Doctor of Philosophy

dissertationWater resources are limited and disproportionately distributed in time and place. Moreover, complex interactions among different components of the water system, changes in population and urbanization growth rates, and climate change have increased the uncertainty influencing water resource planning. The ultimate question arising for water managers considering the complexity of water systems is how to determine if management strategies are effective and improve the performance of a water system. Generally, decision-makers assess the system’s condition based on a univariate measure of reliability or vulnerability. However, these measures do not deliver sufficient information, and present a limited view about the system’s performance. There is a known need to study water resources in an integrated fashion to effectively manage for the present and the future. In this dissertation, a new comprehensive integrated modeling and performance assessment framework is offered. First, a new approach is designed to assess vulnerability of a water system based on important factors including exposure, sensitivity, severity, potential severity, social vulnerability, and adaptive capacity. Then, instead of an individual metric, the joint probability distribution of reliability and vulnerability based on copula function is developed to estimate a new index, the Water System Performance Index (WSPI), to evaluate the reliability and vulnerability of a water system simultaneously. To test the effectiveness of the framework and demonstrate the advances of the new performance index, a practical application is conducted for the Salt Lake City Department of Public Utilities (SLCDPU) water system. For this purpose, an integrated water resource management (IWRM) model is developed using system dynamics approach for the case study. Management alternatives are incorporated into the model using a decision support tool designed for use by water managers and stakeholders. Results of the study show an inconsistency in the degree of vulnerability between traditionally used and the new vulnerability assessment approaches. The use of the integrated model and new vulnerability approach is also shown to provide more informative guidance for decision makers evaluating alternative management strategies during failure events. Furthermore, results illustrate the effectiveness of the WSPI to identify critical conditions when there is a need for a combined measure of performance. In terms of water management decision making, the final results of this dissertation indicate centralized water storage solutions improve water system performance better than rainwater harvesting for the Salt Lake City case study

A Data-Driven Predictive Model of Reliability Estimation Using State-Space Stochastic Degradation Model

The concept of the Industrial Internet of Things (IIoT) provides the foundation to apply data-driven methodologies. The data-driven predictive models of reliability estimation can become a major tool in increasing the life of assets, lowering capital cost, and reducing operating and maintenance costs. Classical models of reliability assessment mainly rely on lifetime data. Failure data may not be easily obtainable for highly reliable assets. Furthermore, the collected historical lifetime data may not be able to accurately describe the behavior of the asset in a unique application or environment. Therefore, it is not an optimal approach anymore to conduct a reliability estimation based on classical models. Fortunately, most of the industrial assets have performance characteristics whose degradation or decay over the operating time can be related to their reliability estimates. The application of the degradation methods has been recently increasing due to their ability to keep track of the dynamic conditions of the system over time. The main purpose of this study is to develop a data-driven predictive model of reliability assessment based on real-time data using a state-space stochastic degradation model to predict the critical time for initiating maintenance actions in order to enhance the value and prolonging the life of assets. The new degradation model developed in this thesis is introducing a new mapping function for the General Path Model based on series of Gamma Processes degradation models in the state-space environment by considering Poisson distributed weights for each of the Gamma processes. The application of the developed algorithm is illustrated for the distributed electrical systems as a generic use case. A data-driven algorithm is developed in order to estimate the parameters of the new degradation model. Once the estimates of the parameters are available, distribution of the failure time, time-dependent distribution of the degradation, and reliability based on the current estimate of the degradation can be obtained

