Bridge monitoring sensor life verification method and system based on scale model

Abstract

The application relates to the technical field of highway infrastructure monitoring, in particular to a bridge monitoring sensor life verification method and system based on a scale model. The method comprises the following steps: preparing a reinforced concrete beam test piece as a scale model, and arranging a bridge monitoring sensor on the scale model; establishing a vehicle load spectrum based on actual traffic flow statistical data, and obtaining test load parameters of the scale model according to a linear cumulative damage equivalent principle; obtaining a temperature and humidity change curve according to environmental parameter statistical data, and applying the temperature and humidity change curve to the scale model; constructing an accelerated verification test of the scale model; carrying out the accelerated verification test on the scale model, and obtaining performance test results of the bridge monitoring sensor; and obtaining life verification results of the bridge monitoring sensor according to the performance test results. The application is helpful for evaluating long-term performance and service life of the sensor in a highway infrastructure monitoring system under a specific arrangement process.

Description

Technical Field

[0001] This invention relates to the field of highway infrastructure monitoring technology, and in particular to a method and system for verifying the lifespan of bridge monitoring sensors based on a scaled-down model. Background Technology

[0002] With the continuous expansion of my country's highway infrastructure and the increasing service life, structural health monitoring systems have become an important technical means to ensure their safe operation. By deploying various sensors in key structural components such as bridges, tunnels, and pavements, structural responses can be sensed in real time, providing data support for structural condition monitoring, assessment, and maintenance decisions. Therefore, the effectiveness and timeliness of structural health monitoring systems are highly dependent on the long-term stability and reliability of the sensors.

[0003] Highway infrastructure has a long service life, and the effects of loads and environmental changes are complex. Engineering practice shows that sensors often experience decreased accuracy or even premature failure under the coupled effects of complex environments and loads. This performance degradation severely impacts the continuity and accuracy of monitoring data. The reasons for this are twofold: firstly, construction techniques and conditions may prevent sensors from achieving ideal installation quality on the structure. Later, as loads and environmental factors influence the system, the coupling performance between the sensor and the structure deteriorates, making it difficult to effectively transmit the actual response of the structure to the sensor; secondly, the performance of the sensor's own sensing components deteriorates, leading to distortion of the structural response sensed by the sensor.

[0004] To address the aforementioned issues, it is essential to optimize sensor selection and deployment processes before deploying highway infrastructure monitoring systems to ensure their long-term stable operation. During sensor selection, deployment optimization, and sensor development, conducting long-term performance and lifespan testing after sensor coupling with the structure is crucial. However, existing sensor lifespan verification methods are largely limited to performance testing of the sensors themselves under ideal laboratory conditions. They fail to fully consider the synergistic effects of environmental factors such as temperature, humidity, and chloride corrosion in actual service environments, as well as vehicle loads, and do not systematically assess the impact of different on-site construction conditions and deployment processes on the long-term performance of sensors. This leads to significant discrepancies between laboratory verification results and actual on-site lifespan, making it difficult to accurately reflect the durability performance of sensors in highway infrastructure.

[0005] Therefore, there is an urgent need to develop a sensor life verification method that can simulate the actual service conditions of sensors on highway infrastructure, comprehensively consider the environmental impact-load coupling effect and the impact of deployment process, and provide technical support for the scientific selection of sensors, optimization of deployment process and improvement of the reliability of monitoring system.

[0006] In summary, the main problems in the field of sensor life verification include: First, there is a lack of standardized testing methods for sensor life verification in highway infrastructure. Existing testing methods are mostly based on stable laboratory conditions, failing to fully consider the impact of load-environment coupling under actual service conditions, making it difficult to accurately predict the service life of the sensor after coupling with the structure. Second, there is a lack of dedicated sensor verification systems for the structural characteristics of highway infrastructure, especially for the numerous reinforced concrete structures. Current sensor fatigue testing methods in some standards and specifications are mostly based on steel beams of equal strength. The material characteristics of the two are fundamentally different, and their deployment process and influencing factors are completely different from those of concrete structures. The verification results differ significantly from actual engineering applications, resulting in insufficient reliability. Third, traditional fatigue testing cannot establish the relationship between the number of fatigue accelerated loading cycles and the service life. Fatigue testing is the main means of structural life verification, but currently, there is a lack of effective methods to distinguish between the equivalent number of fatigue loading cycles and the actual service life, especially considering the equivalent number of load-environment coupling cycles and the actual service life, making it difficult to truly simulate the service life. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a method and system for verifying the lifespan of bridge monitoring sensors based on a scaled-down model.

