Bridge bearing life evaluation method and device, system, storage medium
By establishing a digital life assessment framework for bridge bearings through real-time monitoring and intelligent algorithms, the problem of difficult observation and uncertain assessment of wear damage is solved, enabling real-time and accurate assessment and predictive maintenance of bridge bearing life.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANG HAI CHU CHUANG TU MU KE JI YOU XIAN GONG SI
- Filing Date
- 2026-03-10
- Publication Date
- 2026-06-05
Smart Images
Figure CN122153331A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of bridge engineering and structural health monitoring technology, specifically relating to a method, device, system, and storage medium for assessing the lifespan of bridge bearings. Background Technology
[0002] Bridge bearings endure complex environmental and load conditions during long-term service, including vehicle loads, temperature deformation, shrinkage and creep, and seismic disturbances. Their slippage exhibits both random and cumulative characteristics. When bearings utilize friction supports or movable support devices, wear occurs in the friction pairs during cumulative slippage, leading to changes in the friction coefficient, decreased free-slip performance, and accumulated internal structural damage, ultimately affecting the overall stress and durability of the bridge. Currently, bearing life assessment relies primarily on periodic inspections and empirical judgment, lacking real-time monitoring and quantitative assessment methods. The key factor affecting bearing life is the wear of the slip surface. Bearing wear mainly originates from long-term temperature deformation, repeated shear slippage caused by live loads, and seismic displacement; the wear rate is closely related to the cumulative sliding displacement. However, the actual displacement of the bearing is influenced by multiple factors such as temperature, load, and structural constraints, making accurate prediction from theoretical models difficult. Furthermore, the wear area is often concealed and difficult to observe directly, resulting in significant uncertainty in life assessment. Therefore, constructing a comprehensive life assessment system combining monitoring data, intelligent algorithms, and wear damage models has significant engineering application value and innovative significance. Summary of the Invention
[0003] To address the problems existing in the prior art, this invention provides a method, device, system, and storage medium for assessing the life of bridge bearings. It overcomes the problems in the prior art, such as the inability to directly observe bearing wear damage, the difficulty in accurately obtaining slip displacement from theoretical analysis, the reliance of traditional life assessment methods on periodic manual inspections, the lack of correlation between displacement data and wear status, and the absence of a digital closed-loop assessment framework for the entire process of "displacement-wear-performance-life".
[0004] To achieve the above objectives, the present invention provides the following solution: A method for assessing the life of bridge bearings includes: Real-time acquisition of monitoring data for bridge bearings; Calculate the preliminary wear and damage amount based on the monitoring data; The initial wear damage amount is corrected for probabilistic uncertainty to obtain the statistically corrected wear damage amount; The damage correction amount is obtained through data-driven correction based on monitoring data; The real-time damage is obtained based on the statistically corrected wear damage amount, the damage correction amount, and the actual observed real-time wear amount. Remaining lifetime is estimated based on real-time damage and degradation process models.
[0005] Preferably, the monitoring data for bridge bearings includes: bearing displacement, temperature, and load data.
[0006] As a preferred method, real-time damage is obtained through intelligent iterative updates based on the statistically corrected wear damage amount, damage correction amount, and actual observed real-time wear amount.
[0007] Preferably, the damage correction amount includes: Establish a data-driven fitting damage model: in, f(·) The wear function is fitted by a neural network. T(t) Temperature is a factor that affects the environment. μ(t) The coefficient of friction varies with time. θ(t) These are the weight parameters of the neural network; Support monitoring data The model is driven by multiple input data, including temperature T(t) and friction coefficient μ(t), to calculate the damage correction amount. .
[0008] Preferably, the real-time damage includes: Calculate the corrected wear damage: The actual observed real-time wear amount ; Input the intelligent inversion module, and iteratively update the model parameters θ using either Bayesian update or Extended Kalman Filter (EKF): Wherein, K(t) is the gain matrix, which is dynamically adjusted according to the prediction error.
[0009] As a preferred method, the remaining lifetime is calculated based on a real-time damage and utilization degradation process model as follows: Specified support wear failure threshold Based on the identified real-time damage D(t) and the remaining lifetime RUL(t) estimated using a degradation process model: Where τ is a candidate value for the remaining lifetime.
