4.6 Article

Predicting the settlement of geosynthetic-reinforced soil foundations using evolutionary artificial intelligence technique

Related references

Note: Only part of the references are listed.
Article Engineering, Civil

Effect of aspect ratio of footings on settlement response of geosynthetic-reinforced granular fill-soft soil system

Subinay Saha Roy et al.

Summary: A mathematical model is used to study the effect of aspect ratio of footings on settlement response of unreinforced or geosynthetic-reinforced granular fill-soft soil system. The study shows that aspect ratio has a significant effect on settlement response and mobilised tension in the geosynthetic layer. The effectiveness of the reinforcement decreases with an increase in aspect ratio. Settlement decreases with an increase in ultimate bearing capacity of soft soil or shear modulus of granular fill, but the rate of reduction of settlement decreases as the aspect ratio increases.

EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING (2022)

Article Engineering, Geological

An extreme learning machine model for geosynthetic-reinforced sandy soil foundations

Muhammad Nouman Amjad Raja et al.

Summary: The study used an ELM model to predict the UBC of GRSSF, showing good accuracy in comparison to other data-driven models and traditional methods. The ELM technique is a reliable approach for predicting multivariate non-linear problems in geotechnical engineering.

PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-GEOTECHNICAL ENGINEERING (2022)

Article Construction & Building Technology

An intelligent approach for predicting the strength of geosynthetic-reinforced subgrade soil

Muhammad Nouman Amjad Raja et al.

Summary: This paper explores and evaluates the competency of several intelligent models in estimating the CBR of reinforced soil, with artificial neural network (ANN) being identified as the best model. The study assesses the predictive accuracy of the models using various evaluation methods and sensitivity analysis, providing valuable insights for future research.

INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING (2022)

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)

Article Engineering, Geological

Physical and numerical modelling of strip footing on geogrid reinforced transparent sand

Jianfeng Chen et al.

Summary: This study investigated the mechanical response and geogrid fracture behavior of reinforced soil foundation through experiments and numerical simulations. It was found that increasing the number of geogrid layers significantly improves the bearing capacity and stiffness of the soil foundation.

GEOTEXTILES AND GEOMEMBRANES (2021)

Article Engineering, Geological

Experimental study on repeatedly loaded foundation soil strengthened by wraparound geosynthetic reinforcement technique

Muhammad Nouman Amjad Raja et al.

Summary: The study found that the wraparound reinforced model resulted in less settlement compared to the planar reinforced model, with an increase in efficiency as load amplitude increased and a decrease in the rate of total cumulative settlement with an increase in number of load cycles. The wraparound reinforced model showed about 45% lower average total settlement compared to the unreinforced model.

JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING (2021)

Article Engineering, Geological

Development of genetic-based models for predicting the resilient modulus of cohesive pavement subgrade soils

Behnam Ghorbani et al.

SOILS AND FOUNDATIONS (2020)

Article Engineering, Geological

Ultimate bearing capacity of strip footing resting on soil bed strengthened by wraparound geosynthetic reinforcement technique

Muhammad Nouman Amjad Raja et al.

GEOTEXTILES AND GEOMEMBRANES (2020)

Article Construction & Building Technology

Evaluating and Predicting the Stability of Roadways in Tunnelling and Underground Space Using Artificial Neural Network-Based Particle Swarm Optimization

Xiliang Zhang et al.

TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY (2020)

Article Geosciences, Multidisciplinary

Soft-computing techniques for prediction of soils consolidation coefficient

Manh Duc Nguyen et al.

CATENA (2020)

Article Engineering, Geological

Experimental and theoretical studies on the ultimate bearing capacity of geogrid-reinforced sand

Chao Xu et al.

GEOTEXTILES AND GEOMEMBRANES (2019)

Article Engineering, Geological

Nonlinear Equation for Predicting the Settlement of Reinforced Soil Foundations

Mahsa Khosrojerdi et al.

JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING (2019)

Article Engineering, Geological

Evaluation of equivalent hydraulic aperture (EHA) for rough rock fractures

Fei Xiao et al.

