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Automated guided vehicles (AGVs), a type of unmanned moving robots that move along fixed routes or are directed by laser navigation systems, are increasingly used in modern society to improve efficiency and lower the cost of production. A fleet of AGVs operate together to form a fully automatic transport system, which is known as an AGV system. To date, their added value in efficiency improvement and cost reduction has been sufficiently explored via conducting in-depth research on route optimisation, system layout configuration, and traffic control. However, their safe application has not received sufficient attention although the failure of AGVs may significantly impact the operation and efficiency of the entire system. This issue becomes more markable today particularly in the light of the fact that the size of AGV systems is becoming much larger and their operating environment is becoming more complex than ever before. This motivates the research into AGV reliability, availability and maintenance issues in this thesis, which aims to answer the following four fundamental questions: (1) How could AGVs fail? (2) How is the reliability of individual AGVs in the system assessed? (3) How does a failed AGV affect the operation of the other AGVs and the performance of the whole system? (4) How can an optimal maintenance strategy for AGV systems be achieved? In order to answer these questions, the method for identifying the critical subsystems and actions of AGVs is studied first in this thesis. Then based on the research results, mathematical models are developed in Python to simulate AGV systems and assess their performance in different scenarios. In the research of this thesis, Failure Mode, Effects and Criticality Analysis (FMECA) was adopted first to analyse the failure modes and effects of individual AGV subsystems. The interactions of these subsystems were studied via performing Fault Tree Analysis (FTA). Then, a mathematical model was developed to simulate the operation of a single AGV with the aid of Petri Nets (PNs). Since most existing AGV systems in modern industries and warehouses consist of multiple AGVs that operate synchronously to perform specific tasks, it is necessary to investigate the interactions between different AGVs in the same system. To facilitate the research of multi-AGV systems, the model of a three-AGV system with unidirectional paths was considered. In the model, an advanced concept PN, namely Coloured Petri Net (CPN), was creatively used to describe the movements of the AGVs. Attributing to the application of CPN, not only the movements of the AGVs but also the various operation and maintenance activities of the AGV systems (for example, item delivery, corrective maintenance, periodic maintenance, etc.) can be readily simulated. Such a unique technique provides us with an effective tool to investigate larger-scale AGV systems. To investigate the reliability, efficiency and maintenance of dynamic AGV systems which consist of multiple single-load and multi-load AGVs traveling along different bidirectional routes in different missions, an AGV system consisting of 9 stations was simulated using the CPN methods. Moreover, the automatic recycling of failed AGVs is studied as well in order to further reduce human participation in the operation of AGV systems. Finally, the simulation results were used to optimise the design, operation and maintenance of multi-AGV systems with the consideration of the throughputs and corresponding costs of them. The research reported in this thesis contributes to the design, reliability, operation, and maintenance of large-scale AGV systems in the modern and rapidly changing world.

Nowadays, the avionics software has been becoming more and more critical for both civil and military aircraft. However, the software may become crazy sometimes and may cause the catastrophic result if any failure in software. Therefore, the software safety assessment is not only crucial to the specific software, but also for the system and aircraft. Although there are some industry standards as guidelines for development of software system, applications of these standards to practical software systems are still challenged and hard to operate in practice. This thesis tries to solve this problem. After analyses and summaries of the system safety assessment process and existing software safety assessment process in different fields, research wants to propose the systematic and comprehensive software safety assessment process and method for avionics software. The thesis presents the research process, and proposes one suitable avionics software safety assessment process. Meanwhile, thesis uses a real functional block in flight management system as a case study, and then conducts the software safety requirement assessment based on the proposed software safety assessment method. After analysis the result of case study, this proposed software safety assessment process and methods can quickly and correctly identify the software design errors. So, this analysis can use to prove the feasibility and validity of this proposed software safety assessment process and methods, which will help engineers modify every software design errors at the early stage in order to guarantee the software safety.

