The transition zones between bridges and adjacent tracks in high-speed railway systems arecritical areas where dynamic interactions between the train and track infrastructure can lead to significantmaintenance and structural challenges. These zones are particularly susceptible to issues due to the abruptchange in stiffness between the bridge structure and the adjacent track, resulting in complex stress patterns anddifferential settlement. This study investigates the impact of train travel direction on the dynamic behavior andstress distribution within bridge-transition zones. Advanced numerical modeling techniques, including finitedifference method (FDM) for modeling the rail structure, and the discrete element method (DEM) forsimulating the behavior of sleepers, ballast, and sub-ballast layers, were utilized to provide a comprehensivesimulation of the effects of trains approaching or departing from bridges. The findings reveal that train traveldirection affects structural behavior, track deformation, and differential settlement within these zones. Dynamic loading conditions, which vary depending on whether the train is moving onto or off the bridge, leadto uneven stress distributions. These stress variations contribute to differential settlement, where the track andunderlying materials settle at different rates, exacerbating track wear and increasing maintenance needs. Thisstudy provides key insights into enhancing the design and upkeep of bridge-transition zones by analyzing theimpact of train travel direction. The findings enable engineers and designers to develop strategies to mitigatethe adverse effects of differential settlement and stress concentration, thereby enhancing track longevity,reducing maintenance costs, and improving the overall safety and reliability of high-speed railway systems.

Strength verification of dry deep mixed columns is almost exclusively performed by column penetration tests (KPS) using a probe with two wings shearing into the column. Guidelines specify a constant bearing capacity factor (NKPS ) of 10 to determine the shear strength of the column. This factor has, however, undergone little research, and there are considerable research gaps. Results from laboratory scale tests are presented herein, where cement-improved kaolin columns with three different strengths were tested with KPS and cone penetration tests (CPT). The results showed NKPS ranging from 7.3 to 7.8 at low degrees of confinement around the columns, up to 15.4–16.5 at high degrees of confinement. The column strength did not significantly affect NKPS . The results further indicates that extraction to penetration ratio can be used to predict NKPS . Equivalent bearing capacity factors for CPT were 5.4–7.8 for tests only performed in low degrees of confinement. The findings present new knowledge that degree of confinement is crucial for NKPS, which has important practical implications, particularly that current guidelines oversimplify and might yield unconservative column strengths with increased risk of failure.

The partial factor method is the most common approach to verify structural safety in Eurocode 7. Given the ongoing discussion on the European level to include also underground excavations in rock in the scope of the Eurocodes, there is a clear need to investigate the applicability of partial factors to the design of rock tunnels. However, implementing fixed partial factors, in accordance with the suggestion in the current Eurocode 7, may not be appropriate to account for the large uncertainties and variable conditions prevalent in rock engineering. This paper studies the critical characteristics and underlying assumptions of different reliability-based partial factor formats. The suitability of the analyzed partial factor formats to evaluate safety is analysed and discussed with reference to a design example of a rock-shotcrete interaction system for support against block failure in an underground opening. The results show that reliability-based partial factor methods outperform the traditional partial safety format suggested in the Eurocode in terms of accuracy and consistency.

There is an increasing interest in creating high-resolution 3D subsurface geo-models using multisource retrieved data, i.e., borehole, geophysical techniques, geological maps, and rock properties, for emergency managements. However, dedicating meaningful, and thus interpretable 3D subsurface views from such integrated heterogeneous data requires developing a new methodology for convenient post-modeling analyses. To this end, in the current paper a hybrid ensemble-based automated deep learning approach for 3D modeling of subsurface geological bedrock using multisource data is proposed. The uncertainty then was quantified using a novel ensemble randomly automated deactivating process implanted on the jointed weight database. The applicability of the automated process in capturing the optimum topology is then validated by creating 3D subsurface geo-model using laser-scanned bedrock-level data from Sweden. In comparison with intelligent quantile regression and traditional geostatistical interpolation algorithms, the proposed hybrid approach showed higher accuracy for visualizing and post-analyzing the 3D subsurface model. Due to the use of integrated multi-source data, the approach presented here and the subsequently created 3D model can be a representative reconcile for geoengineering applications.

