Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Faculty of Construction and Environment | en_US |
| dc.creator | Wang, Xiao | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/7179 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Statistical counting methods for strain-based structural condition assessment | en_US |
| dcterms.abstract | Structure suffers from many perilous conditions during its service period due to load effects during its operation and deferred maintenance. A precise and comprehensive structural condition assessment is vitally important to guarantee the safety of a structure. With many advancements in sensor technology for structural health monitoring (SHM) systems, strain-based structural condition assessment has become a favoured dependable approach to evaluate the condition of a structure. Reliability-based structural condition assessment that incorporates strain data is capable of providing accurate information about in-service performance of the structure and accommodating uncertainties in measurement data. Combining the structural reliability assessment with the monitoring strain data by the SHM system is an effective tool to evaluate the structural safety and health. When monitoring data of strain is applied for structural reliability assessment, usually the peak data rather than the whole raw data is used. The peak values of the measurands that illustrate the critical condition/status of the structure are random in nature, therefore it is important to adopt appropriate statistical counting methods to extract favorable peak values for reliability assessment. The result of reliability is influenced by the selection of the statistical counting method. Some methods, such as the sampling method, the peak counting method, and the pointwise counting method, have been proposed for peak counting. | en_US |
| dcterms.abstract | This dissertation compares three most common statistical counting methods for the selection of peak data targeted for strain-based structural reliability assessment, through the application of the above methods for the purpose of constructing peak-stress histograms and formulating probability density functions (PDF) by use of long-term strain monitoring data from an instrumented bridge. Peak covering rate is defined and the relationship between the amount of peak data and the control parameters for peak counting is obtained to help determine the parameters used in the statistical counting methods. Finally, the analysis performs reliability calculations on the same amount of peak data selected by the three statistical counting methods. In summary, the research described in this dissertation chiefly clarifies a systematic framework for strain-based structural condition assessment of applying statistical counting methods. Through the application of the statistical counting methods on the strain monitoring data from the Tsing Ma Bridge (TMB), research results show that the shape of the peak-stress histograms obtained by the three statistical counting methods is varying with the control parameter; the evaluation results of reliability index from the three statistical counting methods are consistent when the control parameters for the three methods are determined according to the peak covering rate as described in this dissertation; and the evaluated values of reliability index from the three statistical counting methods grow with the increase of the amount of peak data used in constructing peak-stress histograms. | en_US |
| dcterms.extent | x, 100 leaves : ill. ; 30 cm. | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2013 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.LCSH | Structural analysis (Engineering) | en_US |
| dcterms.LCSH | Structural analysis (Engineering) -- Statistical methods. | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b26469340.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 4.39 MB | Adobe PDF | View/Open |
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