Papers Structural Health Monitoring
“Structural Health Monitoring” 태그가 달린 논문 197편 · 필터 해제
Fully Data-Driven Model for Increasing Sampling Rate Frequency of Seismic Data using Super-Resolution Generative Adversarial Networks
High-quality data is one of the key requirements for any engineering application. In earthquake engineering practice, accurate data is pivotal in predicting the response of structure or damage detection process in an Str…
Structural Health MonitoringSuper-ResolutionConcrete Surface Crack Detection with Convolutional-based Deep Learning Models
Effective crack detection is pivotal for the structural health monitoring and inspection of buildings. This task presents a formidable challenge to computer vision techniques due to the inherently subtle nature of cracks…
Deep LearningStructural Health MonitoringTransfer LearningOperational modal analysis of under-determined system based on Bayesian CP decomposition
Modal parameters such as natural frequencies, modal shapes, and the damping ratio are useful to understand structural dynamics of mechanical systems. Modal parameters need to be estimated under operational conditions for…
Bayesian InferenceStructural Health MonitoringClassification of Various Types of Damages in Honeycomb Composite Sandwich Structures using Guided Wave Structural Health Monitoring
Classification of damages in honeycomb composite sandwich structure (HCSS) is important to decide remedial actions. However, previous studies have only detected damages using deviations of monitoring signal from healthy …
Feature EngineeringStructural Health MonitoringCNN-Based Structural Damage Detection using Time-Series Sensor Data
Structural Health Monitoring (SHM) is vital for evaluating structural condition, aiming to detect damage through sensor data analysis. It aligns with predictive maintenance in modern industry, minimizing downtime and cos…
Structural Health MonitoringTime SeriesQuantifying the value of information transfer in population-based SHM
Population-based structural health monitoring (PBSHM), seeks to address some of the limitations associated with data scarcity that arise in traditional SHM. A tenet of the population-based approach to SHM is that informa…
ClassificationDomain AdaptationStructural Health MonitoringTransfer LearningPopulation-based wind farm monitoring based on a spatial autoregressive approach
An important challenge faced by wind farm operators is to reduce operation and maintenance cost. Structural health monitoring provides a means of cost reduction through minimising unnecessary maintenance trips as well as…
Structural Health MonitoringSurrogate modeling for stochastic crack growth processes in structural health monitoring applications
Fatigue crack growth is one of the most common types of deterioration in metal structures with significant implications on their reliability. Recent advances in Structural Health Monitoring (SHM) have motivated the use o…
PrognosisStructural Health MonitoringUnsupervised deep learning framework for temperature-compensated damage assessment using ultrasonic guided waves on edge device
Fueled by the rapid development of machine learning (ML) and greater access to cloud computing and graphics processing units (GPUs), various deep learning based models have been proposed for improving performance of ultr…
Cloud ComputingStructural Health MonitoringFull-scale modal testing of a Hawk T1A aircraft for benchmarking vibration-based methods
Research developments for structural dynamics in the fields of design, system identification and structural health monitoring (SHM) have dramatically expanded the bounds of what can be learned from measured vibration dat…
BenchmarkingExperimental DesignStructural Health MonitoringStochastic stiffness identification and response estimation of Timoshenko beams via physics-informed Gaussian processes
Machine learning models trained with structural health monitoring data have become a powerful tool for system identification. This paper presents a physics-informed Gaussian process (GP) model for Timoshenko beam element…
Gaussian ProcessesStructural Health MonitoringFrom Classification to Segmentation with Explainable AI: A Study on Crack Detection and Growth Monitoring
Monitoring surface cracks in infrastructure is crucial for structural health monitoring. Automatic visual inspection offers an effective solution, especially in hard-to-reach areas. Machine learning approaches have prove…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)SegmentationStructural Health MonitoringPredicting Fatigue Crack Growth via Path Slicing and Re-Weighting
Predicting potential risks associated with the fatigue of key structural components is crucial in engineering design. However, fatigue often involves entangled complexities of material microstructures and service conditi…
Decision MakingDimensionality ReductionManagementPhysical Simulations+2YOLO series target detection algorithms for underwater environments
You Only Look Once (YOLO) algorithm is a representative target detection algorithm emerging in 2016, which is known for its balance of computing speed and accuracy, and now plays an important role in various fields of hu…
Structural Health MonitoringDeep Learning Overloaded Vehicle Identification for Long Span Bridges Based on Structural Health Monitoring Data
Overloaded vehicles bring great harm to transportation infrastructures. BWIM (bridge weigh-in-motion) method for overloaded vehicle identification is getting more popular because it can be implemented without interruptio…
Deep LearningStructural Health MonitoringOutput-only Modal Identification of beams with different boundary condition
Structural Health Monitoring (SHM) evaluates the integrity of a structure by observing its dynamic responses by an array of sensors over time to determine the current health state of the structure. The most important ste…
Structural Health MonitoringData-driven Identification of Parametric Governing Equations of Dynamical Systems Using the Signed Cumulative Distribution Transform
This paper presents a novel data-driven approach to identify partial differential equation (PDE) parameters of a dynamical system. Specifically, we adopt a mathematical "transport" model for the solution of the dynamical…
parameter estimationregressionStructural Health MonitoringEnhancing In-Situ Structural Health Monitoring through RF Energy-Powered Sensor Nodes and Mobile Platform
This research contributes to long-term structural health monitoring (SHM) by exploring radio frequency energy-powered sensor nodes (RF-SNs) embedded in concrete. Unlike traditional in-situ monitoring systems relying on b…
Structural Health MonitoringSynergistic Signal Denoising for Multimodal Time Series of Structure Vibration
Structural Health Monitoring (SHM) plays an indispensable role in ensuring the longevity and safety of infrastructure. With the rapid growth of sensor technology, the volume of data generated from various structures has …
DenoisingStructural Health MonitoringTime SeriesA physics-informed machine learning model for reconstruction of dynamic loads
Long-span bridges are subjected to a multitude of dynamic excitations during their lifespan. To account for their effects on the structural system, several load models are used during design to simulate the conditions th…
Physics-informed machine learningPrognosisStructural Health Monitoring