Papers Structural Health Monitoring
“Structural Health Monitoring” 태그가 달린 논문 197편 · 필터 해제
On the use of Statistical Learning Theory for model selection in Structural Health Monitoring
Whenever data-based systems are employed in engineering applications, defining an optimal statistical representation is subject to the problem of model selection. This paper focusses on how well models can generalise in …
Learning TheoryModel SelectionStructural Health MonitoringAutomated Detection and Analysis of Minor Deformations in Flat Walls Due to Railway Vibrations Using LiDAR and Machine Learning
This study introduces an advanced methodology for automatically identifying minor deformations in flat walls caused by vibrations from nearby railway tracks. It leverages high-density Terrestrial Laser Scanner (TLS) LiDA…
Structural Health MonitoringCrackESS: A Self-Prompting Crack Segmentation System for Edge Devices
Structural Health Monitoring (SHM) is a sustainable and essential approach for infrastructure maintenance, enabling the early detection of structural defects. Leveraging computer vision (CV) methods for automated infrast…
Computational EfficiencyCrack Segmentationparameter-efficient fine-tuningSegmentation+1MSCrackMamba: Leveraging Vision Mamba for Crack Detection in Fused Multispectral Imagery
Crack detection is a critical task in structural health monitoring, aimed at assessing the structural integrity of bridges, buildings, and roads to prevent potential failures. Vision-based crack detection has become the …
Image SegmentationMambaSemantic SegmentationStructural Health Monitoring+1Transferring self-supervised pre-trained models for SHM data anomaly detection with scarce labeled data
Structural health monitoring (SHM) has experienced significant advancements in recent decades, accumulating massive monitoring data. Data anomalies inevitably exist in monitoring data, posing significant challenges to th…
Anomaly DetectionSelf-Supervised LearningStructural Health MonitoringUnsupervised Pre-trainingSignal-based online acceleration and strain data fusion using B-splines and Kalman filter for full-field dynamic displacement estimation
Displacement plays a crucial role in structural health monitoring (SHM) and damage detection of structural systems subjected to dynamic loads. However, due to the inconvenience associated with the direct measurement of d…
Structural Health MonitoringWhen does a bridge become an aeroplane?
Despite recent advances in population-based structural health monitoring (PBSHM), knowledge transfer between highly-disparate structures (i.e., heterogeneous populations) remains a challenge. It has been proposed that he…
Structural Health MonitoringTransfer LearningNonSysId: A nonlinear system identification package with improved model term selection for NARMAX models
System identification involves constructing mathematical models of dynamic systems using input-output data, enabling analysis and prediction of system behaviour in both time and frequency domains. This approach can model…
Fault DiagnosisStructural Health MonitoringComparison of Tiny Machine Learning Techniques for Embedded Acoustic Emission Analysis
This paper compares machine learning approaches with different input data formats for the classification of acoustic emission (AE) signals. AE signals are a promising monitoring technique in many structural health monito…
Structural Health MonitoringDeep Learning for Micro-Scale Crack Detection on Imbalanced Datasets Using Key Point Localization
Internal crack detection has been a subject of focus in structural health monitoring. By focusing on crack detection in structural datasets, it is demonstrated that deep learning (DL) methods can effectively analyze seis…
Structural Health MonitoringMulti-temporal crack segmentation in concrete structure using deep learning approaches
Cracks are among the earliest indicators of deterioration in concrete structures. Early automatic detection of these cracks can significantly extend the lifespan of critical infrastructures, such as bridges, buildings, a…
Crack SegmentationDeep LearningSegmentationStructural Health MonitoringDeep Learning-Based Fatigue Cracks Detection in Bridge Girders using Feature Pyramid Networks
For structural health monitoring, continuous and automatic crack detection has been a challenging problem. This study is conducted to propose a framework of automatic crack segmentation from high-resolution images contai…
Crack SegmentationStructural Health MonitoringAddressing the Pitfalls of Image-Based Structural Health Monitoring: A Focus on False Positives, False Negatives, and Base Rate Bias
This study explores the limitations of image-based structural health monitoring (SHM) techniques in detecting structural damage. Leveraging machine learning and computer vision, image-based SHM offers a scalable and effi…
Structural Health MonitoringMulti-input Multi-output Loewner Framework for Vibration-based Damage Detection on a Trainer Jet
Structural health monitoring of aerostructures often faces challenges identifying damage, especially in complex systems. Multi-input multi-output modal parameter identification methods are known to offer enhanced insight…
BenchmarkingCantilever BeamStructural Health MonitoringVery High-Resolution Bridge Deformation Monitoring Using UAV-based Photogrammetry
Accurate and efficient structural health monitoring of infrastructure objects such as bridges is a vital task, as many existing constructions have already reached or are approaching their planned service life. In this co…
Structural Health MonitoringDeep learning-based Visual Measurement Extraction within an Adaptive Digital Twin Framework from Limited Data Using Transfer Learning
Digital Twins technology is revolutionizing decision-making in scientific research by integrating models and simulations with real-time data. Unlike traditional Structural Health Monitoring methods, which rely on computa…
Data IntegrationDecision MakingStructural Health MonitoringTransfer LearningResponse Estimation and System Identification of Dynamical Systems via Physics-Informed Neural Networks
The accurate modelling of structural dynamics is crucial across numerous engineering applications, such as Structural Health Monitoring (SHM), seismic analysis, and vibration control. Often, these models originate from p…
parameter estimationState EstimationStructural Health MonitoringData-driven Framework for Forward and Inverse Problems in Guided Waves-Based Structural Health Monitoring Under Varying Environmental and Operating Conditions
Recently, guided waves-based techniques have garnered increased attention from researchers in the field of Structural Health Monitoring (SHM) for damage detection and quantification. Extracting features that are sensitiv…
Structural Health MonitoringOn the topology and geometry of population-based SHM
Population-Based Structural Health Monitoring (PBSHM), aims to leverage information across populations of structures in order to enhance diagnostics on those with sparse data. The discipline of transfer learning provides…
Geometrical ViewStructural Health MonitoringTransfer LearningTowards an active-learning approach to resource allocation for population-based damage prognosis
Damage prognosis is, arguably, one of the most difficult tasks of structural health monitoring (SHM). To address common problems of damage prognosis, a population-based SHM (PBSHM) approach is adopted in the current work…
Active LearningPrognosisStructural Health Monitoring