4.4 Article

New supervised learning classifiers for structural damage diagnosis using time series features from a new feature extraction technique

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 Computer Science, Interdisciplinary Applications

On model-based damage detection by an enhanced sensitivity function of modal flexibility and LSMR-Tikhonov method under incomplete noisy modal data

Hassan Sarmadi et al.

Summary: The article focuses on locating and quantifying damage using an improved sensitivity function and an iterative regularization method. The enhanced sensitivity function is found to be more sensitive to damage, and the proposed solution method shows robustness under noise-free and noisy modal data.

ENGINEERING WITH COMPUTERS (2022)

Article Engineering, Multidisciplinary

A hybrid sensitivity function and Lanczos bidiagonalization-Tikhonov method for structural model updating: Application to a full-scale bridge structure

Mohammad Rezaiee-Pajand et al.

Summary: This article proposes a sensitivity-based SMU method which updates elemental mass and stiffness matrices simultaneously by deriving a hybrid sensitivity function based on modal strain and kinetic energies. It presents a novel hybrid regularization method to solve the ill-posed inverse problem of SMU in a robust manner by combining the Lanczos bidiagonalization algorithm and Tikhonov regularization technique.

APPLIED MATHEMATICAL MODELLING (2021)

Article Mechanics

Application of supervised learning to validation of damage detection

Hassan Sarmadi et al.

Summary: This article proposes a novel strategy that combines the concepts of unsupervised learning and supervised learning to accurately separate and validate damage detection results. By utilizing two unsupervised learning methods and supervised learning as a validation tool, this strategy proves effective in making accurate decisions in structural health monitoring.

ARCHIVE OF APPLIED MECHANICS (2021)

Article Engineering, Civil

Early damage detection under massive data via innovative hybrid methods: application to a large-scale cable-stayed bridge

Mohammad Hassan Daneshvar et al.

Summary: The paper proposes innovative hybrid methods for damage detection under massive data, utilizing a three-stage algorithm and the ARARX model for response modeling. By employing GMM and an outlier detector, the study effectively detects and analyzes damage, demonstrating the reliability and effectiveness of the proposed methods.

STRUCTURE AND INFRASTRUCTURE ENGINEERING (2021)

Article Engineering, Civil

Model updating of a bridge structure using vibration test data based on GMPSO and BPNN: case study

Zhiyuan Xia et al.

Summary: A hybrid methodology combining GMPSO, BPNN, and LHS techniques is proposed to address model updating issues in high-dimensional and strong-nonlinear optimization processes. Through a case study on an actual bridge, the methodology demonstrates good performance in updating models and achieving consistency in frequencies and mode shapes.

EARTHQUAKE ENGINEERING AND ENGINEERING VIBRATION (2021)

Article Construction & Building Technology

Investigation of Machine Learning Methods for Structural Safety Assessment under Variability in Data: Comparative Studies and New Approaches

Hassan Sarmadi

Summary: This article focuses on the selection of machine learning methods for structural safety assessment and damage detection, proposing automated algorithms for hyperparameter optimization and variability level prediction.

JOURNAL OF PERFORMANCE OF CONSTRUCTED FACILITIES (2021)

Article Engineering, Civil

Bridge health monitoring in environmental variability by new clustering and threshold estimation methods

Hassan Sarmadi et al.

Summary: A new machine-learning method using k-medoids clustering and a new damage indicator, along with an innovative approach for selecting a proper cluster number and a novel probabilistic method for threshold estimation, is proposed for early damage detection under environmental variability. Experimental results demonstrate the accuracy and effectiveness of these methods.

JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING (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 Engineering, Multidisciplinary

A novel data-driven method for structural health monitoring under ambient vibration and high-dimensional features by robust multidimensional scaling

Alireza Entezami et al.

Summary: This article proposes a novel data-driven method for early damage detection of civil engineering structures by robust multidimensional scaling. Extreme value theory is utilized to increase reliability in damage detection, with the introduction of the generalized extreme value distribution for selecting the best model among Gumbel, Frechet, and Weibull distributions. Experimental data sets validate the effectiveness of the proposed method in detecting damage under ambient vibration and high-dimensional features.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2021)

Article Engineering, Civil

Damage detection of 3D structures using nearest neighbor search method

Ali Abasi et al.

Summary: An innovative damage identification method using the nearest neighbor search method for 3D structures is proposed and validated through experimental tests for its accuracy and robustness, showing superior performance compared to artificial neural networks in handling noisy data.

EARTHQUAKE ENGINEERING AND ENGINEERING VIBRATION (2021)

Article Construction & Building Technology

Ensemble learning-based structural health monitoring by Mahalanobis distance metrics

Hassan Sarmadi et al.

Summary: The article proposes a novel ensemble learning method to address the major challenge of environmental variability in structural health monitoring. By using different Mahalanobis distance metrics at multiple levels, the method aims to adapt to environmental changes and improve damage detection capability. The performance and effectiveness of the method are validated using modal features of real bridge structures, demonstrating superior results compared to state-of-the-art techniques.

STRUCTURAL CONTROL & HEALTH MONITORING (2021)

Article Construction & Building Technology

A sensitivity-based finite element model updating based on unconstrained optimization problem and regularized solution methods

Mohammad Rezaiee-Pajand et al.