[0008] To achieve the above objectives, in a first aspect, this invention provides a method for verifying the lifespan of a bridge monitoring sensor based on a scaled-down model. The method includes the following steps: preparing a reinforced concrete beam specimen as a scaled-down model; deploying the bridge monitoring sensor on the scaled-down model; establishing a vehicle load spectrum based on actual traffic flow statistics and obtaining the test load parameters of the scaled-down model according to the principle of linear cumulative damage equivalence; obtaining a temperature and humidity variation curve based on environmental parameter statistics and applying the temperature and humidity variation curve to the scaled-down model; constructing an accelerated verification test of the scaled-down model by combining the test load parameters and the temperature and humidity variation curve; conducting the accelerated verification test on the scaled-down model and obtaining the performance test results of the bridge monitoring sensor; and obtaining the lifespan verification results of the bridge monitoring sensor based on the performance test results. This invention realistically simulates the coupling conditions of the sensor in actual service, breaking the limitations of ideal laboratory testing conditions. The verification results closely match engineering realities, scientifically verifying the sensor's lifespan and providing a core basis for hardware selection.

[0009] Optionally, the preparation of the reinforced concrete beam specimen serves as a scaled-down model, and the bridge monitoring sensors are deployed on the scaled-down model. This includes: preparing the reinforced concrete beam specimen according to a preset scaled-down ratio; and based on the stress characteristics and deployment process of the bridge monitoring sensors in the actual monitored structure, deploying the bridge monitoring sensors inside and on the surface of the reinforced concrete beam specimen. This invention reproduces the actual working state of the sensors in the bridge structure, taking into account different deployment methods such as internal embedding and surface bonding. It can accurately reflect the impact of construction processes on sensor performance, improving the authenticity and comprehensiveness of lifespan verification.

[0010] Optionally, the reinforced concrete beam specimen includes specimens whose dimensions, reinforcement ratio, and material properties are similar to those of the actual monitored structure. This invention ensures that the sensor's stress response during testing closely matches actual service conditions, avoiding distortion of verification results due to model bias and improving the accuracy of lifespan assessment.

[0011] Optionally, the step of establishing a vehicle load spectrum based on actual traffic flow statistics and obtaining the test load parameters of the scaled model according to the linear cumulative damage equivalence principle includes: collecting actual traffic flow data of the target road section; statistically analyzing load distribution characteristics based on the actual traffic flow data to establish the vehicle load spectrum; and converting the vehicle load spectrum into the test load parameters through the linear cumulative damage equivalence principle. This invention, by converting test parameters through damage equivalence, ensures that the test load is consistent with the cumulative damage of the vehicle load actually borne by the bridge, breaking through the limitations of simulated load design without actual data support. This makes the load loading more closely resemble engineering practice and provides a load parameter basis for accelerating testing.

[0012] Optionally, converting the vehicle load spectrum into the test load parameters using the linear cumulative damage equivalence principle includes: processing the load time series data of the vehicle load spectrum using the rainflow counting method to obtain the actual traffic load spectrum and the actual structural stress spectrum; calculating the actual structural cumulative damage over a certain number of years based on the actual traffic load spectrum and the actual structural stress spectrum; setting a target number of loading cycles, and obtaining the equivalent stress amplitude at the target number of loading cycles that makes the cumulative damage of the specimen structure equal to the actual structural cumulative damage; and determining the test load parameters applied to the scaled model using the equivalent stress amplitude. This invention achieves accurate equivalent conversion of actual random vehicle loads to test rule loads, can restore the load damage over a limited number of tests within a finite number of tests, significantly shortens the test cycle, and ensures the accuracy of load equivalence.

[0013] Optionally, obtaining the temperature and humidity variation curve based on environmental parameter statistics and applying the temperature and humidity variation curve to the scaled-down model includes: acquiring temperature parameter variation statistics and humidity parameter variation statistics at the service location of the bridge monitoring sensor as the environmental parameter statistics; obtaining average temperature and average relative humidity based on the environmental parameter statistics over a certain period of time, and obtaining the temperature and humidity variation curve through linear fitting; and applying the temperature and humidity changes cyclically and synchronously to the scaled-down model based on the temperature and humidity variation curve. This invention simulates the periodic temperature and humidity variation characteristics of the sensor's service environment, taking into account the impact of regional environmental differences on the sensor, ensuring that the environmental loading highly matches the actual working conditions, and accurately reflecting the degradation effect of temperature and humidity on sensor performance.

[0014] Optionally, the accelerated verification test of constructing the scaled model by combining the test load parameters and the temperature and humidity change curve includes: within a certain loading time, according to the time synchronization principle, completing the environmental-load equivalent loading of the scaled model by combining the test load parameters and the temperature and humidity change curve to construct the accelerated verification test. This invention recreates the working condition of a sensor under the simultaneous influence of load and environment in actual service, overcoming the limitations of traditional tests that only load the sensor, and accurately reflecting the impact of coupling effects on sensor lifespan.

