Stochastic Subspace Identification (SSI) is a novel time domain identification method, which directly uses operational response data to identify the system model by linear algebraic manipulations such as QR facto...Stochastic Subspace Identification (SSI) is a novel time domain identification method, which directly uses operational response data to identify the system model by linear algebraic manipulations such as QR factorization and Singular Value Decomposition (SVD). This paper deals with SSI and its applications for structural modal identification. The NASA mini mast model is used for simulations to illustrate how to select input parameters, and to demonstrate identification precision. A real building structure, the Heritage Court Tower (HCT) in Canada is analyzed. From the simulation and test researches, the conclusions can be made to instruct how to identify structural modal parameters using SSI method.展开更多
The possibility of determining the integrity of a real structure subjected to non-invasive and non-destructive monitoring,such as that carried out by a series of accelerometers placed on the structure,is certainly a g...The possibility of determining the integrity of a real structure subjected to non-invasive and non-destructive monitoring,such as that carried out by a series of accelerometers placed on the structure,is certainly a goal of extreme and current interest.In the present work,the results obtained from the processing of experimental data of a real structure are shown.The analyzed structure is a lattice structure approximately 9 m high,monitored with 18 uniaxial accelerometers positioned in pairs on 9 different levels.The data used refer to continuous monitoring that lasted for a total of 1 year,during which minor damage was caused to the structure by alternatively removing some bracings and repositioning them in the structure.Two methodologies detecting damage based on decomposition techniques of the acquired data were used and tested,as well as a methodology combining the two techniques.The results obtained are extremely interesting,as all the minor damage caused to the structure was identified by the processing methods used,based solely on the monitored data and without any knowledge of the real structure being analyzed.The results use 15 acquisitions in environmental conditions lasting 10 min each,a reasonable amount of time to get immediate feedback on possible damage to the structure.展开更多
文摘针对现有基于数据驱动的随机子空间(data-driven stochastic subspace identification,DATA-SSI)算法存在的不足,无法实现稳定图中真假模态的智能化筛选,提出了一种新的模态参数智能化识别算法。首先通过引入滑窗技术来实现对输入信号的合理划分,以避免虚假模态和模态遗漏现象的出现;其次通过引入OPTICS(ordering points to identify the clustering structure)密度聚类算法实现稳定图中真实模态的智能化筛选,最后将所提算法运用于某实际大型斜拉桥主梁结构的频率和模态振型识别过程中。结果表明,所提改进算法识别的频率值结果与理论值(MIDAS有限元结果)以及实际值(现场动力特性实测结果)间的误差均在5%以内,且识别的模态振型图与理论模态振型图具有很高的相似性。
文摘Stochastic Subspace Identification (SSI) is a novel time domain identification method, which directly uses operational response data to identify the system model by linear algebraic manipulations such as QR factorization and Singular Value Decomposition (SVD). This paper deals with SSI and its applications for structural modal identification. The NASA mini mast model is used for simulations to illustrate how to select input parameters, and to demonstrate identification precision. A real building structure, the Heritage Court Tower (HCT) in Canada is analyzed. From the simulation and test researches, the conclusions can be made to instruct how to identify structural modal parameters using SSI method.
基金The author N.I.Giannoccaro received funds from the Department of Innovation Engineering,University of Salento,for acquiring the tool Structural Health Monitoring.
文摘The possibility of determining the integrity of a real structure subjected to non-invasive and non-destructive monitoring,such as that carried out by a series of accelerometers placed on the structure,is certainly a goal of extreme and current interest.In the present work,the results obtained from the processing of experimental data of a real structure are shown.The analyzed structure is a lattice structure approximately 9 m high,monitored with 18 uniaxial accelerometers positioned in pairs on 9 different levels.The data used refer to continuous monitoring that lasted for a total of 1 year,during which minor damage was caused to the structure by alternatively removing some bracings and repositioning them in the structure.Two methodologies detecting damage based on decomposition techniques of the acquired data were used and tested,as well as a methodology combining the two techniques.The results obtained are extremely interesting,as all the minor damage caused to the structure was identified by the processing methods used,based solely on the monitored data and without any knowledge of the real structure being analyzed.The results use 15 acquisitions in environmental conditions lasting 10 min each,a reasonable amount of time to get immediate feedback on possible damage to the structure.
