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Computer vision and deep learning–based data anomaly detection method for structural health monitoring

Structural health monitoring, 2019-03, Vol.18 (2), p.401-421

2019

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Convolutional neural network‐based data anomaly detection method using multiple information for structural health monitoring

Structural control and health monitoring, 2019-01, Vol.26 (1), p.e2296-n/a

2019

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Automatic seismic damage identification of reinforced concrete columns from images by a region‐based deep convolutional neural network

Structural control and health monitoring, 2019-03, Vol.26 (3), p.e2313-n/a

2019

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Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images

Structural health monitoring, 2019-05, Vol.18 (3), p.653-674

2019

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Selection of regularization parameter for l1-regularized damage detection

Journal of sound and vibration, 2018-06, Vol.423, p.141-160

2018

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Deep reinforcement learning-based sampling method for structural reliability assessment

Reliability engineering & system safety, 2020-07, Vol.199, p.106901-10, Article 106901

2020

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The State of the Art of Data Science and Engineering in Structural Health Monitoring

Engineering (Beijing, China), 2019-04, Vol.5 (2), p.234-242

2019

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Machine‐learning‐based methods for output‐only structural modal identification

Structural control and health monitoring, 2021-12, Vol.28 (12), p.n/a

2021

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Analyzing and modeling inter-sensor relationships for strain monitoring data and missing data imputation: a copula and functional data-analytic approach

Structural health monitoring, 2019-07, Vol.18 (4), p.1168-1188

2019

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Embedding Compressive Sensing-Based Data Loss Recovery Algorithm Into Wireless Smart Sensors for Structural Health Monitoring

IEEE sensors journal, 2015-02, Vol.15 (2), p.797-808

2015

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LQD-RKHS-based distribution-to-distribution regression methodology for restoring the probability distributions of missing SHM data

Mechanical systems and signal processing, 2019-04, Vol.121, p.655-674

2019

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Machine learning paradigm for structural health monitoring

Structural health monitoring, 2021-07, Vol.20 (4), p.1353-1372

2021

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Clarifying and quantifying the geometric correlation for probability distributions of inter-sensor monitoring data: A functional data analytic methodology

Mechanical systems and signal processing, 2020-04, Vol.138, p.106540, Article 106540

2020

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Compressive sampling–based data loss recovery for wireless sensor networks used in civil structural health monitoring

Structural health monitoring, 2013-01, Vol.12 (1), p.78-95

2013

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Identification of time‐varying cable tension forces based on adaptive sparse time‐frequency analysis of cable vibrations

Structural control and health monitoring, 2017-03, Vol.24 (3), p.np-n/a

2017

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A Study on Data Loss Compensation of WiFi-Based Wireless Sensor Networks for Structural Health Monitoring

IEEE sensors journal, 2016-05, Vol.16 (10), p.3811-3818

2016

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Compressive sampling for accelerometer signals in structural health monitoring

Structural health monitoring, 2011-05, Vol.10 (3), p.235-246

2011

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An Approach of Reliable Data Transmission With Random Redundancy for Wireless Sensors in Structural Health Monitoring

IEEE sensors journal, 2015-02, Vol.15 (2), p.809-818

2015

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Generation of Rayleigh-wave dispersion images from multichannel seismic data using sparse signal reconstruction

Geophysical journal international, 2015-11, Vol.203 (2), p.818-827

2015

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Structural damage identification based on integration of information fusion and shannon entropy

Mechanical systems and signal processing, 2008-08, Vol.22 (6), p.1427-1440

2008

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Adaptive subset searching-based deep neural network method for structural reliability analysis

Reliability engineering & system safety, 2021-09, Vol.213, p.107778, Article 107778

2021

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Optimal policy for structure maintenance: A deep reinforcement learning framework

Structural safety, 2020-03, Vol.83, p.101906, Article 101906

2020

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Emerging data technology in structural health monitoring: compressive sensing technology

Journal of civil structural health monitoring, 2014-04, Vol.4 (2), p.77-90

2014

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An active learning method combining deep neural network and weighted sampling for structural reliability analysis

Mechanical systems and signal processing, 2020-06, Vol.140, p.106684, Article 106684

2020

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Experimental and numerical study on structural performance of reinforced concrete box sewer with localized extreme defect

Underground space (Beijing), 2018-06, Vol.3 (2), p.166-179

2018

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Computer vision and deep learning–based data anomaly detection method for structural health monitoring

Convolutional neural network‐based data anomaly detection method using multiple information for structural health monitoring

Automatic seismic damage identification of reinforced concrete columns from images by a region‐based deep convolutional neural network

Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images

Selection of regularization parameter for l1-regularized damage detection

Deep reinforcement learning-based sampling method for structural reliability assessment

The State of the Art of Data Science and Engineering in Structural Health Monitoring

Machine‐learning‐based methods for output‐only structural modal identification

Analyzing and modeling inter-sensor relationships for strain monitoring data and missing data imputation: a copula and functional data-analytic approach

Embedding Compressive Sensing-Based Data Loss Recovery Algorithm Into Wireless Smart Sensors for Structural Health Monitoring

LQD-RKHS-based distribution-to-distribution regression methodology for restoring the probability distributions of missing SHM data

Machine learning paradigm for structural health monitoring

Clarifying and quantifying the geometric correlation for probability distributions of inter-sensor monitoring data: A functional data analytic methodology

Compressive sampling–based data loss recovery for wireless sensor networks used in civil structural health monitoring

Identification of time‐varying cable tension forces based on adaptive sparse time‐frequency analysis of cable vibrations

A Study on Data Loss Compensation of WiFi-Based Wireless Sensor Networks for Structural Health Monitoring

Compressive sampling for accelerometer signals in structural health monitoring

An Approach of Reliable Data Transmission With Random Redundancy for Wireless Sensors in Structural Health Monitoring

Generation of Rayleigh-wave dispersion images from multichannel seismic data using sparse signal reconstruction

Structural damage identification based on integration of information fusion and shannon entropy

Adaptive subset searching-based deep neural network method for structural reliability analysis

Optimal policy for structure maintenance: A deep reinforcement learning framework

Emerging data technology in structural health monitoring: compressive sensing technology

An active learning method combining deep neural network and weighted sampling for structural reliability analysis

Experimental and numerical study on structural performance of reinforced concrete box sewer with localized extreme defect

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