[0015] Optionally, the accelerated verification test on the scaled model and the acquisition of the performance test results of the bridge monitoring sensor include: conducting the accelerated verification test on the scaled model on an environment-load coupled fatigue testing machine, periodically testing the key performance parameters of the bridge monitoring sensor during the test; and analyzing the parameter change rate of the bridge monitoring sensor based on the key performance parameters to obtain the performance test results. This invention collects the performance change patterns of the sensor during accelerated testing, accurately monitors the degradation of key indicators such as full-scale error, and provides comprehensive and accurate measured data support for subsequent failure determination and life assessment.

[0016] Optionally, obtaining the life verification result of the bridge monitoring sensor based on the performance test results includes: setting a failure threshold for the bridge monitoring sensor based on the performance test results; when the failure threshold is exceeded for the first time, the equivalent number of loading cycles corresponding to this threshold is taken as the sensor's ultimate lifespan, and a correspondence between the number of test cycles and the actual service life is established to obtain the life verification result. This invention provides a unified quantitative standard for sensor lifespan assessment, avoids biases in subjective judgment, accurately predicts the actual service life of the sensor, and guides engineering applications.

[0017] Secondly, this invention provides a bridge monitoring sensor lifespan verification system based on a scaled-down model. The system executes the bridge monitoring sensor lifespan verification method based on a scaled-down model provided by this invention. The system includes an input device, an output device, a processor, and a memory, which are interconnected. The memory stores a computer program, which includes program instructions, and the processor is configured to call these instructions. This invention, through high-performance hardware collaboration, enables the practical implementation of the bridge monitoring sensor lifespan verification method, improves verification efficiency, and provides reliable hardware support for sensor selection and deployment process optimization. Attached Figure Description

[0018] Figure 1 This is a flowchart of a bridge monitoring sensor life verification method based on a scaled model according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the reinforced concrete beam specimen structure according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the fiber optic sensor deployment according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the concrete strain measuring point arrangement according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the test loading device according to an embodiment of the present invention; Figure 6 This is a framework diagram of a bridge monitoring sensor life verification system based on a scaled model, according to an embodiment of the present invention. Detailed Implementation

[0019] 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.

[0020] 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.

[0021] Please see Figure 1 One embodiment of the present invention provides a method for verifying the lifespan of bridge monitoring sensors based on a scaled-down model, the method comprising the following steps: S1. Prepare a reinforced concrete beam specimen as a scaled-down model, and deploy bridge monitoring sensors on the scaled-down model.

[0022] Currently, over 80% of bridges in my country's highway infrastructure are reinforced concrete structures, making concrete structures the primary target for bridge monitoring sensors. Based on this, common reinforced concrete beams were used as test specimens, and scaled-down models of reinforced concrete beam specimens were prepared.

[0023] In this embodiment, the size, reinforcement ratio, and material properties of the reinforced concrete beam specimen are similar to those of the actual monitored structure in which the bridge monitoring sensor is in service. The stress characteristics and deployment process of the sensor in the scaled-down model are consistent with those of the actual monitored structure.

[0024] Specifically, taking a 30m simply supported beam as an example, a 5m beam specimen was made using C50 concrete at a scale of 1 / 6. Based on the actual reinforcement amount, the reinforcement ratio of the specimen section was calculated to be 2.0%.

[0025] Please see Figure 2 The figure shows a schematic diagram of the reinforced concrete beam specimen; the cross-sectional dimensions are 300mm×200mm, the total length of the beam is 5000mm, the top surface is equipped with 2 longitudinal steel bars with a diameter of 12mm and the bottom surface is equipped with 3 longitudinal steel bars with a diameter of 12mm, the concrete cover thickness is 60mm, the stirrup spacing is 180mm, the spacing of the reinforced zone is 100mm, and the length of the reinforced zone is 1m.

[0026] Please see Figure 3 The diagram illustrates the fiber optic sensor deployment. In this embodiment, two main deployment methods are considered for on-site fiber optic sensor installation: slotted embedding and surface bonding. The two sides of the test beam are arranged in slotted embedding and surface bonding methods, respectively. Figure 3The positions of the optical fiber, adhesive layer, and beam are shown in two different deployment methods.

