joint devices that use rubber as the sealing material, this device better ensures the safety of the bridge structure and the comfort of drivers and passengers during long-term service.

[0029] The IoT smart sensing system described in this invention is a smart sensing system based on Internet of Things (IoT) technology. It typically includes various types of sensors, communication devices, and data processing units, which are used to collect, transmit, and process response data (strain, displacement, audio) and environmental data (temperature and humidity) about the expansion joint device.

[0030] Furthermore, the various components of the IoT smart sensing system will be deployed at different locations on the expansion joint device according to the actual data collection needs. Typically, in order not to affect the use of the expansion joint device, the various components of the IoT smart sensing system will be deployed at different locations on the side of the expansion joint device away from the bridge deck.

[0031] Based on the expansion joint device provided in the above embodiments, in an optional embodiment, to better obtain various response data of the expansion joint device during service, so as to evaluate and predict its real-time service performance and long-term service performance, please refer to the following for the sensor types and deployment locations in the IoT smart sensing system. Figure 3 , Figure 3 This is a schematic diagram illustrating the deployment of an IoT smart sensing system provided in an embodiment of the present invention. Figure 3 As shown, the IoT smart sensing system includes a strain sensor, a displacement sensor, an audio sensor, a temperature sensor, and a humidity sensor.

[0032] The strain sensor is used to collect strain data of the expansion joint device during service; the displacement sensor is used to collect displacement data of the expansion joint device during service; the audio sensor is used to collect audio data of the expansion joint device during service; the temperature sensor is used to collect ambient temperature data of the expansion joint device during service; and the humidity sensor is used to collect ambient humidity data of the expansion joint device during service.

[0033] In this embodiment, five sets of strain sensors are specifically included to collect strain data for the entire expansion joint device. Each set of strain sensors is positioned laterally at the center of the expansion joint device on the side facing away from the bridge deck, with a 10cm interval between adjacent sets. Furthermore, each set of strain sensors includes two strain sensors: one is a longitudinal strain sensor 211 positioned along the driving direction to measure the longitudinal strain of the expansion joint device. One is a lateral strain sensor 212 installed along the direction perpendicular to the travel direction to measure the lateral strain of the expansion joint device. The two strain sensors are arranged in a "T" shape to achieve real-time acquisition of transverse and longitudinal strain data at their respective locations.

[0034] In this embodiment, the displacement sensor is a pull-rope type displacement sensor 22. The two ends of the pull-rope type displacement sensor 22 are respectively anchored at the two ends of the expansion joint device. Specifically, any end of the pull-rope type displacement sensor 22 is arranged in the transverse center of the expansion joint device on the side away from the bridge deck.

[0035] In this embodiment, the IoT smart sensing system is equipped with two audio sensors. Each audio sensor is located in the transverse center of the expansion joint device on the side away from the bridge deck. Specifically, one audio sensor 231 is located along the driving direction, and the other audio sensor 232 is located perpendicular to the driving direction.

[0036] In this embodiment, the temperature sensor and humidity sensor in the IoT smart sensing system are integrated temperature and humidity sensors 24, which realize the simultaneous collection of environmental temperature and humidity data of the expansion joint device during its service.

[0037] In one or more alternative embodiments, the IoT smart sensing system further includes a locator 25 for locating the installation location of the expansion joint device. Further, the locator includes a GNSS BeiDou chip-based locator, which is a positioning device that uses satellite positioning technology to provide maintenance personnel with accurate location data, enabling them to know the exact geographical location of the expansion joint device. In case of problems or emergencies, accurate location information can help emergency service personnel reach the scene more quickly.

[0038] S02. Construct a long-term service performance prediction model for the expansion joint device, which includes a remaining service life prediction model and a typical disease prediction model for the expansion joint device.

[0039] The long-term service performance described in this invention refers to the lifespan of an expansion joint device under prolonged exposure to various environmental and operational conditions, maintaining its design performance and functional integrity, as well as the likelihood of typical defects. Lifespan prediction refers to predicting how long the expansion joint device can continue to operate normally without significant performance degradation or failure; lifespan prediction helps in planning maintenance and replacement schedules to ensure the reliability and safety of the device. Defect prediction refers to predicting potential typical defects, damage, or deterioration, such as corrosion, cracks, fatigue, and deformation; by predicting typical defects of the expansion joint device, preventative measures can be taken in advance, reducing the burden of maintenance and replacement, and thus extending the service life of the expansion joint device.

[0040] In an optional embodiment, the long-term service performance prediction model of the expansion joint device constructed in step S02 specifically includes a remaining service life prediction model for the expansion joint device, which satisfies the following formula: ,in, Indicates the expansion joint device Remaining life coefficient during the second expansion / contraction. This represents the temperature correction factor. This represents the humidity correction factor. This represents the lateral strain correction factor. This represents the longitudinal strain correction factor. Indicates the expansion joint device The actual amount of expansion and contraction during each expansion and contraction. This indicates the cumulative expansion and contraction amount at the fatigue limit of the expansion joint device.

