Risk based life management of offshore structures and equipment

Risk based approaches are gaining currency as industry looks for rational, efficient and flexible approaches to managing their structures and equipment. When applied to inspection and maintenance of industrial assets, risk based approaches differ from other approaches mainly in their assessment of failure in its wider context and ramifications. These advanced techniques provide more insight into the causes and avoidance of structural failure and competing risks, as well as the resources needed to manage them. Measuring risk is a challenge that is being met with state of the art technology, skills, knowledge and experience. The thesis presents risk based approaches to solving two specific types of problem in the management of offshore structures and equipments. The first type is finding the optimum timing of an asset life management action such that financial benefit is maximised, considering the cost of the action and the risk (quantified in monetary terms) of not undertaking that action. The approach presented here is applied to managing remedial action in offshore wind farms and specifically to corroded wind turbine tower structures. The second type of problem is how to optimise resources using risk based criteria for managing competing demands. The approach presented here is applied to stocking spares in the shipping sector, where the cost of holding spares is balanced against the risk of failing to meet demands for spares. Risk is the leitmotiv running through this thesis. The approaches discussed here will find application in a variety of situations where competing risks are being managed within constraints

Model based test suite minimization using metaheuristics

Software testing is one of the most widely used methods for quality assurance and fault detection purposes. However, it is one of the most expensive, tedious and time consuming activities in software development life cycle. Code-based and specification-based testing has been going on for almost four decades. Model-based testing (MBT) is a relatively new approach to software testing where the software models as opposed to other artifacts (i.e. source code) are used as primary source of test cases. Models are simplified representation of a software system and are cheaper to execute than the original or deployed system. The main objective of the research presented in this thesis is the development of a framework for improving the efficiency and effectiveness of test suites generated from UML models. It focuses on three activities: transformation of Activity Diagram (AD) model into Colored Petri Net (CPN) model, generation and evaluation of AD based test suite and optimization of AD based test suite. Unified Modeling Language (UML) is a de facto standard for software system analysis and design. UML models can be categorized into structural and behavioral models. AD is a behavioral type of UML model and since major revision in UML version 2.x it has a new Petri Nets like semantics. It has wide application scope including embedded, workflow and web-service systems. For this reason this thesis concentrates on AD models. Informal semantics of UML generally and AD specially is a major challenge in the development of UML based verification and validation tools. One solution to this challenge is transforming a UML model into an executable formal model. In the thesis, a three step transformation methodology is proposed for resolving ambiguities in an AD model and then transforming it into a CPN representation which is a well known formal language with extensive tool support. Test case generation is one of the most critical and labor intensive activities in testing processes. The flow oriented semantic of AD suits modeling both sequential and concurrent systems. The thesis presented a novel technique to generate test cases from AD using a stochastic algorithm. In order to determine if the generated test suite is adequate, two test suite adequacy analysis techniques based on structural coverage and mutation have been proposed. In terms of structural coverage, two separate coverage criteria are also proposed to evaluate the adequacy of the test suite from both perspectives, sequential and concurrent. Mutation analysis is a fault-based technique to determine if the test suite is adequate for detecting particular types of faults. Four categories of mutation operators are defined to seed specific faults into the mutant model. Another focus of thesis is to improve the test suite efficiency without compromising its effectiveness. One way of achieving this is identifying and removing the redundant test cases. It has been shown that the test suite minimization by removing redundant test cases is a combinatorial optimization problem. An evolutionary computation based test suite minimization technique is developed to address the test suite minimization problem and its performance is empirically compared with other well known heuristic algorithms. Additionally, statistical analysis is performed to characterize the fitness landscape of test suite minimization problems. The proposed test suite minimization solution is extended to include multi-objective minimization. As the redundancy is contextual, different criteria and their combination can significantly change the solution test suite. Therefore, the last part of the thesis describes an investigation into multi-objective test suite minimization and optimization algorithms. The proposed framework is demonstrated and evaluated using prototype tools and case study models. Empirical results have shown that the techniques developed within the framework are effective in model based test suite generation and optimizatio

Compound Impact on Private and Public Transport Network Performance on Integration of New Forms of Mobility

Continued evolutions in autonomous drive technologies and pandemic leading to a boom in micro-mobility usage make these new forms of mobility an integral part of investigative research to assess their impacts on transportation networks. This research thesis examines their impacts in terms of: quantification of the penetration rate of autonomous vehicles (AVs), the influence of physical characteristics of the urban road network on macroscopic fundamental parameters in heterogeneous traffic stream, inequities in travel costs equilibrium, assessment of public transport (PuT) network vulnerability against random service disruptions and importance of topography for accurate provision of micro-mobility services. Some benefits for 25-35% inclusion of AVs include enhanced network capacity, improvement in travel time, decrement in travel equilibrium costs. Whereas, the integrated micro-mobility modes reduce the commuter’s dis-utility and perceived journey times by 7.14% in case of disruptions. However, the spill-over effects are to watch out for

The Economics of Lotteries: An Annotated Bibliography

This paper presents an annotated bibliography of all papers relating to the economics of lotteries as of early to mid 2011. All published scholarly papers that could be identified by the authors are included along with the published abstract where available.lotto, lottery, public finance, gambling

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Copyright © 2014 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Environmental Modelling and Software. 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 document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling and Software Vol. 62 (2014), DOI: 10.1016/j.envsoft.2014.09.013The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g. model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts

Assessing Potential Energy Savings in Household Travel: Methodological and Empirical Considerations of Vehicle Capability Constraints and Multi-day Activity Patterns.

The lack of multi-day data for household travel and vehicle capability requirements is an impediment to evaluations of energy savings strategies, since 1) travel requirements vary from day-to-day, and 2) energy-saving transportation options often have reduced capability. This work demonstrates a survey methodology and modeling system for evaluating the energy-savings potential of household travel, considering multi-day travel requirements and capability constraints imposed by the available transportation resources. A stochastic scheduling model is introduced – the multi-day Household Activity Schedule Estimator (mPHASE) – which generates synthetic daily schedules based on “fuzzy” descriptions of activity characteristics using a finite-element representation of activity flexibility, coordination among household members, and scheduling conflict resolution. Results of a thirty-household pilot study are presented in which responses to an interactive computer assisted personal interview were used as inputs to the mPHASE model in order to illustrate the feasibility of generating complex, realistic multi-day household schedules. Study vehicles were equipped with digital cameras and GPS data acquisition equipment to validate the model results. The synthetically generated schedules captured an average of 60 percent of household travel distance, and exhibited many of the characteristics of complex household travel, including day-to-day travel variation, and schedule coordination among household members. Future advances in the methodology may improve the model results, such as encouraging more detailed and accurate responses by providing a selection of generated schedules during the interview. Finally, the Constraints-based Transportation Resource Assignment Model (CTRAM) is introduced. Using an enumerative optimization approach, CTRAM determines the energy-minimizing vehicle-to-trip assignment decisions, considering trip schedules, occupancy, and vehicle capability. Designed to accept either actual or synthetic schedules, results of an application of the optimization model to the 2001 and 2009 National Household Travel Survey data show that U.S. households can reduce energy use by 10 percent, on average, by modifying the assignment of existing vehicles to trips. Households in 2009 show a higher tendency to assign vehicles optimally than in 2001, and multi-vehicle households with diverse fleets have greater savings potential, indicating that fleet modification strategies may be effective, particularly under higher energy price conditions.Ph.D.Natural Resources and EnvironmentUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91567/1/kevinb_1.pd