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机构地区:[1]长安大学公路学院,陕西西安710064 [2]加拿大阿尔伯塔大学土木与环境工程学院 [3]石家庄市京昆高速公路京石管理处,河北石家庄050000
出 处:《公路交通科技》2016年第4期96-101,148,共7页Journal of Highway and Transportation Research and Development
基 金:河北省交通科技计划重点项目(Y-2010156);陕西省交通运输厅科技项目(13-25K)
摘 要:为避免基于结构频率的损伤检测法因温变影响而出现误判,提出一种自联想神经网络(AANN)和奇异检测技术相结合的损伤检测方法,利用某桥梁结构健康监测(SHM)Benchmark试验结构的数值模型,分析温变和损伤对结构频率的影响,用温变影响下健康结构的前十阶竖向弯曲模态频率训练AANN来提取频率和温度的关系。为消除温变影响,构造网络输出与输入间的欧式距离作为损伤识别指标,对比结构未知状态和健康状态的指标值以判定结构是否存在损伤。通过在桥梁Benchmark结构中模拟多级损伤来验证该法的有效性,检测结果表明:该法不仅能可靠地检测温变影响下结构损伤的存在,且能定性地判别损伤程度的大小,并具有较强的抗噪声鲁棒性,可为实际桥梁结构的在线健康监测提供参考。With the intention of avoiding the false diagnoses caused by temperature fluctuation using the frequency-based structural damage detection methods,an approach incorporating an AANN and a novelty detection technique is proposed. A numerical model of a bridge structural health monitoring( SHM)Benchmark structure is used to investigate the influences of temperature fluctuation and damage on structural frequencies. The frequencies of the first 10 vertical flexure modes of the intact Benchmark structure under varying temperatures are used to qualitatively train an AANN for extracting the dependency of frequency on temperature. To eliminate the influence of temperature fluctuation,the Euclidean distance between the outputs and inputs of this network is then formulated,and it is used as a damage identification index. These indices of candidate states are compared with those of intact states to detect damage occurrence. Several simulated damage scenarios are introduced into the Benchmark structure to verify the availability of this approach. The result shows that the proposed approach enables one to detect damage occurrence robustly under influence of temperature fluctuation and to estimate damage severity with high noise-tolerant capacity.Hence,this approach may provide a reference for in-service bridge SHM.
关 键 词:桥梁工程 损伤检测 AANN Benchmark结构 温变 频率
分 类 号:U446[建筑科学—桥梁与隧道工程]
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