3279results about "Elasticity measurement" patented technology

The invention discloses a bridge detection method and system based on a digital twin technology, and relates to the field of bridge structure health monitoring. The method comprises the following steps: acquiring a strain distribution value, a vibration spectrum value and an environmental load spectrum value in real time through a sensor network deployed in a physical bridge, generating a structural response data set, and synchronizing the structural response data set to a digital twinborn body; calculating a damage index value and an accumulated damage quantity value based on the structural response data set; inputting the damage index value and the accumulated damage quantity value into a preset safety criterion, and calculating a safety margin coefficient value and a failure risk grade value; calculating a residual life prediction value based on the safety margin coefficient value and the environmental load spectrum value, and synchronously correcting a degradation rate value of the digital twin; and generating a priority maintenance instruction according to the failure risk grade value, the residual life prediction value and the safety margin coefficient value, and feeding back maintenance effect data to the digital twinborn body to complete updating after execution. The bridge operation and maintenance efficiency and safety are remarkably improved.

A computer implemented system configured for receiving obtaining sensor data comprising one or more displacements as a function of time of one or more targets attached to a structure in response to point loads applied to the structure; optionally receiving image data representing images of the vehicles comprising the point loads traversing the structure; and determining, from the displacements, at least one of a health status of the structure, or forces applied by one or more of the point loads.

The invention discloses a large-span bridge body structure health monitoring system based on deformation analysis, and relates to the technical field of large-span bridge body structure health monitoring, the system comprises a multi-modal sensor fusion module, a data processing module, an adaptive algorithm module and a deep learning and predictive maintenance module; and the multi-modal sensor fusion module is used for acquiring data of a plurality of sensors and carrying out cooperative processing. According to the large-span bridge body structure health monitoring system based on deformation analysis, through intelligent fusion of multi-modal sensors, the system can provide more accurate angle measurement under the complex conditions of dynamic loads, environment changes, sensor aging and the like, the measurement precision is improved through sensor redundancy design and a self-adaptive algorithm, and the system is suitable for popularization and application. And meanwhile, the influence of environmental interference and sensor faults on a measurement result is reduced.

A structural-health-monitoring system is disclosed for real-time detection and autonomous maintenance of physical structures. The system includes a sensor network comprising at least one strain gauge and one tri-axial accelerometer mounted on the structure to generate real-time sensor signals. A perception module filters and normalizes the signals and extracts numerical features such as peak amplitude and dominant frequency. A digital-twin module maintains a finite-element model updated in response to the extracted features. A data-driven surrogate model predicts sensor behavior and refines itself using machine-learning techniques. An anomaly-detection module computes an anomaly score from model residuals or classifier outputs. Upon exceeding a threshold, a maintenance module initiates a maintenance action, including generating an inspection schedule or issuing a control signal to an autonomous inspection or repair device. A learning module continuously improves system performance using reinforcement learning based on historical outcomes. The system supports predictive diagnostics, robotic repair, and automated optimization for long-term structural integrity.

The invention discloses a bridge health prediction device based on a Beidou system and a monitoring analysis method, and relates to the field of bridge health monitoring. The method comprises the following steps: S1, deploying monitoring points at key structure nodes of a bridge, and collecting three-dimensional position information and multi-source data by using a Beidou system and a multi-type sensor; s2, constructing a time-varying displacement field and a continuous deformation tensor, fusing stress and strain to construct a structural fatigue factor, and introducing environmental disturbance to realize dynamic correction of the fatigue factor; s3, a health state function is constructed based on the stress response, the displacement gradient and the deformation tensor, a damage mapping function is constructed in combination with accumulated deformation and a historical threshold value, and a damage thermodynamic diagram is generated; and S4, finally fusing fatigue, health and damage indexes to evaluate bridge risks, and performing dynamic early warning. Through fusion of mechanical tensor features and environmental disturbance modeling, a multi-index linkage fatigue and damage assessment mechanism is constructed, and the precision, robustness and global perception ability of bridge health monitoring are improved.

