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![Determining particle shape through sphericity and roundness, with diagonal dotted lines indicating consistent particle regularity ρ r = (R+S) 2 [2,49].](profile/Firas-Daghistani/publication/372376813/figure/fig1/AS:11431281174888387@1689423957043/Determining-particle-shape-through-sphericity-and-roundness-with-diagonal-dotted-lines.png)
Determining particle shape through sphericity and roundness, with diagonal dotted lines indicating consistent particle regularity ρ r = (R+S) 2 [2,49].
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Particulate materials, such as sandy soil, are everywhere in nature and form the basis for many engineering applications. The aim of this research is to investigate the particle shape, size, and gradation of sandy soil and how they relate to shear strength, which is an essential characteristic that impacts soil stability and mechanical behaviour. T...
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... microscope comes with a built-in Progression system, offers high-quality optics that enable accurate and efficient identification of soil particle shapes and sizes. Several parameters are used to characterize sand particle shape and quality, including sphericity and roundness ( Figure 5). ...
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This study focuses on using various machine learning (ML) models to evaluate the shear behaviors of ultra-high-performance concrete (UHPC) beams reinforced with glass fiber-reinforced polymer (GFRP) bars. The main objective of the study is to predict the shear strength of UHPC beams reinforced with GFRP bars using ML models. We use four different M...
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... Over the last two decades, artificial intelligence (AI) methods have attracted the attention of engineering and geotechnical researchers. Various AI methods have been successfully applied to slope stability (Baghbani et al., 2022a;Bardhan & Samui, 2022), soil mechanics (Daghistani & Abuel-Naga, 2023;Inazumi et al., 2020;Singh et al., 2021), soil dynamics Gatto & Montrasio, 2023;Lee et al., 2003), recycled materials Saad et al., 2023), and soil cracking (Baghbani et al., 2022c;Xu et al., 2022;Zhang et al., 2023). In spite of this, AI methods have not yet been used to predict the shear strength of soil and carpet fibre mixtures, thus presenting a gap in the history of studies that must be addressed. ...
... Despite these known factors, as yet, no detailed study that combines all of these into a single, easy-to-understand model has been conducted, mainly because these factors interact in complicated ways. In artificial intelligence, machine learning algorithms have helped solve complex problems in the geotechnical field, such as understanding soil mechanics behaviour (Daghistani & Abuel-Naga, 2023) and improving the use of recycled materials in soil stabilization (Baghbani et al., 2022d;Daghistani et al., 2023a;Daghistani et al., 2023b). The application of machine learning for predicting the interface friction coefficient of granular materials, considering the aforementioned factors, has not been sufficiently investigated. ...
The escalating global waste crisis underscores the urgent need for comprehensive waste management strategies, with recycling practices at their core. Rubber tyre and carpet fibre waste materials are two of the major contributors to this human-related waste. These materials can be effectively recycled and utilised in various applications, including the geotechnical field. This thesis addresses environmental concerns by exploring the mechanical behaviour of cohesionless soil and investigating its behaviour when mixed with recycled waste materials. The mechanical behaviour examined includes shear strength characteristics, stiffness, compressibility, and peak interface friction. These aspects of mechanical behaviour will be examined under various influence parameters, including void ratio, particle size, particle shape, size ratio, and normal stresses.
An extensive experimental study was conducted using direct shear and oedometer test apparatus. A modified direct shear mould was designed/built and used for the interface shear test in this study. The shape of the granular material was determined using a microscope. The sample was prepared using either the pluvation technique or dynamic compaction. Furthermore, this study utilises an artificial intelligence modelling approach (machine learning) that recently attracted the attention of geotechnical researchers as it can model complicated soil behaviour considering all of its parameters of influence.
In this study, an analysis of both the macro and micromechanical behaviours of coarse-grained soil and sand mixed with recycled waste material was conducted, offering new insights into their properties and enhancing understanding. Generally, the findings indicate that as the mean particle size of the sand grains increases in the sand size range from fine to medium and from medium to coarse, both density and shear strength increase, while specific gravity decreases. In sand-rubber mixtures, the compressibility of rubber affects the consistency of the friction angle. As normal stress increases, the friction angle decreases. Hence, the traditional Mohr–Coulomb criterion may be unsuitable for such mixtures. In a mixture of sand and carpet fibre, the inclusion of the fibre reduces the density and increases shear strength. When sand is reinforced with carpet fibre in a loosely packed state, it provides higher shear strength than pure dense sand. In the interface shear experiment, the shear strength of the sand markedly exceeds the peak interface friction shown at both the smooth steel and rough steel interfaces. Furthermore, an increase in the sample density leads to an increase in the peak friction angle, due to an increase in surface contact. Moreover, machine learning models predict the experiment results with high accuracy, highlighting the influential parameters.
