Random Shape Research Articles

Analysis of Microplastics in Industrial Processes—Systematic Analysis of Digestion Efficiency of Samples from Forestry, Wastewater Treatment Plants and Biogas Industries

Microplastics (MPs) are persistent, globally relevant pollutants that have thus far been rigorously studied in natural waters but have not been as extensively studied in industrial wastewaters. Samples were collected from the forestry industry, wastewater treatment plants and the biogas industry. An enzymatic treatment protocol for MPs’ detection was applied to an assortment of industrial samples ranging from wastewaters, effluents and condensates to sludges and digestates. The effects of selected enzymes were studied systematically to develop a basis for digestion protocols on industrial samples. Further, different methods of detection (micro FTIR and Raman) were compared to each other, and the samples were visually examined using SEM. The developed protocols in this study were then compared with blank samples, contamination controls and samples spiked with artificial microplastics. This research aimed to fill some of the gap in the knowledge regarding the analysis methods and especially in the type of samples screened for microplastics thus far and presents a systematic approach to MPs’ detection in industrial wastewaters. It highlights the issues with the used analytical methods (such as misidentification) and validates the analysis results with milled, random shape and wide-size-range reference MPs that represent real samples better than standardized, ideal round beads. This study provides the first-ever suggestion for an enzymatic digestion protocol for industrial sample analysis.

A Candy Defect Detection Method Based on StyleGAN2 and Improved YOLOv7 for Imbalanced Data.

Quality management in the candy industry is a vital part of food quality management. Defective candies significantly affect subsequent packaging and consumption, impacting the efficiency of candy manufacturers and the consumer experience. However, challenges exist in candy defect detection on food production lines due to the small size of the targets and defects, as well as the difficulty of batch sampling defects from automated production lines. A high-precision candy defect detection method based on deep learning is proposed in this paper. Initially, pseudo-defective candy images are generated based on Style Generative Adversarial Network-v2 (StyleGAN2), thereby enhancing the authenticity of these synthetic defect images. Following the separation of the background based on the color characteristics of the defective candies on the conveyor belt, a GAN is utilized for negative sample data enhancement. This effectively reduces the impact of data imbalance between complete and defective candies on the model's detection performance. Secondly, considering the challenges brought by the small size and random shape of candy defects to target detection, the efficient target detection method YOLOv7 is improved. The Spatial Pyramid Pooling Fast Cross Stage Partial Connection (SPPFCSPC) module, the C3C2 module, and the global attention mechanism are introduced to enhance feature extraction precision. The improved model achieves a 3.0% increase in recognition accuracy and a 3.7% increase in recall rate while supporting real-time recognition scenery. This method not only enhances the efficiency of food quality management but also promotes the application of computer vision and deep learning in industrial production.

Hand drill shape optimization using Open CASCADE library and the steepest descent method

In this article, compliance optimization with the steepest descent method of the hand drill bits shapes for metal drilling is presented. The analysis of stress, displacements and compliance of the solid with random shape can be performed using the finite element method. In the case of a high number of optimization iterations, each analysis needs automatic modifications of geometry, mesh and boundary conditions. The Open CASCADE library can be used in the fast and automatic construction of modified models. It allows for fast reanalysis for each derivative of the objective function in the optimization process. The motivation of this research is to fill the gap in the literature on drilling technology. Most of the contemporary research is devoted to the oil and gas industry, while optimization of the hand drill bits used in metal drilling is rare. Compliance optimization allows us to find the shape which guarantees a greater stiffness for the specified loading conditions and a given amount of material. Although the obtained compliance was low, further experimental research would be needed to apply new solutions in drilling practice. This will involve the construction of the drill bit tips and the development of a heat treatment process

A new combined finite-discrete element method for stability analysis of soil-rock mixture slopes

PurposeThe purpose of this paper is to propose a new combined finite-discrete element method (FDEM) to analyze the mechanical properties, failure behavior and slope stability of soil rock mixtures (SRM), in which the rocks within the SRM model have shape randomness, size randomness and spatial distribution randomness.Design/methodology/approachBased on the modeling method of heterogeneous rocks, the SRM numerical model can be built and by adjusting the boundary between soil and rock, an SRM numerical model with any rock content can be obtained. The reliability and robustness of the new modeling method can be verified by uniaxial compression simulation. In addition, this paper investigates the effects of rock topology, rock content, slope height and slope inclination on the stability of SRM slopes.FindingsInvestigations of the influences of rock content, slope height and slope inclination of SRM slopes showed that the slope height had little effect on the failure mode. The influences of rock content and slope inclination on the slope failure mode were significant. With increasing rock content and slope dip angle, SRM slopes gradually transitioned from a single shear failure mode to a multi-shear fracture failure mode, and shear fractures showed irregular and bifurcated characteristics in which the cut-off values of rock content and slope inclination were 20% and 80°, respectively.Originality/valueThis paper proposed a new modeling method for SRMs based on FDEM, with rocks having random shapes, sizes and spatial distributions.

