Uncertainty analysis of intelligent model of hybrid genetic algorithm and particle swarm optimization with ANFIS to predict threshold bank profile shape based on digital laser approach sensing

Gholami, Azadeh; Bonakdari, Hossein; Ebtehaj, Isa; Mohammadian, Abdolmajid; Gharabaghi, Bahram; Khodashenas, Saeed Reza

doi:10.1016/j.measurement.2018.02.070

Cited by 62 publications

(13 citation statements)

References 68 publications

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“…is is clearly an indication to the performance of the Particle Swarm Optimization algorithm for tuning the internal parameters of the ANFIS model and particularly for simulating the investigated geotechnical problem "i.e., shallow foundation settlement." It is worth to highlight that the ability of the PSO algorithm was approved for optimizing ANFIS model over multiple engineering applications such as channel sediment transport, basin bank shape optimization, compressive strength of intact roach, friction capacity ration of driven piles, oil flocculated asphaltene weight percentage, and several others [73][74][75][76][77].…”

Section: Application Analysis and Discussionmentioning

confidence: 99%

Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro‐Fuzzy Inference System Model

Mohammed

Sharafati

Al‐Ansari

et al. 2020

Advances in Civil Engineering

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Settlement simulating in cohesion materials is a crucial issue due to complexity of cohesion soil texture. This research emphasis on the implementation of newly developed machine learning models called hybridized Adaptive Neuro-Fuzzy Inference System (ANFIS) with Particle Swarm Optimization (PSO) algorithm, Ant Colony optimizer (ACO), Differential Evolution (DE), and Genetic Algorithm (GA) as efficient approaches to predict settlement of shallow foundation over cohesion soil properties. The width of footing (B), pressure of footing (qa), geometry of footing (L/B), count of SPT blow (N), and ratio of footing embedment (Df/B) are considered as predictive variables. Nonhomogeneity and inconsistency of employed dataset is a major concern during prediction modeling. Hence, two different modeling scenarios (i) preprocessed dataset (PP) and (ii) nonprocessed (initial) dataset (NP) were inspected. To assess the accuracy of the applied hybrid models and standalone one, multiple statistical metrics were computed and analyzed over the training and testing phases. Results indicated ANFIS-PSO model exhibited an accurate and reliable prediction data intelligent and had the highest predictability performance against all employed models. In addition, results demonstrated that data preprocessing is highly essential to be performed prior to building the predictive models. Overall, ANFIS-PSO model showed a robust machine learning for settlement prediction.

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Section: Application Analysis and Discussionmentioning

confidence: 99%

Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro‐Fuzzy Inference System Model

Mohammed

Sharafati

Al‐Ansari

et al. 2020

Advances in Civil Engineering

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“…Recently, ANN techniques and GA method has been successfully applied to many industries (Ardabili et al, 2018;Ebtehaj & Bonakdari, 2016;Fotovatikhah et al, 2018;Gholami et al, 2018;Moazenzadeh, Mohammadi, Shamshirband, & Chau, 2018;Najafi, Faizollahzadeh Ardabili, Shamshirband, Chau, & Rabczuk, 2018;Taherei Ghazvinei et al, 2018). Also, cyclone optimization studies have been performed by combing Design of Experiment (DOE) with various modeling methods such as Response Surface Methodology (RSM) and Artificial Neural Network (ANN) methods.…”

Section: Research Backgroundmentioning

confidence: 99%

“…Furthermore, in order to quantitatively estimate and compare the uncertainties of the cyclone models, uncertainty analysis (Gholami et al, 2018) was performed with the 25 CFD test set. To estimate the uncertainties, prediction error (PE), mean prediction error (MPE), standard deviation of prediction error (SDPE) and width of uncertainty band (WUB) and prediction error interval of 95% (PEI) are introduced.…”

Section: Comparison Of Prediction Performance Among Rsm Gmdh and Bpnmentioning

confidence: 99%

Multi-objective optimization and comparison of surrogate models for separation performances of cyclone separator based on CFD, RSM, GMDH-neural network, back propagation-ANN and genetic algorithm

