Railway-induced ground vibrations – a review of vehicle effects

This paper is a review of the effect of vehicle characteristics on ground- and track borne-vibrations from railways. It combines traditional theory with modern thinking and uses a range of numerical analysis and experimental results to provide a broad analysis of the subject area. First, the effect of different train types on vibration propagation is investigated. Then, despite not being the focus of this work, numerical approaches to vibration propagation modelling within the track and soil are briefly touched upon. Next an in-depth discussion is presented related to the evolution of numerical models, with analysis of the suitability of various modelling approaches for analysing vehicle effects. The differences between quasi-static and dynamic characteristics are also discussed with insights into defects such as wheel/rail irregularities. Additionally, as an appendix, a modest database of train types are presented along with detailed information related to their physical attributes. It is hoped that this information may provide assistance to future researchers attempting to simulate railway vehicle vibrations. It is concluded that train type and the contact conditions at the wheel/rail interface can be influential in the generation of vibration. Therefore, where possible, when using numerical approach, the vehicle should be modelled in detail. Additionally, it was found that there are a wide variety of modelling approaches capable of simulating train types effects. If non-linear behaviour needs to be included in the model, then time domain simulations are preferable, however if the system can be assumed linear then frequency domain simulations are suitable due to their reduced computational demand

Mining telecommunication networks to enhance customer lifetime predictions

Customer retention has become a necessity in many markets, including mobile telecommunications. As it becomes easier for customers to switch providers, the providers seek to improve prediction models in an effort to intervene with potential churners. Many studies have evaluated different models seeking any improvement to prediction accuracy. This study proposes that the attributes, not the model, need to be reconsidered. By representing call detail records as a social network of customers, network attributes can be extracted for use in various traditional prediction models. The use of network attributes exhibits a significant increase in the area under the receiver operating curve (AUC) when compared to using just individual customer attributes.nrpages: 14status: publishe

Scoping assessment of building vibrations due to railway traffic

Ministerio de Economía y Competitividad BIA2016-75042-C2Leeds Cheney Award SchemeMinisterio de Educación, Cultura y Deporte PRX18/0011