In the above article [1], Table I, III, and IV should show 'N/m' instead of 'kN/m' and they should also show 'Ns/m' instead of 'kNs/m.' The revised tables are shown below. Also, in (1), “k_pw ” should be changed to “k_pw.” And on page 2952, first line, in the left column, “They also found…” should be changed to “We also found…”.

A novel data-driven framework to monitor the health status of high-speed rail suspension system by measuring train vibrations is proposed herein. Unlike existing methods, this framework does not rely on sophisticated dynamic models or high-fidelity simulations; it combines the power of data and domain knowledge to generate a model that can be trained quickly and adapted easily to different rail systems. In addition, the framework includes a module to generate a training dataset, tackling a typical challenge in real-world system monitoring, namely, the lack of labeled data due to practical limits. Based on the multi-output support vector regression (MSVR), the proposed framework can monitor the stiffness and damping coefficients of the suspension system using vibration signals measured on trains in real time. The framework comprises three modules. First, a simple suspension system dynamics model is built to generate a training dataset. Furthermore, key features are extracted from frequency response curves to reflect the impact of spring and damper degradation. Subsequently, a supervised learning model based on the MSVR is built to predict the stiffness and damping coefficients of suspension systems from features extracted in the second module. Once the model is built, real-time monitoring can be achieved by feeding the vibration signals as they are collected during operations. The proposed framework was evaluated on simulation data for its accuracy and tested on real-world operational data for its practicability.

Many dryland rivers are terminal systems, with small channels undergoing prominent downstream size reductions before ending on channelless floodplains, in wetlands, or at playa margins. Spaceborne Digital Elevation Models (DEMs) provide potential for assessing subtle topographic and hydrodynamic changes in these low-gradient, low-relief settings, but challenges are posed by limitations in vertical and horizontal accuracy. This study evaluates the use of different spaceborne DEMs for topographic characterisation and flood flow modelling of the low-gradient (<0.0006 m m⁻¹) Río Colorado terminal system, Bolivia. A comparison between DEM and field dGPS elevation data (1290 measurement points) reveals that the TanDEM-X DEM 12 m (TDX-12 m) has a RMSE of 0.47 m, far less than those of other frequently used spaceborne DEMs such as ALOS RTC (4.58 m) and SRTM (6.02 m). For hydrodynamic modelling, TDX-12 m data were smoothed (adaptive filter and feature-preserving DEM smoothing) and upscaled. The smoothed TDX-12 m data were mosaiced with a dGPS data-derived river path, surveyed along a reach (mean width <30 m) with a prominent downstream size decrease. The methods enabled effective de-noising of the TDX-12 m data (RMSE 0.29 m) and resulted in a high linear regression correlation coefficient (0.75). HEC-RAS 2D modelling reveals that in the selected reach, overbank flooding starts in the downstream part when discharge is <18 m³/s, with flow initially spreading through crevasse channels and levee topographic lows. As discharge increases, flow spreads farther across the floodplain, ultimately forming connected floodplain flow in distal topographic lows. Satellite imagery and a derived water index indicate the same floodplain flow patterns as the modelling (critical success index 0.77). Wider use of DEMs based on TDX-12 m data for topographic characterization and flood flow modelling along relatively small, low-gradient terminal dryland rivers will result in many scientific and applied benefits.

Condition monitoring, as part of the intelligent infrastructure concept, can significantly improve the reliability, safety and efficiency of rail operations. Degradation in infrastructure can be detected before a problem occurs, without interrupting normal operations. This paper contributes to the development of analytics tools and methods for condition monitoring using sensor data. A data-driven method is proposed to monitor the wheel wear of high-speed trains using onboard vibration sensors. In this method, a number of signal processing techniques and statistical methods are applied and extended to extract useful information from multi-location vibration data and estimate the wheel wear. We test the method using real operational data collected from high-speed trains in China over half a year. Preliminary results show that the method is accurate and can be easily applied to real world operations. The method can be extended to provide fault or degradation predictions, which reduces the in-service failures, and enables predictive maintenance.