Understanding fluid flow in fractured porous media is of great importance in the fields of civil ... more Understanding fluid flow in fractured porous media is of great importance in the fields of civil engineering in general or in soil science particular. This study is devoted to the development and validation of a numerical tool based on the use of the finite element method. To this aim, the problem of fluid flow in fractured porous media is considered as a problem of coupling free fluid and fluid flow in porous media or coupling of the Stokes and Darcy equations. The strong formulation of the problem is constructed, highlighting the condition at the free surface between the Stokes and Darcy regions, following by the variational formulation and numerical integration using the finite element method. Besides, the analytical solutions of the problem are constructed and compared with the numerical solutions given by the finite element approach. Both local properties and macroscopic responses of the two solutions are in excellent agreement, on condition that the porous media are sufficient...
Improvement of compressive strength prediction accuracy for concrete is crucial and is considered... more Improvement of compressive strength prediction accuracy for concrete is crucial and is considered a challenging task to reduce costly experiments and time. Particularly, the determination of compressive strength of concrete using ground granulated blast furnace slag (GGBFS) is more difficult due to the complexity of the composition mix design. In this paper, an approach using random forest (RF), which is one of the powerful machine learning algorithms, is proposed for predicting the compressive strength of concrete using GGBFS. The RF model is first evaluated to determine the best architecture, which constitutes 500 growth trees and leaf size of 1. In the next step, the evaluation of the model is conducted over 500 simulations considering the effect of random sampling. Finally, the best prediction results are given in function of statistical measures such as the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), respectively, which are 0.9729,...
Determination of shear strength of soil is very important in civil engineering for foundation des... more Determination of shear strength of soil is very important in civil engineering for foundation design, earth and rock fill dam design, highway and airfield design, stability of slopes and cuts, and in the design of coastal structures. In this study, a novel hybrid soft computing model (RF-PSO) of random forest (RF) and particle swarm optimization (PSO) was developed and used to estimate the undrained shear strength of soil based on the clay content (%), moisture content (%), specific gravity (%), void ratio (%), liquid limit (%), and plastic limit (%). In this study, the experimental results of 127 soil samples from national highway project Hai Phong-Thai Binh of Vietnam were used to generate datasets for training and validating models. Pearson correlation coefficient (R) method was used to evaluate and compare performance of the proposed model with single RF model. The results show that the proposed hybrid model (RF-PSO) achieved a high accuracy performance (R = 0.89) in the predict...
Understanding fluid flow in fractured porous media is of great importance in the fields of civil ... more Understanding fluid flow in fractured porous media is of great importance in the fields of civil engineering in general or in soil science particular. This study is devoted to the development and validation of a numerical tool based on the use of the finite element method. To this aim, the problem of fluid flow in fractured porous media is considered as a problem of coupling free fluid and fluid flow in porous media or coupling of the Stokes and Darcy equations. The strong formulation of the problem is constructed, highlighting the condition at the free surface between the Stokes and Darcy regions, following by the variational formulation and numerical integration using the finite element method. Besides, the analytical solutions of the problem are constructed and compared with the numerical solutions given by the finite element approach. Both local properties and macroscopic responses of the two solutions are in excellent agreement, on condition that the porous media are sufficient...
Improvement of compressive strength prediction accuracy for concrete is crucial and is considered... more Improvement of compressive strength prediction accuracy for concrete is crucial and is considered a challenging task to reduce costly experiments and time. Particularly, the determination of compressive strength of concrete using ground granulated blast furnace slag (GGBFS) is more difficult due to the complexity of the composition mix design. In this paper, an approach using random forest (RF), which is one of the powerful machine learning algorithms, is proposed for predicting the compressive strength of concrete using GGBFS. The RF model is first evaluated to determine the best architecture, which constitutes 500 growth trees and leaf size of 1. In the next step, the evaluation of the model is conducted over 500 simulations considering the effect of random sampling. Finally, the best prediction results are given in function of statistical measures such as the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), respectively, which are 0.9729,...
Determination of shear strength of soil is very important in civil engineering for foundation des... more Determination of shear strength of soil is very important in civil engineering for foundation design, earth and rock fill dam design, highway and airfield design, stability of slopes and cuts, and in the design of coastal structures. In this study, a novel hybrid soft computing model (RF-PSO) of random forest (RF) and particle swarm optimization (PSO) was developed and used to estimate the undrained shear strength of soil based on the clay content (%), moisture content (%), specific gravity (%), void ratio (%), liquid limit (%), and plastic limit (%). In this study, the experimental results of 127 soil samples from national highway project Hai Phong-Thai Binh of Vietnam were used to generate datasets for training and validating models. Pearson correlation coefficient (R) method was used to evaluate and compare performance of the proposed model with single RF model. The results show that the proposed hybrid model (RF-PSO) achieved a high accuracy performance (R = 0.89) in the predict...
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