A Methodology for Bridge Condition Evaluation

Rashidi, Maria

doi:10.17265/1934-7359/2012.09.007

Cited by 17 publications

(17 citation statements)

References 13 publications

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“…Table 1 presents the vulnerability factor of common materials used in concrete bridges introduced as Mi which is obtained from the work of Valenzuela et al (2010) 6 and validated by the judgements of structural engineers. Based on vulnerability of different materials it varies between 1 and 4 7 .…”

Section: Phase One: Project Rankingmentioning

confidence: 99%

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A New Approach to Bridge Infrastructure Management

Rashidi

Ho²

2014

Proceedings of the International Symposium for Next Generation Infrastructure

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The maintenance of bridges as a key element in transportation infrastructure has become a major concern due to increasing traffic volumes, deterioration of existing bridges and well-publicised bridge failures. The main goal of this study is to develop a requirement-driven decision support methodology for remediation of concrete bridges within acceptable limits of safety, serviceability and sustainability. The proposed model includes two phases: Phase one is focused on condition assessment and priority ranking of bridge projects which makes use of an integrated priority index addressing a variety of factors. Phase two includes a multi criteria decision making technique which is able to select the best remediation strategy at both project and network level. The modified Simple Multi Attribute Rating Technique (SMART) is used as a decision analysis tool that employs the eigenvector approach of the Analytical Hierarchy Process (AHP) for criteria weighting.

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Section: Phase One: Project Rankingmentioning

confidence: 99%

“…The weights of the involved parameters have been estimated using AHP and the associated ratings are defined based on the classifications presented in Table 1. 7 (2)…”

Section: Phase One: Project Rankingmentioning

confidence: 99%

A New Approach to Bridge Infrastructure Management

Rashidi

Ho²

2014

Proceedings of the International Symposium for Next Generation Infrastructure

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“…In through visual inspection, bridge inspectors evaluate the condition of a bridge using their personal experience and following guidelines such as those found in inspector's manuals. The visual inspection incorporates many parameters and human judgments that may produce slightly uncertain and imprecise results [2]. It would be ideal to conduct physical structural tests on each bridge component, but it would be impractical and economically prohibitive to implement them, given the large number of bridges to be inspected within a given period.…”

Section: Introductionmentioning

confidence: 99%

Developing a Bridge Condition Rating Model Based on Limited Number of Data Sets

Yusuf¹,

Hamid²

2018

Bridge Engineering

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This chapter utilizes artificial neural network (ANN) and multiple regression analysis (MRA) to model bridge condition rating based on limited number of data sets. Since data sets are very limited and there is a gap in range of rating scale, two conditions of data sets are used in this study, namely complete data sets and data set with bridge component condition rating data are missing. Five methods are then used to handle the missing bridge component condition rating data. Three commonly used methods and two new methods are explored in this study. It seems that the performance of the model using data sets after handling missing bridge component data to fill the gaps in the range scales of the bridge condition rating improved the performance of the model. In addition, a handling method that substitutes missing data of bridge component ratings with available bridge rating data is favorable. Based on the values of root mean square error (RMSE) and R 2 , the ANN models perform slightly better than MRA to map relationship between bridge components and bridge condition rating. This concluded that ANN is suitable to model bridge condition rating compare to MRA method.

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“…The four condition states are (1) Excellent, (2) Good; (3) Fair; and (4) Poor (see Ministry of Transportation 2008 andAmerican Association of State Highway andTransportation Officials 2007). The accuracy of bridge rating and management decisions of bridge engineers reliy on the accuracy of the bridge condition assessment (Rashidi and Gibson 2012), mainly based on different levels of visual inspection. Different bridge management systems recognize many types of visual inspection such as the typical visual inspection (VI), in-depth VI, and enhanced in-depth VI (see FHWA-RD-01-020, Ministry of Transportation 2008 and American Association of State Highway and Transportation Officials 2000).…”

Section: Introductionmentioning

confidence: 99%

Simplified finite element model for evaluation of ultimate capacity of corrosion-damaged reinforced concrete beam-columns

Mohammed

Almansour

Martı́n-Pérez

2018

Int J Adv Struct Eng

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A simplified nonlinear finite element analysis (NLFEA) based on enhanced inspection, material testing, and nonlinear sectional analysis is introduced as part of a semi-quantitative assessment approach of aged beam-columns. The focus is on the evaluation of the structural performance and residual capacities of slab-on-girder bridge columns subjected to combined external loads and reinforcement corrosion. NLFEA takes into account different levels of geometrical, material, and bond damage due to reinforcement corrosion. At each load step of the nonlinear analysis process, NLFEA establishes the instantaneous stiffness of the structure through effective transfer of the instantaneous axial and flexural rigidities from the sectional level to the element level. The model adopts a displacement field tuning convergence approach that involves single or multiple correction phases satisfying the equilibrium and any user-defined displacement tolerance. The efficiency and accuracy of the proposed NLFEA is verified by comparison with test and analytical results from previous studies conducted on undamaged and corrosion-damaged structural elements. NLFEA proves to have high numerical stability and fast convergence, establishing its adoptability in large structural analysis/assessment frameworks. For corrosion-damaged beamcolumns, it is found that critical design sections do not necessarily remain critical for the structural evaluation.

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A Methodology for Bridge Condition Evaluation

Cited by 17 publications

References 13 publications

A New Approach to Bridge Infrastructure Management

A New Approach to Bridge Infrastructure Management

Developing a Bridge Condition Rating Model Based on Limited Number of Data Sets

Simplified finite element model for evaluation of ultimate capacity of corrosion-damaged reinforced concrete beam-columns

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