A method for testing and evaluating lateral stiffness of small box girder bridges is provided. The method includes the following steps: a deflection sensor collects a deflection value of a measuring point based on a loading process of a loading vehicle; based on the deflection value of the measuring point and an information matrix of the loading vehicle, an influence line of longitudinal bridge deflection of each measuring point is obtained; based on the influence line of longitudinal bridge deflection, the influence line of lateral bridge deflection of small box girder bridge is obtained by interpolation method; and based on the influence line of lateral deflection of small box girder bridge, a stiffness evaluation result is obtained.

The invention relates to the technical field of bridge engineering, in particular to a system and method for predicting the service life of a steel structure bridge after fatigue crack repair. The method comprises the steps that load data and stress data in the operation process of a bridge structure after fatigue crack repairing are collected; dynamic response characteristic parameters are extracted in combination with stress distribution characteristics of the fatigue crack repair area; performing multi-dimensional data fusion and preprocessing on the dynamic response characteristic parameters, and constructing a data set for training a deep learning algorithm; combining a deep learning algorithm and a fracture mechanics theory to construct a crack propagation rate prediction model and a residual life prediction model; according to the operation state of the bridge and the performance degradation law of the repairing area, the remaining life of the repaired structure is predicted, and multi-stage early warning is conducted. According to the method provided by the invention, bridge structure performance evaluation, life prediction and potential risk early warning can be realized, and the safety and reliability of bridge operation are ensured.

A disclosed water droplet sensing system and methods using split-ring resonators, which may be embedded within a material. In use, a component includes at least one split-ring resonator (SRR) which may be embedded within a material of the component. The at least one SRR may be formed from a composite material. Additionally, the at least one SRR may be configured to form a signal that is correlated with a concentration of water proximate to the at least one SRR. In some aspects, each SRR may resonate at a first frequency in response to an electromagnetic ping when the material is in a first state, and may resonate at a second frequency in response to the electromagnetic ping when the material is in a second state. A resonant frequency of the material may be based on physical characteristics of the material (including fluid accumulation on the material).

The invention discloses a bridge expansion joint damage detection and positioning method based on multi-modal signal fusion, and the method comprises the following steps: S1, building a detection platform, and collecting the multi-source data of a vibration signal, a sound signal and a video signal; s2, the vibration signals, the sound signals and the video signals are preprocessed respectively, and respective features including time domain features, frequency domain features and time-frequency domain features are extracted; s3, carrying out sound-vibration fusion on the vibration signal and the sound signal, and carrying out audio-video fusion on the sound signal and the video signal to realize integration of multi-modal features; and S4, outputting a damage identification and positioning result, and providing decision support for maintenance and management of the bridge expansion joint. According to the method, through multi-modal fusion signal analysis, the health condition of the bridge expansion device is monitored in real time, and damage is found in time, so that potential safety risks are prevented, safe operation of the bridge is ensured, and a new technical means is provided for health monitoring and maintenance of the bridge.

The invention belongs to the technical field of plate spring performance analysis and evaluation, and discloses a high-strength two-stage gradual-change-stiffness plate spring performance analysis and evaluation system and method.The method comprises the steps that plate spring initial mechanical data collection and heterogeneous load response integrated processing are conducted on the basis of a multi-dimensional vehicle-mounted test experiment, and plate spring integrated mechanical response data are generated; performing double-rigidity gradient recognition analysis to generate rigidity transformation critical data; performing critical region stress field deconstruction analysis to generate stress field distribution characteristic data; carrying out local microstructure change characterization to generate structural deformation data; constructing a multi-scale stress transfer link, and generating critical region stress transfer path data; performing fatigue damage accumulative mapping to generate accumulative feature data; constructing a fatigue life prediction model, and generating life prediction data; carrying out comprehensive performance index quantitative evaluation to generate a performance evaluation result; executing plate spring optimization improvement scheme generation and verification; and a scientific basis is provided for performance optimization of the two-stage gradual-change-rigidity plate spring.

