☆ 4.6 Article

Prediction of pullout interaction coefficient of geogrids by extreme gradient boosting model

GEOTEXTILES AND GEOMEMBRANES (2022)

Related references

Note: Only part of the references are listed.

Article Engineering, Geological

Analytical solution for calculation of pull out force-deformation of geosynthetics reinforcing unsaturated soils

Mahmoud Ghazavi et al.

Summary: This paper presents an analytical solution for determining the pullout force-deformation variation of planar geosynthetics reinforcing unsaturated cohesive-frictional soils. The solution is validated with experimental results and can be used to study the characteristics of geosynthetic-reinforced soils. The results show that decreasing soil suction and increasing moisture content lead to a decrease in peak pullout force, while increasing matric suction decreases the relative soil-reinforcement displacement.

GEOTEXTILES AND GEOMEMBRANES (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 Engineering, Geological

Multivariate adaptive regression splines model for reinforced soil foundations

M. N. A. Raja et al.

Summary: This study developed a MARS model to predict settlement of shallow reinforced sandy soil foundations, and compared its performance with four other machine learning regression models. The model was validated using data from previous scientific studies and demonstrated superior accuracy in predicting settlement of reinforced foundations.

GEOSYNTHETICS INTERNATIONAL (2021)

Add to Collection

Article Engineering, Civil

Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements

De-Cheng Feng et al.

Summary: This paper introduces a practical and comprehensive implementation of ensemble methods for predicting the shear strength of deep beams with or without web reinforcements. The study utilizes four typical ensemble machine learning models and demonstrates their superior performance over traditional machine learning methods in predicting accuracy and discrepancy.

ENGINEERING STRUCTURES (2021)

Add to Collection

Article Computer Science, Hardware & Architecture

Prediction of coefficient of consolidation in soil using machine learning techniques

Mamta Mittal et al.

Summary: This study proposes an efficient methodology to predict the coefficient of consolidation in soil using machine learning models and feature selection techniques. Experimental results demonstrate that the hybrid approach of ANFIS with LASSO feature selection method provides the best prediction for the coefficient of consolidation in soil.

MICROPROCESSORS AND MICROSYSTEMS (2021)

Add to Collection

Article Environmental Sciences

Predictive modeling of swell-strength of expansive soils using artificial intelligence approaches: ANN, ANFIS and GEP

Fazal E. Jalal et al.

Summary: This study developed new empirical prediction models for evaluating swell pressure and unconfined compression strength of expansive soils using three soft computing methods. The results indicated that the GEP and ANN models showed the best performance in estimating PsUCS-ES. The findings of the study can help researchers, designers, and practitioners evaluate the characteristics of expansive soils and reduce environmental vulnerabilities, promoting sustainable construction practices.

JOURNAL OF ENVIRONMENTAL MANAGEMENT (2021)

Add to Collection

Article Construction & Building Technology

Prediction of Compressive Strength of Concrete with Manufactured Sand by Ensemble Classification and Regression Tree Method

Qiang Ren et al.

Summary: A method based on ensemble classification and regression tree was proposed to predict the compressive strength of manufactured sand concrete, showing superiority in accuracy and robustness. Age was identified as the most significant factor influencing the compressive strength, with stone powder content contributing approximately 25% to the age.

JOURNAL OF MATERIALS IN CIVIL ENGINEERING (2021)

Add to Collection

Article Engineering, Geological

Predicting Resilient Modulus of Cementitiously Stabilized Subgrade Soils Using Neural Network, Support Vector Machine, and Gaussian Process Regression

Xi Hu et al.

Summary: In this study, three machine learning models were developed to predict the resilient modulus of cementitously stabilized subgrade soils. The MLPN model with one hidden layer was determined as the best performing model among the three types of models evaluated. The developed models were compared based on mean squared error values and determination coefficient values.

INTERNATIONAL JOURNAL OF GEOMECHANICS (2021)

Add to Collection

Article Engineering, Civil

Experimental and computational response of strip footing resting on prestressed geotextile-reinforced industrial waste

Sufyan Ghani et al.

Summary: The study investigated the strength and deformation characteristics of ferrochrome slag reinforced with single prestressed geotextile layer through model load tests. It proposed sustainable solutions for industrial waste and found that prestressing of reinforcements is more effective than simple reinforcement in reducing settlement. Artificial neural network and ELM were suggested for settlement prediction, with computational models proving efficient and effective for preliminary assessments.