Application of graph neural networks to predict explosion-induced transient flow

We illustrate an application of graph neural networks (GNNs) to predict the pressure, temperature and velocity fields induced by a sudden explosion. The aim of the work is to enable accurate simulation of explosion events in large and geometrically complex domains. Such simulations are currently out of the reach of existing CFD solvers, which represents an opportunity to apply machine learning. The training dataset is obtained from the results of URANS analyses in OpenFOAM. We simulate the transient flow following impulsive events in air in atmospheric conditions. The time history of the fields of pressure, temperature and velocity obtained from a set of such simulations is then recorded to serve as a training database. In the training simulations we model a cubic volume of air enclosed within rigid walls, which also encompass rigid obstacles of random shape, position and orientation. A subset of the cubic volume is initialized to have a higher pressure than the rest of the domain. The ensuing shock initiates the propagation of pressure waves and their reflection and diffraction at the obstacles and walls. A recently proposed GNN framework is extended and adapted to this problem. During the training, the model learns the evolution of thermodynamic quantities in time and space, as well as the effect of the boundary conditions. After training, the model can quickly compute such evolution for unseen geometries and arbitrary initial and boundary conditions, exhibiting good generalization capabilities for domains up to 125 times larger than those used in the training simulations.

The Quantification of Myocardial Fibrosis on Human Histopathology Images by a Semi-Automatic Algorithm

(1) Background: Considering the increasing workload of pathologists, computer-assisted methods have the potential to come to their aid. Considering the prognostic role of myocardial fibrosis, its precise quantification is essential. Currently, the evaluation is performed semi-quantitatively by the pathologist, a method exposed to the issues of subjectivity. The present research proposes validating a semi-automatic algorithm that aims to quantify myocardial fibrosis on microscopic images. (2) Methods: Forty digital images were selected from the slide collection of The Iowa Virtual Slidebox, from which the collagen volume fraction (CVF) was calculated using two semi-automatic methods: CIELAB-MATLAB® and CIELAB-Python. These involve the use of color difference analysis, using Delta E, in a rectangular region for CIELAB-Python and a region with a random geometric shape, determined by the user’s cursor movement, for CIELAB-MATLAB®. The comparison was made between the stereological evaluation and ImageJ. (3) Results: A total of 36 images were included in the study (n = 36), demonstrating a high, statistically significant correlation between stereology and ImageJ on the one hand, and the proposed methods on the other (p < 0.001). The mean CVF determined by the two methods shows a mean bias of 1.5% compared with stereology and 0.9% compared with ImageJ. Conclusions: The combined algorithm has a superior performance compared to the proposed methods, considered individually. Despite the relatively small mean bias, the limits of agreement are quite wide, reflecting the variability of the images included in the study.

Performance Evaluation of Reconfigurable Intelligent Surfaces-Assisted Millimeter-Wave Network Using Stochastic Geometry-Based Model

Millimeter-wave (mm-wave) bands have received significant attention due to their large bandwidth and high frequency. However, mm-wave signals experience high attenuation primarily as a result of their high directivity and vulnerability to blockage, leading to non-line-of-sight (NLOS) conditions and signal outages. Buildings, structures, and natural obstacles can cause signal blockage, which increases connection challenges and creates coverage gaps. A reconfigurable intelligent surface (RIS) is a potential technique that can improve the coverage of mm-wave networks by intelligently reconfiguring the wireless propagation environment. RIS offers novel solutions to blockage issues by passively reflecting and rerouting mm-wave signals in desired directions. This paper presents a simulation-based evaluation of the signal-to-interference ratio coverage probability in RIS-assisted mm-wave networks based on stochastic geometry. We will use random shape theory to represent the blockages within the network. Furthermore, the impact of increasing the density of RIS and the number of reflective elements on network performance will be examined. The results show that RIS can enhance network coverage and decrease the effects of blockages, with considerable increases in coverage probability when compared to networks without RISs.