[0027] Specifically, two distributed strain sensors were installed on the concrete surface of both sides of the web of the reinforced concrete beam specimen using a slotted embedding process (using carbon fiber leveling adhesive and structural epoxy grouting adhesive respectively) and a surface bonding process (using acrylic oil-based adhesive, K1302 epoxy adhesive and 3M double-sided adhesive respectively). The sensor grating spacing was 1m, and they were symmetrically arranged along the mid-span of the test beam.

[0028] It should be noted that the groove depth and width are both 12mm in the slotted embedded arrangement method. The spacing between slotted embedded optical cables is 10cm, and the spacing between surface-mounted optical cables is 6cm.

[0029] In this embodiment, please refer to Figure 4 The diagram shows a schematic of the concrete strain gauge arrangement. Corresponding to the mid-span grating measurement point of the fiber optic sensor, concrete strain gauges (strain gauges) are placed at the mid-span position on the surface of the reinforced concrete beam specimen. Within the test section, the strain gauges are numbered A, B, and C sequentially from top to bottom. The strain gauges are Yiyang Guangce BFH120-10AA type, and their placement is on the same section as the fiber optic sensor grating measurement point. Meanwhile, Figure 4 The document shows the locations of embedded optical fiber placement (epoxy potting compound and carbon fiber leveling compound), surface-mount optical fiber placement (acrylic oil-based adhesive, 3M double-sided tape and Kraft K1207 solvent adhesive), and specimen size information.

[0030] S2. Establish a vehicle load spectrum based on actual traffic flow statistics, and obtain the test load parameters of the scaled model according to the linear cumulative damage equivalence principle.

[0031] Bridges and other highway infrastructure primarily bear vehicle loads throughout their service life. Since vehicle loads are random, directly applying the actual vehicle load effects to the test components would result in a test duration as long as the actual service life of the monitored structure. Furthermore, existing loading devices are insufficient to achieve the desired effect. Due to limitations in current testing equipment, technology, and testing cycles, unlimited loading is impossible, typically limited to a few million cycles. Moreover, the load amplitude used in tests or numerical simulations cannot vary too much; constant amplitude stress is the simplest approach. Therefore, to accelerate testing, it is necessary to treat the vehicle load effects using equivalent methods.

[0032] In this embodiment, a vehicle load spectrum is established based on actual traffic flow statistics, and then converted into test load parameters using the linear cumulative damage equivalence principle. The specific steps are as follows: First, collect actual traffic flow data for the target road section and statistically analyze load distribution characteristics, including but not limited to the number of vehicles, axle load, and number of axles, and then establish a vehicle load spectrum. Secondly, the rainflow counting method is used to process the load time series data to obtain the actual traffic load spectrum and the actual structural stress spectrum. Then, based on the Palmgren-Miner linear damage hypothesis (Miner's rule), the actual cumulative structural damage caused by vehicle loads to the monitored structure over a certain period of time (taken as the design life of the bridge, 100 years) is calculated. Next, a target number of test loadings is set, such as 2 million times. Under the target number of loadings, the cumulative damage of the specimen structure is made equal to the cumulative damage of the actual structure under actual vehicle loads over 100 years. Based on this, the equivalent stress amplitude applied to the scaled model is calculated. Finally, the equivalent application of vehicle load is carried out: according to the principle that the stress amplitude of the scaled model is equivalent to that of the actual structure, the magnitude and variation amplitude of the load applied to the scaled model are determined based on the similarity between the scaled model and the actual monitored structure, and are used as test load parameters.

[0033] In this embodiment, the equivalent load amplitude of repeated action of vehicle live load on the main beam over 100 years is first calculated based on Miner's linear cumulative damage theory.

[0034] First, the relationship between the stress (or bending moment) amplitude of the steel reinforcement and the fatigue life follows the SN curve relationship:

[0035] The above formula can be rewritten as follows:

[0036] in, To maintain a constant bending moment amplitude The number of cycles (i.e., fatigue life) at which a component reaches fatigue failure under load. These are fatigue performance constants related to the material (the values ​​of which are determined by specifications or material testing). The cotangent of the inclination angle of the standard SN curve of the reinforcing steel is taken as 3-3.5. This represents the amplitude of the mid-span bending moment.

[0037] Secondly, according to Miner's linear cumulative damage theory, the steel reinforcement under constant amplitude bending moment can be obtained. Repetitive action Cumulative damage per instance It satisfies the following relationship:

[0038] in, for Repetitive action The cumulative damage level of each time, The number of times the action is repeated with equal amplitude. For fatigue life, These are fatigue performance constants related to the material. This represents the mid-span bending moment amplitude. The cotangent of the inclination angle of the standard SN curve for reinforcing bars.