With increasing competitive pressures in today‟s market, it has become critical for businesses to recognise the significance of satisfying their customers so as to ensure their economic stability. Various studies have emphasised on the need for customer focus and project satisfaction in the construction industry sector. The industry, however, has not fully embraced the practice of project satisfaction, which is grounded on meeting the needs of the customer. Though most research on project satisfaction has focussed on the client, it is essential that the satisfaction of the project delivery team and in the wider context, the stakeholders be considered. In this case, the client is the centre of gravity of the project team. In order to satisfy the project team, there are challenges in assessing their requirements. This necessitates the need to develop a unique and robust method for capturing and analysing the level of integrated project team satisfaction. In this research, the project delivery team and the stakeholders have been lumped together as an integrated project team. Therefore, integrated project team satisfaction entails recognising the client and project participants‟ requirements that guarantees project successful completion and acceptance by the team. In view of this, this research presents a framework, which has been developed to plug these needs and challenges. The framework, known as the Satisfaction Assessment Integrated Framework (SAIF) involves an integrated approach that considers the participants of a construction project as a tree structure, and each member of that tree as an intermediate or top element. Relationships and interactions of the elements, and how these affect the overall satisfaction levels of a single project, are analysed based on understanding their requirements and invoking modern satisfaction attainment theory. The framework includes a method for understanding and identifying the satisfaction attributes; multi-attribute analysis for prioritising the satisfaction attributes of the clients and project participants; fault tree analysis strategy for defining the satisfaction relationship in a particular project team; and an assessment scoring system (a combination of multi-attribute analysis, and failure mode and effects analysis methodical approach) that evaluates how much each member of the project team meets the requirements or satisfaction attributes of other participants. Hence, SAIF, a novel assessment methodology, investigates and identifies possible links and the influence of integrating the construction project team and their satisfaction attributes with the aim of improving their satisfaction levels as a team. Through the findings of this research, recommendations are made to further explore the implications of satisfying a given participant against dissatisfying the participant; and subsequently improve the satisfaction assessment process.

Safety systems are designed to prevent the occurrence of certain conditions and their future development into a hazardous situation. The consequence of the failure of a safety system of a potentially hazardous industrial system or process varies from minor inconvenience and cost to personal injury, significant economic loss and death. To minimise the likelihood of a hazardous situation, safety systems must be designed to maximise their availability. Therefore, the purpose of this thesis is to propose an effective safety system design optimization scheme. A multi-objective genetic algorithm has been adopted, where the criteria catered for includes unavailability, cost, spurious trip and maintenance down time. Analyses of individual system designs are carried out using the latest advantages of the fault tree analysis technique and the binary decision diagram approach (BDD). The improved strength Pareto evolutionary approach (SPEA2) is chosen to perform the system optimization resulting in the final design specifications. The practicality of the developed approach is demonstrated initially through application to a High Integrity Protection System (HIPS) and subsequently to test scalability using the more complex Firewater Deluge System (FDS). Computer code has been developed to carry out the analysis. The results for both systems are compared to those using a single objective optimization approach (GASSOP) and exhaustive search. The overall conclusions show a number of benefits of the SPEA2 based technique application to the safety system design optimization. It is common for safety systems to feature dependency relationships between its components. To enable the use of the fault tree analysis technique and the BDD approach for such systems, the Markov method is incorporated into the optimization process. The main types of dependency which can exist between the safety system component failures are identified. The Markov model generation algorithms are suggested for each type of dependency. The modified optimization tool is tested on the HIPS and FDS. Results comparison shows the benefit of using the modified technique for safety system optimization. Finally the effectiveness and application to general safety systems is discussed.

Fault tree analysis, FTA, is one of the most commonly used techniques for safety system assessment. Over the past five years the Binary Decision Diagram (BDD) methodology has been introduced which significantly aids the analysis of the fault tree diagram. The approach has been shown to improve both the efficiency of determining the minimal cut sets of the fault tree, and also the accuracy of the calculation procedure used to quantifY the top event parameters. To utilise the BDD technique the fault tree structure needs to be converted into the BDD format. Converting the fault tree is relatively straightforward but requires the basic events of the tree to be placed in an ordering. The ordering of the basic events is -critical to the resulting size of the BDD, and ultimately affects the performance and benefits of this technique. There are a number of variable ordering heuristics in the literature, however the performance of each depends on the tree structure being analysed. These heuristic approaches do not always yield a minimal BDD structure for all trees, some approaches generate orderings that are better for some trees but worse for others. Within this thesis three pattern recognition approaches, that of machine learning classifier systems, multi-layer perceptron networks and radial basis function neural networks, have been investigated to try and select a variable ordering heuristic for a given fault tree from a set of alternatives. In addition a completely new heuristic based on component structural importance measures has been suggested with significant improvement in producing the smallest BDD over those methods currently in the literature.