Reliability Analysis of Continuous Structural Systems

This study mainly deals with developing another approximate method for system reliability analysis and its applications to the continuous structures such as an assembly of stiffened cylindrical and rectangular sections used in Tension Leg Platform (TLP). Various methods developed for the structural system reliability analysis are reviewed The developed system reliability method, called herein the "Extended Incremental Load Method", is an extended approach of the conventional incremental load method. It has been developed in order to extend its applicability to the system reliability analysis of a structure under multiple loadings. It directly uses existing component strength formulae in the system analysis and more realistically takes account of the post-ultimate (post-failure) behaviour of a failed component when assessing the system reliability and ultimate strength. This is an important merit of the method over other methods. The method allows for load re-distribution during the development of elasto-plastic moments in large cross-sections under the action of axial and bending forces and in the presence of lateral hydrostatic and hydrodynamic pressure. The effects of shearing actions are ignored. A search is made for the most important failure modes to give the lowest system safety index. In the method the modified safety margin equation, which has been proposed to use existing strength formulae for principle components of a floating offshore structure, is employed in which the strength modelling parameter is treated as a basic random variable in system reliability analysis as well as component reliability analysis and the concept of the first-order second moment method is adopted to obtain the resistance coefficients and the loading coefficients in the safety margin equation. Details about deriving the safety margin equation by the proposed reliability method, calculation of the total load factor, the procedure of identifying the most important failure modes and flow vectors of principle component are described in the Appendices. Applications to discrete structures are demonstrated to show the validity of the proposed method. The method has been applied to the Hutton TLP and two variants, TLP-A and TLP-B, which are modified models of the Hutton TLP and of the design using TLP Rule Case Committee type loading and improved strength models, under the design environmental loading conditions. Components and systems safety indices of the models, Hutton TLP, TLP-A and TLP-B, are illustrated with three dimensional collapse mechanisms figures. Reserve and reserve strength characteristics are derived for the design as built and for more economical and efficient variations of the design. The TLP form is shown to possess high redundancy and systems safety. Sensitivity studies to changes in stochastic parameters of resistance and loading variables have been carried out. For this purpose the strength modelling parameter, yield stress and certain member sizes are selected as resistance variables, and effects of their mean values and/or coefficients of variation on the system, as well as on the component reliability index, have been investigated. The effects of mean bias and coefficient of variation of load effects, namely, static, quasi-static and dynamic component, on the the system as well as on the component reliability index have also been investigated. The results are discussed with regard to effects of various parameters on safety, with illustrating figures, from which the relative importance of random variables can be seen. As an another important resistance variable, the post-ultimate behaviour of failed components has been taken account of in system reliability assessment, which should be the most important resistance variable affecting the system reliability and the effective residual strength of a structure. Some case studies have been carried out with the simplified non-linear model which has a form of piecewise multi-state (more than two states) and is characterised by the post-ultimate slope and the residual strength. The results are illustrated in figures and tables and discussion made about its effects on the system reliability level. (Abstract shortened by ProQuest.)

Development of Bridge Information Model (BrIM) for digital twinning and management using TLS technology

In the current modern era of information and technology, the concept of Building Information Model (BIM), has made revolutionary changes in different aspects of engineering design, construction, and management of infrastructure assets, especially bridges. In the field of bridge engineering, Bridge Information Model (BrIM), as a specific form of BIM, includes digital twining of the physical asset associated with geometrical inspections and non-geometrical data, which has eliminated the use of traditional paper-based documentation and hand-written reports, enabling professionals and managers to operate more efficiently and effectively. However, concerns remain about the quality of the acquired inspection data and utilizing BrIM information for remedial decisions in a reliable Bridge Management System (BMS) which are still reliant on the knowledge and experience of the involved inspectors, or asset manager, and are susceptible to a certain degree of subjectivity. Therefore, this research study aims not only to introduce the valuable benefits of Terrestrial Laser Scanning (TLS) as a precise, rapid, and qualitative inspection method, but also to serve a novel sliced-based approach for bridge geometric Computer-Aided Design (CAD) model extraction using TLS-based point cloud, and to contribute to BrIM development. Moreover, this study presents a comprehensive methodology for incorporating generated BrIM in a redeveloped element-based condition assessment model while integrating a Decision Support System (DSS) to propose an innovative BMS. This methodology was further implemented in a designed software plugin and validated by a real case study on the Werrington Bridge, a cable-stayed bridge in New South Wales, Australia. The finding of this research confirms the reliability of the TLS-derived 3D model in terms of quality of acquired data and accuracy of the proposed novel slice-based method, as well as BrIM implementation, and integration of the proposed BMS into the developed BrIM. Furthermore, the results of this study showed that the proposed integrated model addresses the subjective nature of decision-making by conducting a risk assessment and utilising structured decision-making tools for priority ranking of remedial actions. The findings demonstrated acceptable agreement in utilizing the proposed BMS for priority ranking of structural elements that require more attention, as well as efficient optimisation of remedial actions to preserve bridge health and safety