The invention discloses a bridge vibration identification method and system based on an artificial intelligence technology, and relates to the technical field of data identification, and the method comprises the steps: dividing a bridge into a plurality of bridge segments, collecting the vibration data of the bridge segments in a preset time, forming a vibration data set, and analyzing the stress bearing capability of the bridge segments based on the data. The method comprises the following steps: identifying a stress bearing limit value of each bridge section, establishing a health safety threshold value of the bridge section, providing indexes for dangerous bridge section identification, inputting real-time monitoring data into a vibration identification model, predicting a vibration influence degree, dynamically monitoring a bridge state, discovering risks in advance and taking measures. And analyzing vibration influence factors, determining influence sources, and helping to optimize coping strategies. Predicting vibration deformation trend, evaluating bridge structure change, avoiding major accidents, reducing overload stress based on a balance strategy, prolonging the service life of the bridge, regularly monitoring the health state, formulating optimized operation time and mechanism, and ensuring safe operation of the bridge.

The invention discloses a bridge deformation monitoring device based on a digital twinborn technology, and the device can carry out the real-time correction of collected data through employing a data optimization algorithm, and eliminates an error caused by an environment factor. The algorithms can automatically adjust the measurement result based on the difference between the theoretical model and the actual sensor data, so that the measurement result is more accurate, and additional hardware investment is not needed. The sensor data is compensated and corrected through an optimization algorithm, the precision and stability of the monitoring system can be effectively improved, it is ensured that the collected data truly reflects the actual deformation state of the bridge, and therefore a more reliable basis is provided for safety monitoring and decision making of the bridge.

The invention discloses a bridge beam plate load testing device for road and bridge construction, and belongs to the technical field of bridge beam plate detection. The device comprises the following modules: an intelligent multi-point collaborative loading module for realizing multi-point synchronous dynamic loading of a beam plate so as to simulate various traffic load conditions; the multi-modal sensing and data acquisition module is used for acquiring dynamic response data of the beam slab in an omnibearing manner; the central control module is used for coordinating and managing the work of each module, processing dynamic response data in real time and dynamically optimizing a test strategy; the self-adaptive safety protection module monitors the health state of the beam slab in real time, predicts potential risks and takes active prevention measures; the environmental simulation and compensation module simulates the influence of various environmental conditions on the beam slab and compensates the interference of environmental factors on a test result in real time; the artificial intelligence data analysis module evaluates the performance of the beam slab and predicts the service life of the beam slab; and the augmented reality interaction and visualization module provides a visual user interface and visualizes the test process and result in real time.

The invention provides a segmental bridge wind vibration performance evaluation method and system, and relates to the technical field of segmental bridge wind vibration performance. Laser scanning is used for obtaining geometric parameters of a segmental bridge and collecting material characteristics, the geometric parameters and the material characteristics are imported into finite element software, a basic model is established, and boundary conditions are set; modal analysis is executed in finite element software, the natural vibration frequency and the vibration mode are calculated through a Lanczos method, and basic data are provided for wind load analysis; simulating a wind load by using a Davenport wind spectrum, setting a wind speed and a wind direction, and then applying the wind speed and the wind direction to the model; finally, the wind load condition is adjusted, the performance of the bridge under different wind loads is tested, and the displacement, acceleration and stress distribution of the bridge are analyzed and calculated through dynamic response to complete the wind vibration performance evaluation of the segmental bridge.

A disclosed airborne vehicle includes split-ring resonators (split ring resonators), which may be embedded within a material. Each split ring resonator may be formed from a three-dimensional (3D) monolithic carbonaceous growth and may detect an electromagnetic ping emitted from a user device. Each split ring resonator may generate an electromagnetic return signal in response to the electromagnetic ping. The electromagnetic return signal may indicate a state of the material in a position proximate to a respective split ring resonator. In some aspects, each may resonate at a first frequency in response to the electromagnetic ping when the material is in a first state, and may resonate at a second frequency in response to the electromagnetic ping when the material is in a second state. A resonant frequency of the 3D monolithic carbonaceous growth may be based on physical characteristics of the material.

