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|Title:||Integration of long-term SHM data into bridge condition assessment||Authors:||Xia, Yunxia||Degree:||Ph.D.||Issue Date:||2017||Abstract:||The past two decades have witnessed a mushrooming of multidisciplinary research and applications of the structural health monitoring (SHM) technology to civil structures. Much attention was paid to long-span bridges because of their complexity, huge investment, and significance to the society. A great expectation has been placed on the long-term SHM to lead to the next significant evolution of design, assessment, and management of bridges. However, the gap between SHM and bridge condition assessment, that exists currently, impedes the bridge managers benefiting from the costly SHM systems. In connection with fifteen years of SHM data collected from the instrumented Tsing Ma Bridge, this thesis aims to develop a methodology to integrate SHM data into bridge condition assessment. A reliability-based framework for evolutionary bridge condition assessment is proposed in the context of statistical inference. With elaborately configured strain gauge arrays, the structural reliability is evaluated at two levels: (i) individual chord level, and (ii) deck cross-section level. For long-term monitoring data, extreme value statistics is advantageous because it not only avoids the cumbersome modelling of multiple load effects but also provides a time reference in terms of a return period. Hence, extreme value distributions of the live load demands are inferred to evaluate the structural reliability indices. The bridge is not equipped with sensors everywhere, and sensor fault may occur sometimes in the SHM system. Thus, inspection results and finite element model (FEM) of the bridge are also essential. To obtain the evaluation results more reliable, a three-dimensional bridge condition rating system, comprising criticality rating, vulnerability rating and inspection rating, is proposed subsequently. This system comprehensively considers the data-driven and FEM-driven condition assessment results, inspection results, and exposure of structural components to adverse effects such as corrosion and ship collision, as well as other relevant information such as as-built report and maintenance record of the bridge. Prior to the above studies, an effective and computationally efficient wavelet-based signal pre-processing scheme is first developed to automatically remove the noises, spikes, and trends embedded in the signals, and to separate the signals into different ingredients such as stress components due to the highway traffic, railway traffic and temperature. In addition, site-specific load models for the highway and railway traffic are developed, so that the full 3D FEM of the bridge can be employed to complement SHM data in the bridge condition rating.||Subjects:||Bridges -- Inspection.
Bridges -- Maintenance and repair.
Structural health monitoring.
Hong Kong Polytechnic University -- Dissertations
|Pages:||xxii, 242 pages : color illustrations|
|Appears in Collections:||Thesis|
View full-text via https://theses.lib.polyu.edu.hk/handle/200/8937
Citations as of May 22, 2022
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