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.

Emissions from infrastructure projects and construction projects have a large impact on the environment. Construction activities and materials, including geotechnical engineering works, account for a great share of that impact and the monetary costs of the projects. In railway projects, crushed bedrock is often used as fill material in the embankments, and less suitable soil is excavated and transported to a landfill causing emissions. Despite that, sustainability assessments are rarely made when comparing the crushed bedrock fill method with other alternative methods, when the geotechnical engineer is designing an embankment. This paper, therefore, shows how climate impact and monetary costs can be compared for two fill methods in a railway embankment in Sweden, namely crushed rock fill and fill made of cement-stabilized sandy till. A comparing life cycle assessment (LCA) of climate impact and a life cycle cost analysis (LCCA) of monetary costs were made for the two methods. Activities and materials used in the production and construction stages were assessed. The results show that the stabilized sandy till method had both a smaller climate impact and lower life cycle cost (LCC) than the crushed bedrock fill method. 

Spetsbärande pålar dimensioneras vanligtvis efter den modell för böjknäckning med sidomotstånd med ekvivalent styvhet som redovisas i Pålkommissionens rapporter. För pålar som är kortare än den elastiska knäcklängden är dock förutsättningarna för denna beräkningsmodell inte giltig. Alternativet är då att använda beräkningsmodell för fri pelare som redovisas i Eurokoder. Föreliggande artikeln redovisar resultatet av ett examensarbete på KTH där skillnaden jämförs för beräkningsmodeller som består av standardmodell för böjknäckning med sidomotstånd, beräkning för axialbelastad fri pelare enligt SS-EN 1993–1, och FEM-modell innefattande initialutböjning.

Driven piles can be severely damaged during driving into soil with high boulder content and can potentially lose their structural integrity, resulting in premature pile refusal. This can cause large additional project cost and delays, including an urgency to change the design in progress. Geotechnical uncertainties related to the spatial variability of soil's boulder content aggravate the complexity of the problem, making it difficult to assess the probability of premature pile refusal due to boulders and to identify the optimal pile design and driving strategy. Existing tools for drivability assessment are mainly deterministic and excessively simplistic, relying on the technical expertise of the personnel involved in the design and execution of the project rather than adopting a systematic treatment of the uncertainties at the specific geological location. A transparent methodology to support the decision-making in pile design is necessary for a cost-effective driving and optimal design solutions. This paper discusses how pile design can be understood from a risk perspective, with specific focus on the assessment of pile drivability. It is found that a new probabilistic approach that provides a correlation between the results from site investigations with the quantities of boulder in the soil layer by inverse analysis can create a solid basis for decision-making in pile design.

Med påbörjad planering och projektering av nya stambanor för järnväg i Sverige, och med en ännu större utveckling och utbyggnad av ballastfria spår världen över, är det viktigt att undersöka möjliga riskområden som kan komma att påverka exempelvis drift och underhåll. Ett av dessa problemområden är övergången mellan järnvägsbank och järnvägsbro. I ett nyligen publicerat examensarbete, utfört på KTH tillsammans med Sweco, har detta problemområde under­sökts med hjälp av numeriska simuleringar med finita element­metoden

Genom att tillämpa livscykelberäkningar där man utvärderar hela konstruktionens livscykel, från råmaterialutvinning till slutskedet, kan ett mer hållbart val av en konstruktions utformning göras. I syfte att skapa förutsättningar för att minska både konstruktionens miljöpåverkan och kostnader utvecklar man nu på avdelningen för Jord- och bergmekanik på KTH just sådana beräkningsmetoder. Här skriver forskarna på KTH om det pågående projektet som sker med finansiering från Sven Tyréns Stiftelse och Trafikverket.