A system, apparatus and method directed to detecting damage to an optical fiber of a medical device. The optical fiber includes one or more core fibers each including a plurality of sensors configured to (i) reflect a light signal based on received incident light, and (ii) alter the reflected light signal for use in determining a physical state of the multi-core optical fiber. The system also includes a console having non-transitory computer-readable medium storing logic that, when executed, causes operations of providing a broadband incident light signal to the multi-core optical fiber, receiving reflected light signals, receiving reflected light signals of different spectral widths of the broadband incident light by one or more of the plurality of sensors, identifying at least one unexpected spectral width or a lack of an expected spectral width, and determining the damage has occurred to the optical fiber based on the identification.

A disclosed apparatus includes sensors incorporated into adhesive material. In use, an apparatus may comprise an adhesive material and at least one split-ring resonator (SRR) disposed on or in the adhesive material. Additionally, the at least one SRR is formed from a carbon-containing material, and the adhesive material is a non-elastomeric material or a semi-rigid material. In some aspects, each SRR may resonate at a first frequency in response to an electromagnetic ping when the adhesive material is in a first state, and may resonate at a second frequency in response to the electromagnetic ping when the adhesive material is in a second state. A resonant frequency of the adhesive material may be based on physical characteristics of the adhesive material.

The invention discloses a whole-span bridge hoisting construction method. The method comprises the steps that prefabricated bridge components are transported to a construction site; in the construction preparation stage, a foundation monitoring mechanical model of a construction site is built, and the optimal position and operation parameters of a crane are determined; before hoisting, a laser emitting device is installed on a bridge component, a laser receiving device is installed at the front end of a crane suspension arm, and a plurality of laser reflection targets are arranged on the periphery of a preset installation position of a pier; in the lifting and translation processes, the laser positioning system monitors the spatial position and attitude information of the bridge component and the suspension arm in real time, and transmits the data to the control center in real time; and the control center compares the actual position deviation with the target position deviation in real time according to the preset hoisting path and the in-position precision requirement, and when the deviation exceeds the threshold value range, a control instruction is sent to a power system and a hydraulic control system of the crane. According to the method, the position and posture of the bridge component can be accurately monitored in real time, the action of the crane is timely and accurately regulated and controlled according to the deviation, and the construction precision and safety are improved.

A disclosed leaky coaxial resonant sensor system and methods. In use, the system includes at least one split-ring resonator (SRR) embedded within a material of the component. The at least one SRR is formed from a composite material. Additionally, the at least one SRR is configured to resonate at a first frequency in response to an interrogation signal from a leaky coaxial cable antenna. In some aspects, each SRR may resonate at a first frequency in response to an electromagnetic ping when the composite material is in a first state, and may resonate at a second frequency in response to the electromagnetic ping when the composite material is in a second state. A resonant frequency of the composite material may be based on physical characteristics of the composite material.

A system for providing an interactive inspection map of an inspection surface inspected by an inspection robot includes an inspection circuit structured to interpret inspection data of the inspection surface from the inspection robot and a user interaction circuit structured to interpret a user focus value from a user device. The user focus value includes an activation state value. The system further includes an inspection visualization circuit structured to provide the interactive inspection map to the user device in response to the user focus value. The interactive inspection map is based on the inspection data. Also, the system includes an inspection data validation circuit that determines an inspection data validity description of the inspection data. The inspection data validity description is provided as a display layer on the interactive inspection map to indicate whether the inspection data is valid.

The invention relates to the technical field of wind turbine generators, particularly provides an aeroelasticity analysis method, system and equipment of a fan blade and a medium, and aims to solve the aeroelasticity analysis problem of a flexible fan blade. Therefore, the aeroelasticity analysis method for the fan blade comprises the following steps: acquiring blade parameters and wind speed data of a to-be-analyzed blade; determining the aerodynamic load of the to-be-analyzed blade based on an aerodynamic model, the blade parameters and the wind speed data; based on the blade parameters and the aerodynamic load of the to-be-analyzed blade, determining the structural response of the to-be-analyzed blade; and carrying out aeroelasticity analysis on the to-be-analyzed blade based on the aerodynamic load and the structural response of the to-be-analyzed blade. The method is suitable for aeroelasticity analysis of the flexible blade of the wind turbine generator, and provides reliable reference for aeroelasticity stability evaluation of the flexible blade of the wind turbine generator.

