The type of subgrade of a railroad foundation is vital to the overall performance of the track structure. With the train speed and tonnage increase, as well as environmental changes, the evaluation and influence of subgrade are even more paramount in the railroad track structure performance. A geomechanics classification for subgrade is proposed coupling the stiffness (resilient modulus) and permanent deformation behaviour evaluated by means of repeated triaxial loading tests. This classification covers from fine- to coarse-grained soils, grouped by UIC and ASTM. For this achievement, we first summarize the main models for estimating resilient modulus and permanent deformation, including the evaluation of their robustness and their sensitivity to mechanical and environmental parameters. This is followed by the procedure required to arrive at the geomechanical classification and rating, as well as a discussion of the influence of environmental factors. This work is the first attempt to obtain a new geomechanical classification that can be a useful tool in the evaluation and modelling of the foundation of railway structures.

In this paper, the performance of different railway structures – ballasted and two types of slab tracks (Rheda system and a ballastless track only constituted by the concrete slab) – is evaluated in terms of stresses and permanent deformations through the selection of some important parameters: wavelength of the unevenness profile, train’s speed, subgrade’s characteristics and spatial variability of the track unevenness profile. The elastodynamic response of 60 case studies is computed through a 2.5D model FEM-PML and the long-term behaviour is assessed by the implementation of a permanent deformation model. The obtained results are analysed through innovative stress and permanent deformation amplification factors in the subgrade layer. This study concludes that the train’s speed and the mechanical properties of the subgrade are the factors that most affect the long-term performance of the subgrade of railway structures, which must be considered for further optimization of the rail track structures.

Joints are the most defining features of fractured rock masses. It has been well established that joint behaviour is mainly defined by wall topography, which is difficult to measure and define. Previous works have employed both contact and non-contact scanning methods and multiple approaches to the analysis of rock surfaces, but a majority focused exclusively on individual surface characterisation without appraising wall–wall interaction. This paper presents an equipment that is easily available for laboratory scanning of rock joints. Surface topographies of eight granite rock joints (16 surfaces, made available to other researchers) are statistically analysed separately and jointly by applying a fitting algorithm to match the top and bottom surfaces that allow mapping and calculation of contact areas and void volumes during closure. Results show that traditional methods for joint profile analysis are not applicable to three-dimensional surfaces, and that often specific details of joint surfaces have opposing influence on the mechanical, dynamic or hydraulic behaviours.

Railway lines require a significant amount of natural raw materials. Industrial by-products can be used instead, reducing the costs of natural aggregate exploration. This work analyzes a ballasted track’s short- and long-term performances when replacing conventional sub-ballast aggregate with steel slag. After an extensive laboratory characterization of the steel slag, the material performance was analyzed in a 3D numerical model of a ballasted track when included in a railway track. An empirical model was implemented and calibrated to predict the long-term permanent deformation induced in the track after many train passages. The results are compared with the allowable deformation limits required for conventional high-speed ballasted track railway lines. An additional analysis was conducted to assess the influence of steel slags on the critical speed of conventional railway tracks when used. The results show a residual impact on the critical speed value compared to the conventional sub-ballast made with natural aggregates.

Motivated by concerns regarding safety and maintenance, the operational speed of a railway line must remain significantly below the critical speed associated with the track–ground system. Given the large number of track sections within a railway corridor that potentially need to be analyzed, the development of efficient predictive tools is of the utmost importance. Based on that, the problem can be analyzed in a few seconds instead of taking several hours of computational effort, as required by a numerical analysis. In this context, and for the first time, machine learning algorithms, namely artificial neural networks and support vector machine techniques, are applied to this particular issue. For its derivation, a reliable and robust dataset was developed by means of advanced numerical methodologies that were previously experimentally validated. The database is available as supplemental data and may be used by other researchers. Regarding the prediction process, the performance of both models was very satisfactory. From the results achieved, it is possible to conclude that the prediction tool is a novel and reliable approach for an almost instantaneous prediction of critical speed in a high number of track sections.