... The size distribution of glass beads was evaluated considering whether the composition is well graded, or not, by the coefficients of uniformity (C U ) and curvature (C C ) commonly employed in soil mechanics for analyzing granular materials [34,35]. The coefficient of uniformity is defined by Equation (1): ...
Pavement marking retroreflectivity, a critical factor for safe driving, depends on the characteristics of both the paint and the embedded glass beads. However, traditional methods for predicting pavement marking service life often overlook these materials properties. This study investigates the influence of paint and glass bead characteristics on pavement marking retroreflectivity performance and addresses the characterization of glass bead size distribution by the coefficient of uniformity and curvature. Three field test sites on a Brazilian highway with various paint and glass bead combinations were evaluated. A statistical model, GAMLSS (Generalized Additive Model for Location, Scale, and Shape), was adjusted to evaluate the performance of the markings’ retroreflectivity as a function of paint and glass bead characteristics. The model revealed that well-graded glass beads increased retroreflectivity by around 10%, while paints with a higher volume of solids improved service life around 65%. Therefore, the results show that acrylic water-based paints with higher volumes of solids and well-graded glass beads with better shape characteristics should be preferred to improve pavement markings’ retroreflectivity and service life. The statistical model identified the key characteristics with the greatest impact on pavement marking retroreflectivity, offering valuable insights for real-world applications, which will assist pavement marking practitioners and road authorities in selecting appropriate materials to achieve enhanced durability.
... The shape of the sand grain is irregular, and the shear strength of the sample will be improved [13,30]. When calcium carbonate is generated in the sand column, the sample is composed of sand grains and calcium carbonate. ...
In recent years, microbial mineralization has aroused attention in soil reinforcement. However, most studies focus on soil and coast on land, and the consolidation of seafloor sediments is rarely reported. In this paper, the hydrate reservoir sediments in different sea areas are strengthened, which provides a new method to solve the formation settlement problem in hydrate exploitation. In this study, the microorganisms were cultivated using both fresh and seawater, and hydrate sediments of varying particle sizes (fine sand and silty sand) were consolidated. Triaxial tests and calcium carbonate content tests were conducted to characterize the consolidation effect and detect calcium carbonate content. X-ray diffraction was used to analyze the crystal form and mineral composition of calcium carbonate generated. SEM was employed to observe the microscopic characteristics of the consolidated samples. X-ray computer tomography (X-ray CT) was utilized to analyze changes in pore throat size and the distribution of calcium carbonate in different samples and environments. The experimental results indicate that the consolidated samples exhibit higher strength, particularly in a seawater environment. Fine sand sediment samples primarily demonstrate increased cohesion, from 3.7 kPa initially to 42.3 and 86.8 kPa after consolidation, with the friction angle increasing by less than 2°. While silty sand sediment samples exhibit a greater increase in friction angle after cementation, from 22 to 24.9° and 27.6°, and the cohesion increased by only about 6%. Additionally, it was discovered that the increase in sample strength is not only related to the calcium carbonate content but also to the crystal form and distribution of calcium carbonate within the samples. Under identical sample conditions, those treated in a seawater environment exhibit a more uniform distribution of calcium carbonate and a greater abundance of calcium carbonate crystal forms, such as calcite and vaterite. Furthermore, after consolidation, among the samples treated with fresh water, the porosity of the fine sand sample decreased from 46.38 to 18.26%, and that of the silty sand sample decreased by 25.62%, indicating that fine sand and silty sand samples still possess connecting pores that are not completely obstructed by calcium carbonate. This provides a pathway for improving grouting cycles and gas release following hydrate decomposition.
... Despite these known factors, as yet, no detailed study that combines all of these into a single, easy-to-understand model has been conducted, mainly because these factors interact in complicated ways. In artificial intelligence, machine learning algorithms have helped solve complex problems in the geotechnical field, such as understanding soil mechanics behaviour [11][12][13] and improving the use of recycled materials in soil stabilisation [14][15][16][17][18][19]. ...
