Winkler springs (p-y curves) for liquefied soil from element tests.

In practice, piles are most often modelled as "Beams on Non-Linear Winkler Foundation" (also known as “p-y spring” approach) where the soil is idealised as p-y springs. These p-y springs are obtained through semi-empirical approach using element test results of the soil. For liquefied soil, a reduction factor (often termed as p-multiplier approach) is applied on a standard p-y curve for the non-liquefied condition to obtain the p-y curve liquefied soil condition. This paper presents a methodology to obtain p-y curves for liquefied soil based on element testing of liquefied soil considering physically plausible mechanisms. Validation of the proposed p-y curves is carried out through the back analysis of physical model tests.

An analysis of manufacturing simulation software.

As a management tool Similation Software deserves greater analysis from both an academic and industrial viewpoint. A comparative study of three packages was carried out from a 'first time' use approach. This allowed the ease of use and package features to be assessed using a simple theoretical benchmark manufacturing process. To back the use of these packages an objective survey on simulation use and package features was carried out within the manufacturing industry.This identified the use of simulation software, its' applicability and preception of user requirements thereby proposing an ideal package.

Applied web traffic analysis for numerical encoding of SQL Injection attack features.

SQL Injection Attack (SQLIA) remains a technique used by a computer network intruder to pilfer an organisation’s confidential data. This is done by an intruder re-crafting web form’s input and query strings used in web requests with malicious intent to compromise the security of an organisation’s confidential data stored at the back-end database. The database is the most valuable data source, and thus, intruders are unrelenting in constantly evolving new techniques to bypass the signature’s solutions currently provided in Web Application Firewalls (WAF) to mitigate SQLIA. There is therefore a need for an automated scalable methodology in the pre-processing of SQLIA features fit for a supervised learning model. However, obtaining a ready-made scalable dataset that is feature engineered with numerical attributes dataset items to train Artificial Neural Network (ANN) and Machine Leaning (ML) models is a known issue in applying artificial intelligence to effectively address ever evolving novel SQLIA signatures. This proposed approach applies numerical attributes encoding ontology to encode features (both legitimate web requests and SQLIA) to numerical data items as to extract scalable dataset for input to a supervised learning model in moving towards a ML SQLIA detection and prevention model. In numerical attributes encoding of features, the proposed model explores a hybrid of static and dynamic pattern matching by implementing a Non-Deterministic Finite Automaton (NFA). This combined with proxy and SQL parser Application Programming Interface (API) to intercept and parse web requests in transition to the back-end database. In developing a solution to address SQLIA, this model allows processed web requests at the proxy deemed to contain injected query string to be excluded from reaching the target back-end database. This paper is intended for evaluating the performance metrics of a dataset obtained by numerical encoding of features ontology in Microsoft Azure Machine Learning (MAML) studio using Two-Class Support Vector Machines (TCSVM) binary classifier. This methodology then forms the subject of the empirical evaluation.