Fire in building structures represents a risk both to life and property that cannot be fully eliminated. It is the aim of fire safety engineering to reduce this risk to an acceptable level through the application of scientific and engineering principles to evaluate the risk posed by fire and to determine the optimal set of protective measures. This is increasingly being achieved through performance-based design methods. Performance-based design sets out performance requirements, typically related to life safety and control of property losses, and the designer is free to choose the most suitable approach to meet these requirements. Accurate performance-based design requires the evaluation of the risks to a structure through the evaluation of the range of hazards that may occur and the resulting structural responses. The purpose of this research is to develop simplified methodologies for the reliability based design of steel framed structures in fire. These methodologies are incorporated into a software package, FireLab, which is intended to act as a tool for practicing engineers to aid in learning and applying performance-based design. FireLab is a Matlab based program that incorporates a number of different models for analysing the response of structural elements exposed to fire. It includes both deterministic and probabilistic analysis procedures. A range of simple fire models are presented for modelling compartment fires. A set of heat transfer processes are discussed for calculating the temperature distribution within common structural elements exposed to fire. A variety of structural models are discussed which may be used to model the effects of fire on a structure. An analytical model for the analysis of composite beams has been implemented in the software program. Interfaces between the software and 2 separate third party programs have also been created to allow for the analysis of composite beams using the finite element method. Analytical methods for the analysis of composite slabs under thermo-mechanical load have been implemented in the software. These methods account for the additional load carrying capacity that slabs have in fire due to the positive effects of tensile membrane action. A numerical analysis method for the vertical stability of structures subjected to multi-floor fires has been implemented using the direct stiffness method. This method uses an elastic 2nd order solution in order to check the stability of a column under the fire induced horizontal loads from sagging floors. These models of potential failure scenarios provide the basis for the probabilistic analysis methods. A variety of methods for reliability analysis are evaluated based on ease of use, accuracy and efficiency. A selection of these methods has been implemented in the software program. A selection of sample cases are examined in order to illustrate the procedures and to evaluate the important input variables. These methods provide the probability of failure of a structure under specific loads. The probability of failure is a useful parameter in comparing the level of safety between various design options. A more comprehensive framework is developed for the evaluation of the probable costs due to fire associated with a given design. This framework is based on an existing framework from earthquake engineering. It involves calculating the statistical spread of both the magnitude and likelihood of occurrence of fire and the resulting structural responses. The damage that occurs from the structural response may be then estimated. Finally, given the likely level of damage that will occur it is possible to estimate the cost of the damage either in terms of monetary cost of repair or downtime due to repair works. This method is applied to a variety of design options for a typical office building in order to illustrate the application of the framework.

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When aggressive aircraft performance goals are set, he integration of new, advanced technologies into next generation aircraft concepts is required to bridge the gap between current capabilities and required capabilities. A large number of technologies exists that can be pursued, and only a subset may practically be selected to reach the chosen objectives. Additionally, the appropriate numerical and physical experimentation must be identified to further develop the selected technologies. These decisions must be made under a large amount of uncertainty because developing technologies introduce phenomena that have not been previously characterized. Traditionally, technology selection decisions are made based on deterministic performance assessments that do not capture the uncertainty of the technology impacts. Model-driven environments and new, advanced uncertainty quantification techniques provide the ability to characterize technology impact uncertainties and pinpoint how they are driving the system performance, which will aid technology selection decisions. Moreover, the probabilistic assessments can be used to plan experimentation that facilitates uncertainty reduction by targeting uncertainty sources with large performance impacts. The thesis formulates and implements a process that allows for risk-informed decision making throughout technology development. It focuses on quantifying technology readiness risk and performance risk by synthesizing quantitative, probabilistic performance information with qualitative readiness assessments. The Quantitative Uncertainty Modeling, Management, and Mitigation (QuantUM3) methodology was tested through the use of an environmentally-motivated aircraft design case study based upon NASAs Environmentally Responsible Aviation (ERA) technology development program. A physics-based aircraft design environment was created that has the ability to provide quantitative system-level performance assessments and was employed to model the technology impacts as probability distributions to facilitate the development of an overall process required to enable risk-informed technology and experimentation decisions. The outcome of the experimental e orts was a detailed outline of the entire methodology and a confirmation that the methodology enables risk-informed technology development decisions with respect to both readiness risk and performance risk. Furthermore, a new process for communicating technology readiness through morphological analysis was created as well as an experiment design process that utilizes the readiness information and quantitative uncertainty analysis to simultaneously increase readiness and decrease technology performance uncertainty.