Comparative electrochemical properties of MO (ZrO2, V2O5) based polyaniline nanocomposites for high-performance supercapacitor applications

Due to its distinctive qualities, such as its moderate energy density, extended service life, rapid discharge–charge rates, and superior safety, supercapacitors (SCs) are gaining more and more attention. A zirconium oxide ZrO2 (Zr) and vanadium oxide V2O5 (VO) based PANI nanocomposites, denoted as (ZrP for ZrO2/PANI, and VOP for V2O5/PANI) are fabricated using hydrothermal technique in this research work. Morphological and phase investigations validated the random particle shapes with good crystallinity and purity of the samples. The N2 sorption/desorption isotherm reveals a mesoporous feature of the electrodes and the highest BET surface area (36.5 m2/g) with large electroactive sites, which offers abundant faradaic reactions for charge storage. The I-V characteristics confirm their excellent conduction capabilities as well. When utilized as electrodes for SCs in the three-electrode setup, the VOP composite electrode attains the highest capacitance of 1372 F g−1 at a scan rate of 5 mV s−1 compared with other active electrodes. Besides that, the VOP electrodes offer superior cyclic stability, with a retention rate of 94.28% even after 7000 consecutive charge/discharge cycles. It has been discovered that the in two electrode VOP asymmetric device exhibited remarkable specific capacitance of 651.36 Fg−1 at 5 mV s−1 demonstrating a significant capacitance retention of 87.6% over 6000 cycles. The results suggest that the material could be a good contender for electrode materials in supercapacitors.

SMFS‐GAN: Style‐Guided Multi‐class Freehand Sketch‐to‐Image Synthesis

AbstractFreehand sketch‐to‐image (S2I) is a challenging task due to the individualized lines and the random shape of freehand sketches. The multi‐class freehand sketch‐to‐image synthesis task, in turn, presents new challenges for this research area. This task requires not only the consideration of the problems posed by freehand sketches but also the analysis of multi‐class domain differences in the conditions of a single model. However, existing methods often have difficulty learning domain differences between multiple classes, and cannot generate controllable and appropriate textures while maintaining shape stability. In this paper, we propose a style‐guided multi‐class freehand sketch‐to‐image synthesis model, SMFS‐GAN, which can be trained using only unpaired data. To this end, we introduce a contrast‐based style encoder that optimizes the network's perception of domain disparities by explicitly modelling the differences between classes and thus extracting style information across domains. Further, to optimize the fine‐grained texture of the generated results and the shape consistency with freehand sketches, we propose a local texture refinement discriminator and a Shape Constraint Module, respectively. In addition, to address the imbalance of data classes in the QMUL‐Sketch dataset, we add 6K images by drawing manually and obtain QMUL‐Sketch+ dataset. Extensive experiments on SketchyCOCO Object dataset, QMUL‐Sketch+ dataset and Pseudosketches dataset demonstrate the effectiveness as well as the superiority of our proposed method.

A novel framework for low-contrast and random multi-scale blade casting defect detection by an adaptive global dynamic detection transformer

The radiographic inspection plays a crucial role in ensuring the casting quality for improving the service life under harsh environments. However, due to the low-contrast between the defects and the image background, the random spatial position distribution, random shapes and aspect ratios of the defects, the development of an accurate defect automatic detection system is still challenging. To address these issues, this paper proposes a novel framework for low-contrast and random multi-scale casting defect detection, which is referred to as adaptive global dynamic detection transformer (AGD-DETR). A novel defect-aware data augmentation method is first proposed to adaptively highlight the feature of the low-contrast defect boundary. A multi-attentional pyramid feature refinement (MPFR) module is then established to refine and fuse the multi-scale defect features of random sizes. Afterwards, a novel global dynamic receptive fusion-transformer (GDRF-Transformer) detection scheme is designed to perform the global perception and feature dynamic extraction of complex internal casting defects. It includes 4D-anchor query and cross-layer box update strategy, query rectification by prior information of defect aspect ratio, and global adaptive-feed forward network (GA-FFN). A dataset comprising turbine blade casting defect radiographic (TBCDR) images, is used to demonstrate the high efficiency of the proposed AGD-DETR. The obtained results show that the proposed method can accurately capture the spatial position distributions and complex defect shapes. Furthermore, it outperforms existing state-of-the-art defect detection methods.