CANADIAN GEOTECHNICAL JOURNAL (2019)

Article Engineering, Geological

Laboratory and numerical modeling of strip footing on geotextile reinforced sand with cement-treated interface

Ahad Ouria et al.

GEOTEXTILES AND GEOMEMBRANES (2018)

Article Engineering, Geological

Load-settlement response of shallow square footings on geogrid-reinforced sand under cyclic loading

Jia-Quan Wang et al.

GEOTEXTILES AND GEOMEMBRANES (2018)

Article Engineering, Geological

Applicability of a CPT-Based Neural Network Solution in Predicting Load-Settlement Responses of Bored Pile

Hossein Moayedi et al.

INTERNATIONAL JOURNAL OF GEOMECHANICS (2018)

Article Engineering, Geological

Estimating the bearing capacity of single reinforced granular fill overlying clay

Gizem Misir et al.

GEOTEXTILES AND GEOMEMBRANES (2018)

Article Engineering, Geological

Laboratory and numerical investigation of machine foundations reinforced with geogrids and geocells

Hasthi Venkateswarlu et al.

GEOTEXTILES AND GEOMEMBRANES (2018)

Article Computer Science, Artificial Intelligence

Load frequency control of interconnected power system using grey wolf optimization

Dipayan Guha et al.

SWARM AND EVOLUTIONARY COMPUTATION (2016)

Article Computer Science, Artificial Intelligence

How effective is the Grey Wolf optimizer in training multi-layer perceptrons

Seyedali Mirjalili

APPLIED INTELLIGENCE (2015)

Article Engineering, Geological

Ultimate bearing capacity analysis of strip footings on reinforced soil foundation

Qiming Chen et al.

SOILS AND FOUNDATIONS (2015)

Article Computer Science, Interdisciplinary Applications

Grey Wolf Optimizer

Seyedali Mirjalili et al.

ADVANCES IN ENGINEERING SOFTWARE (2014)

Review Computer Science, Interdisciplinary Applications

A Review of Feature Reduction Techniques in Neuroimaging

Benson Mwangi et al.

NEUROINFORMATICS (2014)

Article Construction & Building Technology

An evolutionary approach for modeling of shear strength of RC deep beams

Amir Hossein Gandomi et al.

MATERIALS AND STRUCTURES (2013)

Article Engineering, Geological

An experimental evaluation of the behavior of footings on geosynthetic-reinforced sand

Murad Abu-Farsakh et al.

SOILS AND FOUNDATIONS (2013)

Article Automation & Control Systems

Modelling load-settlement behaviour of piles using high-order neural network (HON-PILE model)

A. Ismail et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2011)

Article Engineering, Geological

Settlement of footings on reinforced level sandy ground at peak footing loads

C. -C. Huang

GEOSYNTHETICS INTERNATIONAL (2011)

Article Computer Science, Interdisciplinary Applications

How to avoid a perfunctory sensitivity analysis

Andrea Saltelli et al.

ENVIRONMENTAL MODELLING & SOFTWARE (2010)

Article Engineering, Geological

Comparison of bearing capacity of a strip footing on sand with geocell and with planar forms of geotextile reinforcement

S. N. Moghaddas Tafreshi et al.

GEOTEXTILES AND GEOMEMBRANES (2010)

Article Computer Science, Interdisciplinary Applications

Support vector machine applied to settlement of shallow foundations on cohesionless soils

Pijush Samui

COMPUTERS AND GEOTECHNICS (2008)

Article Engineering, Geological

A new genetic programming model for predicting settlement of shallow foundations

Mohammad Rezania et al.

CANADIAN GEOTECHNICAL JOURNAL (2007)

Article Engineering, Geological

Effects of reinforcement form on the behavior of geosynthetic reinforced sand

G. Madhavi Latha et al.

GEOTEXTILES AND GEOMEMBRANES (2007)

Article Engineering, Geological

Prediction of settlement of shallow foundations on reinforced soils using neural networks

A. Soleimanbeigi et al.

GEOSYNTHETICS INTERNATIONAL (2006)

Article Engineering, Geological

Predicting settlement of shallow foundations using neural networks

MA Shahin et al.

JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING (2002)