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A platform for research: civil engineering, architecture and urbanism
Prediction of California Bearing Ratio from Index Properties of Soils Using Parametric and Non-parametric Models
https://orcid.org/0000-0002-4097-3558
Abstract This work proposes a methodology to obtain from the soils properties the best prediction model for the California bearing ratio index. The methodology proposes three different prediction techniques: (1) the multiple linear regression, a classical parametric technique; and two non-parametric techniques: (2) the local polynomial regression (LPR) and (3) the radial basis network. The LPR is a known statistical method, but in the geotechnical engineering field is not in common use. Besides, although several research works have been published in this field, they do not include a robust procedure for making good comparison between different models. Here, a cross validation method is proposed with this aim. A data set of 96 samples from Peruvian soils is used to illustrate the methodology. To validate the proposed methodology, a data set from the literature is also analyzed.
Prediction of California Bearing Ratio from Index Properties of Soils Using Parametric and Non-parametric Models
https://orcid.org/0000-0002-4097-3558
Abstract This work proposes a methodology to obtain from the soils properties the best prediction model for the California bearing ratio index. The methodology proposes three different prediction techniques: (1) the multiple linear regression, a classical parametric technique; and two non-parametric techniques: (2) the local polynomial regression (LPR) and (3) the radial basis network. The LPR is a known statistical method, but in the geotechnical engineering field is not in common use. Besides, although several research works have been published in this field, they do not include a robust procedure for making good comparison between different models. Here, a cross validation method is proposed with this aim. A data set of 96 samples from Peruvian soils is used to illustrate the methodology. To validate the proposed methodology, a data set from the literature is also analyzed.
Prediction of California Bearing Ratio from Index Properties of Soils Using Parametric and Non-parametric Models
https://orcid.org/0000-0002-4097-3558
Abstract This work proposes a methodology to obtain from the soils properties the best prediction model for the California bearing ratio index. The methodology proposes three different prediction techniques: (1) the multiple linear regression, a classical parametric technique; and two non-parametric techniques: (2) the local polynomial regression (LPR) and (3) the radial basis network. The LPR is a known statistical method, but in the geotechnical engineering field is not in common use. Besides, although several research works have been published in this field, they do not include a robust procedure for making good comparison between different models. Here, a cross validation method is proposed with this aim. A data set of 96 samples from Peruvian soils is used to illustrate the methodology. To validate the proposed methodology, a data set from the literature is also analyzed.
Prediction of California Bearing Ratio from Index Properties of Soils Using Parametric and Non-parametric Models
González Farias, Isabel (author) / Araujo, William (author) / Ruiz, Gaby (author)
2018
Article (Journal)
Electronic Resource
English
BKL:
57.00$jBergbau: Allgemeines
/
38.58
Geomechanik
/
57.00
Bergbau: Allgemeines
/
56.20
Ingenieurgeologie, Bodenmechanik
/
38.58$jGeomechanik
/
56.20$jIngenieurgeologie$jBodenmechanik
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