Prediction of Compression Index of Marine clay Using Artificial Neural Network and Multilinear Regression Models

Compression Index (CI) is one of the frequently used soil parameters for the determination of possible settlement. In this study, the Compression Index of Marine clay is predicted using Artificial Neural network (ANN). Marine clay samples were collected from eight boreholes located at distance varyi...

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Autores principales:	Ramachandiran Saisubramanian, V Murugaiyan
Formato:	article
Lenguaje:	EN
Publicado:	Pouyan Press 2021
Materias:	compression index marine clay artificial neural network multilinear regression Technology T
Acceso en línea:	https://doaj.org/article/3520805e49604e6fa8c3a751522bccd4
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spelling	oai:doaj.org-article:3520805e49604e6fa8c3a751522bccd42021-12-03T15:12:30ZPrediction of Compression Index of Marine clay Using Artificial Neural Network and Multilinear Regression Models2588-287210.22115/scce.2021.287537.1324https://doaj.org/article/3520805e49604e6fa8c3a751522bccd42021-10-01T00:00:00Zhttp://www.jsoftcivil.com/article_141003_104438e517d57cb44f0493efcfcb3b00.pdfhttps://doaj.org/toc/2588-2872Compression Index (CI) is one of the frequently used soil parameters for the determination of possible settlement. In this study, the Compression Index of Marine clay is predicted using Artificial Neural network (ANN). Marine clay samples were collected from eight boreholes located at distance varying from 0.5 Km to 2.5 Km landward from the coastline of Pondicherry. The depth of boring was up to 12m. These samples were used for determining the Plastic Limit (PL), Liquid Limit (LL) and the Natural Moisture Content (NMC) and these were taken as input parameters for computing CI. These input parameters are taken as ‘data set 1’. Similar properties of soil from over 51 boreholes were considered for analysis designated as ‘Data set 2’where the depth of sampling was up to 52. These were located at a distance up to 5.0 Km from the shoreline of Puducherry distributed across the town covering a length of over 5.0 km. In Data set 2, the LL, PL, Plasticity index (PI) Specific Gravity (G), Swell Percentage, ‘N’value and the ratio of PL/LL of the soil samples were taken as input parameters for prediction of CI. The input variables were reduced in successive iterations to determine their influence in the prediction of CI. Multilinear Regression Models using the same set of inputs was compared with that of ANN. Both the analysis methods indicated that the LL and PL of soil are not only easy to determine but are competent to predict CI with a high degree of accuracy.Ramachandiran SaisubramanianV MurugaiyanPouyan Pressarticlecompression indexmarine clayartificial neural networkmultilinear regressionTechnologyTENJournal of Soft Computing in Civil Engineering, Vol 5, Iss 4, Pp 114-124 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	compression index marine clay artificial neural network multilinear regression Technology T
spellingShingle	compression index marine clay artificial neural network multilinear regression Technology T Ramachandiran Saisubramanian V Murugaiyan Prediction of Compression Index of Marine clay Using Artificial Neural Network and Multilinear Regression Models
description	Compression Index (CI) is one of the frequently used soil parameters for the determination of possible settlement. In this study, the Compression Index of Marine clay is predicted using Artificial Neural network (ANN). Marine clay samples were collected from eight boreholes located at distance varying from 0.5 Km to 2.5 Km landward from the coastline of Pondicherry. The depth of boring was up to 12m. These samples were used for determining the Plastic Limit (PL), Liquid Limit (LL) and the Natural Moisture Content (NMC) and these were taken as input parameters for computing CI. These input parameters are taken as ‘data set 1’. Similar properties of soil from over 51 boreholes were considered for analysis designated as ‘Data set 2’where the depth of sampling was up to 52. These were located at a distance up to 5.0 Km from the shoreline of Puducherry distributed across the town covering a length of over 5.0 km. In Data set 2, the LL, PL, Plasticity index (PI) Specific Gravity (G), Swell Percentage, ‘N’value and the ratio of PL/LL of the soil samples were taken as input parameters for prediction of CI. The input variables were reduced in successive iterations to determine their influence in the prediction of CI. Multilinear Regression Models using the same set of inputs was compared with that of ANN. Both the analysis methods indicated that the LL and PL of soil are not only easy to determine but are competent to predict CI with a high degree of accuracy.
format	article
author	Ramachandiran Saisubramanian V Murugaiyan
author_facet	Ramachandiran Saisubramanian V Murugaiyan
author_sort	Ramachandiran Saisubramanian
title	Prediction of Compression Index of Marine clay Using Artificial Neural Network and Multilinear Regression Models
title_short	Prediction of Compression Index of Marine clay Using Artificial Neural Network and Multilinear Regression Models
title_full	Prediction of Compression Index of Marine clay Using Artificial Neural Network and Multilinear Regression Models
title_fullStr	Prediction of Compression Index of Marine clay Using Artificial Neural Network and Multilinear Regression Models
title_full_unstemmed	Prediction of Compression Index of Marine clay Using Artificial Neural Network and Multilinear Regression Models
title_sort	prediction of compression index of marine clay using artificial neural network and multilinear regression models
publisher	Pouyan Press
publishDate	2021
url	https://doaj.org/article/3520805e49604e6fa8c3a751522bccd4
work_keys_str_mv	AT ramachandiransaisubramanian predictionofcompressionindexofmarineclayusingartificialneuralnetworkandmultilinearregressionmodels AT vmurugaiyan predictionofcompressionindexofmarineclayusingartificialneuralnetworkandmultilinearregressionmodels
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Prediction of Compression Index of Marine clay Using Artificial Neural Network and Multilinear Regression Models

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