DAMAGE DETECTION USING SUPPORT VECTOR MACHINE FOR INTEGRITY ASSESSMENT OF CONCRETE STRUCTURES

Damage assessment of bending structures using support vector machine

A damage detection method utilizing Support Vector Machine (SVM) for bending structures is proposed. The SVM was recently proposed as a new technique for pattern recognition. The SVM is a powerful pattern recognition tool applicable to complicated classification problems and is effectively utilized in the method. Based on the modal frequency changes, the damage location and its severity are def...

Quantitative Damage Diagnosis of Shear Structures Using Support Vector Machine

Using Wavelet Support Vector Machine for Fault Diagnosis of Gearboxes

Identifying fault categories, especially for compound faults, is a challenging task in mechanical fault diagnosis. For this task, this paper proposes a novel intelligent method based on wavelet packet transform (WPT) and multiple classifier fusion. An unexpected damage on the gearbox may break the whole transmission line down. It is therefore crucial for engineers and researchers to monitor the...

Structural Damage Detection Using Wavelet Support Vector Machine

Based on wavelet packet decomposition and conditions of the support vector kernel function, a nonlinear wavelet basis is introduced to construct the kernel function of support vector machine (SVM). A tighten wavelet support vector machine (WSVM), which has strong generalization ability is also obtained. In this study, a novel damage classification method based on wavelet support vector machine ...

Outlier Detection for Support Vector Machine using Minimum Covariance Determinant Estimator

The purpose of this paper is to identify the effective points on the performance of one of the important algorithm of data mining namely support vector machine. The final classification decision has been made based on the small portion of data called support vectors. So, existence of the atypical observations in the aforementioned points, will result in deviation from the correct decision. Thus...