TY - JOUR
T1 - Abnormality identification of large-span arch bridge based on BP neural improved novelty detection technique
AU - Wang, Tao
AU - Zhang, Li Sha
AU - Gao, Yan
N1 - Publisher Copyright:
© 2016, Beijing Institute of Technology. All right reserved.
PY - 2016/2/1
Y1 - 2016/2/1
N2 - In order to identify abnormal state of large-span arch bridge, an improved novelty detection technique based on BP neural algorithm was used. The method used BP neural to train a large number of measured data and got the novelty index when bridge state was normal. Then the threshold and whether the bridge abnormal or not was determined. Combined with concrete situation and data analysis, the method can accurately identify the abnormal region. The method, which avoids the effect of the model error, can greatly improve the practical value, decrease false alarm, make the identification result more accurate and more practical.
AB - In order to identify abnormal state of large-span arch bridge, an improved novelty detection technique based on BP neural algorithm was used. The method used BP neural to train a large number of measured data and got the novelty index when bridge state was normal. Then the threshold and whether the bridge abnormal or not was determined. Combined with concrete situation and data analysis, the method can accurately identify the abnormal region. The method, which avoids the effect of the model error, can greatly improve the practical value, decrease false alarm, make the identification result more accurate and more practical.
KW - Abnormality identification
KW - Bridge engineering
KW - Large-span arch bridge
KW - Novelty detection technique
UR - http://www.scopus.com/inward/record.url?scp=84962599176&partnerID=8YFLogxK
U2 - 10.15918/j.tbit1001-0645.2016.02.010
DO - 10.15918/j.tbit1001-0645.2016.02.010
M3 - Article
AN - SCOPUS:84962599176
SN - 1001-0645
VL - 36
SP - 157
EP - 162
JO - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
JF - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
IS - 2
ER -