STRUCTURAL CONTROL & HEALTH MONITORING (2020)

Article Engineering, Civil

Application of wavelet transform in structural health monitoring

Yashodhya Kankanamge et al.

EARTHQUAKE ENGINEERING AND ENGINEERING VIBRATION (2020)

Article Engineering, Civil

Model updating for real time dynamic substructures based on UKF algorithm

Su Tingli et al.

EARTHQUAKE ENGINEERING AND ENGINEERING VIBRATION (2020)

Article Construction & Building Technology

Supervised damage and deterioration detection in building structures using an enhanced autoregressive time-series approach

Vahid Reza Gharehbaghi et al.

JOURNAL OF BUILDING ENGINEERING (2020)

Article Engineering, Civil

An innovative hybrid strategy for structural health monitoring by modal flexibility and clustering methods

Alireza Entezami et al.

JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING (2020)

Article Engineering, Civil

Rapid visual screening of soft-story buildings from street view images using deep learning classification

Qian Yu et al.

EARTHQUAKE ENGINEERING AND ENGINEERING VIBRATION (2020)

Article Engineering, Civil

Automatic modal parameter identification of high arch dams: feasibility verification

Shuai Li et al.

EARTHQUAKE ENGINEERING AND ENGINEERING VIBRATION (2020)

Article Computer Science, Interdisciplinary Applications

Early damage assessment in large-scale structures by innovative statistical pattern recognition methods based on time series modeling and novelty detection

Alireza Entezami et al.

ADVANCES IN ENGINEERING SOFTWARE (2020)

Article Engineering, Civil

Structural damage assessment using improved Dempster-Shafer data fusion algorithm

Ding Yijie et al.

EARTHQUAKE ENGINEERING AND ENGINEERING VIBRATION (2019)

Article Engineering, Civil

Design and application of structural health monitoring system in long-span cable-membrane structure

Tang Teng et al.

EARTHQUAKE ENGINEERING AND ENGINEERING VIBRATION (2019)

Article Engineering, Civil

Damage detection of a thin plate using modal curvature via macrostrain measurement

Ting Yu Hsu et al.

EARTHQUAKE ENGINEERING AND ENGINEERING VIBRATION (2019)

Article Engineering, Civil

Fuzzy rule based seismic risk assessment of one-story precast industrial buildings

Mehmet Palanci

EARTHQUAKE ENGINEERING AND ENGINEERING VIBRATION (2019)

Article Engineering, Mechanical

Data-driven semi-supervised and supervised learning algorithms for health monitoring of pipes

Debarshi Sen et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)

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)

Article Engineering, Multidisciplinary

An unsupervised learning approach by novel damage indices in structural health monitoring for damage localization and quantification

Alireza Entezami et al.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2018)

Article Construction & Building Technology

An iterative order determination method for time-series modeling in structural health monitoring

Mohammad Rezaiee-Pajand et al.

ADVANCES IN STRUCTURAL ENGINEERING (2018)

Article Engineering, Mechanical

Automated structural health monitoring based on adaptive kernel spectral clustering

Rocco Langone et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2017)

Review Computer Science, Interdisciplinary Applications

The Vibration Monitoring Methods and Signal Processing Techniques for Structural Health Monitoring: A Review

D. Goyal et al.

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING (2016)

Article Construction & Building Technology

An improved substructural damage detection approach of shear structure based on ARMAX model residual

Liu Mei et al.

STRUCTURAL CONTROL & HEALTH MONITORING (2016)

Article Engineering, Multidisciplinary

A new iterative model updating technique based on least squares minimal residual method using measured modal data

Hassan Sarmadi et al.

APPLIED MATHEMATICAL MODELLING (2016)

Review Computer Science, Interdisciplinary Applications

Signal Processing Techniques for Vibration-Based Health Monitoring of Smart Structures

Juan Pablo Amezquita-Sanchez et al.

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING (2016)

Article Engineering, Civil

State-of-the-art in structural health monitoring of large and complex civil infrastructures

Hong-Nan Li et al.

JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING (2016)

Article Engineering, Civil

A clustering approach for structural health monitoring on bridges

Alberto Diez et al.

JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING (2016)

Article Engineering, Civil

Comparison of eigensensitivity and ANN based methods in model updating of an eight-story building

K. Prabakaran et al.

EARTHQUAKE ENGINEERING AND ENGINEERING VIBRATION (2015)

Article Engineering, Multidisciplinary

Structural damage identification based on self-fitting ARMAX model and multi-sensor data fusion

Ali M. Ay et al.

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2014)

Article Engineering, Civil

Statistical moment-based structural damage detection method in time domain

J. Zhang et al.

EARTHQUAKE ENGINEERING AND ENGINEERING VIBRATION (2013)

Article Engineering, Mechanical

Identifying damage locations under ambient vibrations utilizing vector autoregressive models and Mahalanobis distances

A. A. Mosavi et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2012)

Article Computer Science, Interdisciplinary Applications

Statistical damage identification for bridges using ambient vibration data

Q. W. Zhang

COMPUTERS & STRUCTURES (2007)

Article Multidisciplinary Sciences

Vibration-based structural damage identification

CR Farrar et al.

PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY OF LONDON SERIES A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2001)

Article Construction & Building Technology

Structural health monitoring using statistical process control

H Sohn et al.

JOURNAL OF STRUCTURAL ENGINEERING-ASCE (2000)