Bayesian inference and failure analysis for risk assessment in quality engineering

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonFailure is the state of not achieving a desired or intended goal. Failure analysis planning in the context of risk assessment is an approach that helps to reduce total cost, increase production capacity, and produce higher-quality products. One of the most common issues that businesses confront are defective products. This issue not only results in monetary loss, but also in a loss of status. Companies must improve their production quality and reduce the quantity of faulty products in order to continue operating in a healthy and profitable manner in today’s very competitive environment. On the other hand, there is the ongoing COVID-19 pandemic, which has thrown the world’s natural order into disarray, and has been designated a Public Health Emergency of International Concern by the World Health Organization. The demand for quality control is rapidly increasing. Failure analysis is thus an useful tool for identifying common failures, their likely causes, and their impact on the health system, as well as plotting strategies to limit COVID-19 transmission. It is now more vital than ever to enhance failure analysis methods. The traditional FMEA (Failure mode and effects analysis) is one of the most widely used approaches for identifying and classifying failure modes (FMs) and failure causes (FCs). It is a risk analysis tool for coping with possible failures and is widely used in the reliability engineering, safety engineering and quality engineering. To prioritize risks of different failure modes, FMEA uses the risk priority number (RPN), which is the product of three risk measures: severity (S), occurrence (O) and detection (D). Traditional FMEA, on the other hand, has drawbacks, such as the inability to cope with uncertain failure data, such as expert subjective evaluations, the failure events’ conditionality, RPN has a high degree of subjectivity, comparing various RPNs is challenging, potential errors may be ignored in the conventional FMEA process, etc. To overcome these limitations, I present an integrated Bayesian approach to FMEA in this thesis. In this proposed approach, I worked with experts in quality engineering and used Bayesian inference to estimate the FMEA risk parameters: S, O and D. The proposed approach is intended to become more practical and less subjective as more data is added. Bayesian statistics is a statistical theory that is based on the Bayesian interpretation of probability, which states that probability expresses a degree of belief or information (knowledge) about an event. Bayesian statistics addresses the issues with uncertainties found in frequentist statistics, such as the distribution of contributing factors, the implications of using specific distributions and specifies that there is some prior probability. A prior can be derived from previous information, such as previous experiments, but it can also be derived from a trained subject-matter expert’s purely subjective assessment. Frequentist statistics (also known as classical statistics) has several limitations, including a lack of uncertainty information in predictions, no built-in regularisation, and no consideration of prior knowledge. Due to the availability of powerful computers and new algorithms, Bayesian methods have seen increased use within statistics in the twenty-first century, and this thesis highlights the effective use of Bayesian analyses to address the shortcomings of the current FMEA with the revamped Bayesian FMEA. As a demonstration of the approach, three case studies are presented. The first case study is a Bayesian risk assessment approach of the modified SEIR (susceptible-exposed-infectious-recovered) model for the transmission dynamics of COVID-19 with an exponentially distributed. The effective reproduction number is estimated based on laboratory-confirmed cases and death data using Bayesian inference and analyse the impact of the community spread of COVID-19 across the United Kingdom. The value of effective reproduction number models the average number of infections caused by a case of an infectious disease in a population that includes not only susceptible people. The FMEA is then applied to evaluate the effectiveness of the action measures taken to manage the COVID-19 pandemic. In the FMEA, the focus was on COVID-19 infections and therefore the failure mode is taken as positive cases. The model is applied to COVID-19 data showing the effectiveness of interventions adopted to control the epidemic by reducing the effective reproduction number of COVID-19. The risk measures were estimated from the case fatality rate (S), the posterior median of the effective reproduction number (O) and the current corrective measures used by government policies (D). The second case study is a Bayesian risk assessment of a coordinate measuring machine (CMM) process using failure mode, effects and criticality analysis (FMECA) and an augmented form error model. The form error is defined as the deviation of a manufactured part from its design or ideal shape, and it is a key characteristic to evaluate in quality engineering and manufacturing. The form error is presented as a probabilistic model using symmetric unimodal distributions. Bayesian inference is then used to identify influence factors associated with the measurement process due to form error, environmental, human and random effects. A risk assessment is then performed by combining Bayesian inference, FMECA and conformity testing, to quantify and minimise the risk of wrong decisions. In the FMECA, the focus was on CMM measurement process and I identified four major FMs that can occur: probe, mechanical, environmental and measurement performance failure. Eleven FCs were also observed, each of which was linked to one of the four FMs. The risk measures were estimated from the posterior probability of failure causes associated with the CMM measurement process (O), the severity of a specific consumer’s risk (S) and the detectability of failures from the posterior standard deviation of the form error model (D). The third case study is a Bayesian risk assessment of a CMM measurement process using an autoregressive (AR) form error model and a combined Fault tree analysis (FTA) and FMEA approach to predict significant failure modes and causes. The main idea is to estimate and predict the form error based on CMM data using Gibbs sampling and analyse the impact of the CMM measurement process on product conformity testing. The FTA is used to compare the actual and predicted form error data from the Bayesian AR plot to determine the likelihood of the CMM measurement process failing using binary data. The acquired binary data is then classified into four states (true positive, true negative, false positive, and false negative) using a confusion matrix, which is subsequently utilized to calculate key classification measures (i.e., error rate, prediction rate, prevalence rate, sensitivity rate, etc). The classification measures were then used to assess the FMEA risk measures S, O, and D, which were critical for determining the RPN and making decisions. Analytical and numerical methods are used in all case studies to highlight the practical implications of our findings and are meant to be practical without complex computing. The proposed methodologies can find applications in numerous disciplines and wide quality engineering

