Prediction of unconfined compressive strength and California bearing capacity of cement- or lime-pozzolan-stabilised soil admixed with crushed stone waste

被引:17
|
作者
Salehi, Mohsen [1 ,2 ]
Bayat, Meysam [1 ]
Saadat, Mohsen [1 ]
Nasri, Masoud [3 ]
机构
[1] Islamic Azad Univ, Dept Civil Engn, Najafabad Branch, Najafabad, Iran
[2] Islamic Azad Univ, Dept Civil Engn, Ardestan Branch, Ardestan, Iran
[3] Islamic Azad Univ, Dept Civil Engn, Dolatabad Branch, Esfahan, Iran
来源
GEOMECHANICS AND GEOENGINEERING-AN INTERNATIONAL JOURNAL | 2023年 / 18卷 / 04期
关键词
Cement; Lime; Stone waste; Sand; multi-layer perceptron; Non-linear regression; NEURAL-NETWORKS; FLY-ASH; BEHAVIOR; REGRESSION; ZEOLITE; UCS;
D O I
10.1080/17486025.2022.2040606
中图分类号
P5 [地质学];
学科分类号
0709 ; 081803 ;
摘要
So far, a limited number of studies have been published on utilisation of crushed stone waste, cement, lime and pozzolan as stabilisers for soil stabilisation. The research conducts soil engineering properties and strength test for various contents of crushed stone waste, cement, lime and pozzolan under various curing times. Increasing the pozzolan or cement content resulted in an increase in UCS; however, the UCS increased as the lime content increased from 0% to 5% and then decreased as lime content violated from 5%. An increase in the granite crushed stone content from 0% to 10% resulted in an increase in the UCS value. The unsoaked CBR and UCS values are almost 1.37 and 1.24 times more than the corresponding soaked ones for all cases, respectively. The CBR value increased with the increase in the pozzolan, lime or cement content. The cement content has more important influence on the increase in CBR value than the lime or pozzolan content. The multi-layer perceptron (MLP) neural network and non-linear regression (NLR) techniques are employed to develop models to predict the CBR and UCS values of the stabilised specimens.
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页码:272 / 283
页数:12
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