4.7 Article

Image-based monitoring of bolt loosening through deep-learning-based integrated detection and tracking

Related references

Note: Only part of the references are listed.
Article Computer Science, Interdisciplinary Applications

Hybrid deep learning architecture for rail surface segmentation and surface defect detection

Yunpeng Wu et al.

Summary: This paper introduces a new rail boundary guidance network (RBGNet) for salient RS detection, utilizing the complementarity between RS and RE, and enhancing accuracy through high-level RS information injection and hybrid loss. Experiments show that the system achieves a high detection rate and good adaptation capability in complex environments.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2022)

Article Computer Science, Interdisciplinary Applications

Uncertainty-assisted deep vision structural health monitoring

Seyed Omid Sajedi et al.

Summary: This paper explores the use of deep learning and Bayesian inference in structural health monitoring, addressing prediction uncertainty and conducting three case studies. The uncertainty metrics show correlations with misclassifications, and a surrogate model is proposed to trigger human interventions.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2021)

Article Computer Science, Interdisciplinary Applications

Real-time regional seismic damage assessment framework based on long short-term memory neural network

Yongjia Xu et al.

Summary: This study proposes a framework for real-time regional seismic damage assessment based on the LSTM neural network architecture, which can rapidly and accurately assess earthquake-induced damage. By establishing a workflow and optimizing model utilization, damage prediction can be performed at a regional scale.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2021)

Article Computer Science, Interdisciplinary Applications

Dual Bayesian inference for risk-informed vibration-based damage diagnosis

Seyedomid Sajedi et al.

Summary: This paper introduces a dual Bayesian inference approach, which improves the reliability of vibration-based damage diagnosis. By using a surrogate deep learning module to transform raw uncertainty output into easily interpretable Prediction Uncertainty Index (PUI), it provides a warning for potential mistakes.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2021)

Article Engineering, Civil

A deep learning approach to rapid regional post-event seismic damage assessment using time-frequency distributions of ground motions

Xinzheng Lu et al.

Summary: Earthquakes cause severe economic losses and casualties globally annually, and timely and accurate assessment of seismic damages is crucial. A rapid regional post-event seismic damage assessment procedure based on convolutional neural network is proposed in this study, utilizing time-frequency distribution graphs to train models for predicting damage states.

EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS (2021)

Article Computer Science, Interdisciplinary Applications

Deep learning for post-hurricane aerial damage assessment of buildings

Chih-Shen Cheng et al.

Summary: This study improves post-disaster preliminary damage assessment using a stacked convolutional neural network trained on UAV imagery from Hurricane Dorian. The model achieves high building localization precision and classification accuracy, with a positive accuracy-confidence correlation for situations where ground-truth information is not readily available. The relationship between building size, number of stories, and disaster damage severity is also examined for damage assessment comparison.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2021)

Article Computer Science, Interdisciplinary Applications

Early damage detection by an innovative unsupervised learning method based on kernel null space and peak-over-threshold

Hassan Sarmadi et al.

Summary: This article proposes an innovative unsupervised learning method for early damage detection and long-term structural health monitoring of civil structures under environmental variability. The method includes a novelty detector, an optimal Gaussian kernel parameter selection approach, and a probabilistic threshold estimation method. The main advantages of the method lie in addressing environmental variations and estimating reliable alarming thresholds.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2021)

Article Computer Science, Interdisciplinary Applications

Balanced semisupervised generative adversarial network for damage assessment from low-data imbalanced-class regime

Yuqing Gao et al.

Summary: In recent years, applying deep learning to assess structural damages in vision-based structural health monitoring has become popular. However, data deficiency and class imbalance have hindered the wide adoption of deep learning in this field. The balanced semisupervised GAN (BSS-GAN) introduced in this work shows better damage detection performance compared to conventional methods.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2021)

Article Computer Science, Interdisciplinary Applications

Structural damage detection and localization using decision tree ensemble and vibration data

Giulio Mariniello et al.

Summary: This paper explores the capabilities of decision tree ensembles (DTEs) for detecting and localizing damage in structural health monitoring (SHM) and proposes a D-2-DTE methodology based on vibration analysis for health assessment. The method is validated for various damage scenarios and shows competitive performances in accuracy and localization errors compared to state-of-the-art methodologies.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2021)

Article Computer Science, Interdisciplinary Applications

Structural health monitoring using extremely compressed data through deep learning

Mohsen Azimi et al.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2020)

Article Computer Science, Interdisciplinary Applications

Postdisaster image-based damage detection and repair cost estimation of reinforced concrete buildings using dual convolutional neural networks

Xiao Pan et al.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2020)

Article Engineering, Multidisciplinary

Fully automated vision-based loosened bolt detection using the Viola-Jones algorithm

Lovedeep Ramana et al.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2019)

Article Construction & Building Technology

Quasi-autonomous bolt-loosening detection method using vision-based deep learning and image processing

Thanh-Canh Huynh et al.