Proposal of a global Total Cost of Ownership Model for FMC Technologies’ suppliers

Masteroppgave i industriell økonomi og informasjonsledelse 2009 – Universitetet i Agder, GrimstadFMC Technologies spends a huge amount of their turnover upstream to their suppliers. With indirect costs, including those that occur when suppliers have a delivery delay or deliver products of low quality, it is expected to be much higher. To be able to estimate these costs, to know the true cost of their suppliers, FMC Technologies would like to have a tool that could help them quantify and calculate these indirect costs. To investigate and create such a tool, questionnaires were sent out, discussions with FMC personnel and in-depth investigation of their ERP (SAP) system were performed to create a foundation of the Total Cost of Ownership (TCO) model. Further, a pilot study on one of FMC’s suppliers was supposed to take place in order to perform an in-depth investigation on how to estimate the TCO. Unfortunately, this task was cancelled due to lack of readily available supplier specific data in the SAP system. Therefore, no TCO analysis case study was performed. Due to this, the thesis changed direction and it started investigating to see, if a TCO model was appropriate to evaluate FMC’s suppliers. In addition to this, a conceptual model was created. When performing this thesis, a framework used in similar approaches was inspirational, as well as the case study approach. Using the case study approach, an empirical inquiry that investigates a contemporary phenomenon within its real-life context was performed, and it is a preferred strategic choice when “how” or “why” questions are posed. To get answers with high reliability, a triangulation of the results was desired. We achieved confirmation of our findings, often with triangulation, when concurrent results were found when either 1) interviewing FMC personnel, 2) investigating SAP, 3) reviewing answers of the questionnaires, or 4) assessing the theory or other performed case studies. All in all, using TCO to rate suppliers could be appropriate in some cases, but when depending on responsive and innovative suppliers, the TCO approach may deliver a too narrow view, as it focuses mainly on financial measurements, and could therefore deliver a short term assessment of suppliers. With the time consuming hard-to-quantify data, more use of subjective rating methods which also focus on the future could be satisfying alternatives, but to recommend this requires more investigation. As there is no use of an activity-based costing system in FMC (which has a critical linkage to TCO), and no plans of implementing it, perhaps a more simple supplier rating system would be the solution. This counts especially for an innovative ETO firm, which produces low volume and customized products. If FMC decides to use a TCO model (or any supplier rating system which focuses on historical data) they need to have a system that gathers the data regarding suppliers, preferably using the already existing SAP system. To have ambition of creating a global TCO model, without global routines seems extremely challengin