[0039] Subsequently, the total fatigue damage of vehicles of each axle type after repeated application based on measured traffic flow satisfies the following relationship:

[0040] in, The total fatigue damage caused by actual traffic load. for Repetitive action The cumulative damage level of each time, For the first The actual number of vehicles passing through the same axle type (or horizontal bending moment). For fatigue life, These are fatigue performance constants related to the material. For the first The amplitude of the mid-span bending moment for vehicle-type axle configurations. The cotangent of the inclination angle of the standard SN curve for reinforcing bars.

[0041] Next, set the total number of experimental cycles. Next, the corresponding equivalent bending moment amplitude The damage degree after 2 million fatigue load tests can be expressed as follows:

[0042] in, The cumulative damage level set for constant amplitude fatigue testing. This represents the total number of loading cycles in the constant amplitude fatigue test (2 million cycles). In bending moment The number of times the lower component reaches fatigue failure. These are fatigue performance constants related to the material. This is the equivalent load amplitude. The cotangent of the inclination angle of the standard SN curve for reinforcing bars.

[0043] Then, let the damage degree of the fatigue test If the cumulative damage of the specimen structure is equal to the cumulative damage of the actual vehicle (cumulative structural damage), then... To test the equivalent load amplitude after 2 million repeated applications, the following relationship must be satisfied:

[0044] in, The equivalent load amplitude for the test is the value of the uniform amplitude repeated loading. For the first The actual number of vehicles passing through the same axle type (or horizontal bending moment). For the first The amplitude of the mid-span bending moment for vehicle-type axle configurations. The cotangent of the inclination angle of the standard SN curve for reinforcing bars.

[0045] It should be noted that, after the above equivalence, the fatigue test with constant amplitude for 2 million cycles can be equivalent to the actual damage to the bridge over 100 years.

[0046] Finally, based on the actual traffic flow data and traffic volume prediction model of a certain highway from 2015 to 2018, the predicted vehicle load spectrum of the highway over 100 years was obtained. The calculation model selected a 15m simply supported hollow slab beam to calculate the damage caused to the structure by vehicle loads over 100 years. Based on the aforementioned equivalent process, the equivalent load amplitude of 2 million load cycles equivalent to vehicle loads over 100 years can be obtained. Based on the mechanical properties of simply supported beam bridges, the lower limit of repeated fatigue load. The bending moment can be taken as the dead load moment, and the upper limit of the repeated load satisfies the following relationship:

[0047] in, The upper limit for repeated loads, This is the lower limit of repetitive fatigue load. This represents the equivalent load amplitude.

[0048] In this embodiment, the equivalent stress amplitude is calculated based on the equivalent load amplitude. The concept of equivalent stress amplitude refers to an equivalent stress amplitude calculated according to Miner's linear cumulative damage theory. Constant amplitude cyclic loading is performed under this stress amplitude. Secondly, the damage it causes is consistent with the total damage caused by 100 years of cyclic loading of variable-amplitude stress on actual vehicles, and the equivalent calculation formula satisfies the following relationship:

[0049] in, It is a constant amplitude cyclic stress amplitude. The number of loops. For the actual traffic load, the first Level stress amplitude The corresponding number of loops, The first generated by the actual vehicle load Level stress amplitude, The number of cycles in the target constant amplitude fatigue test. This represents the slope of the fatigue curve for a material or component.

[0050] In practical applications, fatigue tests are performed with 2 million cycles of constant amplitude stress. Therefore, when determining the equivalent stress amplitude, It can be retrieved 2 million times.

[0051] In this embodiment, based on the above conditions, the stress variation amplitude of the steel reinforcement corresponding to 2 million equivalent loading cycles of the bridge is 40MPa-110MPa. According to the principle of stress equivalence between the model specimen and the actual structure, the load amplitude applied to the specimen can be determined based on the specimen's cross-section and reinforcement.

[0052] S3. Obtain the temperature and humidity change curve based on the environmental parameter statistics, and apply the temperature and humidity change curve to the scaled model.

[0053] In this embodiment, the environmental impact mainly considers the effects of temperature and humidity changes, which have a significant impact on the sensor's service performance. The environmental impact exhibits a periodic variation trend based on regional differences, and the variation pattern remains largely consistent year by year. This can be obtained from one or more years of environmental parameter statistics (including temperature and humidity parameter variation statistics) at the sensor's service location.

[0054] Furthermore, based on environmental parameter statistics, temperature and humidity variation curves (temperature variation curves and humidity variation curves) of the environment at the sensor's service location over a year or more are obtained.

[0055] In this embodiment, the methods for applying environmental influences include methods for simulating and accelerating temperature factors and methods for simulating and accelerating humidity factors.