Real Estate Online Platforms (REOPs) are responsible for providing property-related information to their users. However, most of these users are not satisfied with the information provided to them. This thesis highlights the REOP users’ needs and regrets and the pertinent disruptive digital technologies (DDTs) to address these needs. Two models are developed for assessing the REOPs users’ perception and the two-way relationship between them. Also, the barriers to adoption of the DDTs from a managerial perspective are examined. For assessing the users’ perception, Smart Real Estate Technology Adoption Model (SRETAM) is developed, whereas Risk, Service, Information, System TAM (RSISTAM) is developed to assess the two-way relationship between the perceptions. Concepts of KANO and SISQual are used to assess the perceptions, whereas Decision-Making Trial and Evaluation Laboratory (DEMATEL) is used for assessing the potential two-way relationships. The barriers to adoption of the DDTs are analysed through Fault Tree Analysis. These models, coupled with the users’ needs and DDT adoption barriers, constitute the novel framework adopted in this study. Eight key regrets of the users are identified from the published literature and meta-analyses: complicated buy-sell process, lack of information, housing costs, house size, mortgage, agents, inspections and emotional decision-making. Nine key technologies can help address the REOP users’ needs and regrets. In terms of REOP users’ perception, 31 key factors have been identified, among which 19 are very important based on responses from 407 respondents. Graphical statistics, attractive design, immersive and novel content attract REOPs users, whereas tracing user location, learning tutorials and hyperlinks discourage them. Among possible relations between RSISTAM constructs, nine are categorised as two-way. There are 21 key barriers to adopting DDTs in the Australian real estate sector identified through a survey of 102 real estate managers. High costs, high complexity of systems and lack of government support, regulations and standards are the top reasons for non-adoption. This thesis addresses the users’ regrets and needs related to REOPs-based information through DDTs adoption and provides a novel framework for facilitating such adoption. The users’ perceptions, needs and regrets addressed through DDTs and the elimination of associated barriers can transform Australian real estate into the smart real estate sector.

While the widespread adoption of autonomous vehicles (AVs) has the potential to drastically reduce the rate of traffic collisions, failure to verify their safe operation may expose the public to unacceptable risks. Due to the low frequency of traffic fatalities, verifying AV safety statistically via on-road testing is likely to be cost- and time-prohibitive, driving the need for alternate methods. This thesis examines four potential methods to assess AV safety: simulation, Failure Modes and Effects Analysis (FMEA), Fault Tree Analysis (FTA), and Systems Theoretic Process Analysis (STPA). The findings show two methods with potential: simulation based on data recorded from real environments, and quantitative FTA combined with a secondary analysis of the vehicle's machine learning algorithms. However, both approaches require significant amounts of data which may be expensive to gather. Further research into the safety of machine learning algorithms and further developments in AV simulation technology are required in order to develop more cost-effective methods for assessing AV safety.

Component failures can result in catastrophic behaviors in safety critical embedded systems, sometimes resulting in loss of life. Component failures can be treated as off nominal behaviors (ONBs) with respect to the components and sub systems involved in an embedded system. A lot of research is being carried out to tackle the problem of ONBs. These approaches are mainly focused on the states (i.e., desired and undesired states of a system at a given point of time to detect ONBs). In this paper, an approach is discussed to detect component failures and critical components of an embedded system. The approach is based on fault tree analysis (FTA), applied to the requirements specification of embedded systems at design time to find out the relationship between individual component failures and overall system failure. FTA helps in determining both qualitative and quantitative relationship between component failures and system failure. Analyzing the system at design time helps in detecting component failures and critical components and helps in devising strategies to mitigate component failures at design time and improve overall safety and reliability of a system.

In 2016, more than 35,000 people died in traffic crashes, and human error was the reason for 94% of these deaths. Researchers and automobile companies are testing autonomous vehicles in mixed traffic streams to eliminate human error by removing the human driver behind the steering wheel. However, recent autonomous vehicle crashes while testing indicate the necessity for a more thorough risk analysis. The objectives of this study were (1) to perform a risk analysis of autonomous vehicles and (2) to evaluate the safety impact of these vehicles in a mixed traffic stream. The overall research was divided into two phases: (1) risk analysis and (2) simulation of autonomous vehicles. Risk analysis of autonomous vehicles was conducted using the fault tree method. Based on failure probabilities of system components, two fault tree models were developed and combined to predict overall system reliability. It was found that an autonomous vehicle system could fail 158 times per one-million miles of travel due to either malfunction in vehicular components or disruption from infrastructure components. The second phase of this research was the simulation of an autonomous vehicle, where change in crash frequency after autonomous vehicle deployment in a mixed traffic stream was assessed. It was found that average travel time could be reduced by about 50%, and 74% of conflicts, i.e., traffic crashes, could be avoided by replacing 90% of the human drivers with autonomous vehicles.

