Smart railway maintenance is crucial to the safety and efficiency of railway operations. Successful deployment of technologies such as condition-based monitoring and predictive maintenance will enable railway companies to conduct proactive maintenance before defects and failures take place to improve operation safety and efficiency. In this paper, we first propose to develop a classification-based method to detect rail defects such as localized surface collapse, rail end batter, or rail components—such as joints, turning points, crossings, etc.—by using acceleration data. In order to improve the performance of the classification-based models and enhance their applicability in practice, we further propose a deep learning-based approach for the detection of rail joints or defects by deploying convolutional neural networks (CNN). CNN-based models can work directly with raw data to reduce the heavy preprocessing of feature engineering and directly detect joints located on either the left or the right rail. Two convolutional networks, ResNet and fully convolutional networks (FCN), are investigated and evaluated with the collected acceleration data. The experimental results show both deep neural networks obtain good performance, which demonstrate that the deep learning-based methods are effective for detecting rail joints or defects with the expected performance.

A two-phase flow eXtended Finite Element Method (XFEM) model is presented to analyse the injection and sequestration of carbon dioxide (CO 2 ) in deep saline aquifers. XFEM is introduced to accurately approximate near-injection well pressure behaviour with elements significantly larger than the injection well diameter. We present a vertically averaged multiphase flow model that combines XFEM to approximate the pressure field, with a Streamline Upwind/Finite Element Method/Finite Difference Method (SU-FEM-FDM) to approximate the distribution of CO 2 in the aquifer. Near-well enrichment functions are presented along with the solution procedure for the coupled problem. Two examples are presented: in the first, CO 2 injection into a perfectly horizontal aquifer is modelled with both XFEM and FEM-based methods. The results suggest that XFEM is able to provide low relative errors in the pressure near the well at a reduced computational cost compared with FEM. The impact and selection of the stabilization coefficient of the SU-FEM-FDM is also discussed. In the second example, the XFEM and SU-FEM-FDM model is applied to a more realistic problem of an inclined aquifer to demonstrate the ability of the model to capture the buoyancy-driven migration of CO 2 in a deep saline aquifer.

Carbon capture and storage (CCS) risks depend upon the site geology, potential CO 2 -caprock reactions, anthropogenic pathways (legacy wellbores), and well construction and operation. Herein, we assess the major risks, termed 'georisks', acknowledging that quantitative description must be site-specific, although pathway impact generalisations are possible. We discuss geological and pathway issues to guide general site selection practices to reduce georisks. Events that trigger hazards and the consequences are presented for leakage, low storage capacity/injectivity, the release of hazardous gases and materials, surface uplift, and Induced seismicity. A supplementary literature-sourced hazard tabulation was developed with focus on four largescale North American CCS projects (Quest Project, Weyburn Project, Project Pioneer and FutureGen). Each hazard is classified based on the project phase and trigger activity. The risks of CO 2 , brine, or other fluid leakage through wells (injection, monitoring, decommissioned legacy wells) remain uncertain, but legacy well gas leakage is common, rather than exceptional, despite modern cementing and completion practices.

Downburst winds, which are a source of extreme wind loading and are referred to as high intensity wind (HIW) loads, have caused numerous transmission tower failures around the world. A previous investigation was conducted to study the performance of a transmission tower under downburst wind loading, where the behaviour of the tower was limited to a linear response. In the current study, a nonlinear frame element is used to assess the performance of the tower under downburst wind loading. The behaviour is studied using downburst wind field data obtained from a computational fluid dynamics (CFD) model. In order to assess the geometric nonlinear behaviour of the tower, the results are compared to a previous linear analysis for a number of critical configurations of a downburst. The nonlinear analysis predicted that peak axial loads in certain members can be up to 34% larger than those predicted by the linear analysis.

Summary A computationally efficient numerical model that describes carbon sequestration in deep saline aquifers is presented. The model is based on the multiphase flow and vertically averaged mass balance equations, requiring the solution of two partial differential equations – a pressure equation and a saturation equation. The saturation equation is a nonlinear advective equation for which the application of Galerkin finite element method (FEM) can lead to non‐physical oscillations in the solution. In this article, we extend three stabilized FEM formulations, which were developed for uncoupled systems, to the governing nonlinear coupled PDEs. The methods developed are based on the streamline upwind, the streamline upwind/Petrov–Galerkin and the least squares FEM. Two sequential solution schemes are developed: a single step and a predictor–corrector. The range of Courant numbers yielding smooth and oscillation‐free solutions is investigated for each method. The useful range of Courant numbers found depends upon both the sequential scheme (single step vs predictor–corrector) and also the time integration method used (forward Euler, backward Euler or Crank–Nicolson). For complex problems such as when two plumes meet, only the SU stabilization with an amplified stabilization parameter gives satisfactory results when large time steps are used. Copyright © 2016 John Wiley & Sons, Ltd.