The interface friction between granular materials and continuum surfaces is fundamental in civil engineering, especially in geotechnical projects where sand of varying sizes and shapes contacts surfaces with different roughness and hardness. The aim of this research is to investigate the parameters that influence the peak interface friction, taking into consideration the properties of both sand and continuum surfaces. This will be accomplished by employing a combination of experimental and machine learning techniques. In the experiment, a series of interface shear tests were conducted using a direct shear apparatus under differing levels of normal stress and density. Utilising machine learning techniques, the study considered eleven input features: mean particle size, void ratio, specific gravity, particle regularity, coefficient of uniformity, coefficient of curvature, granular rubber content, carpet fibre content, normal stress, surface roughness, and surface hardness. The output measured was the peak interface friction. The machine learning techniques enable us to explore the complex relationships between the input features and the peak interface friction, and to develop an empirical equation that can accurately predict the interface friction. The experiment findings reveal that density, inclusion of recycled material, and normalised roughness impact peak interface friction. The machine learning findings validate the efficacy of both multiple linear regression and random forest regression models in predicting the peak interface friction, with the latter outperforming the former in terms of accuracy when compared to the experiment results. Furthermore, the most important features from both models were identified.
Subaqueous paleoseismic studies used soft sediment deformation structures (SSDS) to discern the shaking strength of past earthquakes, as the deformation degree of SSDS related to Kelvin Helmholtz Instability evolves from disturbed lamination and folds to intraclast breccia with higher peak ground accelerations (PGA). We lack comparative studies of different sediment types with SSDS related to earthquakes from different seismogenic sources to comprehend how these factors modulate earthquake‐induced deformation. Here, we compile sediment records with seven earthquake‐triggered SSDS from 10 lakes with organic‐, carbonate‐, siliciclastic‐, and diatom‐rich sediment from three subduction zones and one collisional setting. We target basin sequences with slope angles <0.65° to reduce the influence of gravitational downslope stress. We find that even minimal increases in slope angle, maximal 1°, lead to higher deformation degrees and, for some earthquakes, SSDS are only present at >0.65°. Fine‐grained clastics enhance sediment susceptibility to deformation, whereas abundant diatoms reduce it, demonstrating the influence of composition. Deformation correlates best with PGA and the vicinity of the earthquakes, suggesting that high frequency shaking promotes deformation. In addition, deformation only occurs above a minimum magnitude dependent on sediment composition, and higher deformation degrees in our studied basin sedimentary sequences only above Mw 4.9 for all sediment types, suggesting that sufficient duration of shaking—magnitude correlates with duration—is essential for SSDS development. We advise taking multiple cores on gentle slopes to study SSDS—additional to basin cores—to resolve small magnitude local earthquakes and relative differences in frequency content of past events.
Geotechnical engineering is civil engineering constructed in rock and soil and includes three main types: underground, foundation, and slope engineering [...]
Engineers have created increasingly complex correlations based on laboratory and field tests. Over time, geotechnical engineering modeling techniques have evolved from simple analytical methods to complex numerical modeling techniques. Nomographs are traditional computational tools that have been widely employed in engineering. Combining nomographs with computational tools such as numerical models and machine learning algorithms can lead to better outcomes. Thus, this study aimed to develop a nomograph for geotechnical engineering that incorporates machine learning, specifically for the unit weight of soil. Four calibrated models were developed to determine the unit weight of soil: the moist unit weight of coarse-grained soil, the saturated unit weight of coarse-grained soil, the moist unit weight of fine-grained soil, and the saturated unit weight of fine-grained soil. An uncertainty test was conducted for the data used. Our results indicated a strong positive relationship to most of the models. The generated nomographs were tested in Malabon, a city in Metro Manila, where a low unit weight of soil was determined. This low unit weight was validated by the predominance of alluvial deposits and the shallow groundwater table, which soften and weaken the soil.
Mixing carpet fibre in sand offers great potential for enhancing soil properties. In this study, direct shear tests were conducted on two different sands mixed with varying carpet fibre percentages to investigate the effects on soil strength, stiffness, and deformation. Artificial intelligence techniques were used to analyse the data and develop predictive models, including an empirical equation that predicts the shear strength. The results showed that the addition of carpet fibre improved soil properties, with increased strength, stiffness, and reduced deformation. The AI models, including the empirical equation, accurately predicted the mixture's shear strength. Furthermore, this study investigated the importance of each input parameter in predicting the mixture's shear strength. The input parameters are normal stress, void ratio, mean particle size, and the ratio of carpet fibre content to specific gravity. According to the results, normal stress is the most important parameter, and mean particle size is the least important. ARTICLE HISTORY
