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

Jalal, Fazal E.; Xu, Yongfu; Iqbal, Mudassir; Javed, Muhammad Faisal; Jamhiri, Babak

doi:10.1016/j.jenvman.2021.112420

Cited by 153 publications

(94 citation statements)

References 91 publications

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“…The process of the ANN model can be seen in Figure 3 . The activation function used in this study was adopted from previously published articles [ 48 ].…”

Section: Methodsmentioning

confidence: 99%

Analyzing the Compressive Strength of Ceramic Waste-Based Concrete Using Experiment and Artificial Neural Network (ANN) Approach

et al. 2021

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In a fast-growing population of the world and regarding meeting consumer’s requirements, solid waste landfills will continue receiving a substantial amount of waste. The utilization of solid waste materials in concrete has gained the attention of the researchers. Ceramic waste powder (CWP) is considered to be one of the most harmful wastes for the environment, which may cause water, soil, and air pollution. The aim of this study was comprised of two phases. Phase one was based on the characterization of CWP with respect to its composition, material testing (coarse aggregate, fine aggregate, cement,) and evaluation of concrete properties both in fresh and hardened states (slump, 28 days compressive strength, and dry density). Concrete mixes were prepared in order to evaluate the compressive strength (CS) of the control mix, with partial replacement of the cement with CWP of 10 and 20% by mass of cement and 60 prepared mixes. However, phase two was based on the application of the artificial neural network (ANN) and decision tree (DT) approaches, which were used to predict the CS of concrete. The linear coefficient correlation (R2) value from the ANN model indicates better performance of the model. Moreover, the statistical check and k-fold cross validation methods were also applied for the performance confirmation of the model. The mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were evaluated to confirm the model’s precision.

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“…The process of the ANN model can be seen in Figure 3 . The activation function used in this study was adopted from previously published articles [ 48 ].…”

Section: Methodsmentioning

confidence: 99%

Analyzing the Compressive Strength of Ceramic Waste-Based Concrete Using Experiment and Artificial Neural Network (ANN) Approach

et al. 2021

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“…The predictions were considered successful if the ratio of predication to observation fell within 0.5 and 2.0 [58,59]. Both relative squared error (RSE) (Equation ( 18)) and the geometric mean-fold error (GMFE) (Equation ( 19)) were further introduced to describe the difference between predictions and observations [60,61].…”

Section: Model Validationmentioning

confidence: 99%

A Whole-Body Physiologically Based Pharmacokinetic Model Characterizing Interplay of OCTs and MATEs in Intestine, Liver and Kidney to Predict Drug-Drug Interactions of Metformin with Perpetrators

Yang

Zhang

et al. 2021

Pharmaceutics

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Transmembrane transport of metformin is highly controlled by transporters including organic cation transporters (OCTs), plasma membrane monoamine transporter (PMAT), and multidrug/toxin extrusions (MATEs). Hepatic OCT1, intestinal OCT3, renal OCT2 on tubule basolateral membrane, and MATE1/2-K on tubule apical membrane coordinately work to control metformin disposition. Drug–drug interactions (DDIs) of metformin occur when co-administrated with perpetrators via inhibiting OCTs or MATEs. We aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model characterizing interplay of OCTs and MATEs in the intestine, liver, and kidney to predict metformin DDIs with cimetidine, pyrimethamine, trimethoprim, ondansetron, rabeprazole, and verapamil. Simulations showed that co-administration of perpetrators increased plasma exposures to metformin, which were consistent with clinic observations. Sensitivity analysis demonstrated that contributions of the tested factors to metformin DDI with cimetidine are gastrointestinal transit rate > inhibition of renal OCT2 ≈ inhibition of renal MATEs > inhibition of intestinal OCT3 > intestinal pH > inhibition of hepatic OCT1. Individual contributions of transporters to metformin disposition are renal OCT2 ≈ renal MATEs > intestinal OCT3 > hepatic OCT1 > intestinal PMAT. In conclusion, DDIs of metformin with perpetrators are attributed to integrated effects of inhibitions of these transporters.

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“…For the sake of clarity and simplicity, we can suppose that the fuzzy system in discussion contains two input variables (x 1 and x 2 ) and one outcome (z). A standard set of rules contains two IF-THEN rules for the interpretation of a first-order Takagi-Sugeno fuzzy model [74]. Equations ( 3) and ( 4) represent the two IF-THEN rules stated below.…”

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confidence: 99%

“…Symmetry 2021, 13, 2009 8 of 24 interpretation of a first-order Takagi-Sugeno fuzzy model [74]. Equations ( 3) and ( 4) represent the two IF-THEN rules stated below.…”

mentioning

confidence: 99%

“…After uploading the data arrays, the number of hyper-parameters provided in Table 5 were selected next to several initial trials. The Takagi-Sugeno fuzzy interface structure with triangular membership (trimf) in combination with a hybrid optimization procedure (backpropagation and least-squares) gives the best performing model [74]. The ANFIS provides the leading performance in comparison with ANN because of its integrated algorithmic architecture of ANN and fuzzy logic rule set.…”

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confidence: 99%

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Modeling Compressive Strength of Eco-Friendly Volcanic Ash Mortar Using Artificial Neural Networking

et al. 2021

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Forecasting the compressive strength of concrete is a complex task owing to the interactions among concrete ingredients. In addition, an important characteristic of the concrete failure surface is its six-fold symmetry. In this study, an artificial neural network (ANN) and adaptive neuro fuzzy interface system (ANFIS) were employed to model the compressive strength of natural volcanic ash mortar (VAM) by using the six-fold symmetry of concrete failure. The modeling was correlated with four parameters. To train and test the projected models, data for more than 150 samples were collected from the literature. Furthermore, mortar samples with varying proportions of volcanic ash were prepared in the laboratory and tested, and the results were used to validate the models. The performance of the developed models was assessed using numerous statistical measures. The results show that both the ANN and ANFIS models accurately predict the compressive strength of VAM with R-square above 0.9 and lower error statistics. The permutation feature analysis confirmed that the age of specimens affects the strength of VAM the most, followed by the water-to-cement ratio, curing temperature, and percentage of volcanic ash.

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

Cited by 153 publications

References 91 publications

Analyzing the Compressive Strength of Ceramic Waste-Based Concrete Using Experiment and Artificial Neural Network (ANN) Approach

Analyzing the Compressive Strength of Ceramic Waste-Based Concrete Using Experiment and Artificial Neural Network (ANN) Approach

A Whole-Body Physiologically Based Pharmacokinetic Model Characterizing Interplay of OCTs and MATEs in Intestine, Liver and Kidney to Predict Drug-Drug Interactions of Metformin with Perpetrators

Modeling Compressive Strength of Eco-Friendly Volcanic Ash Mortar Using Artificial Neural Networking

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