Semi-supervised networks integrated with autoencoder and pseudo-labels propagation for structural condition assessment

Article Automation & Control Systems

A novel deep convolutional image-denoiser network for structural vibration signal denoising

Qingsong Xiong et al.

Summary: A novel denoising approach based on deep convolutional image-denoiser networks is proposed in this study, which demonstrates powerful capability and efficiency in denoising signal.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2023)

Add to Collection

Article Computer Science, Artificial Intelligence

An efficient clinical support system for heart disease prediction using TANFIS classifier

Jayachitra Sekar et al.

Summary: A novel Internet of Things-based tuned adaptive neuro-fuzzy inference system (TANFIS) classifier is proposed for accurate prediction of heart disease. By optimizing tuning parameters, the method achieves a high accuracy of 99.76% for heart disease prediction, which is improved by 5.4% compared to existing algorithms.

COMPUTATIONAL INTELLIGENCE (2022)

Add to Collection

Article Computer Science, Artificial Intelligence

Combine labeled and unlabeled data for immune detector training with label propagation

Chen Wen et al.

Summary: In this study, an improved detector training algorithm is proposed, which enlarges the self training set and evaluates new samples based on noisy learning theory, and directly generates self-detectors at the locations of self samples. The experimental results show that this algorithm not only improves the time cost of detector training, but also enhances the detection accuracy.

KNOWLEDGE-BASED SYSTEMS (2022)

Add to Collection

Article Chemistry, Analytical

Beam Damage Assessment Using Natural Frequency Shift and Machine Learning

Nicoleta Gillich et al.

Summary: In this paper, machine learning methods are proposed for damage assessment. By creating databases and using random forest and artificial neural network as search tools, the crack location and severity can be accurately estimated.

SENSORS (2022)

Add to Collection

Article Computer Science, Software Engineering

Nearest neighbors algorithm and genetic-based collaborative filtering

Farimah Houshmand Nanehkaran et al.

Summary: The proposed method uses a genetic algorithm to find optimal similarities in big data, improving the performance of user-based collaborative filtering recommendation systems. It provides faster optimal solutions by estimating starting points and achieves better results and recommendations in terms of runtime and quality measures compared to previous methods.

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE (2022)

Add to Collection

Article

Feature Selection and Dwarf Mongoose Optimization Enabled Deep Learning for Heart Disease Detection

S. Balasubramaniam et al.

Computational Intelligence and Neuroscience (2022)

Add to Collection

Article Engineering, Civil

Machine learning-driven seismic failure mode identification of reinforced concrete shear walls based on PCA feature extraction

Qingsong Xiong et al.

Summary: This study explored a novel strategy by using principal component analysis (PCA) for data mining and feature extraction, achieving improved prediction accuracy. The dataset of 181 RC shear walls was used to investigate nine ML algorithms, and the gradient boosting classifier achieved a high accuracy of 0.98 after hyperparameter optimization. Additionally, clustering patterns of experimental parameters and feature importance in fitted model explainability were analyzed, providing valuable information and consistent underlying mechanisms.

STRUCTURES (2022)

Add to Collection

Article Computer Science, Artificial Intelligence

Gradient and Newton boosting for classification and regression

Fabio Sigrist

Summary: The article discusses the differences between gradient boosting, Newton boosting, and a hybrid of the two, showing that Newton boosting generally outperforms the others in terms of predictive accuracy. It also introduces a novel tuning parameter for tree-based Newton boosting, which is interpretable and important for predictive accuracy.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

Add to Collection

Article Engineering, Multidisciplinary

Unsupervised deep learning approach using a deep auto-encoder with an one-class support vector machine to detect structural damage

Zilong Wang et al.

Summary: This article proposes an unsupervised deep learning-based approach for structural damage detection, which utilizes a carefully designed deep auto-encoder and one-class support vector machine for extracting damage-sensitive features and detecting future damage. Experimental and numerical studies confirm the high accuracy and stability of the method in detecting structural damage.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2021)

Add to Collection

Article Engineering, Industrial

A Digital Twin approach based on nonparametric Bayesian network for complex system health monitoring

Jinsong Yu et al.

