Neural Network-Based Damage Detection from Transfer Function Changes

被引：5
|
作者：
Vinayak, Hemant Kumar [1 ]
Kumar, Ashok [2 ]
Agarwal, Pankaj [2 ]
Thakkar, Shashi Kant [2 ]
机构：
[1] Natl Inst Technol Hamirpur, Dept Civil Engn, Hamirpur 177005, Himachal Prades, India
[2] Indian Inst Technol, Dept Earthquake Engn, Roorkee, Uttar Pradesh, India
来源：
JOURNAL OF EARTHQUAKE ENGINEERING | 2010年 / 14卷 / 05期
关键词：
Damage Detection; Neural Network; Transfer Function; Stiffness; Earthquake;
D O I：
10.1080/13632460903414535
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
This article puts forth the work on a neural network-based approach to determine the degree of damaged floors of the building considering changes in the transfer function. The transfer function is considered for that part of forced vibration in which system vibrates linearly after the structure has been damaged considering the building is instrumented during the earthquake. The results showed that accuracy of degree of damage detected increased with the increase in the number of combination of damages. The instrumentation of the first floor is expected to give best results for damage detection based on the transfer function-based approach.
引用
收藏
页码：771 / 787
页数：17
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