Reverse Electrodialysis with Continuous Random Variation in Nanochannel Shape: Salinity Gradient-Driven Power Generation.

The shape of nanochannels plays a crucial role in the ion selectivity and overall performance of reverse electrodialysis (RED) systems. However, current research on two-dimensional nanochannel shapes is largely limited to a few fixed asymmetric forms. This study explores the impact of randomly shaped nanochannels using dimensionless methods, controlling their randomness by varying their length and shape amplitude. The research systematically compares how alterations in the nanochannel length and shape amplitude influence various system performance parameters. Our findings indicate that increasing the nanochannel length can significantly enhance the system performance. While drastic changes in the nanochannel shape amplitude positively affect the system performance, the most significant improvements arise from the interplay between the nanochannel length and shape amplitude. This compounding effect creates a local optimum, resulting in peak system performance. Within the range of dimensionless lengths from 0 to 30, the system reaches its optimal performance at a dimensionless length of approximately 25. Additionally, we explored two other influencing factors: the nanochannel surface charge density and the concentration gradient of the solution across the nanochannel. Optimal performance is observed when the nanochannel has a high surface charge density and a low concentration gradient, particularly with random shapes. This study advances the theoretical understanding of RED systems in two-dimensional nanochannels, guiding research towards practical operational conditions.

Probabilistic analysis of near-field blast loads considering fireball surface instabilities and stochastic detonator location

High speed video analysis of near-field explosive detonations displays distinct stages of emergent hydrodynamic instabilities in the fireball/shock-air interface. Typically, beyond 10 charge radii, the instabilities experienced large growths giving rise to more chaotic behaviour of the interface and thus an increasing uncertainty in surface velocity. These surface instabilities are suggested as the primary cause of blast parameter variability in the near-field. However, as a deterministic tool, numerical simulation of the detonation process and subsequent blast wave propagation is not able to replicate the stochastic nature of fireball surface instabilities and hence near-field blast parameter variability. Therefore, it is necessary to develop new methods to simulate and characterise the stochastic features of the fireball/shock-air interface. This paper proposes an algorithm to generate an explosive charge element with random shape in finite element model in order to simulate irregularities in the fireball/shock-air interface, and therefore produce variabilities comparable to those from direct observation. The effect of chaotic fireball/shock-air interface on near-field loading is explored through a large number of numerical simulations in order to investigate the statistical distribution of parameters including peak overpressure and impulse. Subsequently, the effect of stochastic detonator location is explored in a similar manner. A computational procedure based on the Monte Carlo Method is proposed to establish a probabilistic model of near-field blast loads, termed PSL-Blast. The reliability of design blast loads calculated using the UFC 3-340-02 design manual is then estimated using PSL-Blast, which suggests that reliability decreases with decreasing scaled distance. Finally, reliability-based safety factors of blast loads are calculated based on different blast settings.

Development of new numerical approach to the flow and segregation behaviors of fresh concrete considering rebar restraint and its application in reinforced lightweight aggregate concrete

Although there are many studies on flow simulation methods for fresh concrete, there are very few studies on casting simulation of reinforced concrete, and in these studies the reinforcing bars are treated only as static boundaries without their restraining effect on the concrete flowing between them. In this study, we first developed a new numerical method based on the MPS method for simulating the flow and segregation behaviors of fresh concrete during casting reinforced member, considering the restraint of reinforcing bars by applying a normal stress to the concrete. Fresh concrete was treated as the double-phase granular fluid that follows a non-linear rheological model, and was represented by matrix mortar, and coarse aggregate particles with actual sizes and random shapes. Then, as an application of this numerical method, the flow and segregation behaviors of lightweight aggregate concrete (LWAC) in an L-shaped box with rebars were numerically investigated in addition to measurement. The comparison of the numerical and experimental results shows that this numerical method allows the LCA volume fraction to be calculated within 3 % error in predicting the segregation behavior of LCA in reinforced LWAC, and improves the accuracy of the flow simulation of LWAC by about 1.5 times. Furthermore, this numerical method can double the accuracy of concrete flow prediction, compared to treating the reinforcement as a static boundary.