With approval of UAS for civilian use in the National Airspace System, comes the need for formal integration. Manned and unmanned aircraft will share the same volumes of airspace, for which the safety standards must be upheld. Under manned aircraft operations, certain implicit assumptions exist that must be made explicit and translatable to the unmanned aircraft context. A formal system safety assessment approach through a fault tree analysis was used to identify assumptions contingent on a pilot’s presence inside the fuselage and areas of weakness in operational equivalency of UAS.The UAS fault tree framework developed is applicable to unmanned aircraft systems of different sizes and complexity, while maintaining a semblance to the framework accepted within the manned aircraft community. In addition, a database of UAS incidents and accidents occurring internationally 2001-2016 was developed from published materials and databases of various sources. Database events were categorized according to the UAS Fault Tree Framework Level 1 Subsystems, the International Civil Aviation Organization (ICAO) Aviation Occurrence Categories, and the Human Factors Analysis and Classification System (HFACS). ICAO Aviation Occurrence Category specific fault trees were constructed for the three most commonly occurring categories in the databaseresults.Significant sources of risk for UAS operations lie in Aircraft/System and Flight Crew/Human Factors failures. Commonly occurring Occurrence Categories in the results of the UAS database were different than those identified for fatal accidents occurring in manned commercial aviation operations. Increased system reliability and standardization is needed to ensure equivalent levels of safety for UAS operations in the NAS. Additionally, needs of UAS pilots are different than those for manned and model aircraft. Training requirements must be approached independently and formally evaluated for their effectiveness in risk mitigation.

Small Unmanned Aerial Vehicles (SUAVs) are rapidly being adopted in the National Airspace (NAS) but experience a much higher failure rate than traditional aircraft. These SUAVs are quickly becoming complex enough to investigate alternative methods of failure analysis. This thesis proposes a method of expanding on the Fault Tree Analysis (FTA) method to a Bayesian Belief Network (BBN) model. FTA is demonstrated to be a special case of BBN and BBN can allow for more complex interactions between nodes than is allowed by FTA. A model can be investigated to determine the components to which failure is most sensitive and allow for redundancies or mitigations against those failures. The introduced method is then applied to the Virginia Tech ESPAARO SUAV.

Construction projects are always expected to be delayed, but the likelihood of a delay varies between projects because of the particular circumstances and schedule for that project. It is usually left to the scheduler to estimate these future circumstances of the project when preparing the schedule and determining the duration of the project. However, the schedule of the Critical Path Method (CPM) does not indicate the factors that are assumed to participate in determining the likelihood for delay. These deterministic durations, apart from the relationship between the activities, are the dominant contributor to the critical path in the CPM calculation. Risk management focuses on the processes that are considered critical, although delay may emerge from non-critical paths.In this study, a new fuzzy model is proposed to provide a subjective assessment of the likelihood of delay for activities in different periods. Using this model, the scheduler’s assessment of the likelihood of delay for each activity can be combined to determine the likelihood of a project delay. This process is done utilizing fuzzy logic and fault-tree analysis and is then combined with the CPM schedule of the project. The result is a fuzzy fault-tree that shows the potential delay of the project and its contributing paths.Applying this method on a sample project, the results show that risk of delay comes not only from critical paths but also from non-critical paths. Consequently, the CPM schedule duration can be reevaluated such that the project can be rescheduled to account for the new findings and, at the very least, the risk of delay can be accounted for.

Small unmanned aerial vehicles (SUAVs) are plagued by alarmingly high failure rates. Because these systems are small and built at lower cost than full-scale aircraft, high quality components and redundant systems are often eschewed to keep production costs low. This thesis proposes a process to ``design in'' reliability in a cost-effective way. Fault Tree Analysis is used to evaluate a system's (un)reliability and Dempster-Shafer Theory (Evidence Theory) is used to deal with imprecise failure data. Three unique sensitivity analyses highlight the most cost-effective improvement for the system by either spending money to research a component and reduce uncertainty, swap a component for a higher quality alternative, or add redundancy to an existing component. A MATLAB$^{\circledR}$ toolbox has been developed to assist in practical design applications. Finally, a case study illustrates the proposed methods by improving the reliability of a new SUAV design: Virginia Tech's SPAARO UAV.