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Eight optimizer methods are combined with a perceptron neural network to achieve an optimal network and minimize errors for predicting the heat transfer rate of a ribbed triple-tube heat exchanger operating with the graphene nanoplatelets-based nanofluid. The optimization techniques consist of Harris Hawks Optimizer (HHO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Ant Lion Optimizer (ALO), Biogeography-Based Optimization (BBO), and Dragonfly Algorithm (DA). The required data are provided with the aid of numerical simulations. The structural parameters are considered using different rib pitches and heights. The ALO algorithm is the best method for estimating the output. The best performance of this algorithm is gained by population size of 350. By this method, the heat transfer rate is estimated with the Root Mean Square Error (RMSE) values of about 0.0310 and 0.0385 for the training and testing data samples, respectively. Unlabelled Image • Predicting heat transfer of a triple-tube heat exchanger operating with nanofluid. • Eight advanced optimizer algorithms to enhance the performance of neural network. • Using nanofluid having graphene platelets decorated with platinum as working fluid. • ALO is the best method for estimating the output due to lowest deviations. [ABSTRACT FROM AUTHOR]

Blasting is the predominant rock fragmentation technique in civil constructions, underground and surface mines. Flyrock is the unwanted throw of rock fragments during blasting and is the major cause of considerable damage in and around the mines. The present research aimed to propose a new intelligence-based method to predict flyrock. In this regard, the recurrent fuzzy neural network (RFNN) combined with the genetic algorithm (GA) is proposed. For checking the suitability of the RFNN-GA model, artificial neural network (ANN), hybrid ANN and GA and a nonlinear regression model were also employed. To achieve the aims of the research, data for 70 blasting sites including four input parameters (spacing, burden, stemming and maximum charge per delay) and one output parameter (flyrock) were gathered from two quarry mines at the Shur River dam, Iran. The performance of the proposed prediction methods was then assessed with statistical evaluation criteria, i.e., R-square and root mean square error. The results indicate the proposed RFNN-GA model was more superior for prediction of flyrock than the GA-ANN, ANN and nonlinear regression models. According to a sensitivity analysis, the maximum charge per delay was the most influential parameter in flyrock prediction in this case. [ABSTRACT FROM AUTHOR]

In the present work, we employed artificial neural network (ANN) that is optimized with two hybrid models, namely imperialist competition algorithm (ICA) as well as particle swarm optimization (PSO) in the case of the problem of bearing capacity of shallow circular footing systems. Many types of research have shown that ANNs are valuable techniques for estimating the bearing capacity of the soils. However, most ANN training models have some drawbacks. This study aimed to focus on the application of two well-known hybrid ICA–ANN and PSO–ANN models to the estimation of bearing capacity of the circular footing lied in layered soils. In order to provide the training and testing datasets for the predictive network models, extensive finite element (FE) modelling (a database includes 2810 training datasets and 703 testing datasets) are performed on 16 soil layer sets (weaker soil rested on stronger soil and vice versa). Note that all the independent variables of ICA and PSO algorithms are optimized utilizing a trial and error method. The input includes upper layer thickness/foundation width (h/B) ratio, footing width (B), top and bottom soil layer properties (e.g., six of the most critical soil characteristics), vertical settlement of circular footing (s), where the output was taken ultimate bearing capacity of the circular footing (Fult). Based on coefficient of determination (R2) and Root Mean Square Error (RMSE), amounts of (0.979, 0.076) and (0.984, 0.066) predicted for training dataset and amounts of (0.978, 0.075) and (0.983, 0.066) indicated in the case of the testing dataset of proposed PSO–ANN and ICA–ANN models of prediction network, respectively. It demonstrates a higher reliability of the presented PSO–ANN model for predicting ultimate bearing capacity of circular footing located on double sandy layer soils. [ABSTRACT FROM AUTHOR]

Slump is a workability-related characteristic of concrete mixture. This paper investigates the efficiency of a novel optimizer, namely ant lion optimization (ALO), for fine-tuning of a neural network (NN) in the field of concrete slump prediction. Two well-known optimization techniques, biogeography-based optimization (BBO) and grasshopper optimization algorithm (GOA), are also considered as benchmark models to be compared with ALO. Considering seven slump effective factors, namely cement, slag, water, fly ash, superplasticizer (SP), fine aggregate (FA), and coarse aggregate (CA), the mentioned algorithms are synthesized with a neural network to determine the best-fitted neural parameters. The most appropriate complexity of each ensemble is also found by a population-based sensitivity analysis. The findings revealed that the proposed ALO-NN model acquires a good approximation of concrete slump, regarding the calculated root mean square error (RMSE = 3.7788) and mean absolute error (MAE = 3.0286). It also outperformed both BBO-NN (RMSE = 4.1859 and MAE = 3.3465) and GOA-NN (RMSE = 4.9553 and MAE = 3.8576) ensembles. [ABSTRACT FROM AUTHOR]