[0010] The present invention also provides a bridge bearing life assessment device, comprising: The first processing module is used to collect monitoring data of bridge bearings in real time; the monitoring data of bridge bearings includes: bearing displacement, temperature and load data. The second processing module is used to calculate the initial wear damage based on the monitoring data; The third processing module is used to correct the initial wear damage amount through probability uncertainty to obtain the statistically corrected wear damage amount; The fourth processing module is used to obtain the damage correction amount by data-driven correction based on the monitoring data; The fifth processing module is used to obtain the real-time damage based on the statistically corrected wear damage amount, the damage correction amount, and the actual observed real-time wear amount; The sixth processing module is used to estimate the remaining lifetime based on real-time damage and degradation process models.
[0011] As a preferred option, the fifth processing module obtains the real-time damage through intelligent iterative updates based on the statistically corrected wear damage amount, the damage correction amount, and the actual observed real-time wear amount.
[0012] The present invention also provides a bridge bearing life assessment system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a bridge bearing life assessment method when executed by the processor.
[0013] The present invention also provides a storage medium storing a computer program, which executes a bridge bearing life assessment method when running.
[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: Using bearing displacement monitoring as the driving force for wear damage inversion; establishing a mapping relationship of "displacement-wear-performance degradation-life"; introducing intelligent algorithms to achieve adaptive correction of physical model parameters; supporting an "integrated damage assessment framework" for performance evaluation of different types of bearings; providing remaining life (RUL) estimation and failure risk warning; and promoting the transformation of bridge operation and maintenance from passive inspection to predictive maintenance. Attached Figure Description
[0015] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0016] Figure 1 This is a flowchart of the bridge bearing life assessment method according to an embodiment of the present invention. Detailed Implementation
[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0019] Example 1 like Figure 1 As shown, the present invention provides a method for assessing the life of bridge bearings, comprising: S1, Real-time monitoring of support displacement S1.1 Collect cumulative relative displacement data of the support under operating loads, temperature, and random events using displacement sensors (such as eddy current sensors, laser displacement gauges, fiber optic sensors, or MEMS). .
[0020] The data acquisition frequency of S1.2 can be set according to the bridge operation requirements (e.g., 10Hz~100Hz).
[0021] S1.3 Uploads the raw displacement data to the data storage module for subsequent processing.
[0022] S2. Preliminary Calculation of Wear Damage S2.1 Cumulative Relative Displacement Data Input the support load F(t) into the physical model to calculate the preliminary wear damage. : Where K is the wear coefficient, F(t) is the force applied to the support (load), and H is a parameter related to the hardness of the friction pair material.
[0023] S2.2 Based on the calculation As a priori that can explain the amount of wear damage.
[0024] S3, Probability Uncertainty Correction S3.1 Establish a probabilistic degradation model, and the preferred degradation model is the Gamma process degradation model.
[0025] S3.2 will Input the probabilistic degradation model to obtain the statistically corrected wear damage amount.
[0026] in, When fully trusting monitoring data, adopt the following approach: When fully trusting model data, take Other adjustments will be made based on the actual situation.
[0027] S3.3 Based on the probabilistic degradation model, the damage confidence interval is output to provide an uncertainty estimate for lifetime prediction.
[0028] S4, Data-Driven Correction S4.1 Establishing a data-driven fitting damage model: in, f(·) The wear function is fitted by a neural network. T(t) Temperature is a factor that affects the environment. μ(t) The coefficient of friction varies with time. θ(t) These are the weight parameters of the neural network, which are dynamically updated by intelligent inversion.
[0029] S4.2 will monitor the bearing data. The model is driven by multiple input data, including temperature T(t) and friction coefficient μ(t), to calculate the damage correction amount. .
[0030] S5, Intelligent Iterative Updates and Real-time Damage Recognition S5.1 Calculate the corrected wear damage: S5.2 Real-time wear data obtained from actual observations ; The S5.3 input intelligent inversion module combines Bayesian update or extended Kalman filter (EKF) to iteratively update the model parameters θ: Wherein, K(t) is the gain matrix, which is dynamically adjusted according to the prediction error.
[0031] S6. Life Prediction and Health Status Assessment S6.1 specifies the support wear failure threshold. Based on the identified real-time damage D(t) and the remaining lifetime RUL(t) estimated using a degradation process model: Where τ is a candidate value for the remaining lifetime.
[0032] S6.2 outputs the life prediction curve and confidence interval, and classifies the health level (normal, mild degradation, moderate damage, severe risk). S6.3 Generate maintenance suggestions and alarm information based on the predicted remaining lifespan and warning cycle.
