Behaviour of displacement piles in sand under cyclic axial loading

Field experiments have demonstrated that piles driven into sand can respond to axial cyclic loading in Stable, Unstable or Meta-Stable ways, depending on the combinations of mean and cyclic loads and the number of cycles. An understanding of the three styles of responses is provided by experiments involving a highly instrumented model displacement pile and an array of soil stress sensors installed in fine sand in a pressurised calibration chamber. The different patterns of effective stress developing on and around the shaft are reported, along with the results of static load tests that track the effects on shaft capacity. The interpretation links these observations to the sand's stress strain behaviour. The interface-shear characteristics, the kinematic yielding, the local densification, the growth of a fractured interface-shear zone and the restrained dilatancy at the pile soil interface are all found to be important. The model tests are shown to be compatible with the full-scale behaviour and to provide key information for improving the modelling and the design rules. (C) 2012 The Japanese Geotechnical Society. Production and hosting by Elsevier B.V. All rights reserved.

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

Background: This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. Results: The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. Conclusions: We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.