相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Potential of corn husk fiber for reinforcing cemented soil with high water content
Nga Thanh Duong et al.
CONSTRUCTION AND BUILDING MATERIALS (2021)
Estimation of effective cohesion using artificial neural networks based on index soil properties: A Singapore case
Yongmin Kim et al.
ENGINEERING GEOLOGY (2021)
Applying several machine learning approaches for prediction of unconfined compressive strength of stabilized pond ashes
Manju Suthar
NEURAL COMPUTING & APPLICATIONS (2020)
Constitutive models for fibre reinforced soil bricks
Mahgoub M. Salih et al.
CONSTRUCTION AND BUILDING MATERIALS (2020)
Experimental investigation and modelling the deformation properties of demolition wastes subjected to freeze-thaw cycles using ANN and SVR
Behnam Ghorbani et al.
CONSTRUCTION AND BUILDING MATERIALS (2020)
Multivariate regression models for predicting the compressive strength of bone ash stabilized lateritic soil for sustainable building
Ifeyinwa Ijeoma Obianyo et al.
CONSTRUCTION AND BUILDING MATERIALS (2020)
Predicting and evaluating the engineering properties of unstabilized and cement stabilized fibre reinforced rammed earth blocks
Satya Sai Deep Raavi et al.
CONSTRUCTION AND BUILDING MATERIALS (2020)
An artificial neural network approach for under-reamed piles subjected to uplift forces in dry sand
Hossein Moayedi et al.
NEURAL COMPUTING & APPLICATIONS (2019)
Fiber-reinforced internally unstable soil against suffusion failure
Kuo-Hsin Yang et al.
CONSTRUCTION AND BUILDING MATERIALS (2019)
Prediction of geotechnical properties of clayey soils stabilised with lime using artificial neural networks (ANNs)
Ismehen Taleb Bahmed et al.
INTERNATIONAL JOURNAL OF GEOTECHNICAL ENGINEERING (2019)
Improvement of mechanical behavior of cemented soil reinforced with waste cornsilk fibers
Khiem Quang Tran et al.
CONSTRUCTION AND BUILDING MATERIALS (2018)
Effect of waste cornsilk fiber reinforcement on mechanical properties of soft soils
Khiem Quang Tran et al.
TRANSPORTATION GEOTECHNICS (2018)
Hydraulic response of fibre-reinforced sand subject to seepage
K-H. Yang et al.
GEOSYNTHETICS INTERNATIONAL (2017)
STUDY ON STRENGTH BEHAVIOR OF CEMENT STABILIZED SLUDGE REINFORCED WITH WASTE CORNSILK FIBER
Khiem Quang Tran et al.
INTERNATIONAL JOURNAL OF GEOMATE (2017)
Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results
R. A. T. M. Ranasinghe et al.
JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING (2017)
Models for predicting the seepage velocity and seepage force in a fiber reinforced silty soil
A. R. Estabragh et al.
COMPUTERS AND GEOTECHNICS (2016)
Prediction of unconfined compressive strength of geopolymer stabilized clayey soil using Artificial Neural Network
Ruhul Amin Mozumder et al.
COMPUTERS AND GEOTECHNICS (2015)
Physical, mechanical and durability properties of soil building blocks reinforced with natural fibres
Humphrey Danso et al.
CONSTRUCTION AND BUILDING MATERIALS (2015)
Prediction of Incipient Breaking Wave-Heights Using Artificial Neural Networks and Empirical Relationships
Bryson Robertson et al.
COASTAL ENGINEERING JOURNAL (2015)
Improving piping resistance using randomly distributed fibers
A. R. Estabragh et al.
GEOTEXTILES AND GEOMEMBRANES (2014)
Influence of Randomly Distributed Coir Fibers on Shear Strength of Clay
Treasa Maliakal et al.
GEOTECHNICAL AND GEOLOGICAL ENGINEERING (2013)
A simple review of soil reinforcement by using natural and synthetic fibers
Sayyed Mahdi Hejazi et al.
CONSTRUCTION AND BUILDING MATERIALS (2012)
Application of Artificial Intelligence to Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilized Soil
Sarat Kumar Das et al.
GEOTECHNICAL AND GEOLOGICAL ENGINEERING (2011)
Experiments on the piping behavior of geofiber-reinforced soil
A. Das et al.
GEOSYNTHETICS INTERNATIONAL (2010)
An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data
JD Olden et al.
ECOLOGICAL MODELLING (2004)
Illuminating the black box: a randomization approach for understanding variable contributions in artificial neural networks
JD Olden et al.
ECOLOGICAL MODELLING (2002)
分享论文