The feasibility and success of petroleum development projects depend to a large degree on well construction costs. Well construction cost estimates often contain high levels of uncertainty. In many cases, these costs have been estimated using deterministic methods that do not reliably account for uncertainty, leading to biased estimates. The primary objective of this work was to improve the reliability of deterministic well construction cost estimates by incorporating probabilistic methods into the estimation process. The method uses historical well cost estimates and actual well costs to develop probabilistic correction factors that can be applied to future well cost estimates. These factors can be applied to the entire well cost or to individual cost components. Application of the methodology to estimation of well construction costs for horizontal wells in a shale gas play resulted in well cost estimates that were well calibrated probabilistically. Overall, average estimated well cost using this methodology was significantly more accurate than average estimated well cost using deterministic methods. Systematic use of this methodology can provide for more accurate and efficient allocation of capital for drilling campaigns, which should have significant impacts on reservoir development and profitability.

Levees are earth structures used for flood protection. Due to their easy availability and low permeability, clays are the most common material used for the construction of levees. Clays are susceptible to desiccation cracks when subjected to long dry spells during summers. There has been an increased interest in studying the occurrence of cracks in soil mass. In particular, many experimental investigations for soils have been undertaken to learn about the crack pattern in earth embankment. However, there is a dearth of work that focuses on the numerical modeling of desiccation cracks effects on levees. This study has been undertaken to analyze the effect of desiccation cracking on the hydraulic behavior of an earth embankment under flooding conditions. A numerical model was developed using the finite element package CODE_BRIGHT. The model was validated from the data obtained from a small scale embankment experiment under controlled environmental conditions. As the phenomenon of desiccation cracking is highly random, a simple random model was developed to capture the variability in crack geometry. The random crack geometry was then passed on to the finite element mesh, so that a probabilistic analysis can be carried out using a Monte Carlo approach, for assessing the embankment’s integrity. The results obtained from the analysis such as time to steady state saturation and steady state flow rate at the outward slope were very interesting to study and provided an insight on the effect of desiccation cracks on unsaturated earth embankments.

It is well recognised that the inherent soil variability and the effect of vegetation, in particular the effect of tree root reinforcement, have a significant effect on the stability of a natural slope. However, in practice, these factors are not commonly considered in routine slope stability analysis. This is due mainly to the fact that the effects of soil variability and vegetation are complex and difficult to quantify. Furthermore, the available slope stability analysis computer programs used in practice, which adopt conventional limit equilibrium methods, are unable to consider these factors. To predict the stability of a natural slope more accurately, especially the marginally stable one, the effects of soil variability and vegetation needs to be taken into account. The research presented in this thesis focuses on investigating and quantifying the effects of soil variability and vegetation on the stability of natural slopes. The random finite element method (RFEM), developed by Griffiths and Fenton (2004), is adopted to model the effect of soil variability on slope stability. The soil variability is quantified by the parameters called the coefficient of variation (COV) and scale of fluctuation (SOF), while the safety of a slope is assessed using probability of failure. In this research, extensive parametric studies are conducted, using the RFEM, to investigate the influence of COV and SOF on the probability of failure of a cohesive slope (i.e. undrained clay slope) with different geometries. Probabilistic stability charts are then developed using the results obtained from the parametric studies. These charts can be used for a preliminary assessment of the probability of failure of a spatially random cohesive slope. In addition, the effect of soil variability on c'–ϕ' slopes is also studied. The available RFEM computer program (i.e. rslope2d) is limited to analysing slopes with single-layered soil profile. Therefore, in this research, this computer program is modified to analyse slopes with two-layered soil profiles. The modified program is then used to investigate the effect of soil variability on two-layered spatially random cohesive slopes. It has been demonstrated that the spatial variability of soil variability has a significant effect on the reliability of both single and two-layered soil slopes. Artificial neural networks (ANNs), which are a powerful data-mapping tool for determining the relationship between a set of input and output variables, are used in an attempt to predict the probability of failure of a spatially random cohesive slope. The aim is to provide an alternative tool to the RFEM and the developed probabilistic stability charts because the RFEM analyses are computationally intensive and time consuming. The results obtained from the parametric studies of a spatially random cohesive slope are used as the database for the ANN model development. It has been demonstrated that the ANN models developed in this research are capable of predicting the probability of failure of a spatially random cohesive slope with high accuracy. The developed ANN models are then transformed into relatively simple formulae for direct application in practice. The effect of root reinforcement caused by vegetation is modelled as additional cohesion to the soils, known as root cohesion, cr. The areas affected by tree roots (i.e. root zone) are incorporated in the finite element slope stability model. The extent of the root zone is defined by the depth of root zone, hr. Parametric studies are conducted and the results are used to develop a set of stability charts that can be used to assess the contribution of root reinforcement on slope stability. Furthermore, ANN models and formulae are also developed based on the results obtained from the parametric studies. It has been demonstrated that the factor of safety of a slope increase linearly with the values cr and hr, and the contribution of root reinforcement to a marginally stable slope is significant. In addition, probabilistic slope stability analysis considering both the variability of the soils and root cohesion are conducted using the modified RFEM computer program. It has been demonstrated that the spatial variability of root cohesion has a significant effect on the probability of slope failure. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1349971 / Thesis (Ph.D.) - University of Adelaide, School of Civil, Environmental and Mining Engineering, 2009

