Ever since the first steam locomotives in 1804, trains have transformed transportation of both people and freight. The 140,000 miles of track in the U.S. supports transport for essential goods like agricultural and manufactured products but also offers a sustainable travel option. Some 1.5 billion tons of goods move annually and more than 28 million passengers traveled via rail in 2023, according to the ASCE 2025 Report Card for America’s Infrastructure. Regular inspections are intended to ensure smooth and safe operations, using tools including GPS, automatic equipment identification readers, electronic data exchange, video inspection, and handheld field tables. Typical parameters tracked include gage, alignment, profile, crosslevel, warp, twist, and cant. Analyzing this data is critical to minimize wheel damage and derailment risks. Using tensor analysis allows researchers to study relationships and properties across coordinate systems by manipulating multidimensional data.
Authors Petros Woldemariam and Nii Attoh-Okine explore the use of tensor analysis on railway track data in a new study, “Multiway Analytics Applied to Railway Track Geometry and Ballast Conditions,” for the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. This research focused on evaluating track geometry and ballast conditions and tensor decomposition methods and their applicability to the field of railway engineering. They used an inspection data set for a U.S. railroad as a case study to test their multiway data modeling. Learn more about how this visual data analysis (biplots and heatmaps) can help decision-makers prioritize maintenance at https://doi.org/10.1061/AJRUA6.RUENG-1367. The abstract is below.
Abstract
Railroad systems generate large amounts of data, which, when effectively analyzed, can significantly enhance maintenance decisions to improve safety and system performance. Tensor decomposition, as an advanced multidimensional data analysis tool, offers unique advantages over traditional two-way matrix factorizations, such as the uniqueness of the optimal solution and component identification, even with substantial data missing. This paper introduces the basic concepts of tensor decomposition and specifically demonstrates its application in analyzing railway track geometry and subsurface conditions. By applying tensor analysis to multidimensional data sets, the study identifies critical patterns in track geometry and ballast conditions. Key findings indicate that tensor-based models can effectively predict track deformations and align maintenance schedules more accurately, thus optimizing repair operations and extending the lifespan of railway infrastructure.
See how this analysis technique can help engineers better decide where maintenance is needed in the ASCE Library: https://doi.org/10.1061/AJRUA6.RUENG-1367.