Summary: This paper introduces a Digital Twin approach for health monitoring, using a nonparametric Bayesian network to construct a model that represents the dynamic degradation process and uncertainty propagation. Real-time model updating based on GPF and DPMM is proposed to enhance adaptability and reduce uncertainty, ensuring the effectiveness of health monitoring.

JOURNAL OF MANUFACTURING SYSTEMS (2021)

Add to Collection

Article Engineering, Civil

Deep learning-based detection of structural damage using time-series data

Hung V. Dang et al.

Summary: This study extends the applicability of four prominent deep learning algorithms to structural damage detection using raw data, showing insights on their performance in various structures through quantitative evaluations of detection accuracy, time complexity, and required data storage in multi-damage scenarios.

STRUCTURE AND INFRASTRUCTURE ENGINEERING (2021)

Add to Collection

Review Computer Science, Artificial Intelligence

Artificial Neural Networks and Deep Learning in the Visual Arts: a review

Iria Santos et al.

Summary: This article provides an exhaustive analysis of Artificial Neural Networks and Deep Learning in the Visual Arts, highlighting their contributions in prediction, classification, generation, and identification. The investigation results show that their use in the Visual Arts continues to evolve and has recently experienced significant growth.

NEURAL COMPUTING & APPLICATIONS (2021)

Add to Collection

Article Automation & Control Systems

Visual high-precision detection method for tool damage based on visual feature migration and cutting edge reconstruction

Zukun Ye et al.

Summary: A visual high-precision detection method for tool damage based on visual feature migration and cutting edge reconstruction is proposed to automatically identify the location of tool damage and accurately measure the amount of damage. By extracting and fusing image information, the method achieves tool damage location recognition and accurate measurement of geometric characteristics. The method outperforms existing methods in tool damage detection accuracy by at least 20%.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2021)

Add to Collection

Article Computer Science, Information Systems

The improved AdaBoost algorithms for imbalanced data classification

Wenyang Wang et al.

Summary: This study proposes a method to improve the AdaBoost algorithm by using new weighted vote parameters to enhance the classification performance of weak classifiers, with a focus on the accuracy rate of the positive class. Experimental results show that this method outperforms traditional algorithms, especially in terms of the F=1 evaluation criterion.

INFORMATION SCIENCES (2021)

Add to Collection

Article Engineering, Civil

Substructure Damage Identification Based on Model Updating of Frequency Response Function

Youliang Fang et al.

Summary: Model updating of large-scale structures for damage identification using frequency response function (FRF) is challenging due to the complexity of global system matrices. A substructure damage identification method based on acceleration FRF model updating is proposed in this paper, which simplifies the model and improves the identification stability by introducing L1 norm regularization. Numerical simulation and experiments have confirmed the effectiveness and robustness of the proposed method.

INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS (2021)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Classification optimization for training a large dataset with Naive Bayes

Thi Thanh Sang Nguyen et al.

JOURNAL OF COMBINATORIAL OPTIMIZATION (2020)

Add to Collection

Article Construction & Building Technology

Dynamic damage identification using complex mode shapes

E. Lofrano et al.

STRUCTURAL CONTROL & HEALTH MONITORING (2020)

Add to Collection

Article Construction & Building Technology

Deep learning-based automatic recognition of water leakage area in shield tunnel lining

Yadong Xue et al.

TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY (2020)

Add to Collection

Proceedings Paper Computer Science, Theory & Methods

Optimization of the Random Forest Algorithm

Niva Mohapatra et al.

ADVANCES IN DATA SCIENCE AND MANAGEMENT (2020)

Add to Collection

Article Engineering, Civil

Dynamic structural health monitoring for concrete gravity dams based on the Bayesian inference

Giacomo Sevieri et al.

JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING (2020)

Add to Collection

Article Engineering, Aerospace

Enabling Damage Identification of Structures Using Time Series-Based Feature Extraction Algorithms

Hong Pan et al.

JOURNAL OF AEROSPACE ENGINEERING (2019)

Add to Collection

Editorial Material Hematology

Logistic regression

Aaron S. Hess et al.

TRANSFUSION (2019)

Add to Collection

Article Construction & Building Technology

Vibration-based structural condition assessment using convolution neural networks

Hamid Khodabandehlou et al.

STRUCTURAL CONTROL & HEALTH MONITORING (2019)

Add to Collection

Article Engineering, Multidisciplinary

Data-driven damage diagnosis under environmental and operational variability by novel statistical pattern recognition methods

Alireza Entezami et al.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2019)

Add to Collection

Article Engineering, Multidisciplinary

Unsupervised novelty detection-based structural damage localization using a density peaks-based fast clustering algorithm

Young-Jin Cha et al.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2018)

Add to Collection

Article Construction & Building Technology

Bayesian Modeling Approach for Forecast of Structural Stress Response Using Structural Health Monitoring Data

Hua-Ping Wan et al.

JOURNAL OF STRUCTURAL ENGINEERING (2018)

Add to Collection

Article Construction & Building Technology

Bayesian Modeling Approach for Forecast of Structural Stress Response Using Structural Health Monitoring Data

Hua-Ping Wan et al.

JOURNAL OF STRUCTURAL ENGINEERING (2018)

Add to Collection

Review Chemistry, Multidisciplinary

A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

John J. Moughty et al.

APPLIED SCIENCES-BASEL (2017)

Add to Collection

Article Engineering, Civil

Damage detection of a highway bridge under severe temperature changes using extended Kalman filter trained neural network

Chenhao Jin et al.

JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING (2016)

Add to Collection

Review Engineering, Civil

Vibration-based damage detection techniques used for health monitoring of structures: a review

Swagato Das et al.

JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING (2016)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Structural Damage Detection Using Modal Strain Energy and Hybrid Multiobjective Optimization

Young-Jin Cha et al.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2015)

Add to Collection

Article Acoustics

On robust regression analysis as a means of exploring environmental and operational conditions for SHM data

N. Dervilis et al.

JOURNAL OF SOUND AND VIBRATION (2015)

Add to Collection

Article Automation & Control Systems

Vibration-based estimation of tool major flank wear in a turning process using ARMA models

B. H. Aghdam et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2015)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Road Crack Detection Using Visual Features Extracted by Gabor Filters

Eduardo Zalama et al.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2014)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Image-Based Automated 3D Crack Detection for Post-disaster Building Assessment

Matthew M. Torok et al.

JOURNAL OF COMPUTING IN CIVIL ENGINEERING (2014)

Add to Collection

Article Engineering, Mechanical

A multiresolution approach to cointegration for enhanced SHM of structures under varying conditions - An exploratory study

K. Worden et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2014)

Add to Collection

Article Engineering, Multidisciplinary

Output-only structural health monitoring in changing environmental conditions by means of nonlinear system identification

Edwin Reynders et al.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2014)

Add to Collection

Article Engineering, Mechanical

Damage detection using data-driven methods applied to moving-load responses

Filipe Cavadas et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2013)

Add to Collection

Article Engineering, Mechanical

Long-term monitoring and data analysis of the Tamar Bridge

E. J. Cross et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2013)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Long-Term Monitoring and Identification of Bridge Structural Parameters

Serdar Soyoz et al.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2009)

Add to Collection

Article Instruments & Instrumentation

Statistical novelty detection within the Yeongjong suspension bridge under environmental and operational variations

Chang Kook Oh et al.

SMART MATERIALS AND STRUCTURES (2009)

Add to Collection

Semi-supervised networks integrated with autoencoder and pseudo-labels propagation for structural condition assessment

Related references

Export Citation

Share Paper