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Continuous wave measurements in a network of transducers for structural health monitoring of a large concrete floor slab

Structural health monitoring, 2016-07, Vol.15 (4), p.403-412

2016

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Bayesian forces identification in cable networks with small bending stiffness

Structural health monitoring, 2024-07, Vol.23 (4), p.2135-2160

2024

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Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection

Structural health monitoring, 2018-09, Vol.17 (5), p.1110-1128

2018

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Monitoring of a civil structure’s state based on noncontact measurements

Structural health monitoring, 2013-09, Vol.12 (5-6), p.411-429

2013

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Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Structural Health Monitoring, 2022-07, Vol.21 (4), p.1906-1955

2022

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Data fusion approaches for structural health monitoring and system identification: Past, present, and future

Structural Health Monitoring, 2020-03, Vol.19 (2), p.552-586

2020

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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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Optimum wavelet selection for nonparametric analysis toward structural health monitoring for processing big data from sensor network: A comparative study

Structural health monitoring, 2022-05, Vol.21 (3), p.803-825

2022

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A review of computer vision–based structural health monitoring at local and global levels

Structural Health Monitoring, 2021-03, Vol.20 (2), p.692-743

2021

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Determination of structural and damage detection system influencing parameters on the value of information

Structural health monitoring, 2022-01, Vol.21 (1), p.19-36

2022

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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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Country-scale InSAR monitoring for settlement and uplift damage calculation in architectural heritage structures

Structural health monitoring, 2021-09, Vol.20 (5), p.2317-2336

2021

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A research on an improved Unet-based concrete crack detection algorithm

Structural health monitoring, 2021-07, Vol.20 (4), p.1864-1879

2021

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Thermal response of timber connections using densified wood dowels under fire

Journal of fire sciences, 2024-06

2024

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Optimization of sensor placement for structural health monitoring: a review

Structural Health Monitoring, 2019-05, Vol.18 (3), p.963-988

2019

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Farm-wide virtual load monitoring for offshore wind structures via Bayesian neural networks

Structural health monitoring, 2024-05, Vol.23 (3), p.1641-1663

2024

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Predictive probability of detection curves based on data from undamaged structures

Structural health monitoring, 2024-05, Vol.23 (3), p.1725-1741

2024

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Bayesian data-driven framework for structural health monitoring of composite structures under limited experimental data

Structural health monitoring, 2024-04

2024

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Toward data anomaly detection for automated structural health monitoring: Exploiting generative adversarial nets and autoencoders

Structural health monitoring, 2021-07, Vol.20 (4), p.1609-1626

2021

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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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Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm

Structural health monitoring, 2022-09, Vol.21 (5), p.2244-2263

2022

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Crack and Noncrack Classification from Concrete Surface Images Using Machine Learning

Structural health monitoring, 2019-05, Vol.18 (3), p.725-738

2019

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Polynomial Chaos-Kriging metamodel for quantification of the debonding area in large wind turbine blades

Structural health monitoring, 2022-03, Vol.21 (2), p.666-682

2022

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Structural health monitoring of offshore wind turbines using automated operational modal analysis

Structural health monitoring, 2014-11, Vol.13 (6), p.644-659

2014

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Three decades of statistical pattern recognition paradigm for SHM of bridges

Structural Health Monitoring, 2022-11, Vol.21 (6), p.3018-3054

2022

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1 – 25 von 4.537

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Continuous wave measurements in a network of transducers for structural health monitoring of a large concrete floor slab

Bayesian forces identification in cable networks with small bending stiffness

Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection

Monitoring of a civil structure’s state based on noncontact measurements

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Data fusion approaches for structural health monitoring and system identification: Past, present, and future

Machine learning paradigm for structural health monitoring

Optimum wavelet selection for nonparametric analysis toward structural health monitoring for processing big data from sensor network: A comparative study

A review of computer vision–based structural health monitoring at local and global levels

Determination of structural and damage detection system influencing parameters on the value of information

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

Country-scale InSAR monitoring for settlement and uplift damage calculation in architectural heritage structures

A research on an improved Unet-based concrete crack detection algorithm

Thermal response of timber connections using densified wood dowels under fire

Optimization of sensor placement for structural health monitoring: a review

Farm-wide virtual load monitoring for offshore wind structures via Bayesian neural networks

Predictive probability of detection curves based on data from undamaged structures

Bayesian data-driven framework for structural health monitoring of composite structures under limited experimental data

Toward data anomaly detection for automated structural health monitoring: Exploiting generative adversarial nets and autoencoders

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

Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm

Crack and Noncrack Classification from Concrete Surface Images Using Machine Learning

Polynomial Chaos-Kriging metamodel for quantification of the debonding area in large wind turbine blades

Structural health monitoring of offshore wind turbines using automated operational modal analysis

Three decades of statistical pattern recognition paradigm for SHM of bridges

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