☆ 4.7 Article

Soil database development with the application of machine learning methods in soil properties prediction

ENGINEERING GEOLOGY (2022)

Related references

Note: Only part of the references are listed.

Article Engineering, Geological

Prediction of soil-water characteristic curves using two artificial intelligence (AI) models and AI aid design method for sands

Yao Li et al.

Summary: This paper develops two artificial intelligence models, artificial neural networks (ANN) and multivariate adaptive regression splines (MARS), to predict the soil-water characteristic curve (SWCC) using the cumulative percentiles from the grain-size distribution (GSD) curve as input information. The results show a strong correlation between the SWCC and GSD curves and suggest that the ANN model provides higher accuracy while the MARS model offers stable performance.

CANADIAN GEOTECHNICAL JOURNAL (2022)

Add to Collection

Article Engineering, Geological

Robust Analysis and Design of Bored Pile Considering Uncertain Parameters

Alimzhan Oteuil et al.

Summary: This article introduces the study of developing a framework for the analysis and design of axial and lateral capacity of bored piles. The research found that a robust design can be achieved with just a few soil parameters.

INDIAN GEOTECHNICAL JOURNAL (2022)

Add to Collection

Article Engineering, Geological

Mapping shear strength and compressibility of soft soils with artificial neural networks

Peiyuan Lin et al.

Summary: This study develops a simple, efficient, and accurate tool for prompt assessments of shear strength and compressibility parameters of soft soils in the Guangdong-Hong Kong-Macao Greater Bay Area of China. By employing the artificial neural network (ANN) technique, the study maps the shear strength and compressibility properties of soft soils based on their physical parameters and evaluates the accuracy of the ANN models. The study helps save time and cost in geotechnical investigation for soft soils in the area.

ENGINEERING GEOLOGY (2022)

Add to Collection

Article Geosciences, Multidisciplinary

Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization

Wengang Zhang et al.

Summary: This study applies XGBoost and RF methods to predict the USS of soft clays, showing that these approaches outperform traditional methods. Bayesian optimization is used to determine model hyperparameters, leading to more accurate and robust models.

GEOSCIENCE FRONTIERS (2021)

Add to Collection

Article Geosciences, Multidisciplinary

Spatial variation of shear strength properties incorporating auxiliary variables

Sabrina C. Y. Ip et al.

Summary: Soil shear strength is a critical parameter in slope stability and its spatial variation is complex. The study shows that Random Forest model has higher accuracy and spatial heterogeneity in predicting soil shear strength, and it is more sensitive to sample size.

CATENA (2021)

Add to Collection

Article Chemistry, Multidisciplinary

Comparative Analysis of Kazakhstani and European Design Specifications: Raft Foundation, Pile Foundation, and Piled Raft Foundation

Assel Zhanabayeva et al.

Summary: The study compared Kazakhstani and European approaches in geotechnical design of foundations, finding Eurocode to be more conservative than Kazakhstani building regulations. Sensitivity analysis of foundation parameters supported the conclusions of the study.

APPLIED SCIENCES-BASEL (2021)

Add to Collection

Article Engineering, Geological

Estimation of effective cohesion using artificial neural networks based on index soil properties: A Singapore case

Yongmin Kim et al.

Summary: This study developed a multi-layer perceptron (MLP) model and used ordinary kriging analysis to analyze the variability of effective cohesion for residual soils in Singapore. By utilizing four index properties as training parameters, the effective cohesion of residual soils from different geological formations can be accurately estimated, and the variations can be clearly differentiated through spatial analysis.

ENGINEERING GEOLOGY (2021)

Add to Collection

Review Engineering, Geological

35 Years of (AI) in Geotechnical Engineering: State of the Art

Ahmed M. Ebid

Summary: The application of artificial intelligence techniques in geotechnical engineering has seen significant development over the past 35 years. Research in this field is increasing exponentially, with artificial neural networks being the most commonly used technique. Correlating soil and rock properties is the most addressed subject of study.

GEOTECHNICAL AND GEOLOGICAL ENGINEERING (2021)

Add to Collection

Article Engineering, Geological

Estimation of the soil-water characteristic curve from the grain size distribution of coarse-grained soils

Qian Zhai et al.

ENGINEERING GEOLOGY (2020)

Add to Collection

Article Engineering, Geological

Straightforward prediction for air-entry value of compacted soils using machine learning algorithms

Han-Lin Wang et al.

ENGINEERING GEOLOGY (2020)

Add to Collection

Article Computer Science, Information Systems

Application of Ordinary Kriging and Regression Kriging Method for Soil Properties Mapping in Hilly Region of Central Vietnam

Tung Gia Pham et al.

ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2019)

Add to Collection

Article Plant Sciences

Effect of weather conditions on leans of one Eugenia Grandis tree in Singapore

Yangyang Li et al.

URBAN FORESTRY & URBAN GREENING (2019)

Add to Collection

Article Water Resources

Random forest, M5P and regression analysis to estimate the field unsaturated hydraulic conductivity

Parveen Sihag et al.

APPLIED WATER SCIENCE (2019)

Add to Collection

Article Engineering, Geological

Role of unsaturated soil mechanics in geotechnical engineering

Harianto Rahardjo et al.

INTERNATIONAL JOURNAL OF GEO-ENGINEERING (2019)

Add to Collection

Article Geosciences, Multidisciplinary

Prediction of shear strength of soft soil using machine learning methods

Binh Thai Pham et al.

CATENA (2018)

Add to Collection

Article Engineering, Geological

Estimation of unimodal water characteristic curve for gap-graded soil

Alfrendo Satyanaga et al.

SOILS AND FOUNDATIONS (2017)

Add to Collection

Article Engineering, Geological

Variability in unsaturated hydraulic properties of residual soil in Singapore

Qian Zhai et al.

ENGINEERING GEOLOGY (2016)

Add to Collection

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Add to Collection

Article Engineering, Geological

Modeling the unsaturated soil zone in slope stability analysis

L. L. Zhang et al.

CANADIAN GEOTECHNICAL JOURNAL (2014)

Add to Collection

Article Geosciences, Multidisciplinary

Artificial Neural Network (ANN) and Regression Tree (CART) applications for the indirect estimation of unsaturated soil shear strength parameters

D. P. Kanungo et al.

FRONTIERS OF EARTH SCIENCE (2014)

Add to Collection

Article Engineering, Geological

Variability of residual soil properties

Harianto Rahardjo et al.

ENGINEERING GEOLOGY (2012)

Add to Collection

Article Engineering, Geological

Influence of spatial variability of permeability property on steady state seepage flow and slope stability analysis

Amit Srivastava et al.

ENGINEERING GEOLOGY (2010)

Add to Collection

Article Agriculture, Multidisciplinary

Testing the performance of spatial interpolation techniques for mapping soil properties

TP Robinson et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2006)

Add to Collection

Article Engineering, Geological

Characteristics of residual soils in Singapore as formed by weathering

H Rahardjo et al.

ENGINEERING GEOLOGY (2004)

Add to Collection

© Peeref 2019-2024. All rights reserved.