X.-X. Yuan

A nonlinear mixed-effects model for degradation data obtained from in-service inspections

Monitoring of degradation and predicting its progression using periodic inspection data are important to ensure safety and reliability of engineering systems. Traditional regression models are inadequate in modeling the periodic inspection data, as it ignores units specific random effects and potential correlation among repeated measurements. This paper presents an advanced nonlinear mixed-effects (NLME) model, generally adopted in bio-statistical literature, for modeling and predicting degradation in nuclear piping system. The proposed model offers considerable improvement by reducing the variance associated with degradation of a specific unit, which leads to more realistic estimates of risk.

A Bayesian approach to modeling and predicting pitting flaws in steam generator tubes

Steam generators in nuclear power plants have experienced varying degrees of under-deposit pitting corrosion. A probabilistic model to accurately predict pitting damage is necessary for effective life-cycle management of steam generators. This paper presents an advanced probabilistic model of pitting corrosion characterizing the inherent randomness of the pitting process and measurement uncertainties of the in-service inspection (ISI) data obtained from eddy current (EC) inspections. A Markov chain Monte Carlo simulation-based Bayesian method, enhanced by a data augmentation technique, is developed for estimating the model parameters. The proposed model is able to predict the actual pit number, the actual pit depth as well as the maximum pit depth, which is the main interest of the pitting corrosion model. The study also reveals the significance of inspection uncertainties in the modeling of pitting flaws using the ISI data: Without considering the probability-of-detection issues and measurement errors, the leakage risk resulted from the pitting corrosion would be under-estimated, despite the fact that the actual pit depth would usually be over-estimated.

A correlated bidding model for markup size decisions

Whereas competitive bidding models have been studied for more than five decades with many factors being considered and statistical methods proposed, the correlation among bids of different companies and its effects on markup decisions have not been explored. Through a multivariate competitive bidding model, the significance of the correlation is investigated in this paper. Mechanistic arguments and probabilistic analysis based on a breakdown of cost estimates show that bid ratios are positively correlated to one another. This fact is then incorporated as a priori information into a Bayesian statistical method to estimate the correlation coefficients from historical data with missing values. The effectiveness of the proposed Bayesian method has been demonstrated through a case study. The proposed bidding model has a flexible mathematical structure, which allows one to better characterize actual varying bidding patterns. It also includes the Friedman and Carr models as its special cases. Moreover, through the use of the streamlined Bayesian method, the new model can be implemented easily in practice.

A Comparison of Probabilistic Models of Deterioration for Life Cycle Management of Structures

The probabilistic modelling of deterioration in the time-dependent reliability analysis is a necessary step for developing a risk-based approach to the life cycle management of infrastructure systems. The decisions regarding the time and frequency of inspection, maintenance and replacement are confounded by sampling and temporal uncertainties associated with the deterioration of structural resistance. To account for these uncertainties, probabilistic models of deterioration have been developed under two broad categories, namely the random variable model and stochastic process model. The paper presents a conceptual exposition of these two models and highlights their profound implications to the age-based and condition-based preventive maintenances policies. The proposed stochastic gamma process model of deterioration is more versatile than the random rate model commonly used in the structural reliability literature.

Reliability Analysis of Soil Nail Walls against External Failures in Layered Ground

AbstractThis study presents reliability analyses of soil nail walls (SNWs) against global and sliding stability failures in layered ground. The load and resistance factor design (LRFD) has been studied and advocated for SNW design over the past few years. However, the majority of those studies were exclusively focused on SNWs in single-layer ground and few have investigated the case of layered ground. In this paper, the reliability is evaluated using the first-order reliability method (FORM) for SNWs that are designed based on traditional working stress design (WSD) methods. Based on analyses of a great variety of combinations of two soils, a stiff clay layer and a dense sand layer, reliability indices calibrated from traditional WSD exceed 3.0 for global stability designs but may be lower than 3.0 for sliding stability designs. This study also reveals that the sequence and the thickness ratio of layers are two important factors for the reliability of both global and sliding stability modes. SNWs in layer...

Bayesian method for the correlated competitive bidding model

A multivariate competitive bidding model takes into account the correlation among competitors in determination of markup size. However, parameter estimation for the multivariate model is a challenging issue. A simplified, piecemeal style statistical method was proposed for low-dimension problems. However, this method may cause significant estimation errors when applied to complex bidding situations. A refined Bayesian statistical method based on Markov chain Monte Carlo (MCMC) simulation is developed that can be employed in practical bidding problems. To deal with missing values in bid data, a data augmentation technique is integrated in the MCMC process. The proposed Bayesian method is shown through case studies to be robust for complex bidding situations and also insensitive to the selection of the prior models of the correlation matrix. An important feature of the proposed Bayesian method is that it allows a project manager to quantify statistical uncertainties of parameter estimation and their effects on markup decisions. The optimal markup is represented by a posterior distribution which paints a complete picture of the uncertainties involved in the markup size decision.

