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The efficacy of saving energy standards depends on the ability to anticipate the heat loss of buildings. Environmentally friendly materials, also known as eco-friendly or sustainable materials, have a minimal negative impact on the environment throughout their life cycle. These materials are designed to conserve resources, reduce pollution, and promote sustainability. The characteristics of non-stationary and non-linear heat loss through environmentally friendly materials make it challenging to anticipate accurately. At the same time, many of the industry's presently accessible computational models have been created with this in mind; the majority call for powerful computers and time-consuming computations. The artificial neural network (ANN) has been utilized for prediction, and ground-breaking research has shown the viability of this strategy. This research proposes an artificial neural network (ANN) prototype to estimate construction cooling load usage. ANN is integrated with the vortex search algorithm (VS), stochastic fractal search (SFS), and multi-verse optimizer (MVO) models to compare the three models' outcomes and suggest a more accurate strategy. These techniques make a linear mapping among the output and input parameters, often utilized for modeling and regression. The value of the multiple determination coefficient is also determined. The values of the training R2 (coefficient of multiple determination) are 0.9464, 0.99827, and 0.99522 for VS-MLP, SFS-MLP, and MVO-MLP, respectively, with an unknown dataset which is acceptable. The training RMSE amounts for VS-MLP, SFS-MLP, and MVO-MLP are 0.06433, 0.00619, and 0.01028 for the unknown dataset, which is acceptable. According to the MAE values of 0.0082902, 0.0047834, and 0.0076534 in the training phase for VS-MLP, SFS-MLP, and MVO-MLP approaches and the values of testing MAE error of 0.029107, 0.018167, and 0.029212 for VS-MLP, SFS-MLP, and MVO-MLP approaches, respectively, it is obtained that the SFS-MLP has a lower MAE value. The lowest RMSE value and the higher R2 value indicate the favorable accuracy of the SFS-MLP technique. [ABSTRACT FROM AUTHOR]

Manual calculation of the compressive strength of concrete (CSC) is an expensive and time-consuming process. Soft computing methods outperform the statistical methods used to resolve these problems. Nonetheless, complicated prediction models are still incomplete and require more exploration. Artificial neural networks (ANNs) provide a better and faster technique featuring solitary hidden layers and have improved the generalization capacity. The present paper presents three ANN-based (shuffled complex evolution, evaporation rate based water cycle algorithm (ERWCA), and Cuckoo optimization algorithm) prediction models to anticipate the compressive strength of concrete efficiently. An available database from the UCI repository is employed to develop and access the model performance. A comparison is made between the prediction accuracies of the above three techniques. Using all models, a comparative investigation has been conducted to predict the compressive strength of concrete at the curing ages of 91, 56, and 28 days. The experimental findings obtained from the ERWCA-MLP method indicate its capability of robust CSC prediction. On average, this method achieves the minimum RMSE of 0.55314 and 0.43329 and R2 of 0.99803 and 0.99824. The statistical significance test and the comparative analysis of simulation results indicate the superiority of ERWCA-MLP in predicting the compressive strength of concrete. [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated the high efficiency of metaheuristic algorithms for various optimization engineering problems. The main focus of the present study is to apply a novel notion of stochastic search methods, namely evaporation rate-based water cycle algorithm (ER-WCA) to the problem of soil shear strength (SSS) prediction. The ER-WCA, as the name indicates, is a modified version of the water cycle algorithm that is used to computationally modify an artificial neural network (ANN) for the mentioned purpose. The sensitivity analysis showed that the most proper values for the number of rivers + sea and the population size are 5 and 300, respectively. The performance of the ER-WCA–ANN hybrid is compared to an ANN typically trained by the Levenberg–Marquardt algorithm to evaluate the effectiveness of the proposed metaheuristic technique. The findings showed that incorporation of the ER-WCA results in reducing the root-mean-square error by 5.87% and 4.92% in the training and testing phases, respectively. Meanwhile, the coefficient of determination rose from 84.27 to 86.11% and from 78.80 to 80.83% in these phases. It indicates that the weights and biases suggested by the ER-WCA can construct a considerably more reliable ANN. Therefore, the introduced method is recommended for practical uses in the early prediction of the SSS in civil engineering projects. [ABSTRACT FROM AUTHOR]

