The Experts below are selected from a list of 6 Experts worldwide ranked by ideXlab platform
Fernando Salazar - One of the best experts on this subject based on the ideXlab platform.
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Engaging soft computing in material and modeling uncertainty quantification of Dam engineering problems
Soft Computing, 2019Co-Authors: Mohammad Amin Hariri-ardebili, Fernando SalazarAbstract:Due to complex nature of nearly all infrastructures (and more specifically concrete Dams), the uncertainty quantification is an inseparable part of risk assessment. Uncertainties might be propagated in different aspects depending on their relative importance such as epistemic and aleatory, or spatial and temporal. The objective of this paper is to focus on the material and modeling uncertainties, and to couple them with soft computing techniques aiming to reduce the computational burden of the conventional Monte Carlo-based finite element simulations. Several scenarios are considered in which the concrete and foundation material properties, the water level, and the Dam geometry are assumed as random variables. Five soft computing techniques (i.e., random forest, boosted regression trees, multi-adaptive regression splines, artificial neural networks, and support vector machines) are employed to predict various quantities of interest based on different training sizes. It is argued that the artificial neural network is the most accurate algorithm in majority of cases, with enough accuracy as to be useful in reliability analysis as a complement to numerical models. The results with 200 samples in the training set are enough for reaching useful accuracy in most cases. For the simple prediction tasks, the results were predicted with less than 1% error. It is observed that increasing the number of input parameters increases the prediction error. The partial dependence plots provided most sensitive variables in Dam Design, which were consistent with the physics of the problem. Finally, several practical recommendations are provided for future applications.
Mohammad Amin Hariri-ardebili - One of the best experts on this subject based on the ideXlab platform.
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Engaging soft computing in material and modeling uncertainty quantification of Dam engineering problems
Soft Computing, 2019Co-Authors: Mohammad Amin Hariri-ardebili, Fernando SalazarAbstract:Due to complex nature of nearly all infrastructures (and more specifically concrete Dams), the uncertainty quantification is an inseparable part of risk assessment. Uncertainties might be propagated in different aspects depending on their relative importance such as epistemic and aleatory, or spatial and temporal. The objective of this paper is to focus on the material and modeling uncertainties, and to couple them with soft computing techniques aiming to reduce the computational burden of the conventional Monte Carlo-based finite element simulations. Several scenarios are considered in which the concrete and foundation material properties, the water level, and the Dam geometry are assumed as random variables. Five soft computing techniques (i.e., random forest, boosted regression trees, multi-adaptive regression splines, artificial neural networks, and support vector machines) are employed to predict various quantities of interest based on different training sizes. It is argued that the artificial neural network is the most accurate algorithm in majority of cases, with enough accuracy as to be useful in reliability analysis as a complement to numerical models. The results with 200 samples in the training set are enough for reaching useful accuracy in most cases. For the simple prediction tasks, the results were predicted with less than 1% error. It is observed that increasing the number of input parameters increases the prediction error. The partial dependence plots provided most sensitive variables in Dam Design, which were consistent with the physics of the problem. Finally, several practical recommendations are provided for future applications.
Prakash Indra - One of the best experts on this subject based on the ideXlab platform.
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A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil
Switzerland, 2020Co-Authors: Pham, Binh Thai, Qi Chongchong, Nguyen-thoi Trung, Al-ansari Nadhi, Nguyen, Manh Duc, Nguyen, Huu Duy, Ly Hai-bang, Hiep Van ,le, Prakash IndraAbstract:Determination of shear strength of soil is very important in civilengineering 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 softcomputing model (RF-PSO) of random forest (RF) and particle swarm optimization (PSO) wasdeveloped 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 (%). Inthis 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. Pearsoncorrelation coefficient (R) method was used to evaluate and compare performance of the proposedmodel with single RF model. The results show that the proposed hybrid model (RF-PSO) achieveda high accuracy performance (R = 0.89) in the prediction of shear strength of soil. Validation of themodels also indicated that RF-PSO model (R = 0.89 and Root Mean Square Error (RMSE) = 0.453) issuperior to the single RF model without optimization (R = 0.87 and RMSE = 0.48). Thus, theproposed hybrid model (RF-PSO) can be used for accurate estimation of shear strength which canbe used for the suitable Designing of civil engineering structures.Validerad;2020;Nivå 2;2020-03-16 (johcin)
Pham, Binh Thai - One of the best experts on this subject based on the ideXlab platform.
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A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil
Switzerland, 2020Co-Authors: Pham, Binh Thai, Qi Chongchong, Nguyen-thoi Trung, Al-ansari Nadhi, Nguyen, Manh Duc, Nguyen, Huu Duy, Ly Hai-bang, Hiep Van ,le, Prakash IndraAbstract:Determination of shear strength of soil is very important in civilengineering 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 softcomputing model (RF-PSO) of random forest (RF) and particle swarm optimization (PSO) wasdeveloped 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 (%). Inthis 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. Pearsoncorrelation coefficient (R) method was used to evaluate and compare performance of the proposedmodel with single RF model. The results show that the proposed hybrid model (RF-PSO) achieveda high accuracy performance (R = 0.89) in the prediction of shear strength of soil. Validation of themodels also indicated that RF-PSO model (R = 0.89 and Root Mean Square Error (RMSE) = 0.453) issuperior to the single RF model without optimization (R = 0.87 and RMSE = 0.48). Thus, theproposed hybrid model (RF-PSO) can be used for accurate estimation of shear strength which canbe used for the suitable Designing of civil engineering structures.Validerad;2020;Nivå 2;2020-03-16 (johcin)
Qi Chongchong - One of the best experts on this subject based on the ideXlab platform.
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A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil
Switzerland, 2020Co-Authors: Pham, Binh Thai, Qi Chongchong, Nguyen-thoi Trung, Al-ansari Nadhi, Nguyen, Manh Duc, Nguyen, Huu Duy, Ly Hai-bang, Hiep Van ,le, Prakash IndraAbstract:Determination of shear strength of soil is very important in civilengineering 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 softcomputing model (RF-PSO) of random forest (RF) and particle swarm optimization (PSO) wasdeveloped 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 (%). Inthis 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. Pearsoncorrelation coefficient (R) method was used to evaluate and compare performance of the proposedmodel with single RF model. The results show that the proposed hybrid model (RF-PSO) achieveda high accuracy performance (R = 0.89) in the prediction of shear strength of soil. Validation of themodels also indicated that RF-PSO model (R = 0.89 and Root Mean Square Error (RMSE) = 0.453) issuperior to the single RF model without optimization (R = 0.87 and RMSE = 0.48). Thus, theproposed hybrid model (RF-PSO) can be used for accurate estimation of shear strength which canbe used for the suitable Designing of civil engineering structures.Validerad;2020;Nivå 2;2020-03-16 (johcin)
