Regression-based models for the prediction of unconfined compressive strength of artificially structured soil

被引:76
作者
Sharma, L. K. [1 ]
Singh, T. N. [1 ]
机构
[1] Indian Inst Technol, Dept Earth Sci, Bombay 400076, Maharashtra, India
关键词
Regression; Artificial soil; Unconfined compressive strength; Lime; Mountainous soil; EXPANSIVE CLAYEY SOIL; ABSOLUTE ERROR MAE; GEOTECHNICAL PROPERTIES; NEURAL-NETWORK; LIME; CEMENT; ROCK; MICROSTRUCTURE; CLASSIFICATION; PERFORMANCE;
D O I
10.1007/s00366-017-0528-8
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Unconfined compressive strength (UCS) of soil is a critical and important geotechnical property which is widely used as input parameters for the design and practice of various geoengineering projects. UCS controls the deformational behavior of soil by measuring its strength and load bearing capacity. The laboratory determination of UCS is tedious, expensive and being a time-consuming process. Therefore, the present study is aimed to establish empirical equations for UCS using simple and multiple linear regression methods. The accuracy of the developed equations are tested by employing coefficient of determination (R-2), root mean square error (RMSE) and mean absolute percentage error (MAPE). It has been found that the developed equations are reliable and capable to predict UCS with acceptable degree of confidence. Among all the developed models, model-I consist of lime content, curing time, plastic limit, liquid limit, potential of hydrogen, primary ultrasonic wave velocity, optimum moisture content and maximum dry density as independent parameters shows highest prediction capacity with R (2), RMSE and MAPE are 0.96, 25.89 and 16.59, respectively.
引用
收藏
页码:175 / 186
页数:12
相关论文
共 46 条
[1]   Multiple Regression Model for the Prediction of Unconfined Compressive Strength of Jet Grout Columns [J].
Akan, Recep ;
Keskin, S. Nilay ;
Uzundurukan, Soner .
WORLD MULTIDISCIPLINARY EARTH SCIENCES SYMPOSIUM, WMESS 2015, 2015, 15 :299-303
[2]   Investigating the effect of correlation-based feature selection on the performance of neural network in reservoir characterization [J].
Akande, Kabiru O. ;
Owolabi, Taoreed O. ;
Olatunji, Sunday O. .
JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING, 2015, 27 :98-108
[3]   Stabilization of a Saudi calcareous marl soil [J].
Al-Amoudi, Omar Saeed Baghabra ;
Khan, Khaqan ;
Al-Kahtani, Nasser Saban .
CONSTRUCTION AND BUILDING MATERIALS, 2010, 24 (10) :1848-1854
[4]   Microstructure and geotechnical properties of lime-treated expansive clayey soil [J].
Al-Mukhtar, Muzahim ;
Khattab, Suhail ;
Alcover, Jean-Francois .
ENGINEERING GEOLOGY, 2012, 139 :17-27
[5]   Behaviour and mineralogy changes in lime-treated expansive soil at 20 °C [J].
Al-Mukhtar, Muzahim ;
Lasledj, Abdelmadjid ;
Alcover, Jean-Francois .
APPLIED CLAY SCIENCE, 2010, 50 (02) :191-198
[6]   A simple regression based approach to estimate deformation modulus of rock masses [J].
Alemdag, Selcuk ;
Gurocak, Zulfu ;
Gokceoglu, Candan .
JOURNAL OF AFRICAN EARTH SCIENCES, 2015, 110 :75-80
[7]  
[Anonymous], 2016, C15492016 ASTM INT, DOI DOI 10.1520/C1549-16
[8]  
[Anonymous], 1992, ANN BOOK ASTM STAND
[9]  
[Anonymous], 2012, ASTM D882
[10]  
[Anonymous], 2015, D555006 ASTM