[0056] 1) Simulation and acceleration methods for temperature factors: In this embodiment, the temperature at a specific time point every day over the past 10 years at the bridge's location is statistically analyzed to obtain the average temperature at that time point for each day over the 10 years. Finally, the average temperature is used as a control parameter for the annual temperature variation, and the annual temperature variation is obtained through linear fitting. The fitted annual temperature variation is then used as a cycle to simulate the bridge's annual temperature variation.

[0057] Based on the aforementioned method for determining the equivalent load amplitude, 2 million equivalent load cycles correspond to a service life of 100 years, with 20,000 load cycles per year. Using a 1Hz loading frequency on the testing machine, one year of equivalent loading is completed in 6 hours. The test cycle is chosen to be 6 hours. Following the annual average temperature change curve, a heating and cooling cycle is performed once within 6 hours, applied synchronously to the specimen with the load. This means that the 6-hour test simulates one year of temperature change in the bridge structure, with an acceleration factor of 1460 times. The number of temperature-load coupling cycles determines the number of years simulated. For example, 10 cycles simulate 10 years of actual temperature change in the bridge during operation.

[0058] 2) Simulation and acceleration methods for humidity factors: Similar to the application of temperature effects, the average relative humidity at a certain point in time within a year is used as the control parameter for the annual relative humidity variation. The annual relative humidity variation is obtained through linear fitting. The annual relative humidity variation obtained from the linear fitting is used as a cycle, and one cycle can simulate the annual humidity variation of the bridge.

[0059] Following this approach, the number of cycles and loading frequency of the equivalent load can be arbitrarily determined based on the equivalent method of fatigue load, and an appropriate test acceleration factor can be selected. Under the condition that allows, the lowest possible test acceleration factor should be selected.

[0060] S4. Accelerated verification test of the scaled model is constructed by combining the test load parameters and the temperature and humidity change curves.

[0061] Engineering practice shows that when sensors operate on highway infrastructure structures, they are mainly affected by the coupling effect of vehicle loads and environmental changes. Therefore, load and environmental factors are the main factors causing sensor performance degradation. To simulate actual service conditions, accelerated verification tests need to apply the loads and environmental influences actually experienced by the monitored structure to the tested component.

[0062] In this embodiment, the equivalent loading time of the vehicle load within one year is determined based on the loading frequency of the fatigue testing machine and the number of times the equivalent vehicle load is applied within one year. Then, the statistical change curves of temperature and humidity within one year are applied to the scaled-down model according to the principle of synchronizing with the load loading time. An equivalent environmental-load loading for one year is completed to construct an accelerated verification test, which can be considered equivalent to the sensor serving for one year.

[0063] S5. Conduct the accelerated verification test on the scaled-down model and obtain the performance test results of the bridge monitoring sensor.

[0064] Accelerated verification tests were conducted on an environmental-load coupled fatigue testing machine, and key performance parameters of the sensors were tested periodically during the loading process.

[0065] In this embodiment, the lifespan of a typical sensor is measured in years. Therefore, after an equivalent year of loading (i.e., every 20,000 cycles), loading is paused to perform sensor performance testing and analyze the changes in the sensor's full-scale error to obtain performance test results.

[0066] Specifically, a scaled-down model was installed on an environmental-load coupled fatigue testing machine. The calculated span of the scaled-down model was 4.8m. During loading, a 1m long distribution beam was placed at the mid-span to ensure that the mid-span was in a pure bending state, and the jacks were applied at the middle position of the distribution beam. Based on the principle of equivalent stress amplitude, the fatigue load amplitude applied in the test was determined to be 4.4kN-17.0kN, corresponding to a stress amplitude of 40MPa-110MPa in the steel reinforcement at the bottom and mid-span of the beam, with a loading frequency of 1Hz.

[0067] Please see Figure 5 The diagram shows a schematic of the test loading device, illustrating the scaled-down model, loading position, distribution beam, and relative positions of the rubber bearings.

[0068] In this embodiment, the beam is preloaded before the accelerated verification test to verify the performance of the testing system. The preload amplitude is the upper limit of the fatigue load. After the fatigue test begins, temperature and humidity environmental effects are applied to the scaled-down model. At the same time, a reciprocating load is applied to the top of the beam via actuators to simulate the effect of live load during the bridge's service life.

[0069] Specifically, during the experiment, every 20,000 load cycles, equivalent to one year of sensor service, were used to conduct three static load tests on the scaled model. The scaled model was subjected to a static load test with the upper limit of the load amplitude. The static load loading process was divided into five levels. After each level of loading stabilized for 5 minutes, the strain change of the fiber optic strain sensor at each measuring point was tested. By comparing the data with the strain gauges placed at the same location, the working performance of the fiber optic strain sensor under the condition of working together with the concrete structure was verified.

