A new collection of compressed damage indices for multi-damage detection of cold formed steel shear walls based on neural network ensembles

作者：Tajrishi Fatemeh Zahedi^*; Roshan Alireza Mirza Goltabar; Zeynalian Mehran; Amiri Javad Vaseghi

来源：Canadian Journal of Civil Engineering, 2016, 43(12): 1034-1043.

DOI：10.1139/cjce-2015-0417

• damage detection
• neural network ensemble
• frequency response function
• principal component analysis
• cold formed steel
• strap brace
• time series
• damage index
• shear wall

This study presents a methodology that utilizes a new combination of two compressed damage indices as input data of an artificial neural network (ANN) ensemble to detect multi-damages in the braces of cold formed steel shear walls. To identify an efficient input data for ANN, first, three main groups of damage indices are considered: modal parameter-based damage indices; frequency response functions (FRFs)-based damage indices and time series-based damage indices. Furthermore, principal component analysis (PCA) technique is applied to reduce the dimensions of FRFs and time series-based input pattern. By a sensitivity study, two suitable damage indices of PCA-compressed time series data and PCA-compressed FRFs are identified and then combined to produce a new efficient input data for a hierarchy of ANN ensembles. The numerical results show that the ANN ensemble-based damage detection approach with the proposed collection of two damage indices is effective and reliable.

• 出版日期2016-12