Aggregate-aware model with bidirectional edge generation for medical image segmentation

Accurate segmentation of lesion areas plays an important role in medical imaging-assisted diagnosis and treatment. Accurate boundary information can help doctors develop precise surgical plans and improve patient prognosis. However, automatic segmentation methods often struggle to accurately segment edges due to the random shape, size, and location of regions of interest (ROI). This problem is compounded in medical images, where the difference in pixel intensity between foreground and background is significantly smaller than in natural images. In this study, we propose an aggregate-aware model with bidirectional edge generation (Ambeg) for medical image segmentation. To overcome the problem of blurred edges between foreground and background in medical images, we design a deep learning model via a multi-task learning strategy and obtain richer visual features to guide segmentation. Furthermore, an Edge Feature Fusion (EFF) module is developed to combine spatial correlation information of lesion edges between adjacent images for more accurate edge segmentation. Finally, we design a new evaluation metric, the Boundary DSC segmentation consistency measure, to evaluate the edge segmentation accuracy of medical image segmentation methods. We utilize dilation and erosion operations in morphological methods to construct lesion edge labels. In addition, we use expansion and erosion rates to regulate the dimensions of the edge region to assess the requirements of different diseases for edge segmentation accuracy. The proposed approach is particularly noteworthy for achieving state-of-the-art results on medical image segmentation datasets, including BraTS 2022 (MRI), BraTS 2020 (MRI) and COVID-19–20 (CT), which have different modalities of datasets. It has an impressive Hausdorff distance of 4.62 mm and a sensitivity score of 92.45 % on BraTS 2020. Compared with existing assessment methods such as Dice score, Boundary DSC segmentation consistency measure focuses on the hard-to-segment lesion edge region rather than the easy-to-segment lesion center region, which provides a more comprehensive reference for physicians to choose automatic segmentation methods. In addition, since the boundary shapes of medical images are complex and diverse, we utilize morphological methods to obtain the boundary labels to ensure the smoothness of the boundary. Moreover, the approach is easy to implement and has a low computational cost, making it an attractive option for practical medical imaging applications.

Improving the accuracy of interferometer testing with absolute surface calibration and power spectral density analysis

A novel absolute surface calibration method for interferometer testing based on power spectral density analysis has been proposed. The method involves obtaining the loss of signal at different frequencies as a function of various rotation angles by signal analysis to choose the rotation angle for calibration. A model is developed to evaluate the calibration method by creating random surface shapes based on the Power Spectral Density. Using this algorithm, the precision of the absolute testing method during the testing process exceeds 0.28 %. Error propagation during the experimental process, such as testing optic clamping, angular errors, eccentricity, vibrations and airflow disturbances, were calibrated. Ultimately, the absolute detection test results were verified using high-precision flat mirrors. The experimental result indicates an RMS of 4.275 nm, 4.263 nm, and 4.265 nm in different rotation angle, and the calibrated result shows an RMS of 4.880 nm. The shape of surface errors in the absolute test results and calibration results is consistent.

Max-convolution processes with random shape indicator kernels

In this paper, we introduce a new class of models for spatial data obtained from max-convolution processes based on indicator kernels with random shape. We show that these models have appealing dependence properties including tail dependence at short distances and independence at long distances. We further consider max-convolutions between such processes and processes with tail independence, in order to separately control the bulk and tail dependence behaviors, and to increase flexibility of the model at longer distances, in particular, to capture intermediate tail dependence. We show how parameters can be estimated using a weighted pairwise likelihood approach, and we conduct an extensive simulation study to show that the proposed inference approach is feasible in relatively high dimensions and it yields accurate parameter estimates in most cases. We apply the proposed methodology to analyze daily temperature maxima measured at 100 monitoring stations in the state of Oklahoma, US. Our results indicate that our proposed model provides a good fit to the data, and that it captures both the bulk and the tail dependence structures accurately.