Permanent deformation in ballast layers is a major contributing factor to the railway track geometry deterioration. In spite of a considerable amount of research on understanding and predicting performance of ballast layers, accurately capturing their settlements remains a challenge. In order to contribute to solving this important issue, a new numerical method for predicting ballast settlements is presented in this paper. This method is based on the finite element (FE) method combined with a constitutive model that captures permanent deformation accumulation in unbound materials under cyclic loading. This allows predicting permanent deformations of large structures and at large number of load cycles in a computationally efficient manner. The developed constitutive model is validated based on triaxial test measurements over wide range of loading conditions. Stress state in ballast layers has been examined with a 3D FE model, for several embankment structures and traffic load magnitudes. The determined stress distributions and loading frequencies were used as an input of the constitutive model to evaluate permanent strains and settlements of ballast layer. The influence of embankment structural designs and traffic loading magnitudes on the ballast layers settlements is examined and the results obtained are compared with the existing empirical performance models.

Modelling railway projects has a main challenge in the discrete element method (DEM). The granular material of the embankment consists of millions of fine angular particles which are difficult to model due to the long computational time. The long computational time also prevents the modeling of the higher number of loading cycles. As a result, researchers prefer to simulate the project in 2D to accelerate the simulation. While 2D simulations present a seemingly simple option for modeling railways, they tend to oversimplify the intricacies of particle interactions and the distribution of stress. Nonetheless, the extent to which these simplifications affect the authenticity of the simulations has remained ambiguous. In this study, the periodic cell replication method is used to build extensive long railway tracks significantly faster than conventional methods. Then, this DEM model is calibrated against the measurement results of a physical full-scale ballasted track. The model is then used to simulate several railway projects with different initial particle arrangements and model dimensions in both 2D and 3D. The results show that the 2D models are more dependant on the initial particle arrangement which shows different behavior for the same model. In addition, 2D simulations are incapable of reproducing the principal stress rotation in granular layers due to the moving load of the train wheel. As a result, 3D DEM simulations using the periodic cell replication method is suggested for studying the railway tracks.

In the context of geo-infrastructures and specifically tunneling projects, analyzing the large-scale sensor-based measurement-while-drilling (MWD) data plays a pivotal role in assessing rock engineering conditions. However, handling the big MWD data due to multiform stacking is a time-consuming and challenging task. Extracting valuable insights and improving the accuracy of geoengineering interpretations from MWD data necessitates a combination of domain expertise and data science skills in an iterative process. To address these challenges and efficiently normalize and filter out noisy data, an automated processing approach integrating the stepwise technique, mode, and percentile gate bands for both single and peer group-based holes was developed. Subsequently, the mathematical concept of a novel normalizing index for classifying such big datasets was also presented. The visualized results from different geo-infrastructure datasets in Sweden indicated that outliers and noisy data can more efficiently be eliminated using single hole-based normalizing. Additionally, a relational unified PostgreSQL database was created to store and automatically transfer the processed and raw MWD as well as real time grouting data that offers a cost effective and efficient data extraction tool. The generated database is expected to facilitate in-depth investigations and enable application of the artificial intelligence (AI) techniques to predict rock quality conditions and design appropriate support systems based on MWD data.

Considering that a large part of Sweden is covered by glacial till, which is classified as an unsorted sediment formed by glaciers that can contain fragments of rock known as boulders, driving piles constitutes a substantial economic risk. Piles driven into glacial till may encounter boulders and undergo structural damages leading to premature refusal and to the loss of piles. Even though geotechnical investigations as of today form a solid basis for the design of pile foundations, the unpredictable presence of boulders and their hard resistance to breakage, makes it challenging to penetrate boulders by standard investigation methods. Currently, the only available source of information used by the Swedish construction industry to confirm the existence of boulders is a dynamic penetration test known as soil–rock sounding. Relying on the results from only one testing method may for most projects underestimate the existence of boulders and their potential impact to piles, leading to an unsuitable design of the entire piling system. This paper discusses the benefit in using the input from soil–rock soundings for quantifying the probability of boulder encounters in glacial till based on Poisson point process.