文献类型：期刊文献

英文题名：Damage detection of wood beams using the differences in local modal flexibility

作者：Hu, Chuanshuang[1] Xiao, Min[2] Zhou, Haibin[3] Wen, Wei[1] Yun, Hong[1]

第一作者：Hu, Chuanshuang

通信作者：Hu, CS[1]

机构：[1]S China Agr Univ, Dept Wood Sci & Engn, Fac Forestry, Guangzhou 510642, Guangdong, Peoples R China;[2]S China Univ Technol, Sch Architecture, Guangzhou 510642, Guangdong, Peoples R China;[3]Chinese Acad Forestry, Res Inst Wood Ind, Beijing 100081, Peoples R China

年份：2011

卷号：57

期号：6

起止页码：479-483

外文期刊名：JOURNAL OF WOOD SCIENCE

收录：;EI(收录号：20114514492787);Scopus(收录号：2-s2.0-80255134579);WOS:【SCI-EXPANDED(收录号:WOS:000296517500003)】；

基金：The authors would like to express their thanks to the Department of Finance of Guangdong Province (Project No. 2008A020100013) and the State Key Laboratory of Subtropical Building Science, South China University of Technology.

语种：英文

外文关键词：Damage detection; Wood products; Modal flexibilities

摘要：Local damage such as knots, decay, and cracks can result in a reduction of service life due to mechanical and environmental loadings. In wood construction, it is very important to evaluate the weakest location and to detect damage at the earliest possible stage to avoid future catastrophic failure. In this study, modal testing was carried out on wood beams to generate the first two mode shapes. A novel statistical algorithm was proposed to extract the damage indicator by computing the local modal flexibility before and after damage in timbers. Different damage severities, damage locations, and damage counts were simulated by removing mass from intact beams to verify the algorithm. The results indicated that the proposed statistical algorithm is effective and suitable for the damage scenarios considered. The algorithm was reliable for detecting and locating local damage under different damage scenarios. The peak values of the damage indicators computed from the first two mode shapes were sensitive to different damage severities and locations. This approach was also reliable for detecting multiple defects.