[0033] Example 2 The present invention also provides a bridge bearing life assessment device, comprising: The first processing module is used to collect monitoring data of bridge bearings in real time; the monitoring data of bridge bearings includes: bearing displacement, temperature and load data. The second processing module is used to calculate the initial wear damage based on the monitoring data; The third processing module is used to correct the initial wear damage amount through probability uncertainty to obtain the statistically corrected wear damage amount; The fourth processing module is used to obtain the damage correction amount by data-driven correction based on the monitoring data; The fifth processing module is used to obtain the real-time damage based on the statistically corrected wear damage amount, the damage correction amount, and the actual observed real-time wear amount; The sixth processing module is used to estimate the remaining lifetime based on real-time damage and degradation process models.
[0034] As one embodiment of the present invention, the fifth processing module obtains the real-time damage embodiment 3 through intelligent iterative updates based on the statistically corrected wear damage amount, the damage correction amount, and the actual observed real-time wear amount. The present invention also provides a bridge bearing life assessment system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a bridge bearing life assessment method when executed by the processor.
[0035] Example 4 The present invention also provides a storage medium storing a computer program, which executes a bridge bearing life assessment method when running.
[0036] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.
Claims
1. A method for assessing the lifespan of bridge bearings, characterized in that, include: Real-time acquisition of monitoring data for bridge bearings; Calculate the preliminary wear and damage amount based on the monitoring data; The initial wear damage amount is corrected for probabilistic uncertainty to obtain the statistically corrected wear damage amount; The damage correction amount is obtained through data-driven correction based on monitoring data; The real-time damage is obtained based on the statistically corrected wear damage amount, the damage correction amount, and the actual observed real-time wear amount. Remaining lifetime is estimated based on real-time damage and degradation process models.
2. The bridge bearing life assessment method as described in claim 1, characterized in that, The monitoring data for bridge bearings includes: bearing displacement, temperature, and load data.
3. The bridge bearing life assessment method as described in claim 2, characterized in that, Based on the statistically corrected wear damage, the damage correction, and the actual observed real-time wear, the real-time damage is obtained through intelligent iterative updates.
4. The bridge bearing life assessment method as described in claim 3, characterized in that, The damage correction amounts include: Establish a data-driven fitting damage model: in, f(·) The wear function is fitted by a neural network. T(t) Temperature is a factor that affects the environment. μ(t) The coefficient of friction varies with time. θ(t) These are the weight parameters of the neural network; Support monitoring data The model is driven by multiple input data, including temperature T(t) and friction coefficient μ(t), to calculate the damage correction amount. .
5. The bridge bearing life assessment method as described in claim 4, characterized in that, Real-time damage includes: Calculate the corrected wear damage: The actual observed real-time wear amount ; Input the intelligent inversion module, and iteratively update the model parameters θ using either Bayesian update or Extended Kalman Filter (EKF): Wherein, K(t) is the gain matrix, which is dynamically adjusted according to the prediction error.
6. The bridge bearing life assessment method as described in claim 5, characterized in that, The remaining lifetime is calculated based on a real-time damage and utilization degradation process model as follows: Specified support wear failure threshold Based on the identified real-time damage D(t) and the remaining lifetime RUL(t) estimated using a degradation process model: Where τ is a candidate value for the remaining lifetime.
7. A bridge bearing life assessment device, characterized in that, include: The first processing module is used to collect monitoring data of bridge bearings in real time; the monitoring data of bridge bearings includes: bearing displacement, temperature and load data. The second processing module is used to calculate the initial wear damage based on the monitoring data; The third processing module is used to correct the initial wear damage amount through probability uncertainty to obtain the statistically corrected wear damage amount; The fourth processing module is used to obtain the damage correction amount by data-driven correction based on the monitoring data; The fifth processing module is used to obtain the real-time damage based on the statistically corrected wear damage amount, the damage correction amount, and the actual observed real-time wear amount; The sixth processing module is used to estimate the remaining lifetime based on real-time damage and degradation process models.
8. The bridge bearing life assessment device as described in claim 7, characterized in that, The fifth processing module obtains the real-time damage through intelligent iterative updates based on the statistically corrected wear damage amount, the damage correction amount, and the actual observed real-time wear amount.
9. A bridge bearing life assessment system, characterized in that, include: A memory and a processor, wherein the memory stores a computer program executed by the processor, the computer program performing the bridge bearing life assessment method as described in any one of claims 1-6 when executed by the processor.
10. A storage medium, characterized in that, The storage medium stores a computer program that, when executed, performs the bridge bearing life assessment method as described in any one of claims 1-6.