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A dataset on void ratio limits and their range for cohesionless soils
A database, which consists of maximum and minimum void ratio limits and their range, particle size, distribution and shape characteristics, is compiled. More specifically, minimum and maximum void ratios (e(min) and e(max)) along with their range (e(max)-e(min)), particle roundness (R) and spherecit...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6859216/ https://www.ncbi.nlm.nih.gov/pubmed/31763383 http://dx.doi.org/10.1016/j.dib.2019.104696 |
Sumario: | A database, which consists of maximum and minimum void ratio limits and their range, particle size, distribution and shape characteristics, is compiled. More specifically, minimum and maximum void ratios (e(min) and e(max)) along with their range (e(max)-e(min)), particle roundness (R) and spherecity (S), fines content (FC), coefficient of uniformity (C(u)), mean grain size (D(50)) data are compiled from natural cohesionless soils and reconstituted grained material (e.g.: rice, glass beads, mica) mixtures. The final dataset is composed of 636, mostly soil samples. Out of 636 samples, 496, 474 and 603 of them have e(max), e(min) or e(max)-e(min) data, respectively. Similarly, for 593, 419, 171, 126 and 93 soils, D(50), C(u), R, S and FC data exists, respectively. Not for every sample, USCS based soil classification designation is available, hence for the missing ones, soil classification is performed based on mean particle diameter-based classification as suggested by ASTM D2487 – 17: Standard Practice for Classification of Soils for Engineering Purposes (Unified Soil Classification System) [1]. The dataset consists of 19 silts and clays, 527 sands (357 fine sands, 153 medium sands, 17 coarse sands) and 47 gravels (44 fine gravels, 3 coarse gravels). A spreadsheet summary of the dataset is provided. This dataset is later used for the development of probability-based void ratio predictive models. |
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