scholarly journals DAMAGE DETECTION OF TRUSS STRUCTURES BY APPLYING MACHINE LEARNING ALGORITHMS

2019 ◽  
Vol 16 (54) ◽  
Author(s):  
Koji Unno
Keyword(s):  
Machine Learning ◽  
Damage Detection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Truss Structures

Machine learning algorithms for damage detection: Kernel-based approaches

2016 ◽  
Vol 363 ◽  
pp. 584-599 ◽  
Author(s):  
Adam Santos ◽  
Eloi Figueiredo ◽  
M.F.M. Silva ◽  
C.S. Sales ◽  
J.C.W.A. Costa
Keyword(s):  
Machine Learning ◽  
Damage Detection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals Wind Turbine Blade Damage Detection Using Supervised Machine Learning Algorithms

2017 ◽  
Vol 139 (6) ◽  
Author(s):  
Taylor Regan ◽  
Christopher Beale ◽  
Murat Inalpolat
Keyword(s):  
Machine Learning ◽  
Damage Detection ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Learning Algorithms ◽  
Turbine Blades ◽  
Machine Learning Algorithms ◽  
Test Rig ◽  
Wind Turbine Blades ◽  
Baseline Characteristics

Wind turbine blades undergo high operational loads, experience variable environmental conditions, and are susceptible to failure due to defects, fatigue, and weather-induced damage. These large-scale composite structures are fundamentally enclosed acoustic cavities and currently have limited, if any, structural health monitoring (SHM) in place. A novel acoustics-based structural sensing and health monitoring technique is developed, requiring efficient algorithms for operational damage detection of cavity structures. This paper describes the selection of a set of statistical features for acoustics-based damage detection of enclosed cavities, such as wind turbine blades, as well as a systematic approach used in the identification of competent machine learning algorithms. Logistic regression (LR) and support vector machine (SVM) methods are identified and used with optimal feature selection for decision-making via binary classification algorithms. A laboratory-scale wind turbine with hollow composite blades was built for damage detection studies. This test rig allows for testing of stationary or rotating blades, of which time and frequency domain information can be collected to establish baseline characteristics. The test rig can then be used to observe any deviations from the baseline characteristics. An external microphone attached to the tower will be utilized to monitor blade health while blades are internally ensonified by wireless speakers. An initial test campaign with healthy and damaged blade specimens is carried out to arrive at several conclusions on the detectability and feature extraction capabilities required for damage detection.

Machine learning algorithms to damage detection under operational and environmental variability

2010 ◽  
Author(s):  
Eloi Figueiredo ◽  
Gyuhae Park ◽  
Charles R. Farrar ◽  
Keith Worden ◽  
Joaquim Figueiras
Keyword(s):  
Machine Learning ◽  
Damage Detection ◽  
Environmental Variability ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals A data-based structural health monitoring approach for damage detection in steel bridges using experimental data

2021 ◽  
Author(s):  
Bjørn T. Svendsen ◽  
Gunnstein T. Frøseth ◽  
Ole Øiseth ◽  
Anders Rønnquist
Keyword(s):  
Machine Learning ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Damage ◽  
Learning Algorithms ◽  
Steel Bridges ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Unsupervised Machine Learning

AbstractThere is a need for reliable structural health monitoring (SHM) systems that can detect local and global structural damage in existing steel bridges. In this paper, a data-based SHM approach for damage detection in steel bridges is presented. An extensive experimental study is performed to obtain data from a real bridge under different structural state conditions, where damage is introduced based on a comprehensive investigation of common types of steel bridge damage reported in the literature. An analysis approach that includes a setup with two sensor groups for capturing both the local and global responses of the bridge is considered. From this, an unsupervised machine learning algorithm is applied and compared with four supervised machine learning algorithms. An evaluation of the damage types that can best be detected is performed by utilizing the supervised machine learning algorithms. It is demonstrated that relevant structural damage in steel bridges can be found and that unsupervised machine learning can perform almost as well as supervised machine learning. As such, the results obtained from this study provide a major contribution towards establishing a methodology for damage detection that can be employed in SHM systems on existing steel bridges.

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