Explicit data-driven models for prediction of pressure fluctuations occur during turbulent flows on sloping channels

Samadi, Mehrshad; Sarkardeh, Hamed; Jabbari, Ebrahim

doi:10.1007/s00477-020-01794-0

Cited by 33 publications

(11 citation statements)

References 65 publications

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“…On the other hand, OBJ creation is used to establish how well a data-driven model works as a whole by combining the R 2 and MAE values obtained in both training and testing phases. The following expression is used to determine OBJ creation value [ 119 ].

where

, and

are the number of samples, determination coefficient, and MAE values, respectively, for the training dataset, and

and

are also indicate the same parameters, bur for the testing dataset.…”

Section: Resultsmentioning

confidence: 99%

Modelling Compression Strength of Waste PET and SCM Blended Cementitious Grout Using Hybrid of LSSVM Models

et al. 2022

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Nowadays, concretes blended with pozzolanic additives such as fly ash (FA), silica fume (SF), slag, etc., are often used in construction practices. The utilization of pozzolanic additives and industrial by-products in concrete and grouting materials has an important role in reducing the Portland cement usage, the CO2 emissions, and disposal issues. Thus, the goal of the present work is to estimate the compressive strength (CS) of polyethylene terephthalate (PET) and two supplementary cementitious materials (SCMs), namely FA and SF, blended cementitious grouts to produce green mix. For this purpose, five hybrid least-square support vector machine (LSSVM) models were constructed using swarm intelligence algorithms, including particle swarm optimization, grey wolf optimizer, salp swarm algorithm, Harris hawks optimization, and slime mold algorithm. To construct and validate the developed hybrid models, a sum of 156 samples were generated in the lab with varying percentages of PET and SCM. To estimate the CS, five influencing parameters, namely PET, SCM, FLOW, 1-day CS (CS1D), and 7-day CS (CS7D), were considered. The performance of the developed models was assessed in terms of multiple performance indices. Based on the results, the proposed LSSVM-PSO (a hybrid model of LSSVM and particle swarm optimization) was determined to be the best performing model with R2 = 0.9708, RMSE = 0.0424, and total score = 40 in the validation phase. The results of sensitivity analysis demonstrate that all the input parameters substantially impact the 28-day CS (CS28D) of cementitious grouts. Among them, the CS7D has the most significant effect. From the experimental results, it can be deduced that PET/SCM has no detrimental impact on CS28D of cementitious grouts, making PET a viable alternative for generating sustainable and green concrete. In addition, the proposed LSSVM-PSO model can be utilized as a novel alternative for estimating the CS of cementitious grouts, which will aid engineers during the design phase of civil engineering projects.

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where

, and

are the number of samples, determination coefficient, and MAE values, respectively, for the training dataset, and

and

are also indicate the same parameters, bur for the testing dataset.…”

Section: Resultsmentioning

confidence: 99%

Modelling Compression Strength of Waste PET and SCM Blended Cementitious Grout Using Hybrid of LSSVM Models

et al. 2022

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“…In complicated engineering problems, data-driven methods, such as artificial neural networks (ANNs); adaptive neurofuzzy inference system (ANFIS); wavelet-hybrid (W-hybrid) data-driven methods; evolutionary polynomial regression (EPR); Support Vector Machines (SVMs); classification and regression trees (CART); multivariate adaptive regression splines (MARS); gene expression programming (GEP) and group method of data handling (GMDH), which are frequently used and have been applied in many civil engineering fields, especially in water-related problems (Samadi et al 2015(Samadi et al , 2020a(Samadi et al , 2020bMojaradi et al 2018;Torabi et al 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Generally, the data-driven models are classified into white-box and black-box models. White-box data-driven models give mathematical equations that enable the relationship between the variables and dependent variables to be understood and interpreted directly (Samadi et al 2020a(Samadi et al , 2020b. By contrast, black-box models predict output parameters using numerical values rather than a straightforward equation between the input and output variables (Samadi et al 2021a(Samadi et al , 2021b.…”

Section: Introductionmentioning

confidence: 99%

Estimating the permeability coefficient of soil using CART and GMDH approaches

2022

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Permeability coefficient of soil (k) is one of the most important parameters in groundwater studies. This study, two robust explicit data-driven methods, including Classification and Regression Trees (CART) and the Group Method of Data Handling (GMDH) were developed using characteristics of soil, i.e., clay content (CC), water content (ω), liquid limit (LL), plastic limit (PL), specific density (γ), void ratio (e) to generate predictive equations for prediction of k. When compared to CART; Mean Absolute Error (MAE) = 0.0051, Root Mean Square Error (RMSE) = 0.0088, Scatter Index (SI) = 64.00%, Correlation Coefficient (R) = 0.7841, Index agreement (Ia) = 0.8830; the GMDH equation produced the lowest error values; MAE = 0.0044, RMSE = 0.0072, SI = 52.17%, R = 0.8493, Ia = 0.9184; in testing stage. Although, GMDH had better performance, however, CART and GMDH could be considered effective approaches for the prediction of k.

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“…The data-driven (soft computing) approach is another technique that has also been used to predict the C d using experimental data during train and test phases. These models are capable of extracting complex and hidden relationships among dependent and independent variables (Samadi et al 2014(Samadi et al , 2015(Samadi et al , 2020. In this regard, researchers used an artificial neural network (ANN), group method of data handling (GMDH), gene expression programming (GEP), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS).…”

Section: Introductionmentioning

confidence: 99%

Using data mining methods to improve discharge coefficient prediction in Piano Key and Labyrinth weirs

et al. 2021

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As a remarkable parameter, the discharge coefficient (Cd) plays an important role in determining weirs' passing capacity. In this research work, the SVM and the GEP algorithms were assessed to predict Cd of piano key weir (PKW), rectangular labyrinth weir (RLW), and trapezoidal labyrinth weir (TLW) with gathered experimental data set. Using dimensional analysis, various combinations of hydraulic and geometric non-dimensional parameters were extracted to perform simulation. The superior model for the SVM and the GEP predictor for PKW, RLW, and TLW included , and respectively. The results showed that both algorithms are potential in predicting discharge coefficient, but the coefficient of determination (RMSE, R2, Cd(DDR)max) illustrated the superiority of the GEP performance over the SVM. The results of the sensitivity analysis determined the highest effective parameters for PKW, RLW, and TLW in predicting discharge coefficients are , , and Fr respectively.

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Explicit data-driven models for prediction of pressure fluctuations occur during turbulent flows on sloping channels

Cited by 33 publications

References 65 publications

Modelling Compression Strength of Waste PET and SCM Blended Cementitious Grout Using Hybrid of LSSVM Models

Modelling Compression Strength of Waste PET and SCM Blended Cementitious Grout Using Hybrid of LSSVM Models

Estimating the permeability coefficient of soil using CART and GMDH approaches

Using data mining methods to improve discharge coefficient prediction in Piano Key and Labyrinth weirs

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