Stochastic failure analysis of the gusset plates in the Mississippi River Bridge

Mahmoodian, Mojtaba; Alani, Amir M.; Tee, Kong Fah

doi:10.1504/ijfe.2012.050415

Cited by 23 publications

(15 citation statements)

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“…The main advantages of RSM are because it is not restrained by the number of random variables and it is also not limited by the form of the limit state function whether it is implicit or explicit (Ellingwood and Galambos 1982, Mahmoodian et al 2012, Jiang et al 2012, Fang et al 2014. In addition, the RSM is simple to perform with high accuracy (Bai et al 2014, Fang et al 2013, Su et al 2015.…”

Section: Introductionmentioning

confidence: 99%

Structural reliability analysis using response surface method with improved genetic algorithm

Fang¹,

Tee²

2017

Structural Engineering and Mechanics

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Abstract. For the conventional computational methods for structural reliability analysis, the common limitations are long computational time, large number of iteration and low accuracy. Thus, a new novel method for structural reliability analysis has been proposed in this paper based on response surface method incorporated with an improved genetic algorithm. The genetic algorithm is first improved from the conventional genetic algorithm. Then, it is used to produce the response surface and the structural reliability is finally computed using the proposed method. The proposed method can be used to compute structural reliability easily whether the limit state function is explicit or implicit. It has been verified by two practical engineering cases that the algorithm is simple, robust, high accuracy and fast computation.

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Section: Introductionmentioning

confidence: 99%

Structural reliability analysis using response surface method with improved genetic algorithm

Fang¹,

Tee²

2017

Structural Engineering and Mechanics

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“…Industries have welcomed the importance of risk-based tactics as an instrument for inspection planning and maintenance of components or assets carrying highest risk [1,13]. Well established, good practices and standards for the application of methodical risk analysis for systems inspection planning have been applied in bridges [6,7,14,15], plant integrity in petroleum industry [12], pipelines [16,17], mechanical systems [18,19], etc.…”

Section: Introductionmentioning

confidence: 99%

Risk-based inspection on highway assets with category 2 defects

Ekpiwhre¹,

Tee²,

Aghagba³

et al. 2016

Int. J. SAFE

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This paper presents a study piloted on highway assets Category 2 defects. Imminent hazards on highway road networks are significantly accelerated by structural deterioration of highway infrastructures. The inspection and maintenance strategy for highway infrastructure requires continuous improvements to reduce high occurrence of defective highway assets. Combined risk-based inspection (RBI) and stochastic (STOC) techniques is considered in this investigation to give an in-depth understanding of highway asset maintenance response. Appropriate data information is extracted from Network Maintenance Management System and complementary information elicited from maintenance experts as well as recommended standards. Safety inspections piloted within the period of 5 years is evaluated using the projected RBI-STOC approach. The RBI incorporates the consequences and likelihood of the defects and the combined STOC techniques utilised defines the actual maintenance interval operated. The RBI-STOC approach proposes reclassification of highway asset defect repair intervals, appropriate maintenance task response and efficient maintenance prioritisation of highway assets in equivalence with contribution to system average mean time to repair and downtime.

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“…Methods of reliability analysis such as the first-order reliability method (FORM), second-order reliability method (SORM), point estimate method (PEM), Monte Carlo simulation (MCS), gamma process, probability density evolution method (PDEM), etc. are available in literature (Sivakumar Babu and Srivastava 2010; Mahmoodian et al 2012;Fang et al 2013;Tee and Khan 2014;Tee et al 2015). As failure is an exception rather than the rule in properly designed systems, therefore, an engineering reliability analysis involves a rare event simulation.…”

Section: Introductionmentioning

confidence: 99%

Risk-Cost Optimization of Buried Pipelines Using Subset Simulation

Khan

Tee

2016

J. Infrastruct. Syst.

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On the basis of time-dependent reliability analysis, a computational framework called subset simulation (SS) has been applied for risk-cost optimization of flexible underground pipeline networks. SS can provide better resolution for rare failure events that are commonly encountered in pipeline engineering applications. Attention in this work is devoted to scrutinize the robustness of SS in risk-cost optimization of pipelines. SS is first employed to estimate the reliability of flexible underground pipes subjected to externally applied loading and material corrosion. Then SS is extended to determine the intervention year for maintenance and to identify the most appropriate renewal solution and renewal priority by minimizing the risk of failure and whole life-cycle cost. The efficiency of SS compared to genetic algorithm has been demonstrated by numerical studies with a view to prevent unexpected failure of flexible pipes at minimal cost by prioritizing maintenance based on failure severity and system reliability. This paper shows that SS is a more robust method in the decision-making process of reliabilitybased management for underground pipeline networks.

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Stochastic failure analysis of the gusset plates in the Mississippi River Bridge

Cited by 23 publications

References 0 publications

Structural reliability analysis using response surface method with improved genetic algorithm

Structural reliability analysis using response surface method with improved genetic algorithm

Risk-based inspection on highway assets with category 2 defects

Risk-Cost Optimization of Buried Pipelines Using Subset Simulation

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