Loess as a Collapsible Soil: Some Basic Particle Packing

Loess is the most important collapsible soil; possibly the only engineering soil in which real collapse occurs. A real collapse involves a diminution in volume - it would be an open metastable packing being reduced to a more closely packed, more stable structure. Metastability is at the heart of the collapsible soils problem. To envisage and to model the collapse process in a metastable medium, knowledge is required about the nature and shape of the particles, the types of packings they assume (real and ideal), and the nature of the collapse process - a packing transition upon a change to the effective stress in a media of double porosity. Particle packing science has made little progress in geoscience discipline - since the initial packing paradigms set by Graton and Fraser (1935) - nevertheless is relatively well-established in the soft matter physics discipline. The collapse process can be represented by mathematical modelling of packing – including the Monte Carlo simulations - but relating representation to process remains difficult. This paper revisits the problem of sudden packing transition from a micro-physico-mechanical viewpoint (i.e. collapse imetan terms of structure-based effective stress). This cross-disciplinary approach helps in generalization on collapsible soils to be made that suggests loess is the only truly collapsible soil, because it is only loess which is so totally influenced by the packing essence of the formation process.

Probabilistic Graphical Modelling for Software Product Lines: A Frameweork for Modeling and Reasoning under Uncertainty

This work provides a holistic investigation into the realm of feature modeling within software product lines. The work presented identifies limitations and challenges within the current feature modeling approaches. Those limitations include, but not limited to, the dearth of satisfactory cognitive presentation, inconveniency in scalable systems, inflexibility in adapting changes, nonexistence of predictability of models behavior, as well as the lack of probabilistic quantification of model’s implications and decision support for reasoning under uncertainty. The work in this thesis addresses these challenges by proposing a series of solutions. The first solution is the construction of a Bayesian Belief Feature Model, which is a novel modeling approach capable of quantifying the uncertainty measures in model parameters by a means of incorporating probabilistic modeling with a conventional modeling approach. The Bayesian Belief feature model presents a new enhanced feature modeling approach in terms of truth quantification and visual expressiveness. The second solution takes into consideration the unclear support for the reasoning under the uncertainty process, and the challenging constraint satisfaction problem in software product lines. This has been done through the development of a mathematical reasoner, which was designed to satisfy the model constraints by considering probability weight for all involved parameters and quantify the actual implications of the problem constraints. The developed Uncertain Constraint Satisfaction Problem approach has been tested and validated through a set of designated experiments. Profoundly stating, the main contributions of this thesis include the following: • Develop a framework for probabilistic graphical modeling to build the purported Bayesian belief feature model. • Extend the model to enhance visual expressiveness throughout the integration of colour degree variation; in which the colour varies with respect to the predefined probabilistic weights. • Enhance the constraints satisfaction problem by the uncertainty measuring of the parameters truth assumption. • Validate the developed approach against different experimental settings to determine its functionality and performance.