Randomness of Shapes and Statistical Inference on Shapes via the Smooth Euler Characteristic Transform

In this article, we establish the mathematical foundations for modeling the randomness of shapes and conducting statistical inference on shapes using the smooth Euler characteristic transform. Based on these foundations, we propose two Chi-squared statistic-based algorithms for testing hypotheses on random shapes. Simulation studies are presented to validate our mathematical derivations and to compare our algorithms with state-of-the-art methods to demonstrate the utility of our proposed framework. As real applications, we analyze a dataset of mandibular molars from four genera of primates and show that our algorithms have the power to detect significant shape differences that recapitulate known morphological variation across suborders. Altogether, our discussions bridge the following fields: algebraic and computational topology, probability theory and stochastic processes, Sobolev spaces and functional analysis, analysis of variance for functional data, and geometric morphometrics. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Structural characterization of strawberry pomace

Strawberries are a nutrient dense food rich in vitamins, minerals, non-nutrient antioxidant phenolics, and fibers. Strawberry fiber bioactive structures are not well characterized and limited information is available about the interaction between strawberry fiber and phenolics. Therefore, we analyzed commercial strawberry pomace in order to provide a detailed carbohydrate structural characterization, and to associate structures with functions. The pomace fraction, which remained after strawberry commercial juice extraction, contained mostly insoluble (49.1 % vs. 5.6 % soluble dietary fiber) dietary fiber, with pectin, xyloglucan, xylan, β-glucan and glucomannan polysaccharides; glucose, fructose, xylose, arabinose, galactose, fucose and galacturonic acid free carbohydrates; protein (15.6 %), fat (8.34 %), and pelargonidin 3-glucoside (562 μg/g). Oligosaccharides from fucogalacto-xyloglucan, methyl-esterified rhamnogalacturonan I with branched arabinogalacto-side chains, rhamnogalacturonan II, homogalacturonan and β-glucan were detected by MALDI-TOF MS, NMR and glycosyl-linkage analysis. Previous reports suggest that these oligosaccharide and polysaccharide structures have prebiotic, bacterial pathogen anti-adhesion, and cholesterol-lowering activity, while anthocyanins are well-known antioxidants. A strawberry pomace microwave acid-extracted (10 min, 80 °C) fraction had high molar mass (2376 kDa) and viscosity (3.75 dL/g), with an extended rod shape. A random coil shape, that was reported previously to bind to phenolic compounds, was observed for other strawberry microwave-extracted fractions. These strawberry fiber structural details suggest that they can thicken foods, while the polysaccharide and polyphenol interaction indicates great potential as a multiple-function bioactive food ingredient important for gut and metabolic health.

Random bridge generator as a platform for developing computer vision-based structural inspection algorithms

Recent advances in computer vision algorithms have transformed the bridge visual inspection process. Those algorithms typically require large amounts of annotated data, which is lacking for generic bridge inspection scenarios. To address this challenge efficiently, this research designs, develops, and demonstrates a platform that can provide synthetic datasets and testing environments, termed Random Bridge Generator (RBG). The RBG produces photo-realistic 3D synthetic environments of six types of bridges randomly, automatically, and procedurally. Following relevant standards and design practice, the RBG creates random cross-sectional shapes, converts those shapes into bridge components, and assembles the components into bridges. The effectiveness of the RBG is demonstrated by producing a dataset (RBG Dataset) containing 10,753 images with pixel-wise annotations, rendered in 250 different synthetic environments. Significant diversity of the photo-realistic bridge inspection environments has been achieved, while all structural components strictly conform to the definitions derived from structural engineering documents. The use of the RBG dataset has been demonstrated by training a deep semantic segmentation algorithm with 101 convolutional layers, showing successful segmentation results for both major and minor structural components. The developed RBG is expected to enhance the level of automation in bridge visual inspection process. The Python code for RBG is made public at: https://github.com/chenghaojia2323/Random-Bridge-Generator.git.

Analysis of the Load-Bearing Capacity of Pebble Aggregates

The load-bearing capacity of pebble aggregates plays a pivotal role in influencing the operational performance of uncontrolled trucks on arrester beds. The complexity of this phenomenon stems from the nonuniformity in the shapes of the pebbles and their stochastic arrangement within the beds, presenting notable challenges for traditional mathematical modelling techniques in precisely evaluating the contact dynamics of these aggregates. This study leverages the discrete element method (DEM) to extensively analyse the arrester bed aggregate of a standard truck escape ramp. The aforementioned mechanism entails the gathering of morphological parameters of irregularly shaped aggregate particles and introduces a novel method for constructing random shapes that adhere to the observed distribution characteristics. A discrete element model, grounded in the physical properties of these aggregates, is formulated. This study focuses on the aggregate’s load-bearing capabilities, scrutinising the mechanical behaviour of the aggregate particles at the macroscopic and microscopic scales. These insights offer substantial scientific contributions and practical implications for assessing the safety of escape ramps and determining essential parameters for the brake bed design.