2009
DOI: 10.2172/961604
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Abstract: The real-world structures are subjected to operational and environmental condition changes that impose difficulties in detecting and identifying structural damage. The aim of this report is to detect damage with the presence of such operational and environmental condition changes through the application of the Los Alamos National Laboratory's statistical pattern recognition paradigm for structural health monitoring (SHM). The test structure is a laboratory three-story building, and the damage is simulated thro… Show more

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Cited by 196 publications
(176 citation statements)
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References 19 publications
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“…In this section, the experimental data [42] of a three-story building structure made of aluminum As illustrated in Figure 5, the proposed Kriging method can effectively identify the stiffness reduction caused by damage in this numerical simulation experiment. A great improvement has been made by introducing the FDAC in the objective function as the AF approach contains obvious errors.…”
Section: Experimental Studymentioning
confidence: 99%
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“…In this section, the experimental data [42] of a three-story building structure made of aluminum As illustrated in Figure 5, the proposed Kriging method can effectively identify the stiffness reduction caused by damage in this numerical simulation experiment. A great improvement has been made by introducing the FDAC in the objective function as the AF approach contains obvious errors.…”
Section: Experimental Studymentioning
confidence: 99%
“…In this section, the experimental data [42] of a three-story building structure made of aluminum tested at Los Alamos National Laboratory (LANL), shown in Figure 6, is used to verify the effectiveness and the practicability of the proposed approach. The test-bed structure consists of 4 plates and 12 columns assembled by bolted joints and was installed on rails that allow it to slide in only one direction.…”
Section: Experimental Studymentioning
confidence: 99%
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“…In the Structural Health Monitoring field, PCA may be used for three primary purposes: i) evaluation of patterns in the data; ii) data cleansing; iii) data compression [23].…”
Section: Moving Principal Component Analysis (Mpca)mentioning
confidence: 99%