☆ 4.6 Article

Research on the Identification of Bridge Structural Damage Using Variational Mode Decomposition and Convolutional Self-Attention Neural Networks

APPLIED SCIENCES-BASEL (2023)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Optics

Deep learning assisted non-contact defect identification method using diffraction phase microscopy

Subrahmanya Keremane Narayan et al.

Summary: In this paper, a deep-learning-based method for identifying defects in optical fringe patterns is proposed. By attributing the defect information to the phase gradient of the fringe pattern, the spatial phase derivatives are computed using a deep learning model, and the gradient map is applied to localize the defect. The robustness of the proposed method is demonstrated on numerically synthesized fringe pattern defects with various noise levels. Furthermore, the practical utility of the proposed method is substantiated in experimental defect identification in diffraction phase microscopy.

APPLIED OPTICS (2023)

添加到收藏夹

Article Chemistry, Multidisciplinary

A Robust Deep Learning-Based Damage Identification Approach for SHM Considering Missing Data

Fan Deng et al.

Summary: This paper proposes a deep learning-based method that integrates missing data imputation and damage identification tasks into a single framework.

APPLIED SCIENCES-BASEL (2023)

添加到收藏夹

Article Chemistry, Multidisciplinary

Numerical Verification of the Drive-By Monitoring Method for Identifying Vehicle and Bridge Mechanical Parameters

Kyosuke Yamamoto et al.

Summary: In this paper, it is demonstrated through numerical simulation that the PRE method can identify stiffness reductions in bridge models, thus increasing the feasibility of bridge inspection based on vehicle vibration. The PRE method uses vehicle vibration data to estimate the mechanical parameters of the vehicle and bridge, as well as the road unevenness. The MCMC algorithm is adopted to search for better combinations of the mechanical parameters, and the numerical simulation results show that bridge-stiffness reduction can be reasonably estimated.

APPLIED SCIENCES-BASEL (2023)

添加到收藏夹

Article Chemistry, Multidisciplinary

Application of Vehicle-Based Indirect Structural Health Monitoring Method to Railway Bridges—Simulation and In Situ Test

Michael Reiterer et al.

Applied Sciences-Basel (2023)

添加到收藏夹

Article Optics

Atmospheric visibility prediction by using the DBN deep learning model and principal component analysis

Yufeng Wang et al.

Summary: This study proposes a deep learning model that utilizes principal component analysis and a deep belief network to predict atmospheric visibility. By analyzing data from 2016 to 2019, an input dataset for atmospheric visibility prediction is constructed. The deep belief network model achieves high accuracy and determination coefficient compared to other neural network models. Additionally, the model can predict short- and long-term trends in atmospheric visibility and demonstrates good practicality under different weather conditions.

APPLIED OPTICS (2022)

添加到收藏夹

Article Engineering, Civil

Experimental and numerical investigation of an existing railway bridge during train crossing

Michael Reiterer

BAUTECHNIK (2020)

添加到收藏夹

Article Engineering, Multidisciplinary

Structural damage detection and localization using a hybrid method and artificial intelligence techniques

Azadeh Noori Hoshyar et al.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2020)

添加到收藏夹

Article Chemistry, Analytical

A Data-Driven Damage Identification Framework Based on Transmissibility Function Datasets and One-Dimensional Convolutional Neural Networks: Verification on a Structural Health Monitoring Benchmark Structure

Tongwei Liu et al.

SENSORS (2020)

添加到收藏夹

Article Engineering, Civil

Mitigating effects of temperature variations through probabilistic-based machine learning for vibration-based bridge scour detection

Wei Zheng et al.

JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING (2020)

添加到收藏夹

Article Optics

Damage identification using deep learning and long-gauge fiber Bragg grating sensors

Wenda Zhang et al.

APPLIED OPTICS (2020)

添加到收藏夹

Article Optics

Method of damage location determination based on a neural network using a single fiber Bragg grating sensor

Bangtian Xu et al.

APPLIED OPTICS (2019)

添加到收藏夹

Review Construction & Building Technology

Modal Strain Energy-based Structural Damage Identification: A Review and Comparative Study

Shuqing Wang et al.

STRUCTURAL ENGINEERING INTERNATIONAL (2019)

添加到收藏夹

Article Optics

Deep learning enhancement of infrared face images using generative adversarial networks

Axel-Christian Guei et al.

APPLIED OPTICS (2018)

添加到收藏夹

Article Engineering, Multidisciplinary

Hurst based vibro-acoustic feature extraction of bearing using EMD and VMD

Satish Mohanty et al.

MEASUREMENT (2018)

添加到收藏夹

Article Chemistry, Analytical

A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network

Sheng Guo et al.

SENSORS (2018)

添加到收藏夹

Article Construction & Building Technology

Convolutional neural networks for automated damage recognition and damage type identification

Ceena Modarres et al.

STRUCTURAL CONTROL & HEALTH MONITORING (2018)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Structural Damage Detection with Automatic Feature-Extraction through Deep Learning

Yi-zhou Lin et al.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2017)

添加到收藏夹

Article Computer Science, Hardware & Architecture

ImageNet Classification with Deep Convolutional Neural Networks

Alex Krizhevsky et al.

COMMUNICATIONS OF THE ACM (2017)

添加到收藏夹

Article Engineering, Civil

Adjustable hybrid resampling approach to computationally efficient probabilistic inference of structural damage based on vibration measurements

Wei Zheng et al.

JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING (2016)

添加到收藏夹

Article Engineering, Electrical & Electronic

Variational Mode Decomposition

Konstantin Dragomiretskiy et al.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2014)

添加到收藏夹

Article Engineering, Mechanical

Bayesian analysis of the Phase II IASC-ASCE structural health monitoring experimental benchmark data

J Ching et al.

JOURNAL OF ENGINEERING MECHANICS-ASCE (2004)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.