It is well recognised that the inherent soil variability and the effect of vegetation, in particular the effect of tree root reinforcement, have a significant effect on the stability of a natural slope. However, in practice, these factors are not commonly considered in routine slope stability analysis. This is due mainly to the fact that the effects of soil variability and vegetation are complex and difficult to quantify. Furthermore, the available slope stability analysis computer programs used in practice, which adopt conventional limit equilibrium methods, are unable to consider these factors. To predict the stability of a natural slope more accurately, especially the marginally stable one, the effects of soil variability and vegetation needs to be taken into account. The research presented in this thesis focuses on investigating and quantifying the effects of soil variability and vegetation on the stability of natural slopes. The random finite element method (RFEM), developed by Griffiths and Fenton (2004), is adopted to model the effect of soil variability on slope stability. The soil variability is quantified by the parameters called the coefficient of variation (COV) and scale of fluctuation (SOF), while the safety of a slope is assessed using probability of failure. In this research, extensive parametric studies are conducted, using the RFEM, to investigate the influence of COV and SOF on the probability of failure of a cohesive slope (i.e. undrained clay slope) with different geometries. Probabilistic stability charts are then developed using the results obtained from the parametric studies. These charts can be used for a preliminary assessment of the probability of failure of a spatially random cohesive slope. In addition, the effect of soil variability on c'–ϕ' slopes is also studied. The available RFEM computer program (i.e. rslope2d) is limited to analysing slopes with single-layered soil profile. Therefore, in this research, this computer program is modified to analyse slopes with two-layered soil profiles. The modified program is then used to investigate the effect of soil variability on two-layered spatially random cohesive slopes. It has been demonstrated that the spatial variability of soil variability has a significant effect on the reliability of both single and two-layered soil slopes. Artificial neural networks (ANNs), which are a powerful data-mapping tool for determining the relationship between a set of input and output variables, are used in an attempt to predict the probability of failure of a spatially random cohesive slope. The aim is to provide an alternative tool to the RFEM and the developed probabilistic stability charts because the RFEM analyses are computationally intensive and time consuming. The results obtained from the parametric studies of a spatially random cohesive slope are used as the database for the ANN model development. It has been demonstrated that the ANN models developed in this research are capable of predicting the probability of failure of a spatially random cohesive slope with high accuracy. The developed ANN models are then transformed into relatively simple formulae for direct application in practice. The effect of root reinforcement caused by vegetation is modelled as additional cohesion to the soils, known as root cohesion, cr. The areas affected by tree roots (i.e. root zone) are incorporated in the finite element slope stability model. The extent of the root zone is defined by the depth of root zone, hr. Parametric studies are conducted and the results are used to develop a set of stability charts that can be used to assess the contribution of root reinforcement on slope stability. Furthermore, ANN models and formulae are also developed based on the results obtained from the parametric studies. It has been demonstrated that the factor of safety of a slope increase linearly with the values cr and hr, and the contribution of root reinforcement to a marginally stable slope is significant. In addition, probabilistic slope stability analysis considering both the variability of the soils and root cohesion are conducted using the modified RFEM computer program. It has been demonstrated that the spatial variability of root cohesion has a significant effect on the probability of slope failure. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1349971 / Thesis (Ph.D.) - University of Adelaide, School of Civil, Environmental and Mining Engineering, 2009