INNOVATIVE INFRASTRUCTURE SOLUTIONS (2021)

Add to Collection

Article Engineering, Chemical

Stick-slip behavior of dry fly ash

Aali Pant et al.

PARTICULATE SCIENCE AND TECHNOLOGY (2020)

Add to Collection

Article Engineering, Geological

Bottom ash as a backfill material in reinforced soil structures

Aali Pant et al.

GEOTEXTILES AND GEOMEMBRANES (2019)

Add to Collection

Article Engineering, Geological

Pullout response of strengthened geosynthetic interacting with fine sand

A. Mirzaalimohammadi et al.

GEOTEXTILES AND GEOMEMBRANES (2019)

Add to Collection

Article Green & Sustainable Science & Technology

Coal combustion residue as structural fill material for reinforced soil structures

Aali Pant et al.

JOURNAL OF CLEANER PRODUCTION (2019)

Add to Collection

Article Engineering, Geological

Application of Machine Learning Techniques for Predicting the Dynamic Response of Geogrid Reinforced Foundation Beds

Shivpreet Sharma et al.

GEOTECHNICAL AND GEOLOGICAL ENGINEERING (2019)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Soil-Compressibility Prediction Models Using Machine Learning

Scott P. E. Kirts et al.

JOURNAL OF COMPUTING IN CIVIL ENGINEERING (2018)

Add to Collection

Article Engineering, Geological

Prediction of Bearing Capacity of Geogrid-Reinforced Stone Columns Using Support Vector Regression

P. Debnath et al.

INTERNATIONAL JOURNAL OF GEOMECHANICS (2018)

Add to Collection

Article Engineering, Geological

Modelling interference between the geogrid bearing members under pullout loading conditions

G. Cardile et al.

GEOTEXTILES AND GEOMEMBRANES (2017)

Add to Collection

Article Engineering, Geological

Experimental and PIV evaluation of grain size and distribution on soil-geogrid interactions in pullout test

M. R. Abdi et al.

SOILS AND FOUNDATIONS (2017)

Add to Collection

Article Engineering, Geological

Imperial smelting furnace (zinc) slag as a structural fill in reinforced soil structures

P. S. Prasad et al.

GEOTEXTILES AND GEOMEMBRANES (2016)

Add to Collection

Article Green & Sustainable Science & Technology

Recycled Construction and Demolition Wastes as filling material for geosynthetic reinforced structures. Interface properties

Castorina Silva Vieira et al.

JOURNAL OF CLEANER PRODUCTION (2016)

Add to Collection

Proceedings Paper Engineering, Multidisciplinary

Effects of Fines Contents on Engineering Properties of Sand-Fines Mixtures

Vu To-Anh Phan et al.

PROCEEDING OF SUSTAINABLE DEVELOPMENT OF CIVIL, URBAN AND TRANSPORTATION ENGINEERING (2016)

Add to Collection

Article Engineering, Geological

A study of geogrid-reinforced ballast using laboratory pull-out tests and discrete element modelling

Cheng Chen et al.

GEOMECHANICS AND GEOENGINEERING-AN INTERNATIONAL JOURNAL (2013)

Add to Collection

Article Engineering, Geological

Pullout tests conducted on clay reinforced with geogrid encapsulated in thin layers of sand

M. R. Abdi et al.

GEOTEXTILES AND GEOMEMBRANES (2011)

Add to Collection

Article Construction & Building Technology

Experimental study of the effect of fines content on dynamic compaction grouting in completely decomposed granite of Hong Kong

Shanyong Wang et al.

CONSTRUCTION AND BUILDING MATERIALS (2009)

Add to Collection

Article Engineering, Geological

Pullout resistance of individual longitudinal and transverse geogrid ribs

Sidnei H. C. Teixeira et al.

JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING (2007)

Add to Collection

Article Engineering, Geological

Factors affecting the pullout behaviour of extruded geogrids embedded in a compacted granular soil

Nicola Moraci et al.

GEOTEXTILES AND GEOMEMBRANES (2006)

Add to Collection

Article Engineering, Geological

Influence of longitudinal and transverse members on geogrid pullout behavior during deformation

AMN Alagiyawanna et al.

GEOTEXTILES AND GEOMEMBRANES (2001)

Add to Collection

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)

Add to Collection

Article Engineering, Geological

Interaction of foundry sands with geosynthetics

MJ Goodhue et al.

JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING (2001)

Add to Collection

© Peeref 2019-2024. All rights reserved.