Part 2: Keynote Lectures

Cruz, Paulo J. S.; Frangopol, Dan M.; Neves, Luis C. Canhoto

doi:10.1201/b18175-6

Cited by 5 publications

(2 citation statements)

References 16 publications

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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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Section: Markov Chain Deterioration Modelmentioning

confidence: 99%

Deriving the transition probability matrix using computational mechanics

Riveros

Rosario-Pérez

2018

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“…The role of the test loading research in the bridge management system is significant in the world. The publication (Casas, 2006;Wiśniewski et al, 2012) presented the current situation and future trends related to the assessment of the condition and bearing capacity of a structure. The importance of diagnostic test loading was emphasized to be the most accurate tool to assess the structure's bearing capacity.…”

Section: Introductionmentioning

confidence: 99%

Practical Considerations Regarding Results From Static and Dynamic Load Testing of Bridges

Olaszek

Casas

2019

Front. Built Environ.

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Bridge tests are a helpful tool for bridge assessment and evaluation. Both in the case of a static and dynamic load testing, each element of the test: the load selection and application, the creation of a numerical model to follow the progress of the test or to check the validity of the test results, the measurement process itself and the comparative analysis of experimental results and calculations could be a source of errors in the bridge final evaluation if these errors and uncertainties are not properly considered. The article presents some of the most important factors that may bring errors in the interpretation of the test results and their comparison to targeted values or values derived from a numerical model. This, at the end, may result in the adoption of decisions that are not accurate and appropriate. The selected sources of feasible errors are presented with the division into static and dynamic loading tests. The presented examples of bridge load testing show how the use of improper test methods could lead to significant errors in bridge assessment and evaluation and, consequently, to wrong decisions.

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