Monitoring Information and Probabilistic-Based Prediction Models for the Performance Assessment of Concrete Structures

Strauß, Alfred; Vidović, Anja; Zambon, Ivan; Grossberger, Hirut; Bergmeister, Konrad

doi:10.1061/(asce)cf.1943-5509.0000834

Cited by 7 publications

(5 citation statements)

References 25 publications

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“…How to reasonably handle the distantly provided monitored data is one of the main difficulties in structural health monitoring. A sound number of studies about data processing are mainly focused on the modal parameter identification, structural damage detection technology, data modeling, and so on, [1][2][3][4][5] while for the research of bridge performance prediction and assessment based on monitored data, some achievements are conducted, for example, performance analysis of structural member based on reliability, 6 performance prediction analysis of concrete structures based on monitoring information and probability, 7 fatigue life prediction method of concrete structures based on numerical and monitoring, 8 reliability updating and prediction of bridge structures based on proof loads and monitored data, 9 extreme stress prediction of bridges based on multiple Bayesian dynamic linear models (BDLM), 10 reliability assessment of long-span truss bridge, 11 and structural performance prediction based on monitored extreme data. 12 It is noticed that these examples are all studied based on the uniform data or theoretical analysis model.…”

Section: Introductionmentioning

confidence: 99%

Bridge extreme stress prediction based on Bayesian dynamic linear models and non-uniform sampling

Fan

2017

Structural Health Monitoring

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Bridge monitoring systems produce a large amount of data, including uniform and non-uniform sampled data in the long-term service periods; the proper handling of these data is one of the main difficulties in structural health monitoring. To properly predict structural non-uniform extreme stress and deal with the uncertainties of the monitored data, the objectives of this article are to present (a) Bayesian dynamic linear models about non-uniform extreme stress, (b) monitoring mechanism about the Bayesian dynamic linear models based on single and cumulative Bayes’ factors, and (c) an effective use of the Bayesian dynamic linear models to incorporate the dynamic monitored data into structural non-uniform extreme stress prediction. The proposed models and procedure are applied to the monitored data obtained from the I-39 Northbound Bridge over the Wisconsin River in Wausau, Wisconsin, USA.

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Section: Introductionmentioning

confidence: 99%

Bridge extreme stress prediction based on Bayesian dynamic linear models and non-uniform sampling

Fan

2017

Structural Health Monitoring

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“…This index only considers the weak points of a system and it is narrowly defined. ρ stiffness,Var1 = 0.028 < ρ stiffness,Var2 = 0.076 (14) The probabilistic robustness index can be formulated by comparing the failure probability of the damaged system with that of the intact system as shown in Equations ( 15)- (20). All possible failure paths must be determined in advance.…”

Section: Case Studies Of Computations Of Robustness Performance Indicatorsmentioning

confidence: 99%

“…For this study, a MATLAB algorithm based on Finite Element Modelling (FEM) was developed which is presented herein. The outcome of the study aims to provide insight to the effects of redundancy on structural robustness, and to serve as a paradigm for performance-based design for structural robustness [13,14]. The practical significance of robustness indicators proposed by current standards and directives is also examined.…”

Section: Introductionmentioning

confidence: 99%

Robustness Assessment of Redundant Structural Systems Based on Design Provisions and Probabilistic Damage Analyses

Spyridis

Strauß

2020

Buildings

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Typically in structural design, foreseeable loads are assumed in a structural design and dimensioning exercise and design material properties may be handled in a semi-probabilistic approach. Structures can, however, be exposed to largely unforeseeable events such as intense environmental phenomena, accidents, malicious acts, and planning or execution errors, in addition to degradation with time. Recent significant collapses have highlighted the fact that robustness is an indispensable integral part of the structural design and provisions in upcoming codes are currently expanding in this respect. The paper examines the practical significance of quantitative robustness indicators included in recent research and upcoming standards and it assesses their efficiency based on case studies. Moreover, it proposes a probabilistic numerical methodology for robustness assessment under uncertainty, and it demonstrates its practical applicability based on computations with indicative structural truss systems, i.e., multi-component systems. The proposed method allows for quantifiable and comparable robustness measures, which can be integrated in reliability-based design and structural health monitoring of engineering systems. The redundancy aspect of robustness is pronounced as a plausible quantitative performance indicator for multi-component systems. In particular, the robustness index combining reliability and redundancy of the elements is proven to be the most useful one out of the examined approaches. This probabilistic elaboration does not only account for the reasonable treatment of variability and randomness, but it allows for an inverse identification of the critical failure paths and the characterization of weak links in the systems.

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“…The literature revealed that Markov models are extensively used for infrastructure deterioration Micevski et al, 2002;DeStefano and Grivas, 1998) with bridges being a frequent candidate (Agrawal et al, 2008;Bocchini et al, 2013;Casas, 2013;Strauss et al, 2016) followed by pavements (Ortiz-Garcia et al, 2006) and sewer pipes (Micevski et al, 2002;Baik et al, 2006). The Markov chain prediction model is a stochastic process that is discrete in time, has a finite state space and establishes that future state of the Computational mechanics process depends only on its present state.…”

Section: Markov Chain Deterioration Modelmentioning

confidence: 99%

Deriving the transition probability matrix using computational mechanics

Riveros

Rosario-Pérez

2018

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Purpose The combined effects of several complex phenomena cause the deterioration of elements in steel hydraulic structures (SHSs) within the US lock system: corrosion, cracking and fatigue, impact and overloads. Predicting the future condition state of these structures by the use of current condition state inspection data can be achieved through the probabilistic chain deterioration model. The purpose of this study is to derive the transition probability matrix using final elements modeling of a miter gate. Design/methodology/approach If predicted accurately, this information would yield benefits in determining the need for rehabilitation or replacement of SHS. However, because of the complexity and difficulties on obtaining sufficient inspection data, there is a lack of available condition states needed to formulate proper transition probability matrices for each deterioration case. Findings This study focuses on using a three-dimensional explicit finite element analysis (FEM) of a miter gate that has been fully validated with experimental data to derive the transition probability matrix when the loss of flexural capacity in a corroded member is simulated. Practical implications New methodology using computational mechanics to derive the transition probability matrices of navigation steel structures has been presented. Originality/value The difficulty of deriving the transition probability matrix to perform a Markovian analysis increases when limited amount of inspection data is available. The used state of practice FEM to derive the transition probability matrix is not just necessary but also essential when the need for proper maintenance is required but limited amount of the condition of the structural system is unknown.

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Monitoring Information and Probabilistic-Based Prediction Models for the Performance Assessment of Concrete Structures

Cited by 7 publications

References 25 publications

Bridge extreme stress prediction based on Bayesian dynamic linear models and non-uniform sampling

Bridge extreme stress prediction based on Bayesian dynamic linear models and non-uniform sampling

Robustness Assessment of Redundant Structural Systems Based on Design Provisions and Probabilistic Damage Analyses

Deriving the transition probability matrix using computational mechanics

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