107,380 research outputs found
Damage prediction for regular reinforced concrete buildings using the decision tree algorithm
To overcome the problem of outlier data in the regression analysis for numerical-based damage spectra, the C4.5 decision tree learning algorithm is used to predict damage in reinforced concrete buildings in future earthquake scenarios. Reinforced concrete buildings are modelled as single-degree-of-freedom systems and various time-history nonlinear analyses are performed to create a dataset of damage indices. Subsequently, two decision trees are trained using the qualitative interpretations of those indices. The first decision tree determines whether damage occurs in an RC building. Consequently, the second decision tree predicts the severity of damage as repairable, beyond repair, or collapse
Site investigation for the effects of vegetation on ground stability
The procedure for geotechnical site investigation is well established but little attention is currently given to investigating the potential of vegetation to assist with ground stability. This paper describes how routine investigation procedures may be adapted to consider the effects of the vegetation. It is recommended that the major part of the vegetation investigation is carried out, at relatively low cost, during the preliminary (desk) study phase of the investigation when there is maximum flexibility to take account of findings in the proposed design and construction. The techniques available for investigation of the effects of vegetation are reviewed and references provided for further consideration. As for general geotechnical investigation work, it is important that a balance of effort is maintained in the vegetation investigation between (a) site characterisation (defining and identifying the existing and proposed vegetation to suit the site and ground conditions), (b) testing (in-situ and laboratory testing of the vegetation and root systems to provide design parameters) and (c) modelling (to analyse the vegetation effects)
A survey of random processes with reinforcement
The models surveyed include generalized P\'{o}lya urns, reinforced random
walks, interacting urn models, and continuous reinforced processes. Emphasis is
on methods and results, with sketches provided of some proofs. Applications are
discussed in statistics, biology, economics and a number of other areas.Comment: Published at http://dx.doi.org/10.1214/07-PS094 in the Probability
Surveys (http://www.i-journals.org/ps/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Simple trees in complex forests: Growing Take The Best by Approximate Bayesian Computation
How can heuristic strategies emerge from smaller building blocks? We propose
Approximate Bayesian Computation as a computational solution to this problem.
As a first proof of concept, we demonstrate how a heuristic decision strategy
such as Take The Best (TTB) can be learned from smaller, probabilistically
updated building blocks. Based on a self-reinforcing sampling scheme, different
building blocks are combined and, over time, tree-like non-compensatory
heuristics emerge. This new algorithm, coined Approximately Bayesian Computed
Take The Best (ABC-TTB), is able to recover a data set that was generated by
TTB, leads to sensible inferences about cue importance and cue directions, can
outperform traditional TTB, and allows to trade-off performance and
computational effort explicitly
Intraneuronal information processing, directional selectivity and memory for spatio-temporal sequences.
Interacting intracellular signalling pathways can perform computations on a scale that is slower, but more fine-grained, than the interactions between neurons upon which we normally build our computational models of the brain (Bray D 1995 Nature 376 307-12). What computations might these potentially powerful intraneuronal mechanisms be performing? The answer suggested here is: storage of spatio-temporal trajectories; thus, neurons have some of the capacities required to perform such a task. In the retina, it is suggested that calcium-induced calcium release (CICR) may provide the basis for directional selectivity. In the cortex, if activation mechanisms with different delays could be separately reinforced at individual synapses then each such Hebbian super-synapse would store a memory trace of the delay between pre- and post-synaptic activity, forming an ideal basis for the memory and response to phase sequences
- …