The heating load calculation is the first step of the iterative heating, ventilation, and air conditioning (HVAC) design procedure. In this study, we employed six machine learning techniques, namely multi-layer perceptron regressor (MLPr), lazy locally weighted learning (LLWL), alternating model tree (AMT), random forest (RF), ElasticNet (ENet), and radial basis function regression (RBFr) for the problem of designing energy-efficient buildings. After that, these approaches were used to specify a relationship among the parameters of input and output in terms of the energy performance of buildings. The calculated outcomes for datasets from each of the above-mentioned models were analyzed based on various known statistical indexes like root relative squared error (RRSE), root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R2), and relative absolute error (RAE). It was found that between the discussed machine learning-based solutions of MLPr, LLWL, AMT, RF, ENet, and RBFr, the RF was nominated as the most appropriate predictive network. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the training dataset to be 0.9997, 0.19, 0.2399, 2.078, and 2.3795, respectively. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the testing dataset to be 0.9989, 0.3385, 0.4649, 3.6813, and 4.5995, respectively. These results show the superiority of the presented RF model in estimation of early heating load in energy-efficient buildings. [ABSTRACT FROM AUTHOR]

Current study aimed to combine the multi-layer Perceptron (MLP) neural network technique with five metaheuristic computational algorithms, namely invasive weed optimization (IWO-MLP), dragonfly algorithm (DA-MLP), evolution strategy (ES-MLP), genetic algorithm (GA-MLP), and imperialist competitive algorithm (ICA-MLP) for estimating the monthly natural gas consumption (NGC). For the case of this study, the NGC data was collected from the United States (U.S.) was chosen. To achieve this, a dataset composed of eight independent factors, namely, dry gas production, NGPL production, gross withdrawals, supplemental gaseous fuels, net storage withdrawals, marketed production, net imports, and balancing items, along with a dependent variable of natural gas consumption is provided. Out of 39 samples, a 3-fold ratio is taken to select the datasets of training and testing randomly. During the calculation phase, and through a trial and error procedure, the optimal parameters of the MLP, IWO-MLP, DA-MLP, ES-MLP, GA-MLP, ICA-MLP networks are taken into consideration. To assess the reliability of the proposed technique with the real collected data, three well-known statistical criteria, including mean square error (MSE) and root mean square error (RMSE) as well as coefficient of determination (R ²), are introduced. These indices help to measure the level of accuracy of the used models. It is found that the IWO can be a better predictive network comparing to other hybridized techniques reducing the error performance of the MLP method. The predicted results reveal that hybridizing optimization algorithms could improve the prediction accuracy and helps the MLP to perform more efficiently. The recorded in testing MSE for the IWO-MLP, DA-MLP, ES-MLP, GA-MLP, ICA-MLP prediction techniques (i.e., used for U.S. natural gas consumption estimation) were found 0.000013, 0.002358, 0.024744, 0.013244, and 0.048572, respectively. In the sense of U.S. natural gas consumption prediction, the training R 2 value to 0.9999, 0.9996, 0.9971, 0.9996, and 1.0000 and testing R 2 value of 0.9999, 0.9995, 0.9956, 0.9984, and 0.8115 show that the outputs of the ensemble models are more correlated with the real data. [ABSTRACT FROM AUTHOR]

Adding small amounts of drag-reducing agents (DRA) to the crude oil can effectively reduce energy losses and its associated pressure drop in transportation pipelines. They do their roles by reducing the turbulent friction factor of the flowing liquids in the pipeline. In this study, the artificial neural networks (ANN) are utilized for estimation of drag reduction (DR) in crude oil pipelines as a function of Reynolds number, concentration, and type of drag-reducing agents, temperature, and type of pipe. Two different training algorithms, i.e. Levenberg-Marquardt (LM) and imperialist competition algorithm (ICA) are used for adjustment of parameters of multi-layer perceptron (MLP) networks. Predictive accuracies of trained MLP by Levenberg-Marquardt (LM-MLP) and imperialist competition algorithm (ICA-MLP) for estimating drag reduction in crude oil pipelines were compared. The comparisons were done by three statistical criteria, i.e. root mean square error (RMSE), the coefficient of determination (R2), and mean absolute error (MAE). The obtained results show that replacing LM by ICA evolutionary algorithm for adjustment of parameters of MLP leads to increasing R2 from 0.9585 to 0.9791 in the training phase, and from 0.9310 to 0.9698 in the testing phase. Furthermore, ICA has reduced the RMSE and MAE of testing group from 3.0396 to 2.2616 to the 2.0080 and 1.5579, respectively. Finally, a simple ANN-based formulation is provided for using our developed ICA-MLP model to estimate DR in the crude oil pipelines. • Prediction of pressure drop has been investigated in crude oil pipelines. • Artificial neural network employed to predict the drag reduction. • The models performed based on the five influential variables. [ABSTRACT FROM AUTHOR]