Fatigue Reliability Analysis Using Nondestructive Inspection

Zhang, Ruoxue; Mahadevan, Sankaran

doi:10.1061/(asce)0733-9445(2001)127:8(957)

Cited by 53 publications

(27 citation statements)

References 9 publications

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“…Uncertainties in the fatigue loading, material fatigue properties, geometry effects, and fatigue crack non-destructive inspection (NDI) are significant and must be considered explicitly in any comprehensive fatigue assessment procedure [14][15], so the probabilistic fracture mechanics model using the Paris equation and the Monte-Carlo simulation technique is developed for determining the fatigue failure probability of existing steel bridges in this study ( Figure 9). At first a deterministic fracture mechanics calculation of crack growth with stochastic inputs for crack growth rate, fracture toughness, initial and critical crack size, and load history is proposed.…”

Section: Evaluation Approach Based On Probabilistic Fracture Mechanicmentioning

confidence: 99%

Fatigue Service Life Evaluation of Existing Steel and Concrete Bridges

Wang¹,

Zhai²,

Duan³

et al. 2015

Volume 11 Number 3

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ABSTRACT:In recent years, traffic loads and vehicle speeds on both existing steel and concrete bridges have increased to provide fast and sufficient transportation service in China. Chinese government and bridge engineers are paying more attention to the structural fatigue life, service safety, maintenance intervals and the ability to improve capacity of such infrastructure. To study evaluation methods using modern technologies to achieve economical maintenance and extend the sustainable service life, a deterministic evaluation approach was proposed in this paper based on investigation of linear fatigue-damage accumulation theory and fracture mechanics, in which the fatigue failure behaviour of existing steel and concrete bridges was reflected. According to the stochastic fatigue failure mechanism of existing bridges, the member fatigue reliability assessment models were proposed using probabilistic fracture mechanics. Furthermore, based on a Monte-Carlo method, a fatigue reliability analysis program was developed to calculate member fatigue failure probability of existing bridges. The deterministic and probabilistic assessment methods proposed in this paper were applied to evaluate typical existing steel and concrete bridges. According to assessment results, the deterministic and probabilistic remaining fatigue life, safe inspection intervals and maintenance strategies were determined, which can avoid fatigue failure accidents and reduce the life-cycle cost of these bridge structures. INTRODUCTIONExisting steel and concrete bridges are aging and experiencing increased levels of fatigue deterioration due to increased demands in traffic volume, vehicular weights and environmental corrosion. As a result, the service safety of existing steel and concrete bridges is declining. In recent years, bridge engineers are paying more attention to the actual fatigue life, service safety, maintenance intervals and load carrying capacity of such infrastructure. Researchers devoted to develop effective evaluation methods, and then provide structural behavior prediction for existing steel and concrete bridges, which can make reasonable maintenance and management decisions to ensure the structural safety [1-5].To acquire reliable fatigue life evaluation technique, this paper proposed deterministic and probabilistic evaluation approaches, which reflect fatigue failure behavior of existing steel and concrete bridges. According to the stochastic fatigue failure mechanism of existing bridges, the fatigue reliability assessment models for member were proposed using probabilistic fracture mechanics. Furthermore, based on a Monte-Carlo method, a fatigue reliability analysis program was developed to calculate member fatigue failure probability of existing bridges. To verify the effectiveness of proposed evaluation approaches, five typical existing steel bridges and concrete bridges were selected as case studies. Continuous dynamic monitoring technique was adopted to acquire real fatigue stress spectrum, in order to evaluate the real fat...

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Section: Evaluation Approach Based On Probabilistic Fracture Mechanicmentioning

confidence: 99%

Fatigue Service Life Evaluation of Existing Steel and Concrete Bridges

Wang¹,

Zhai²,

Duan³

et al. 2015

Volume 11 Number 3

View full text Add to dashboard Cite

show abstract

“…In order to be able to characterize the deterioration using inspection outcomes the uncertainty associated with inspection outcomes needs to be characterized. These uncertainties reflect the inspection technique's features and several parameters can be used to quantify them, (Zhang & Mahadevan, 2001), namely:  The probability of detection, POD(x), evaluates the capability of inspection technique to detect a given defect size. Practically, an inspection technique can't detect all sizes of a defect with certainty.…”

Section: Imperfect Inspectionmentioning

confidence: 99%

“…The POD has been modeled in form of an exponential function of actual fatigue crack depth while the relationship between the actual and measured crack depth size is expressed with a linear function. The PFA is obtained as POD when the actual defect size equals zero (Zhang & Mahadevan, 2001). Pandey (1997) presented a probabilistic analysis framework to estimate the pipeline reliability incorporating the impact of inspection and repair activities planned over the service life.…”

Section: Review Of Imperfect Inspection Modelsmentioning

confidence: 99%

“…In order to evaluate the inspection uncertainties, they used POD, PFA, measurement error and reportability. These measures were modeled similarly to Zhang & Mahadevan (2001) …”

Section: Review Of Imperfect Inspection Modelsmentioning

confidence: 99%

“…The measurement error is a known inspection uncertainty, which is often presented, as a normally distributed random variable with known variance and specific mean value (Zhang & Mahadevan, 2001). In order to take into account the measurement error at inspection with deterioration model, the actual defect size X t a insp is presented in form of: (3b) where X t a insp is the actual defect size, X t m insp is the measured defect size, which is modelled as a normally distributed random variable in this paper, and X  is the measurement error.…”

Section: Measurement Error Representationmentioning

confidence: 99%

See 2 more Smart Citations

Imperfect inspection characterization for gamma process structural deterioration model

Ohadi¹,

Micic²

2014

Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures

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A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

Feng

Zhao

et al. 2023

Adv Eng Mater

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Fatigue fracture of welded joints is an important cause for engineering accidents. Due to the coexistence of so many influencing factors, the current prediction model of fatigue behavior cannot be used for all service conditions. The progress of each module (database establishment, data processing, data augmentation, dimension reduction, and comprehensive consideration of influencing factors) during the process of constructing a prediction model is summarized herein. Specifically, the state‐of‐art prediction models of fatigue strength, fatigue crack growth rate, fatigue life, and fatigue reliability are introduced. Moreover, suggestions for future work are given at the end of each section. Finally, according to the influential prediction methods, the establishment of fatigue performance prediction methods of welded joints via data‐driven method is summarized.

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Fatigue Reliability Analysis Using Nondestructive Inspection

Cited by 53 publications

References 9 publications

Fatigue Service Life Evaluation of Existing Steel and Concrete Bridges

Fatigue Service Life Evaluation of Existing Steel and Concrete Bridges

Imperfect inspection characterization for gamma process structural deterioration model

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

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