Automated machine learning recognition to diagnose flood resilience of railway switches and crossings

Abstract

The increase in demand for railway transportation results in a significant need for higher train axle load and faster speed. Weak and sensitive trackforms such as railway switches and crossings (or called ‘turnout’) can suffer from such an increase in either axle loads or speeds. Moreover, railway turnout supports can deteriorate from other incidences due to extreme weather such as floods which undermine cohesion between ballast leading to ballast washaway or loss of support under turnout structures. In this study, new intelligent automation based on machine learning pattern recognition has been built to detect and predict the deterioration of railway turnouts exposed to flooding conditions which is the scope of this study. Since the turnout system is very complex by nature, different features and smart filtering are explored to find the potential features for deep learning. Nonlinear finite element models validated by actual field measurements are used to mimic the dynamic behaviors of turnout supports under flooding conditions. The study exhibits that the novel recognition model can achieve more than 98% accuracy, yielding the potential capability to recognize and classify turnout support deteriorations facing extreme weather conditions which will be beneficial for responsible parties to schedule and plan maintenance activities.

Automated Flood Prediction along Railway Tracks Using Remotely Sensed Data and Traditional Flood Models

“…The DEM provides the location's height above the mean sea level. In addition, the DEM is used to derive slope, TWI, and SPI [29][30][31]. The slope is calculated for each raster cell at 30 m spatial resolution [32].…”

“…Sresakoolchai et al [31] proposed using automated machine learning recognition to determine flood resilience of railway switches and crossings. They used nonlinear finite element models validated by field measurements to mimic the dynamic characteristics of turnout supports under flooding scenarios.…”

Financial Loss Assessment for Weather-Induced Railway Accidents Based on a Deep Learning Technique Using Weather Indicators

“…Accordingly, Rail resilience will be an important factor in determining the safety and sustainability of the system. In accordance with the flow of these changes, many studies have been conducted on global warming, climate change, and natural disasters regarding railway accidents and railway resilience in past studies [7][8][9][10][11][12][13]. However, in previous studies, there is a lack of compressive studies that empirically quantify the risk of railroad accidents linked to detailed weather factors.…”

Multi-defect risk assessment in high-speed rail subgrade infrastructure in China

“…Huang et al (2023) organized seismic damage data of bridges, then used RF to predict seismic damage levels, and used a two-parameter normal distribution function to draw empirical susceptibility curves for seismic damage risk assessment 13 . Sresakoolchai et al (2023) developed a novel intelligent automated system based on machine learning pattern recognition for detecting and predicting the deterioration of railroad turnouts exposed to flood conditions 14 . Although machine learning gained frequent application in railroad safety risk assessment, it tends to focus on the disturbance of railroad operational status by external small-scale disasters, and overlooked the impact of structural changes in the subgrade itself on overall railroad risk in the climatic and geographical environment.…”