Previous earthquakes demonstrated destructive effects of soil-structure interaction on structural response. For example, in the 1970 Gediz earthquake in Turkey, part of a factory was demolished in a town 135 km from the epicentre, while no other buildings in the town were damaged. Subsequent investigations revealed that the fundamental period of vibration of the factory was approximately equal to that of the underlying soil. This alignment provided a resonance effect and led to collapse of the structure. Another dramatic example took place in Adapazari, during the 1999 Kocaeli earthquake where several foundations failed due to either bearing capacity exceedance or foundation uplifting, consequently, damaging the structure. Finally, the Christchurch 2012 earthquakes have shown that significant nonlinear action in the soil and soil-foundation interface can be expected due to high levels of seismic excitation and spectral acceleration. This nonlinearity, in turn, significantly influenced the response of the structure interacting with the soil-foundation underneath. Extensive research over more than 35 years has focused on the subject of seismic soil-structure interaction. However, since the response of soil-structure systems to seismic forces is extremely complex, burdened by uncertainties in system parameters and variability in ground motions, the role of soil-structure interaction on the structural response is still controversial. Conventional design procedures suggest that soil-structure interaction effects on the structural response can be conservatively ignored. However, more recent studies show that soil-structure interaction can be either beneficial or detrimental, depending on the soil-structure-earthquake scenarios considered. In view of the above mentioned issues, this research aims to utilise a comprehensive and systematic probabilistic methodology, as the most rational way, to quantify the effects of soil-structure interaction on the structural response considering both aleatory and epistemic uncertainties. The goal is achieved by examining the response of established rheological single-degree-of-freedom systems located on shallow-foundation and excited by ground motions with different spectral characteristics. In this regard, four main phases are followed. First, the effects of seismic soil-structure interaction on the response of structures with linear behaviour are investigated using a robust stochastic approach. Herein, the soil-foundation interface is modelled by an equivalent linear cone model. This phase is mainly considered to examine the influence of soil-structure interaction on the approach that has been adopted in the building codes for developing design spectrum and defining the seismic forces acting on the structure. Second, the effects of structural nonlinearity on the role of soil-structure interaction in modifying seismic structural response are studied. The same stochastic approach as phase 1 is followed, while three different types of structural force-deflection behaviour are examined. Third, a systematic fashion is carried out to look for any possible correlation between soil, structural, and system parameters and the degree of soil-structure interaction effects on the structural response. An attempt is made to identify the key parameters whose variation significantly affects the structural response. In addition, it is tried to define the critical range of variation of parameters of consequent. Finally, the impact of soil-foundation interface nonlinearity on the soil-structure interaction analysis is examined. In this regard, a newly developed macro-element covering both material and geometrical soil-foundation interface nonlinearity is implemented in a finite-element program Raumoko 3D. This model is then used in an extensive probabilistic simulation to compare the effects of linear and nonlinear soil-structure interaction on the structural response. This research is concluded by reviewing the current design guidelines incorporating soil-structure interaction effects in their design procedures. A discussion is then followed on the inadequacies of current procedures based on the outcomes of this study.

Components are widely seen by software engineers as an important technology to address the "software crisis''. An important aspect of components in other areas of engineering is that system reliability can be estimated from the reliability of the components. We show how commonly proposed methods of reliability estimation and composition for software are inadequate because of differences between the models and the actual software systems, and we show where the assumptions from system reliability theory cause difficulty when applied to software. This thesis provides an approach to reliability that makes it possible, if not currently plausible, to compose component reliabilities so as to accurately and safely determine system reliability. Firstly, we extend previous work on input sub-domains, or partitions, such that our sub-domains can be sampled in a statistically sound way. We provide an algorithm to generate the most important partitions first, which is particularly important when there are an infinite number of input sub-domains. We combine analysis and testing to provide useful reliabilities for the various input sub-domains of a system, or component. This provides a methodology for calculating true reliability for a software system for any accurate statistical distribution of input values. Secondly, we present a calculus for probability density functions that permits accurately modeling the input distribution seen by each component in the system - a critically important issue in dealing with reliability of software components. Finally, we provide the system structuring calculus that allows a system designer to take components from component suppliers that have been built according to our rules and to determine the resulting system reliability. This can be done without access to the actual components. This work raises many issues, particularly about scalability of the proposed techniques and about the ability of the system designer to know the input profile to the level and kind of accuracy required. There are also large classes of components where the techniques are currently intractable, but we see this work as an important first step.