[0041] Furthermore, the coefficients in the remaining life prediction model of the expansion joint device are set according to the following rules: when the temperature hour, The value is 1, otherwise, The value is 1.5; when the humidity... hour, The value is 1, otherwise, The value is 1.2; when the longitudinal strain hour, The value is 1, otherwise, The value is 1.5; when the transverse strain hour, The value is 1, otherwise, The value is 1.3.

[0042] In an optional embodiment, the long-term service performance prediction model of the expansion joint device constructed in step S02 further includes a typical defect prediction model. Specifically, constructing the typical defect prediction model includes the following steps:

[0043] S021. Obtain the short-time energy characteristics of various typical diseases, and build a classification model for typical diseases based on the short-time energy characteristics.

[0044] It is understandable that the short-term energy characteristics corresponding to different typical diseases are different. Therefore, step S021 uses the short-term energy corresponding to typical diseases as the distinguishing feature to build a classification model for multiple typical diseases. Furthermore, the typical features include loosening and cracking, water seepage and leakage, foreign object blockage, misalignment and vehicle jumping, etc.

[0045] The typical disease classification model is a machine model that establishes a relationship between the audio characteristics (short-time energy characteristics) of different typical diseases and their corresponding disease types, so as to identify and classify typical diseases existing in expansion joint devices. Furthermore, the typical disease classification model can employ various machine learning techniques, such as support vector machines, neural networks, or other classification algorithms.

[0046] S022. Based on the classification results of the typical disease classification model, construct a typical disease prediction model for the expansion joint device.

[0047] In this embodiment, the typical disease prediction model constructed based on the classification results of the above-mentioned typical disease classification model satisfies the following formula: ,in, Indicates the expansion joint device for the first Early warning assessment coefficients for typical diseases This indicates the number of times the expansion joint device was tested for typical defects. Indicates the first The test results identified it as the first The number of short-term energy characteristics of a typical disease. Representing the characterization of the first The number of short-term energy characteristics of a typical disease.

[0048] S03. Using the long-term service performance prediction model combined with the response data and the environmental data, predict the long-term service performance of the expansion joint device.

[0049] It is readily understood that step S03 can predict the long-term performance of the expansion joint device by using various long-term service performance evaluation models, combined with corresponding response data and environmental data. Furthermore, implementing step S03 allows for advance understanding of the long-term performance and potential problems of the expansion joint device, supporting maintenance and management decisions, thereby extending the service life of the expansion joint device, improving its reliability and performance, and ensuring that it maintains its design performance and functional integrity even under long-term exposure to various environmental and operational conditions.

[0050] In an optional embodiment, to predict the remaining lifespan of the expansion joint device, the real-time response data acquired in step S03 includes various performance response data (strain, expansion, etc.) of the expansion joint device under the influence of external factors (load factors, environmental factors, etc.) during its service life, as well as environmental data (temperature, humidity) of the expansion joint device. In this embodiment, the above-mentioned expansion joint device remaining lifespan prediction model, combined with the corresponding response data and environmental data, can obtain the remaining lifespan prediction data of the expansion joint device in the [missing information - likely a specific timeframe or timeframe]. Remaining life coefficient after secondary expansion Specifically, the remaining lifetime factor The larger the value, the higher the value of the expansion joint device in the first stage. The longer the remaining lifespan after each expansion and contraction.

[0051] In an optional embodiment, to predict the possibility of a certain typical defect in the expansion joint device, the response data obtained in step S03 includes audio data from long-term service, which can be obtained through an audio sensor. In this embodiment, based on the above-mentioned typical defect prediction model combined with the corresponding response data, the early warning assessment coefficient of the expansion joint device for different typical defects can be obtained. Specifically, when the expansion joint device is for the first Typical Disease Early Warning Assessment Coefficient The larger the value, the more likely the expansion joint device has a certain performance under long-term service conditions. The greater the likelihood of a typical disease.

[0052] In an optional embodiment, to better implement the above-described method for predicting the long-term service performance of expansion joint devices based on IoT smart sensing, a system for predicting the long-term service performance of expansion joint devices based on IoT smart sensing is also provided. Please refer to [link to system description]. Figure 4 , Figure 4 This is a schematic diagram of the long-term service performance prediction system for expansion joint devices based on IoT smart sensing, provided in an embodiment of the present invention.

[0053] like Figure 4 As shown, the long-term service performance prediction system for expansion joint devices based on IoT smart sensing includes an input device, a processor, a memory, and an output device. The input device, the processor, the memory, and the output device are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions to execute the above-mentioned method for predicting the long-term service performance of expansion joint devices based on IoT smart sensing.

[0054] The input device is used to receive various data and information from the expansion joint device. This data may include real-time response data (such as strain, temperature, humidity, sound, etc.), environmental data (such as meteorological information, traffic flow, etc.), and other relevant sensor data.

[0055] The processor is the central computing unit of the system, responsible for executing program instructions in the computer program and processing, analyzing, and evaluating input data. The processor uses algorithms and models to evaluate the long-term performance of the expansion joint device and generates corresponding outputs. Specifically, the processor can be a high-performance computer, an embedded system, or a cloud server, depending on the scale and requirements of the system.