AUTOMATION IN CONSTRUCTION (2019)

Article Computer Science, Interdisciplinary Applications

Image-based post-disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization

Xiao Liang

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2019)

Article Engineering, Civil

A novel unsupervised deep learning model for global and local health condition assessment of structures

Mohammad Hossein Rafiei et al.

ENGINEERING STRUCTURES (2018)

Article Chemistry, Analytical

Image Registration-Based Bolt Loosening Detection of Steel Joints

Xiangxiong Kong et al.

SENSORS (2018)

Article Construction & Building Technology

RBFN-based temperature compensation method for impedance monitoring in prestressed tendon anchorage

Thanh-Canh Huynh et al.

STRUCTURAL CONTROL & HEALTH MONITORING (2018)

Article Computer Science, Interdisciplinary Applications

Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types

Young-Jin Cha et al.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2018)

Article Construction & Building Technology

New method for modal identification of super high-rise building structures using discretized synchrosqueezed wavelet and Hilbert transforms

Zhijun Li et al.

STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS (2017)

Article Instruments & Instrumentation

Quantification of temperature effect on impedance monitoring via PZT interface for prestressed tendon anchorage

Thanh-Canh Huynh et al.

SMART MATERIALS AND STRUCTURES (2017)

Article Engineering, Geological

NEEWS: A novel earthquake early warning model using neural dynamic classification and neural dynamic optimization

Mohammad Hossein Rafiei et al.

SOIL DYNAMICS AND EARTHQUAKE ENGINEERING (2017)

Article Construction & Building Technology

A novel machine learning-based algorithm to detect damage in high-rise building structures

Mohammad Hossein Rafiei et al.

STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS (2017)

Article Computer Science, Artificial Intelligence

A New Neural Dynamic Classification Algorithm

Mohammad Hossein Rafiei et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2017)

Proceedings Paper Engineering, Mechanical

Automated Vision-Based Loosened Bolt Detection Using the Cascade Detector

Lovedeep Ramana et al.

SENSORS AND INSTRUMENTATION, VOL 5 (2017)

Article Computer Science, Artificial Intelligence

Evolutionary learning based sustainable strain sensing model for structural health monitoring of high-rise buildings

Byung Kwan Oh et al.

APPLIED SOFT COMPUTING (2017)

Article Construction & Building Technology

Supervised Deep Restricted Boltzmann Machine for Estimation of Concrete

Mohammad Hossein Rafiei et al.

ACI MATERIALS JOURNAL (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Mask R-CNN

Kaiming He et al.

2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2017)

Article Construction & Building Technology

Vision-based detection of loosened bolts using the Hough transform and support vector machines

Young-Jin Cha et al.

AUTOMATION IN CONSTRUCTION (2016)

Article Automation & Control Systems

New methodology for modal parameters identification of smart civil structures using ambient vibrations and synchrosqueezed wavelet transform

Carlos A. Perez-Ramirez et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2016)

Article Acoustics

Modal identification of simple structures with high-speed video using motion magnification

Justin G. Chen et al.

JOURNAL OF SOUND AND VIBRATION (2015)

Article Chemistry, Analytical

Damage Detection with Streamlined Structural Health Monitoring Data

Jian Li et al.

SENSORS (2015)

Article Instruments & Instrumentation

Synchrosqueezed wavelet transform-fractality model for locating, detecting, and quantifying damage in smart highrise building structures

Juan P. Amezquita-Sanchez et al.

SMART MATERIALS AND STRUCTURES (2015)

Article Construction & Building Technology

Vision-based technique for bolt-loosening detection in wind turbine tower

Jae-Hyung Park et al.

WIND AND STRUCTURES (2015)

Proceedings Paper Computer Science, Artificial Intelligence

Fast R-CNN

Ross Girshick

2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2015)

Review Computer Science, Information Systems

Review of Bolted Connection Monitoring

Tao Wang et al.

INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS (2013)

Review Engineering, Civil

Temperature effect on vibration properties of civil structures: a literature review and case studies

Yong k Xia et al.

JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING (2012)

Article Instruments & Instrumentation

Detection of bolt loosening in C-C composite thermal protection panels: II. Experimental verification

JK Yang et al.

SMART MATERIALS AND STRUCTURES (2006)

Article Computer Science, Artificial Intelligence

MLESAC: A new robust estimator with application to estimating image geometry

PHS Torr et al.

COMPUTER VISION AND IMAGE UNDERSTANDING (2000)