Francisco F. Martins

Prediction of the mechanical behavior of the Oporto granite using Data Mining techniques

Highlights? Granite weathering characteristics and physical properties vs weathering degree index. ? Uniaxial compressive strength and the modulus of elasticity of granitic rocks. ? Artificial neural networks (ANN) and support vector machines (SVM). ? Free porosity (N48), dry bulk density (d) and ultrasonic velocity (v). ? SVM model gave the best results with N48 and v as input variables. The determination of mechanical properties of granitic rocks has a great importance to solve many engineering problems. Tunnelling, mining and excavations are some examples of these problems. The purpose of this paper is to apply Data Mining (DM) techniques such as multiple regressions (MR), artificial neural networks (ANN) and support vector machines (SVM), to predict the uniaxial compressive strength and the deformation modulus of the Oporto granite. This rock is a light grey, two-mica, medium-grained, hypidiomorphic granite and is located in Oporto (Portugal) and surrounding areas. Begonha (1997) and Begonha and Sequeira Braga (2002) studied this granite in terms of chemical, mineralogical, physical and mechanical properties. Among other things, like the weathering features, those authors applied correlation analysis to investigate the relationships between two properties either physical or mechanical or physical and mechanical. This study took the data published by those authors to build a database containing 55 rock sample records. Each record contains the free porosity (N48), the dry bulk density (d), the ultrasonic velocity (v), the uniaxial compressive strength (?c) and the modulus of elasticity (E). It was concluded that all the models obtained from DM techniques have good performances. Nevertheless, the best forecasting capacity was obtained with the SVM model with N48 and v as input parameters.

Estimation of the Rock Deformation Modulus and RMR Based on Data Mining Techniques

In this work Data Mining tools are used to develop new and innovative models for the estimation of the rock deformation modulus and the Rock Mass Rating (RMR). A database published by Chun et al. (Int J Rock Mech Min Sci 46:649–658, 2008) was used to develop these models. The parameters of the database were the depth, the weightings of the RMR system related to the uniaxial compressive strength, the rock quality designation, the joint spacing, the joint condition, the groundwater condition and the discontinuity orientation adjustment, the RMR and the deformation modulus. As a modelling tool the R program environment was used to apply these advanced techniques. Several algorithms were tested and analysed using different sets of input parameters. It was possible to develop new models to predict the rock deformation modulus and the RMR with improved accuracy and, additionally, allowed to have an insight of the importance of the different input parameters.

Using genetic algorithms to generate test plans for functionality testing

Like in other fields, computer products (applications, hardware, etc.), before being marketed, require some level of testing to verify whether they meet their design and functional specifications -- called functionality test. The general process of performing functionality test consists in the production of a test plan that is then executed by humans or by automated software tools. The main difficulty in this entire process is the definition of such test plan. How can we know what a good sequence (test plan) is? The rule of thumb is to trust on people who understand the workings of the application being tested and who can decide what should be tested. The danger is that experts, due to their over-confidence on their knowledge, may become blind to issues that should otherwise be easy to see. This paper describes a technique based on genetic algorithms that is able to generate good test plans in an unbiased way and with minimum expert interference.

On the Idea of Using Nature-Inspired Metaphors to Improve Software Testing

The number of software defects found in software applications today costs users and companies billions of dollars annually. In general, these defects occur due to an inadequate software development process that does not give the necessary importance to testing. Another contributor to these costs is the lack of adequate automated tools that can find “bugs” that would not otherwise be verified by experts. This paper looks at the combinatorial characteristics of the problem of testing — tools essentially search among all test cases for those that are promising (find existing bugs in the application) — and the effect that abstractions inspired by nature, such as genetic algorithms and swarm intelligence, may have in the construction of more “intelligent” testing tools. The paper argues that these abstractions may be used to construct automated tools that are more powerful, less biased, and able to incorporate expert knowledge while maintaining the ability to discover new, never-thought-of software defects.

Prediction of the mechanical properties of granites under tension using DM techniques

This work was partly financed by FEDER funds through the Competitivity Factors Operational Programme - COMPETE and by national funds through FCT – Foundation for Science and Technology within the scope of the project POCI-01-0145-FEDER-007633.