The invention relates to the technical field of data processing, in particular to a vehicle load deflection integrated intelligent analysis method, system and device and a medium, and the method comprises the steps: obtaining a vehicle basic data set and a vehicle load data set corresponding to all vehicles on a to-be-detected bridge within a preset time period; acquiring a bridge state data set and an environment data set of the to-be-detected bridge in a preset time period; determining a target vehicle distribution load data set according to the vehicle basic data set and the vehicle load data set; determining a bridge load deflection parameter set according to the bridge state data set, the environment data set and the target vehicle distribution load data set; and performing bridge damage evaluation on each bridge load deflection parameter in the bridge load deflection parameter set to obtain a bridge damage evaluation result. According to the method, a more accurate and more comprehensive bridge damage assessment result can be obtained, and the effectiveness of bridge maintenance management and the accuracy of bridge structure optimization can be improved.

Resonant sensors for environmental health risk detection are disclosed. A mechanical member may include at least one meso-scale or micro-scale resonator disposed on a surface of the mechanical member. Additionally, the at least one meso-scale or micro-scale resonator may include a plurality of first carbon particles configured to uniquely resonate in response to an electromagnetic ping based at least in part on a concentration level of the first carbon particles within the at least one meso-scale or micro-scale resonator. Further, the at least one meso-scale or micro-scale resonator may be configured to resonate at a first frequency in response to the electromagnetic ping when the mechanical member is in a first state, and may be configured to resonate at a second frequency in response to the electromagnetic ping when the mechanical member is in a second state.

The invention provides a bridge structure monitoring system and method combined with active early warning, and the method comprises the steps: obtaining dynamic stress data and static stress data of each bridge monitoring point in a bridge section, and constructing a plurality of fatigue stress spectrums according to the dynamic stress data of each bridge monitoring point; determining a damage accumulation coefficient of a bridge structure at each bridge monitoring point according to a stress distribution difference between the fatigue stress spectrums, and extracting a plurality of monitoring key points by combining all the damage accumulation coefficients with dead load characteristics of a bridge section; determining the rigidity of the bridge structure corresponding to each monitoring key point according to the static stress data of each monitoring key point, and determining a confidence interval for performing active early warning on the structural damage of the bridge section according to all the rigidity of the bridge structure; and performing structural damage early warning on the bridge section based on the confidence interval and the current bridge stress at each bridge monitoring point. Accurate early warning of bridge structure damage can be realized under the influence of multi-dimensional load response.

The invention belongs to the technical field of bridge damage identification, and relates to a bridge structure intelligent damage identification method based on a physical information neural network, which comprises the following steps: 1, acquiring spatial relation data of a bridge structure, 2, acquiring real-time data, 3, preprocessing the data after the data is acquired, 4, performing hyper-parameter adjustment, 5, constructing a model, 6, carrying out model training and evaluation; 7, carrying out early warning and repairing scheme matching; the health state of the bridge can be monitored in real time, data support can be provided for future maintenance and repair, the safety of the bridge is improved, and the service life of the bridge is prolonged; therefore, a novel efficient tool is provided for bridge health monitoring and damage assessment through a unique data and physical driving method, an efficient and intelligent bridge monitoring and assessment system is constructed, and the safety and maintenance efficiency of the bridge are improved.