Park

Cha

Kim

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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Pressure drop (p) and collection efficiency (η) are used to evaluate the separation performance of the cyclone separator. In this study, we conducted comparative study of cyclone models using response surface methodology (RSM), back propagation neural network (BPNN), and group method of data handling (GMDH) networks to develop optimal predictive cyclone models. Also, we conducted multi-objective optimization for maximizing model and minimizing model using genetic algorithm (GA). CFD was performed instead of experimental method to get the estimated values for modeling of p and η. The validation results of CFD showed 0.5% and 2% errors for p and η, respectively, compared with the experimental data. Second, design of experiment (DOE) analysis for 10 cyclone geometrical parameters was executed to obtain the significant geometrical parameters. Vortex finder diameter D x , inlet width a, inlet height b and cone height H co have a significant effect on η and p. However, interaction effects between the geometrical parameters have small effects. The cyclone models by RSM, BPNN and GMDH based on 25 CFD training set were developed. The predictive performance results by the cyclone models were compared by 25 CFD test set. The GMDH method achieved the best prediction for p (R 2 = 99.7, RMSE = 0.102) R 2 adjusted = 98.99, RMSE = 0.0119) than the RSM, BPNN cyclone models. Additionally, uncertainty analysis was performed to estimate the quantitative performance of cyclone models. The results show that the uncertainty width of GMDH models achieved the best prediction (η: ±0.0065, p: ±0.0188). Finally, GA was applied to optimize the GMDH models simultaneously. GA generated 70 non-dominant solutions. Reproducibility of five optimal points was validated by using CFD. The trade-off optimal point showed improvement by 24.31%, 18% and 8.79% for p d 50 and η, respectively.

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“…Over seven million people have lost their lives in floods between 1990 and 2017 in Asia alone [1,2]. Predicting flood water levels, the design of stable alluvial channels, and protecting lives and properties during major flood events is one of the greatest challenges of the 21st century [3,4]. Open channel levees are used extensively in hydraulic and environmental engineering applications.…”

Section: Introductionmentioning

confidence: 99%

Hydraulic Modeling and Evaluation Equations for the Incipient Motion of Sandbags for Levee Breach Closure Operations

Sattar

Bonakdari

Gharabaghi

et al. 2019

Water

Self Cite

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Open channel levees are used extensively in hydraulic and environmental engineering applications to protect the surrounding area from inundation. However, levees may fail to produce an unsteady flow that is inherently three dimensional. Such a failure may lead to a destructive change in morphology of the river channel and valley. To avoid such a situation arising, hydraulic laboratory modeling was performed on an open channel levee breach model capturing velocity, in x, y and z plans, at selected locations in the breach. Sandbags of various shapes and sizes are tested for incipient motion by the breach flow. We found that a prism sandbag has a better hydrodynamic characteristic and more stability than spherical bags with the same weight. Experimental results are then used to evaluate existing empirical equations and to develop more accurate equations for predicting critical flow velocity at the initial stage of sandbag motion. Results showed the superior predictions a few of the equations could be considered with an uncertainty range of ±10%. These equations explained the initial failed attempts of the United States Army Corps of Engineers (USACE) for breach closure of the case study, and confirmed the experimental results are simulating the case study of breach closure.

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Uncertainty analysis of intelligent model of hybrid genetic algorithm and particle swarm optimization with ANFIS to predict threshold bank profile shape based on digital laser approach sensing

Cited by 62 publications

References 68 publications

Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro‐Fuzzy Inference System Model

Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro‐Fuzzy Inference System Model

Multi-objective optimization and comparison of surrogate models for separation performances of cyclone separator based on CFD, RSM, GMDH-neural network, back propagation-ANN and genetic algorithm

Hydraulic Modeling and Evaluation Equations for the Incipient Motion of Sandbags for Levee Breach Closure Operations

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