[0070] For basic information on the scaled-down model (reinforced concrete beam specimen), please refer to Table 1: Table 1

[0071] S6. Based on the performance test results, obtain the life verification results of the bridge monitoring sensor.

[0072] In this embodiment, based on the performance test results, the change in full-scale error is used as the metric. As the failure threshold of the sensor, the equivalent number of loading cycles corresponding to the first time the full-scale error change exceeds the threshold is taken as the sensor's limit life. A correspondence between the number of test cycles and the actual service life is established to obtain the sensor life verification results.

[0073] Specifically, my country's industrial instruments have 16 accuracy classes, with the 15th class being the full-scale error variation... As the stress fatigue limit threshold for optical fibers, the performance of fiber Bragg grating strain sensors deteriorates sharply when the full-scale error exceeds 4%. Therefore, this embodiment uses the change in full-scale error as a key indicator. As an engineering threshold for judging when sensor performance enters a stage of significant degradation, i.e. A value below 4% indicates the sensor is functioning normally, while a value above 4% indicates the sensor has failed. The equivalent number of load cycles corresponding to this value can be considered the sensor's maximum lifespan. For full scale, This represents the maximum strain error corresponding to full scale.

[0074] Furthermore, fatigue tests were conducted based on the equivalent stress amplitude determined in S5. 2 million fatigue cycle loads are equivalent to a structural service life of 100 years. The sensor was tested every 20,000 loads per year, for a total of 1 million cycles, representing an equivalent service life of 50 years. The full-scale error of the sensor under different deployment conditions was calculated based on the test results, as shown in Table 2. Table 2

[0075] As shown in Table 2, the full-scale error of the sensors using the two embedded deployment processes remained below 4% throughout the entire 1 million loading cycles, thus not reaching their service life. Among the sensors using the three surface-mount deployment processes, the sensor using acrylic oil-based adhesive had a full-scale error below 4%, also not reaching its service life; the sensor using 3M double-sided adhesive first exceeded 4% in full-scale error at 580,000 loading cycles, with a service life of 29 years; and the sensor using Kraft K1207 solvent-based adhesive first exceeded 4% in full-scale error at 540,000 loading cycles, with a service life of 27 years.

[0076] Please see Figure 6 In an optional embodiment, the present invention provides a bridge monitoring sensor life verification system based on a scaled model. The system includes an input device, an output device, a processor, and a memory, all interconnected. The memory stores a computer program comprising program instructions, and the processor is configured to invoke the program instructions to execute specific steps as described in the relevant embodiments of the bridge monitoring sensor life verification method based on a scaled model provided by the present invention. The bridge monitoring sensor life verification system based on a scaled model provided by the present invention has a complete structure, is objective and stable, and improves the overall applicability and practical application capability of the present invention.

[0077] In an optional embodiment, a bridge monitoring sensor lifetime verification system based on a scaled-down model includes: 1) Specimen and sensor deployment subsystem, including: Standard reinforced concrete beam specimens: dimensions, materials, reinforcement, etc. are determined based on the principle of equivalence with the verification object; Sensor deployment process: In order to reflect the service status of the sensor on the actual monitored structure, and to ensure that the stress state and deployment process of the sensor deployment location are consistent with the actual monitored structure.

[0078] 2) Loading subsystem, including: Electro-hydraulic servo fatigue testing machine: maximum loading capacity 1000kN, frequency range 1Hz-10Hz.

[0079] 3) Environment loading subsystem, including: Environmental simulation chamber: Temperature range -40℃ to 80℃, humidity range 30%RH to 95%RH.

[0080] 4) Data acquisition and processing subsystem, including: Multi-channel data acquisition instrument: sampling frequency not less than 100Hz; Reference sensor array: Reference sensors are placed on the specimen after each segment of fatigue loading is completed. During the static load test, the reference sensor readings are used as the true strain values ​​at the measured location to compare and verify the performance of the measured sensors. Data processing software: It has functions such as rainflow counting and damage calculation.

[0081] In summary, the present invention provides a bridge monitoring sensor life verification method and system based on a scaled model. By simulating accelerated tests under load and environmental influences under actual sensor service conditions, it scientifically evaluates the lifespan of sensors deployed on concrete structures. It can also be used for experimental research on sensor performance degradation patterns. The method of the present invention is easy to understand, simple to calculate, requires less workload, and is convenient for engineering applications, providing a theoretical basis and technical support for the further development of highway infrastructure monitoring technology.