The invention relates to the technical field of bridge monitoring, and provides a multi-mode bridge vibration monitoring system. The system comprises a data acquisition and processing module, a bridge monitoring module, a monitoring precision optimization module and a bridge health assessment and early warning module. Intelligent analysis and health state evaluation of multi-dimensional data are realized through a bridge safety fusion network; according to the system, a k and r two-parameter optimization algorithm is adopted to dynamically adjust hyper-parameters of the bridge safety fusion network, the learning rate, regularization parameters and batch size are optimized to adapt to different bridge environments and operation conditions, and the monitoring precision and long-term stability are improved; through multi-modal data fusion, intelligent optimization and adaptive adjustment, the comprehensiveness, accuracy and adaptability of bridge vibration monitoring are improved, and efficient and scientific technical guarantee is provided for bridge safety management.

The invention belongs to the technical field of vibration control, and provides a CFD numerical simulation-based TMDI control bridge rigid main beam low-frequency vertical vortex vibration research method, which comprises the following steps of: establishing a rigid main beam-TMDI vertical coupling vibration equation in a wind field, carrying out order reduction processing on the coupling vibration equation by constructing a state equation, embedding a solving process into Fluent software by utilizing a user-defined function, and carrying out low-frequency vertical vortex vibration research on a rigid main beam-TMDI control bridge rigid main beam. And the vertical vortex-induced vibration response of the controlled main beam is obtained by alternately solving the flow field domain and the structural domain. Compared with a theoretical method, the fluid-solid coupling effect can be directly simulated, a vortex-induced force model does not need to be assumed, and the result is more reliable; compared with a wind tunnel test method, various parameters of the structure and the TMDI can be accurately set, physical space constraint is avoided, machining errors do not exist, the method is not limited to low wind speed, and the vibration suppression effect can be verified more conveniently; compared with an on-site actual measurement method, the method does not need to passively wait for vertical vortex vibration of a real bridge for a long time, can obtain richer information, facilitates the development of vibration suppression mechanism analysis, and greatly reduces the research difficulty and cost.

The invention provides a multi-factor correction and real-time early warning method and system based on bridge deflection, and the method comprises the steps: deploying a high-precision deflection sensor at a key position of a bridge, and collecting the deflection data of the bridge under different load and temperature conditions in real time; establishing an initial finite element model of a vehicle load and a temperature effect on the basis of the collected data, and correcting and optimizing the model through methods of static and dynamic load tests, weighted moving average and the like so as to accurately reflect the actual stress state of the bridge; combining the design standard, the safety margin and the historical monitoring data of the bridge to calculate a reasonable threshold value of the deflection of the bridge; in the real-time monitoring process, when actual deflection data exceed a set threshold value, the system automatically triggers an alarm mechanism, and alarm information is sent to a manager through a preset alarm channel; the functions of intelligent monitoring and early warning of the deflection of the bridge are achieved, and the safety and reliability of the bridge structure are improved.

The present application discloses a regional traffic heavy load digital weighing method and synergy system, the weighting method includes: based on data of a physical weighing system available in a region and basic data of a bridge in the region, selecting a reference physical weighing system at a position and a number M and determining a reasonable number N of bridge group transfer layers; based on the reference physical weighing system, constructing a corresponding single digital bridge scale model; obtaining a branch scale network model based on the reference physical weighing system through migration learning and correction; composing a regional digital scale network model based on branch scale network models corresponding to different reference physical weighing systems.

The invention discloses a mechanical property testing method for an inflatable wing, and belongs to the technical field of aerospace testing. The method comprises the following steps: S1, wing installation: clamping an inflatable wing on a test board, and enabling a chord plane of the inflatable wing to be perpendicular to a loading direction; s2, applying a load: driving a loading mechanism to apply a vertical load to the inflatable wing through an external universal tester; s3, data acquisition: synchronously acquiring a deflection curve and stress distribution data by using a displacement measurement unit and a strain detection unit; and S4, failure judgment: when monitoring that the shape of the deflection curve suddenly changes or the strain data exceeds a threshold value, judging that the inflatable wing is subjected to buckling failure, stopping loading and recording the maximum load. The test bench serves as an expansion platform of a universal tester, can be adapted to a large-size inflatable wing, does not need special equipment, reduces the cost, realizes cooperative monitoring of displacement and strain data, realizes closed-loop control of inflation air pressure-deformation-load, and remarkably improves the test precision and the data reliability.