共 3 条
Identification of multi-axle vehicle loads on beam type bridge based on minimal residual norm steepest descent method
被引:5
|作者:
Chen, Zhen
[1
,2
,3
]
Fang, Yubo
[1
]
Kong, Xuan
[2
]
Deng, Lu
[2
]
机构:
[1] North China Univ Water Resources & Elect Power, Sch Civil Engn & Commun, Zhengzhou 450045, Peoples R China
[2] Hunan Univ, Key Lab Damage Diag Engn Struct Hunan Prov, Changsha 410082, Peoples R China
[3] North China Univ Water Resources & Elect Power, Collaborat Innovat Ctr Efficient Utilizat Water Re, Zhengzhou 450046, Peoples R China
基金:
中国国家自然科学基金;
关键词:
moving force identification;
multi -axle vehicle loads;
identification accuracy;
noise insensitivity;
minimal residual norm steepest descent;
method;
MOVING FORCE IDENTIFICATION;
VALUE DECOMPOSITION ALGORITHM;
SPARSE REGULARIZATION;
PARAMETERS;
SIGNALS;
SYSTEM;
D O I:
10.1016/j.jsv.2023.117866
中图分类号:
O42 [声学];
学科分类号:
070206 ;
082403 ;
摘要:
Moving force identification (MFI) from dynamic responses of bridges is a typical inverse problem in the field of structural dynamics. To deal with the difficulties in identifying multi-axle vehicle loads caused by the interference of adjacent axle loads, a novel minimal residual norm steepest descent method (MRNSD) is proposed. Firstly, the identification accuracy and noise insensitivity of the new method is verified by identifying two-axle vehicle with different speeds. Secondly, the influence of impact force and vehicle wheelbase is investigated to illustrate the applicability of the proposed method. Then, the effectiveness of the MRNSD method is verified by identifying several different types of multi-axle vehicles. Finally, experimental studies are conducted to further validate the effectiveness of the MRNSD method. Numerical simulation and experimental studies show that the MRNSD method has high identification accuracy and strong robustness in different force identification cases. With these advantages, the proposed method provides a favorable choice for force identification in practice.
引用
收藏
页数:21
相关论文