[0056] The memory is used to store computer programs, input data, intermediate processing results, and generated output data. This includes program instructions, model parameters, historical performance data, and evaluation results. Specifically, the memory may include hard disk drives, solid-state drives, cloud storage, or database systems to meet data storage and access needs.

[0057] The output device is used to present the system's evaluation results, typically in a highly readable format such as text reports, graphics, charts, alarms, or notifications. This allows relevant maintenance personnel or operators to take timely and necessary actions. Specifically, the output device can be a computer screen, printer, alarm system, mobile application, or web interface.

[0058] The long-term service performance prediction system for expansion joint devices based on IoT smart sensing provided by this invention combines IoT smart sensing technology to perform long-term performance prediction and typical disease prediction, providing important information and decision support for maintenance and management personnel.

[0059] 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 the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for predicting the long-term service performance of an expansion joint device based on IoT smart sensing, characterized in that, Includes the following steps: An IoT smart sensing system is deployed on the expansion joint device, and the IoT smart sensing system is used to collect response data and environmental data of the expansion joint device during service. The response data includes strain data, displacement data and audio data, and the environmental data includes temperature data and humidity data. A long-term service performance prediction model for the expansion joint device is constructed, which includes a remaining service life prediction model and a typical disease prediction model for the expansion joint device. The remaining life prediction model for the expansion joint device satisfies the following formula: ,in, Indicates the expansion joint device Remaining life coefficient during the second expansion / contraction. This represents the temperature correction factor. This represents the humidity correction factor. This represents the lateral strain correction factor. This represents the longitudinal strain correction factor. Indicates the expansion joint device The actual amount of expansion and contraction during each expansion and contraction. This indicates the cumulative expansion and contraction amount at the fatigue limit of the expansion joint device. The typical disease prediction model satisfies the following formula: ,in, Indicates the expansion joint device for the first Early warning assessment coefficients for typical diseases This indicates the number of times the expansion joint device was tested for typical defects. Indicates the first The test results identified it as the first The number of short-term energy characteristics of a typical disease. Representing the characterization of the first The number of short-term energy characteristics of a typical disease; The long-term service performance prediction model is used in conjunction with the response data and the environmental data to predict the long-term service performance of the expansion joint device.

2. The method for predicting the long-term service performance of an expansion joint device based on IoT smart sensing according to claim 1, characterized in that, The expansion joint device includes an elastic expansion component and an edge fixing component; The elastic expansion component is formed by casting spring steel from a high-molecular elastic polymer material. One end of the elastic expansion component is fixedly connected to one end of the bridge joint through the edge fixing component, and the other end of the elastic expansion component is fixedly connected to the other end of the bridge joint through the edge fixing component.

3. The method for predicting the long-term service performance of an expansion joint device based on IoT smart sensing according to claim 2, characterized in that, The IoT smart sensing system is deployed along the driving direction on the side of the elastic telescopic component away from the driving surface.

4. The method for predicting the long-term service performance of an expansion joint device based on IoT smart sensing according to claim 3, characterized in that, The IoT smart sensing system includes strain sensors, displacement sensors, audio sensors, temperature sensors, and humidity sensors. The strain sensor is used to collect real-time strain data of the expansion joint device during service; the displacement sensor is used to collect real-time displacement data of the expansion joint device during service; the audio sensor is used to collect real-time sound wave data of the expansion joint device during service; the temperature sensor is used to collect ambient temperature data of the expansion joint device during service; and the humidity sensor is used to collect ambient humidity data of the expansion joint device during service.

5. The method for predicting the long-term service performance of an expansion joint device based on IoT smart sensing according to claim 1, characterized in that, The remaining lifespan of the expansion joint device is positively correlated with the magnitude of the remaining lifespan coefficient.

6. The method for predicting the long-term service performance of an expansion joint device based on IoT smart sensing according to claim 1, characterized in that: When temperature hour, The value is 1, otherwise, The value is 1.5; When humidity hour, The value is 1, otherwise, The value is 1.2; When transverse strain hour, The value is 1, otherwise, The value is 1.5; When longitudinal strain hour, The value is 1, otherwise, The value is 1.

3.

7. The method for predicting the long-term service performance of an expansion joint device based on IoT smart sensing according to claim 1, characterized in that, The expansion joint device suffered from the first The likelihood of a typical disease and the first The magnitudes of the early warning assessment coefficients for typical diseases are positively correlated.

8. The method for predicting the long-term service performance of an expansion joint device based on IoT smart sensing according to any one of claims 1-7, characterized in that, The method for predicting the long-term service performance of an expansion joint device based on IoT smart sensing is applicable to a system for predicting the long-term service performance of an expansion joint device based on IoT smart sensing. The system includes an input device, a processor, a memory, and an output device, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to call the program instructions to execute the method for predicting the long-term service performance of an expansion joint device based on IoT smart sensing as described in any one of claims 1 to 7.

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