[0082] 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 verifying the lifespan of bridge monitoring sensors based on a scaled-down model, characterized in that, Includes the following steps: A reinforced concrete beam specimen was prepared as a scaled-down model, and bridge monitoring sensors were deployed on the scaled-down model. A vehicle load spectrum is established based on actual traffic flow statistics, and the test load parameters of the scaled model are obtained according to the linear cumulative damage equivalence principle. The temperature and humidity change curves are obtained based on environmental parameter statistics, and then applied to the scaled-down model. Accelerated verification tests were conducted on the scaled model by combining the test load parameters and the temperature and humidity change curves. The accelerated verification test was carried out on the scaled-down model, and the performance test results of the bridge monitoring sensor were obtained. The lifespan verification results of the bridge monitoring sensor were obtained based on the performance test results. The process of establishing a vehicle load spectrum based on actual traffic flow statistics and obtaining the test load parameters of the scaled model according to the linear cumulative damage equivalence principle includes: Collect actual traffic flow data of the target road section, and statistically analyze the load distribution characteristics based on the actual traffic flow data to establish the vehicle load spectrum; The vehicle load spectrum is converted into the test load parameters using the linear cumulative damage equivalence principle. The process of converting the vehicle load spectrum into the test load parameters using the linear cumulative damage equivalence principle includes: The load time series data of the vehicle load spectrum are processed by the rainflow counting method to obtain the actual traffic load spectrum and the actual structural stress spectrum. The actual cumulative structural damage over a certain number of years is calculated based on the actual traffic load spectrum and the actual structural stress spectrum. Set a target number of loading cycles, and at the target number of loading cycles, obtain the equivalent stress amplitude that makes the cumulative damage of the specimen structure equal to the cumulative damage of the actual structure. The test load parameters applied to the scaled-down model are determined by the equivalent stress amplitude. The accelerated verification test on the scaled-down model and the acquisition of the performance test results of the bridge monitoring sensor include: The accelerated verification test was carried out on the scaled model on an environmental-load coupled fatigue testing machine, and the key performance parameters of the bridge monitoring sensor were tested periodically during the test. The performance test results are obtained by analyzing the parameter change rate of the bridge monitoring sensor based on the key performance parameters.

2. The method for verifying the lifespan of bridge monitoring sensors based on a scaled model according to claim 1, characterized in that, The preparation of the reinforced concrete beam specimen serves as a scaled-down model, and bridge monitoring sensors are deployed on the scaled-down model, including: The reinforced concrete beam specimens were prepared according to a preset scale ratio; Based on the stress characteristics and deployment process of the bridge monitoring sensor in the actual monitored structure, the bridge monitoring sensor is deployed inside and on the surface of the reinforced concrete beam specimen.

3. The method for verifying the lifespan of bridge monitoring sensors based on a scaled model according to claim 2, characterized in that, The reinforced concrete beam specimen includes: The dimensions, reinforcement ratio, and material properties of the reinforced concrete beam specimens are similar to those of the actual monitored structure.

4. The method for verifying the lifespan of bridge monitoring sensors based on a scaled model according to claim 1, characterized in that, The step of obtaining the temperature and humidity change curve based on environmental parameter statistics and applying the temperature and humidity change curve to the scaled model includes: At the service location of the bridge monitoring sensor, statistical data on temperature parameter changes and humidity parameter changes are acquired as environmental parameter statistical data. Within a certain number of years, the average temperature and average relative humidity are obtained based on the statistical data of the environmental parameters, and the temperature and humidity change curves are obtained by linear fitting. Based on the temperature and humidity change curves, temperature and humidity changes are cyclically and synchronously applied to the scaled-down model.

5. The method for verifying the lifespan of bridge monitoring sensors based on a scaled model according to claim 1, characterized in that, The accelerated verification test for constructing the scaled model by combining the test load parameters and the temperature and humidity change curves includes: Within a certain loading time, based on the time synchronization principle, and in conjunction with the test load parameters and the temperature and humidity change curve, the environmental-load equivalent loading of the scaled model is completed to construct the accelerated verification test.

6. The method for verifying the lifespan of bridge monitoring sensors based on a scaled model according to claim 1, characterized in that, The process of obtaining the lifespan verification results of the bridge monitoring sensor based on the performance test results includes: Based on the performance test results, a failure threshold for the bridge monitoring sensor is set. When the failure threshold is exceeded for the first time, the corresponding equivalent number of loading cycles is taken as the sensor's limit lifespan, and a correspondence between the number of test cycles and the actual service life is established to obtain the lifespan verification result.

7. A bridge monitoring sensor life verification system based on a scaled model, characterized in that, The system includes an input device, an output device, a processor, and a memory, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to invoke the program instructions to execute the bridge monitoring sensor life verification method based on a scaled model as described in any one of claims 1-6.

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