Local Calibration of the MEPDG Distress and Performance Models for Ontario’s Flexible Roads: Overview, Impacts, and Reflection

Built upon a seven-year local calibration study of Ontario’s flexible pavements, this paper provides a summary of the calibration results and design impact and, more importantly, shares the experience and lessons learned from the process. The best results have been achieved on the local calibration of the rutting, bottom-up fatigue cracking, and international roughness index (IRI) distress models minimizing the residual sum of squares (RSS) while maintaining the average bias at zero. Significant efforts have been made to calibrate the other distress models with limited success. A design impact study found that local calibration of the rutting models was very important, whereas the alligator fatigue cracking did not usually govern the design in Ontario, although the global model was found to under-predict the cracking damage. The performance of the calibrated IRI model in the design of heavy traffic freeways for both reconstructed and rehabilitated sections was unsatisfactory and needs further study. The paper also presents several open questions for future research. These include the handling of section-length effects of observed cracking data, the determination of initial IRI, the updating of standard deviation functions and the overall reliability models, and the prioritization of pavement research under the new paradigm of the Mechanistic–Empirical Pavement Design Guide (MEPDG).

A low-cost video-based pavement distress screening system for low-volume roads

ABSTRACT This study investigates the use of consumer-grade video camera to develop a low-cost pavement distress screening system, with a particular focus on low-volume roads. Due to the oblique view of video data captured in the car front, existing crack detection algorithms that are tailor-made for nadir view cannot be used directly. Instead, we propose and develop a Video-based PAvement Distress Screening (VPADS) system, which includes an automatic data processing workflow by first defining a Region of Interest (RoI) through robust line fitting of the two side lanes. The pavement condition can be assessed within the RoI by implementing a multi-scale ridge detection filter followed by a boundary contour analysis. Since the proposed VPADS system is designed for preliminary screening purpose, each video image scene is examined if there exists any potential crack or distress feature(s). We tested the proposed system on video data collected for two low-volume roads (Highway 624 and 668) in Northern Ontario region, Canada. The overall accuracy of detecting crack and distress features yielded 80%. Instead of replacing traditional field inspection or high-end multi-sensor pavement evaluation system, the proposed VPADS system aims to provide a computer-aided screening solution for transportation authorities of a vast rural road/highway network and with limited inspection budget. The scope of the work can be further expanded by developing a crowdsensing inspection network built by any regular road user, who can also act as a data provider for the regional/municipal pavement distress screening system.

Reliability Analysis for the Feeder Subject to Wall Loss

Wall thinning is one of the most common degradation mechanisms experienced in piping system. Gradual wall thinning can cause the pipe to leak or in the worst scenario, to rupture. Wall thinning due to FAC of feeder pipe in CANDU® reactors has been identified as an active degradation mechanism, and local thinning has been observed in various locations such as elbows/bends and Grayloc. The assessment of structural integrity is important for the fitness-for-service of those feeders whose wall thickness is predicted to be lower than the required minimum wall thickness before their design life and therefore subject to costly repair or replacement. Among various probabilistic methods, the first-order reliability method (FORM) is adopted in this paper to evaluate the structural reliability of feeders subject to wall thinning, while the wall thickness, one of the key parameters in the reliability analysis, is modeled by three methods based on the wall thickness measurements. They are linear regression analysis, random thinning rate analysis and gamma process modeling. The difference and limitation of the methods for reliability analysis are addressed.© 2012 ASME

Advances and Research Needs in Risk-Informed In-Service Inspection Methodology

While risk-informed in-service inspection (RI-ISI) program has been applied in several countries to enhance the traditional periodic inspection program (PIP), many other countries are waiting for more successful implementation experiences to be accumulated. Canadian Nuclear Safety Commission (CNSC), the regulatory body of nuclear industry in Canada, became increasingly interested in the risk-informed decision making methodology. Several small-scale pilot studies on RI-ISI have been initiated by Canadian utilities during the past few years. Nevertheless, a RI-ISI methodology appropriate for the CANDU technology that can be accepted by the stakeholders has yet to be developed. The development of the RI-ISI methodologies derived from the PWR/BWR operating experiences is first reviewed, followed by an examination of Canadian periodic inspection standard CSA N285.4 and its evolution from a RI-ISI perspective. Finally several key technical issues and research needs in developing an advanced RI-ISI methodology for nuclear power plants are identified.Copyright