000019944 001__ 19944
000019944 005__ 20170118182347.0
000019944 04107 $$aeng
000019944 046__ $$k2017-01-09
000019944 100__ $$aBakhshi, Ali
000019944 24500 $$aStructural Health Monitoring in Multi-Story Frames  Based on Signal Processing and Rbf Neural Networks

000019944 24630 $$n16.$$pProceedings of the 16th World Conference on Earthquake Engineering
000019944 260__ $$b
000019944 506__ $$arestricted
000019944 520__ $$2eng$$aIn the past few decades, using signal processing tools in structural health monitoring has risen considerably due to recent advances in the field of sensors and other electronic technologies. These advances provide a wide range of response signals such as velocity, acceleration and displacement caused by low to high intensity earthquakes and environmental loads on building structures and bridges. Structural health monitoring i.e. the detection of presence, location and type of damage in structure in order to quantify the amount of damage and predicting the remaining lifetime of structure for service. In this research, wavelet packet transform has been employed in combination with Hilbert transform due to its favorable performance in detection of the structural damages and also its capability for denoising of response signals. In the proposed method, radial basis function (RBF) neural network has been used with the aim of reducing the number of required sensors in order to identify the location and determine the severity of damage caused to the structure. To achieve the proposed goal, the extracted data from each response signal should be increased to provide some information with regard to the higher modes. Finally, the obtained data is used to train the RBF neural network. The performance of the proposed method has been verified by means of numerical examples. To demonstrate the capabilities of the proposed algorithm, numerical simulations are performed on a four-story two-bay shear frame with different damage scenarios using OpenSees. The results show that this method can detect the occurrence, location and severity of damage with good accuracy even in the presence of measurement noise.

000019944 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000019944 653__ $$astructural health monitoring; signal processing; wavelet packet transform; neural network; multi-story frames

000019944 7112_ $$a16th World Conference on Earthquake Engineering$$cSantiago (CL)$$d2017-01-09 / 2017-01-13$$gWCEE16
000019944 720__ $$aBakhshi, Ali$$iTehrani, Hamed Amini
000019944 8560_ $$ffischerc@itam.cas.cz
000019944 8564_ $$s336208$$uhttps://invenio.itam.cas.cz/record/19944/files/4765.pdf$$yOriginal version of the author's contribution as presented on USB, paper 4765.
000019944 962__ $$r16048
000019944 980__ $$aPAPER