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Please use this identifier to cite or link to this item: http://10.10.120.238:8080/xmlui/handle/123456789/490
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dc.rights.licenseAll Open Access, Green-
dc.contributor.authorGarg A.en_US
dc.contributor.authorWani I.en_US
dc.contributor.authorZhu H.en_US
dc.contributor.authorKushvaha V.en_US
dc.date.accessioned2023-11-30T08:35:35Z-
dc.date.available2023-11-30T08:35:35Z-
dc.date.issued2022-
dc.identifier.issn1861-1125-
dc.identifier.otherEID(2-s2.0-85119211150)-
dc.identifier.urihttps://dx.doi.org/10.1007/s11440-021-01411-6-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/490-
dc.description.abstractRecently, incentives have been provided in many countries, including Canada and Denmark, to produce biochar for construction usage. This is done because biochar is carbon negative and can help achieve the emission reduction goal of 2030. This technical note aims to analyse the efficiency of biochar in soils with varying grain size distributions for enhancing soil–water characteristic curve (SWCC). The combinations of biochar content and grain size distributions corresponding to the maximum and minimum efficiencies were explored. Artificial neural network-based model for predicting SWCC as a function of soil suction and grain size distribution was developed. A new factor (the ratio of fine (silt + clay) and coarse (sand) content) was proposed for the interpretation of the efficiency of biochar in soils. The newly developed model is able to predict SWCC reasonably well. Biochar amendment is found to influence both dry and wet sides of soils with a clay content lower than threshold content (6–8%). Beyond threshold content, the influence of biochar appears to reduce. However, in the case of high sand content soils (90%), the normalized water content value on the drier side is generally higher as compared to soils with lower sand content. Based on the sensitivity analysis, it was found that the ratio of fine to sand content is the most influential, while biochar content is the least influential. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.en_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceActa Geotechnicaen_US
dc.subjectArtificial neural networken_US
dc.subjectBiocharen_US
dc.subjectRatioen_US
dc.subjectSoil suctionen_US
dc.subjectSoil–water characteristic curveen_US
dc.titleExploring efficiency of biochar in enhancing water retention in soils with varying grain size distributions using ANN techniqueen_US
dc.typeJournal Articleen_US
Appears in Collections:Journal Article

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