Papers Structural Health Monitoring
“Structural Health Monitoring” 태그가 달린 논문 197편 · 필터 해제
A Machine Learning-Driven Wireless System for Structural Health Monitoring
The paper presents a wireless system integrated with a machine learning (ML) model for structural health monitoring (SHM) of carbon fiber reinforced polymer (CFRP) structures, primarily targeting aerospace applications. …
Structural Health MonitoringDamage detection in an uncertain nonlinear beam based on stochastic Volterra series
The damage detection problem in mechanical systems, using vibration measurements, is commonly called Structural Health Monitoring (SHM). Many tools are able to detect damages by changes in the vibration pattern, mainly, …
input filteringStructural Health MonitoringActive learning for regression in engineering populations: A risk-informed approach
Regression is a fundamental prediction task common in data-centric engineering applications that involves learning mappings between continuous variables. In many engineering applications (e.g.\ structural health monitori…
Active LearningregressionStructural Health MonitoringMultitask learning for improved scour detection: A dynamic wave tank study
Population-based structural health monitoring (PBSHM), aims to share information between members of a population. An offshore wind (OW) farm could be considered as a population of nominally-identical wind-turbine structu…
Anomaly DetectionStructural Health MonitoringDamage identification for bridges using machine learning: Development and application to KW51 bridge
The available tools for damage identification in civil engineering structures are known to be computationally expensive and data-demanding. This paper proposes a comprehensive machine learning based damage identification…
Structural Health MonitoringStructural damage detection via hierarchical damage information with volumetric assessment
Structural health monitoring (SHM) is essential for ensuring the safety and longevity of infrastructure, but complex image environments, noisy labels, and reliance on manual damage assessments often hinder its effectiven…
Structural Health MonitoringTriple ClassificationQuantifying the value of positive transfer: An experimental case study
In traditional approaches to structural health monitoring, challenges often arise associated with the availability of labelled data. Population-based structural health monitoring seeks to overcomes these challenges by le…
Decision MakingStructural Health MonitoringTransfer LearningDeep learning architectures for data-driven damage detection in nonlinear dynamic systems
The primary goal of structural health monitoring is to detect damage at its onset before it reaches a critical level. The in-depth investigation in the present work addresses deep learning applied to data-driven damage d…
Structural Health MonitoringEnhancing robustness of data-driven SHM models: adversarial training with circle loss
Structural health monitoring (SHM) is critical to safeguarding the safety and reliability of aerospace, civil, and mechanical infrastructure. Machine learning-based data-driven approaches have gained popularity in SHM du…
Structural Health MonitoringMultiple-input, multiple-output modal testing of a Hawk T1A aircraft: A new full-scale dataset for structural health monitoring
The use of measured vibration data from structures has a long history of enabling the development of methods for inference and monitoring. In particular, applications based on system identification and structural health …
DescriptiveStructural Health MonitoringLeveraging SPD Matrices on Riemannian Manifolds in Quantum Classical Hybrid Models for Structural Health Monitoring
Realtime finite element modeling of bridges assists modern structural health monitoring systems by providing comprehensive insights into structural integrity. This capability is essential for ensuring the safe operation …
Structural Health MonitoringOn the Condition Monitoring of Bolted Joints through Acoustic Emission and Deep Transfer Learning: Generalization, Ordinal Loss and Super-Convergence
This paper investigates the use of deep transfer learning based on convolutional neural networks (CNNs) to monitor the condition of bolted joints using acoustic emissions. Bolted structures are critical components in man…
DenoisingSensor FusionStructural Health MonitoringTransfer LearningDeterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks
The calibration of constitutive models from full-field data has recently gained increasing interest due to improvements in full-field measurement capabilities. In addition to the experimental characterization of novel ma…
Bayesian InferenceStructural Health MonitoringStochastic Inference of Plate Bending from Heterogeneous Data: Physics-informed Gaussian Processes via Kirchhoff-Love Theory
Advancements in machine learning and an abundance of structural monitoring data have inspired the integration of mechanical models with probabilistic models to identify a structure's state and quantify the uncertainty of…
Gaussian ProcessesStructural Health MonitoringUncertainty QuantificationFoundation Models for Structural Health Monitoring
Structural Health Monitoring (SHM) is a critical task for ensuring the safety and reliability of civil infrastructures, typically realized on bridges and viaducts by means of vibration monitoring. In this paper, we propo…
Anomaly DetectionKnowledge DistillationStructural Health MonitoringSmart structural health monitoring (SHM) system for on-board localization of defects in pipes using torsional ultrasonic guided waves
Most reported research for monitoring health of pipelines using ultrasonic guided waves (GW) typically utilize bulky piezoelectric transducer rings and laboratory-grade ultrasonic non-destructive testing (NDT) equipment.…
C++ codeStructural Health MonitoringExploring Challenges in Deep Learning of Single-Station Ground Motion Records
Contemporary deep learning models have demonstrated promising results across various applications within seismology and earthquake engineering. These models rely primarily on utilizing ground motion records for tasks suc…
Deep LearningStructural Health MonitoringTime SeriesAnomaly Detection in Offshore Wind Turbine Structures using Hierarchical Bayesian Modelling
Population-based structural health monitoring (PBSHM), aims to share information between members of a population. An offshore wind (OW) farm could be considered as a population of nominally-identical wind-turbine structu…
Anomaly DetectionStructural Health MonitoringMechanics-Informed Autoencoder Enables Automated Detection and Localization of Unforeseen Structural Damage
Structural health monitoring (SHM) ensures the safety and longevity of structures like buildings and bridges. As the volume and scale of structures and the impact of their failure continue to grow, there is a dire need f…
Data CompressionStructural Health MonitoringSHM-Traffic: DRL and Transfer learning based UAV Control for Structural Health Monitoring of Bridges with Traffic
This work focuses on using advanced techniques for structural health monitoring (SHM) for bridges with Traffic. We propose an approach using deep reinforcement learning (DRL)-based control for Unmanned Aerial Vehicle (UA…
Deep Reinforcement LearningEdge DetectionStructural Health MonitoringTransfer Learning