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Geotechnical and Geological Engineering

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10.01.2019 | Original Paper

Estimating the Strength of Stabilized Dispersive Soil with Cement Clinker and Fly Ash

verfasst von: Samaptika Mohanty, Nagendra Roy, Suresh Prasad Singh, Parveen Sihag

Erschienen in: Geotechnical and Geological Engineering | Ausgabe 4/2019

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Abstract

In this study, the potential of four popular artificial intelligence techniques random forest (RF), Gaussian process (GP), M5P tree and artificial neural network (ANN) are assessed for estimating the strength of stabilized dispersive soil with cement clinker and fly ash. GP, M5P and ANN models were providing a good estimate of performance, whereas the RF model outperforms them. For this study, a dataset containing 52 observations obtained from the laboratory experiments. Total data set (52 observations) has been segregated in two different groups. The larger group (36) was used for model development and the smaller group (16) was used for testing the models. Input dataset consists of dispersive soil (%), cement clinker (%), fly ash (%) and curing time (days), whereas unconfined compressive strength (UCS) of soil (MPa) was taken as a target. Sensitivity testing results conclude that the curing time is the most essential factor in estimating the strength of dispersive soil with cement clinker and fly ash for RF-based modelling. The results of this study also suggest that the combined mix of cement clinker and fly ash are used to increase the UCS of dispersive soil than an alone mix.

Vorheriger Artikel Integrating Remotely Sensed and GIS Data for the Detailed Geological Mapping in Semi-Arid Regions: Case of Youks les Bains Area, Tebessa Province, NE Algeria

Nächster Artikel Investigation of Influence Factors of the Fracture Toughness of Shale: A Case Study of the Longmaxi Formation Shale in Sichuan Basin, China

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Titel: Estimating the Strength of Stabilized Dispersive Soil with Cement Clinker and Fly Ash
verfasst von: Samaptika Mohanty
Nagendra Roy
Suresh Prasad Singh
Parveen Sihag
Publikationsdatum: 10.01.2019
Verlag: Springer International Publishing
Erschienen in: Geotechnical and Geological Engineering / Ausgabe 4/2019
Print ISSN: 0960-3182
Elektronische ISSN: 1573-1529
DOI: https://doi.org/10.1007/s10706-019-00808-1

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