Application of Wavelet Packet Transform and Neural Network to Detect Damage of I-section Steel Beam
Most existing civil engineering structures which have approached their normal life span., such as bridges, big dams, high architectures etc., Almost all of these architecture structures are subjected to damage due to external loads, environmental effect, excessive service, natural disaster, initial design defect etc. Structural damage detection and assessment has been becoming a focus of increasing interest in civil engineering field. However, At present, the study on structural damage detection is still at initial stage and the adopted main approaches are theoretical analysis and numerical simulation, but physical models are scarce. This leads to the yielded theories and methods are not sufficiently applicable for practical engineering application. Aiming at this, this paper focuses on developing effective methods of using wavelet and neural networks to detect the damage of bridge structures due to their extensive applications in civil engineering. The different damage states are set up in a bridge model, a simple-supporting Ⅰ- section steel beam, through different damage location, different damage quantification, single damage, many damages, etc. Experimental exploration the feasibility and validity of applying wavelet transform and natural network to detect damage of Ⅰ- section, steel beam based on many is the main characteristics and contribution of the paper.
Wavelet packet transform Neural network Wavelet packet component energy Damage detection Ⅰ-section steel beam Bridge structure
Donghai Xie Hongwei Tang Jishou Wang
Shandong Urban Construction Vocational College, Jinan 250014, China Shandong University, Jinan 250014, China Shandong Taihang Architecture Design Co., Ltd., Jinan 250001, China
国际会议
南京
英文
441-446
2007-10-16（万方平台首次上网日期，不代表论文的发表时间）