Evaluating the Effects of Spalling on the Capacity of Reinforced Concrete Bridge Girders

Corrosion of the reinforcing steel is a primary deterioration mechanism for reinforced concrete bridges. Heavy use of de-icing salts is believed to be a major contributor in Ontario to severe girder soffit spalling in certain cases. This thesis develops an assessment methodology to evaluate spalled bridges based on ultimate limit states. Specifically, a deterministic program is developed for assessment. It is subsequently compared to laboratory test results and used as a basis for a probabilistic reliability study. A modified area concept is proposed in this thesis to consider the effects of exposing reinforcement at various locations along the girder length. A multipoint analysis program, BEST (Bridge Evaluation Strength Tool), is developed that employs this concept, along with graphical spalling surveys and structural drawings, to evaluate reinforced concrete bridge girders. The program is adapted for a full bridge analysis and to consider the other effects of corrosion, such as bar section loss and bond deterioration. A case study bridge is evaluated to show that the BEST program offers a viable tool for the rapid assessment of spalled bridge girders and to facilitate the prioritization of rehabilitation projects. This evaluation indicates that the spatial distribution of the spalling along a girder, relative to bar splices and laps, has the most significant influence on structural capacity. Single girders show strength deficiencies in flexure and shear due to spalling. In general, the consideration of system effects improves the predicted bridge condition, while considering section loss and bond deterioration has the opposite effect. Laboratory work is used to validate the proposed model and identify a number of areas for future research. The laboratory test results also suggest that the current repair methods are effective in restoring bond and strength. In order to further explore potential uses for the BEST program, modifications are made so that it can be used to perform reliability analyses using Monte-Carlo simulation techniques. A simplified approach for estimating the reliability index as a function of the deterministic resistance ratio is proposed based on the reliability analysis results

Reliability Assessment of Legacy Safety-Critical Systems Upgraded with Fault-Tolerant Off-the-Shelf Software

This paper presents a new way of applying Bayesian assessment to systems, which consist of many components. Full Bayesian inference with such systems is problematic, because it is computationally hard and, far more seriously, one needs to specify a multivariate prior distribution with many counterintuitive dependencies between the probabilities of component failures. The approach taken here is one of decomposition. The system is decomposed into partial views of the systems or part thereof with different degrees of detail and then a mechanism of propagating the knowledge obtained with the more refined views back to the coarser views is applied (recalibration of coarse models). The paper describes the recalibration technique and then evaluates the accuracy of recalibrated models numerically on contrived examples using two techniques: u-plot and prequential likelihood, developed by others for software reliability growth models. The results indicate that the recalibrated predictions are often more accurate than the predictions obtained with the less detailed models, although this is not guaranteed. The techniques used to assess the accuracy of the predictions are accurate enough for one to be able to choose the model giving the most accurate prediction

The safety case and the lessons learned for the reliability and maintainability case

This paper examine the safety case and the lessons learned for the reliability and maintainability case