British Steel (United Kingdom)

In cold-formed steel (CFS) connections, self-tapping screws have increasingly been used with pre-punched dimples to improve assembly accuracy and installation efficiency. However, the structural behaviour of such connections remains poorly understood, and current international design standards provide no specific guidance for their design. This paper presents an experimental investigation on single-lap screwed connections, both with and without dimples, to examine their shear behaviour, ultimate strength, and failure modes. The test matrix comprised four steel sheet thicknesses and various plate arrangements relative to the screw heads. The dimpled holes were automatically pre-punched with an indent depth of 1.5 mm and hole diameters ranging from 4.9 mm to 5.3 mm, accommodating 14g-20×14 mm screws with a nominal diameter of 6.3 mm. Non-dimpled specimens typically failed by screw tilting or a combination of tilting and bearing, whereas dimpled specimens predominantly failed in bearing. The presence of dimples improved resistance to screw tilting due to the interlocking mechanism developed at the connection interface. Measured shear resistances were compared with predictions from EN 1993-1-3:2006, ANSI/SDI AISI S100:2024, and AS/NZS 4600:2018. For bearing failures, current design equations provided relatively accurate predictions for non-dimpled connections; however, no design provisions exist for dimpled-hole connections. These findings highlight the need for new design rules that explicitly account for the influence of dimples, particularly under bearing failure. A companion paper presents new design equations for shear-loaded CFS screwed connections with dimpled holes, calibrated against the experimental results reported herein and validated through nonlinear finite element analyses in an extended parametric study. • A single-lap shear test configuration was developed to evaluate screwed connections with dimples. • Screwed connections with and without dimples were tested using four different steel sheet thicknesses. • Non-dimpled specimens typically failed by screw tilting or a combination of tilting and bearing. • Dimpled specimens exhibited improved resistance to screw tilting due to an interlocking mechanism. • No design provisions currently exist in standards for the design of screwed connections with dimples in shear.

Abstract Lateral resistance, principally dictated by the interaction between sleepers (ties) and ballast, is a fundamental property of ballasted railway track which is critical to overall track stability and safety. This study investigates the influence of sleeper spacing on lateral resistance to support overall track system design and track safety management. A novel multi-sleeper push test (MSPT) methodology, utilising both a kinematic restraint to better replicate track conditions and a state-of-the-art digital image correlation (DIC) system to measure surface ballast movement, has been used to evaluate the lateral resistance behaviour of two different sleeper types (concrete and steel) under seven different sleeper spacing conditions (550–850 mm). This is the first direct comparison of modern concrete and steel sleeper variants under these conditions. It is found that both sleeper types exhibit similar lateral resistance behaviour at small displacements (less than 3 mm) but, regardless of the spacing used, steel sleepers have significantly greater lateral resistance beyond this point. Although it depends on the assessment method used, concrete sleepers demonstrated an optimum spacing of approximately 675 mm, whilst steel sleepers showed highest resistance at approximately 706 mm. However, concrete sleepers showed less sensitivity to changes in spacing compared to steel sleepers, which retained high resistance over a narrower range of spacings. These findings contribute to track safety management by improving buckling mitigation strategies and component selection, whilst unlocking the ability to assess system-level impacts to aid the pursuit of more sustainable railway infrastructure design.

In a process called rail-sanding, the acceleration and deceleration of train can be regulated by applying sand at the wheel-rail interface via an on-board system. A dataset of 78 high-speed video recordings of rail-sanding process using a full-scale testing platform is provided in this data note. The videos are recorded for various case studies, namely different positioning of the sander hose aiming at the rail, nip, and wheel with various angles, and different materials used as rail-sand. The velocities of sand particles is extracted from these high-speed videos using particle image velocimetry software. The spread angle of the particles as they flow out of the sander hose is also measured with the use of image processing software. The data extracted from these high-speed recording can be utilised for calibration, validation, and verification of experimental and numerical set-ups, as well as for training artificial intelligence models.

This paper presents a new independency metric for blind source separation (BSS) problem. It is mathematically proved that the metric value of any linear combination of source signals is less than the largest one of sources under a loose condition. Further, the global optimization of this new metric is achieved by formulating it as a generalized eigenvalue problem. Subsequently, we guarantee to find out a correct de-mixing matrix through maximizing the proposed metric to separate the sources. The simulation results have shown its success in separating the linear combinations of sub-Gaussian and super-Gaussian sources with at most one Gaussian signal.

For premium grades (and rail surface treatments such as ‘laser cladding’) there is little plastic damage expected during typical operating conditions, and smooth surface contact models predict a long life. However, damage still develops, including rolling contact fatigue, affecting premium steels from multiple suppliers. A model is described predicting stress intensity factors and indicative crack growth rates for cracks driven by surface roughness stresses. Accompanying field data quantify the asperity separation and tip radii for rails from metro, mixed traffic and a freight line in ground or unground conditions. The results predict that in rough surface contact small surface cracks (50–100 μm, exceeding microstructural dimensions) can grow faster than crack removal by wear, even when smooth surface models predict no growth. The model has application in identifying how rail grinding marks or wheel roughness may be an important factor in determining rail life through raised stresses at the rail surface (leading to wear), and increased stress intensities and growth rates for incipient cracks.

Mechanical property prediction plays a crucial role in the steel industry, enabling better materials selection and enhancing production efficiency. In this study, we propose a novel framework that facilitates optimal materials selection for steel’s alloying elements, based on accurate predictions of the Jominy hardness curve. Leveraging Gaussian Process (GP) regression, we provide probabilistic predictions of steel hardenability characteristics from alloy element composition. Taking it a step further, our framework incorporates these accurate predictions into a constrained optimization process, yielding optimal compositions that reduce overall spending while meeting performance specifications. Through data obtained from 1080 steel samples, our GP regression model exhibits high accuracy, achieving an RMSE of 1.37 and showcasing significant improvements in the field. Moreover, our constrained optimization utilizing the GP model and historical market data reveals an average cost reduction of 18% on alloying element expenses, highlighting the tangible cost-saving potential of this approach. By leveraging Gaussian Process (GP) regression, we not only achieve accurate predictions of the Jominy hardness curve based on alloy element composition, but we also introduce a crucial element of uncertainty quantification. This empowers us to place trust in the results of our optimization process, ensuring robust and reliable materials selection. The integration of GP regression and optimization provides a powerful tool for achieving cost-effective materials selection and marks a significant advancement compared to existing studies. This research underscores the promise of machine learning in the steel industry, demonstrating its ability to yield substantial cost savings and enhance decision-making in materials selection.

Estimation of breeding values (EBVs) is combined with estimation of accuracies for particular traits. These accuracies are important as they can tell the breeder how trustworthy the EBVs are. For hard to measure, low heritability traits or those measured at the end of life, EBV accuracy values tend to be low. This study aimed to examine the impact of including genotypic information in genetic evaluations to improved EBV accuracy. We analysed California mastitis test (CMT) and footrot (FR) records and over 10,000 genotypes collected on more than 30 UK Texel sheep farms. Results showed that the inclusion of genomic information based on Single Nucleotide Polymorphism (SNP) may increase the accuracy of prediction, especially for non-phenotyped animals.