Article
Engineering, Geological
Ivan Gratchev
Summary: This study aims to investigate the cyclic behavior of fine-grained soils and clarify the mechanism of earthquake-triggered landslides. Six soils with different plasticity and mineralogy were tested in undrained ring-shear experiments. The results showed that low-plasticity soil mixtures containing kaolinite or illite were susceptible to liquefaction, while more plastic soil specimens containing smectite were resistant to liquefaction. The failure in plastic fine-grained soils was not driven by excess pore water pressure, but rather by large permanent shear displacements developed during cyclic loading.
SOIL DYNAMICS AND EARTHQUAKE ENGINEERING
(2023)
Article
Engineering, Geological
Merita Tafili, Carlos Grandas, Theodoros Triantafyllidis, Torsten Wichtmann
Summary: This paper proposes a model that combines the frameworks of elastoplasticity and hypoplasticity, which successfully reproduces the eight-shaped stress hysteresis under cyclic loading. The model introduces a historiotropic surface and a hypoplastic strain rate.
INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS
(2022)
Article
Engineering, Geological
Rita Tufano, Giuseppe Formetta, Domenico Calcaterra, Pantaleone De Vita
Summary: Soil thickness and stratigraphic settings covering slopes potentially control susceptibility to rainfall-induced shallow landslides due to their local effect on slope hydrological response. This study explores the spatial variability of soil thickness and stratigraphic settings, factors poorly considered in literature, through a case study involving ash-fall pyroclastic soils. The modeling approach used provides insights into the effects of irregular bedrock topography, soil thickness variability, and hydraulic heterogeneity on hillslope hydrological regime and slope stability.
Article
Biotechnology & Applied Microbiology
Vitalii Kleshchevnikov, Artem Shmatko, Emma Dann, Alexander Aivazidis, Hamish W. King, Tong Li, Rasa Elmentaite, Artem Lomakin, Veronika Kedlian, Adam Gayoso, Mika Sarkin Jain, Jun Sung Park, Lauma Ramona, Elizabeth Tuck, Anna Arutyunyan, Roser Vento-Tormo, Moritz Gerstung, Louisa James, Oliver Stegle, Omer Ali Bayraktar
Summary: Cell2location, a Bayesian model, can resolve the spatial distribution of cell types and create comprehensive cellular maps of tissues. By accounting for technical variation and borrowing statistical strength, cell2location has higher sensitivity and resolution than existing tools. Our results demonstrate that cell2location is a versatile analysis tool for mapping tissue architectures in a comprehensive manner.
NATURE BIOTECHNOLOGY
(2022)
Article
Computer Science, Interdisciplinary Applications
Vikram Singh, Sam Stanier, Britta Bienen, Mark F. Randolph
Summary: This paper presents a new viscoplastic strain-softening-hardening constitutive model that captures the dual behavior of soil through non-local regularization, strain rate dependency, and consolidation-induced recovery of sensitivity. The model is validated through simulation tests and successfully applied in large deformation finite element analyses of T-bar penetration in different soils.
COMPUTERS AND GEOTECHNICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Sanchari Mondal, Mahdi Disfani, Guillermo A. Narsilio
Summary: This study investigates the ultimate lateral load capacity of battered minipiles in fine-grained soil through field testing and numerical modeling. The results show that the combination of normal and shear stresses along the minipile shaft plays a significant role in determining the optimum batter angle.
COMPUTERS AND GEOTECHNICS
(2022)
Article
Geology
A. N. T. O. N. Y. D. A. V. I. D. REYNOLDS
Summary: Fluvially-dominated, fine-grained, shallow-water deltas are more variable than commonly recognized. They exhibit diverse river-mouth deposits and various modes of channel elongation. These variations are often not considered in models of fluvially-dominated deltas, which could have significant implications for land remediation and resource extraction.
Article
Engineering, Environmental
S. Bulolo, E. C. Leong, R. Kizza
Summary: The study discusses the effects of drying and wetting cycles on soil, particularly in relation to crack formation and tensile strength. Existing tensile strength models for unsaturated soils were found to have limitations, leading to the proposal of a new model that can be applied to both coarse-grained and fine-grained soils. The new model was shown to outperform existing models in terms of performance.
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
(2021)
Article
Engineering, Civil
Zoheir Bellia, Moulay Smaine Ghembaza
Summary: New thermomechanical models for saturated soils are more accurate in predicting thermal cyclic deformations of slightly overconsolidated soils by introducing the concept of reference line (RL) and thermal stabilisation line (TSL). Calculating the hardening law independently for thermal and mechanical loading cases helps to improve the prediction accuracy of thermomechanical behavior in saturated soils.
EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Mustafa Ozsagir, Caner Erden, Ertan Bol, Sedat Sert, Askin Ozocak
Summary: This study used machine learning algorithms to predict and evaluate the potential of soil liquefaction, and the decision tree algorithm was found to perform the best in terms of accuracy, providing decision-makers with a simple and effective evaluation tool.
COMPUTERS AND GEOTECHNICS
(2022)
Article
Computer Science, Artificial Intelligence
Xiaoqian Ruan, Guosheng Lin, Cheng Long, Shengli Lu
Summary: This paper proposes a novel model SACN to address the problem of few-shot fine-grained recognition, utilizing three modules for feature extraction and fusion, and achieving superior performance on three fine-grained datasets.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Xovee Xu, Zhiyuan Wang, Qiang Gao, Ting Zhong, Bei Hui, Fan Zhou, Goce Trajcevski
Summary: The Fine-grained urban flow inference (FUFI) problem aims to reduce electricity, maintenance, and operation costs by inferring fine-grained flow maps from coarse-grained ones, benefiting various smart-city applications. Existing models in FUFI use image super-resolution techniques and have achieved good performance, but they often rely on supervised learning with a large amount of training data, lack generalization capability, and face overfitting. This study presents a new solution called Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference (STCF), which enhances efficiency and performance by attracting and distancing spatial-temporally similar and dissimilar flow maps within the representation space. Comprehensive experiments on two large-scale, real-world urban flow datasets show that STCF reduces inference error by up to 13.5%, requiring significantly fewer data and model parameters than previous approaches.
IEEE TRANSACTIONS ON BIG DATA
(2023)
Article
Engineering, Geological
Houman Soleimani-Fard, Diethard Koenig, Meisam Goudarzy
Summary: This research evaluated the shear strength parameters of fine-grained soils, finding that ductile behavior was more noticeable in samples with lower suctions and higher straw contents. The shear strength significantly increased with an increase in suction, and the shear band inclination was influenced by both suction and straw content.
Article
Engineering, Civil
Xuanxuan Chu, Andrew Dawson, Nick Thom
Summary: The study found a positive correlation between resilient modulus and consistency index of soil, leading to the proposal of two prediction models to assess resilient modulus. The models were validated and proved to be a reasonable alternative for determining resilient modulus of fine-grained soils for foundation design.
TRANSPORTATION GEOTECHNICS
(2021)
Article
Engineering, Electrical & Electronic
Min Wang, Peng Zhao, Xin Lu, Fan Min, Xizhao Wang
Summary: In this paper, a new spatial-frequency feature fusion (SFFF) perspective is proposed to tackle the challenge of fine-grained visual categorization. The SFFF method extracts, selects, and fuses features by designing a heterogeneous feature extraction loss function, a global and local fusion SFFF network, and an importance-sparsity selection strategy. Experimental results on popular datasets demonstrate the effectiveness and outstanding performance of the SFFF approach.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2023)