Time-dependent reliability analysis of existing RC structures in a marine environment using hazard associated with airborne chlorides

Akiyama, Mitsuyoshi; Frangopol, Dan M.; Yoshida, Ikumasa

doi:10.1016/j.engstruct.2010.08.021

Cited by 119 publications

(21 citation statements)

References 24 publications

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“…One is physical-model-based approach, e.g. Frangopol et al (1997aFrangopol et al ( , 1997b, Akiyama et al (2010), Yoshida and Akiyama (2013). The other is statistical approach, e.g.…”

Section: Introductionmentioning

confidence: 99%

A Group of Deterioration Curves of Bridges Estimated by Extended EM Algorithm

Yoshida

Otake

Honjo

2014

Vulnerability, Uncertainty, and Risk

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This study proposes a method to separate data into several groups and perform regression analysis for each group at the same time statistically by using EM (Expectation and Maximization) algorithm or its extended formulation to nonlinear regression problem. With using the proposed method, a group of deterioration curves is estimated based on database of health index of 341 bridges by visual inspection in Gifu prefecture, Japan. Ordinary EM algorithm can treat linear regression only, so that the algorithm is extended to nonlinear regression problems. In our proposed method, the hidden parameter and standard deviation of error are estimated following ordinary EM algorithm, whereas nonlinear coefficients are estimated by using Particle Swarm Optimization, which is one of global optimization methods. Deterioration curves of the four bridge groups are estimated with the inspection database by the proposed algorithm.

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“…One is physical-model-based approach, e.g. Frangopol et al (1997aFrangopol et al ( , 1997b, Akiyama et al (2010), Yoshida and Akiyama (2013). The other is statistical approach, e.g.…”

Section: Introductionmentioning

confidence: 99%

A Group of Deterioration Curves of Bridges Estimated by Extended EM Algorithm

Yoshida

Otake

Honjo

2014

Vulnerability, Uncertainty, and Risk

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“…In spite of all the advances achieved in defining both better structural models (Alipour et al, 2013;Biondini and Vergani, 2015;Lim et al, 2016) and the probabilistic models (Akiyama et al, 2010;Papakonstantinou and Shinozuka, 2013), the uncertainty in future corrosion is very high. This is not a matter of lack of knowledge or understanding, but a consequence of the manifold of factors, including environmental, use, quality of construction and workmanship, which influence the progression of corrosion and cannot be predicted beforehand.…”

Section: Time-dependent To Time-independent Safetymentioning

confidence: 99%

On the robustness to corrosion in the life cycle assessment of an existing reinforced concrete bridge

Cavaco

Neves

Casas

2017

Structure and Infrastructure Engineering

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Management of existing structures has traditionally been based on condition assessment, based on visual inspections, disregarding the susceptibility of different structural types to aging and deterioration. Robustness, as a measure of the effects of unpredictable damage to structural safety can be a complementary information to the results of inspection. Although robustness has mostly been used to evaluate the consequences of extreme events, a similar framework can be used to investigate the result of aging, allowing a better understanding of the potential effects of deterioration and allowing a better allocation of available maintenance funding. In this work, a probabilistic structural robustness indicator is used to quantify the susceptibility of structures to corrosion. The methodology is exemplified through a case-study comprising an existing reinforced concrete bridge deck, heav- * Corresponding author Email address: joan.ramon.casas@upc.edu (Joan R. Casas)Preprint submitted to Structure and Infrastructure Engineering December 29, 2016 ily damaged due to reinforcement corrosion, and finally demolished due to safety concerns. Robustness measures the bridge deck safety tolerance to reinforcement corrosion. The principal effects of corrosion, including loss of area and bond between concrete and steel are modelled using a non-linear finite element model, coupled with a Response Surface Method to compute the bridge reliability as a function of the corrosion level, and finally used to assess robustness. Results show that the redundancy of the bridge allow significant redistribution of loads between elements with different corrosion levels. As a result, the bridge presents significant robustness and tolerance to reinforcement corrosion.

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“…Spatial variability research carried out to date has been mainly focused on predicting the performance of corroding structures and spatial variability of the common parameters such as material, dimension and environmental properties Karimi et al 2005;Stewart 2005;Darmawan and Stewart 2007;Marsh and Frangopol 2008;Kenshel and O'Connor 2009;Akiyama et al 2010;Zhai and Stewart 2010). A major shortcoming in the work to date is that the spatial variability and corresponding correlation associated with applied loads and load effects has not been allowed for.…”

Section: Introductionmentioning

confidence: 99%

The sensitivity of bridge safety to spatial correlation of load and resistance

2016

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Publication informationStructures, 5 : 23-34 Publisher ElsevierItem record/more information http://hdl.handle.net/10197/6997 Publisher's statement þÿ T h i s i s t h e a u t h o r s v e r s i o n o f a w o r k t h a t w a s a c c e p t e d f o r p u b l i c a t i o n i n S t r u c t u r e s .Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this AbstractRandom Field theory has emerged in recent years to model the statistical correlation of resistance in concrete structures and to determine its influence on the probability of structural failure. A major shortcoming in the work carried out to date is the spatial variability and corresponding correlation associated with applied traffic loads. In this paper the influence of spatial correlation of both traffic load and resistance is considered in the context of bridge safety assessment. The current study, explores, the nature of the problem by three theoretical examples. As a general trend, examples show that while traffic loads are weakly correlated, load effects are strongly correlated as the same heavy vehicle often causes extremes of load effect in different parts of the bridge which is due to the transverse sharing of load (measured here using a load sharing factor).It is found that the strength of correlation of load effect depends greatly on the load sharing factor which is treated in a simple way in many studies. In a more sophisticated beam-and-slab bridge example, load sharing factors are derived from a finite element analysis to assess transverse load sharing, and are shown to vary by girder number, girder segment and by load location. Despite the fact that load effect at points along the length of a bridge are strongly correlated, the combined influence of correlation in load and resistance on probability of failure is small

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Time-dependent reliability analysis of existing RC structures in a marine environment using hazard associated with airborne chlorides

Cited by 119 publications

References 24 publications

A Group of Deterioration Curves of Bridges Estimated by Extended EM Algorithm

A Group of Deterioration Curves of Bridges Estimated by Extended EM Algorithm

On the robustness to corrosion in the life cycle assessment of an existing reinforced concrete bridge

The sensitivity of bridge safety to spatial correlation of load and resistance

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