This study aimed to assess particle swarm optimization (PSO) enhanced with an artificial neural network (ANN) for the prediction of the Pul (pullout resistance of helical piles in pure cohesionless soils). To make data sets of training and testing as to the expanded predictive models, 36 laboratory experiments were performed. The database involved 27 experiments for the training data sets (e.g., 80% of the whole database) and 9 experiments for the testing data sets (e.g., a range of 20% of the whole database). In addition, variables of the ANN algorithm (e.g., neurons in each hidden layer) and PSO algorithm, such as population size, were optimized utilizing a series of trial-and-error processes. The input parameters that were used in the analysis include density, embedment ratio, shaft base diameter, and shaft diameter, where the output was taken and the laboratory Pul determined. The selection of these parameters is because the Pul is affected by the geologic conditions, helical pile's configuration, and penetration depth. After performing the sensitivity analysis, the optimized architecture, 4 × 8 × 1 is specified for the structure of ANN. As a result, both models, ANN and PSO–ANN, presented excellent prediction performance. Note that the PSO–ANN method can provide a more accurate network outputs in predicting measured values compared to the conventional ANN model. Based on R2, RMSE and VAF values of, 0.999, 0.0350 and 99.9978, as well as, 0.999, 0.0379 and 99.998, were determined for the normalized training and testing data sets in the optimized PSO–ANN network, respectively. However, in the ANN model with four neurons in each hidden layer, the R2 as well as RMSE for the non-normalized training and testing data sets were (0.99985 and 7.99007) and (0.99990 and 11.97427), respectively, which confirms the superiority of the proposed PSO–ANN model in predicting the Pul. [ABSTRACT FROM AUTHOR]

The present study aimed to optimize the artificial neural network (ANN) with one of the well-established optimization algorithms called particle swarm optimization (PSO) for the problem of ground response approximation in short structures. Various studies showed that ANN-based solutions are a reliable method for complex engineering problems. Predicting the ground surface respond to seismic loading is one of the engineering problems that still has not received any ANN solution. Therefore, this paper aimed to assess the application of hybrid PSO-based ANN models to the calculation of horizontal deflection of columns in short building after being subjected to a significant seismic loading (e.g., The Chi-Chi earthquake used as one of the input databases). To prepare both of the training and testing datasets, for the ANN and PSO-ANN network models, a series of finite element (FE) modeling were performed. The used FEM simulation database consists of 8324 training datasets and 2081 testing datasets that is equal to 80% and 20% of the whole database, respectively. The input includes Chi-Chi earthquake dynamic time (s), friction angle (φ), dilation angle (ψ), unit weight (γ), soil elastic modulus (E), Poisson's ratio (v), structure axial stiffness (EA), and bending stiffness (EI) where the output was taken horizontal deflection of the columns at their highest level (Ux). The result indicates higher reliability of the PSO-ANN model in estimating the ground response and horizontal deflection of structural columns in short structures after being subjected to earthquake loading. [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]

Regular optimization techniques have been widely used in landslide-related problems. This paper outlines two novel optimizations of artificial neural network (ANN) using grey wolf optimization (GWO) and biogeography-based optimization (BBO) metaheuristic algorithms in the Ardabil province, Iran. To this end, these algorithms are synthesized with a multi-layer perceptron (MLP) neural network for optimizing its computational parameters. The used spatial database consists of fourteen landslide conditioning factors, namely elevation, slope aspect, land use, plan curvature, profile curvature, soil type, distance to river, distance to road, distance to fault, rainfall, slope degree, stream power index (SPI), topographic wetness index (TWI) and lithology. 70% of the identified landslides are randomly selected to train the proposed models and the remaining 30% is used to evaluate the accuracy of them. Also, the frequency ratio theory is used to analyze the spatial interaction between the landslide and conditioning factors. Obtained values of area under the receiver operating characteristic curve, as well as mean square error and mean absolute error showed that both GWO and BBO hybrid algorithms could efficiently improve the learning capability of the MLP. Besides, the BBO-based ensemble surpasses other implemented models. [ABSTRACT FROM AUTHOR]

• EHO, SFLA, SSA, and WDO are Hybridized with MLP. • SSA-MLP presents the most efficient prediction. • Computational weights and biases of the MLP and proposed hybrid algorithms are optimized. Due to the vital significance of precise determination of soil shear strength (SSS) in many civil engineering projects, this study is dedicated to proposing novel intelligent models for estimating this parameter. To this end, elephant herding optimization (EHO), shuffled frog leaping algorithm (SFLA), salp swarm algorithm (SSA), and wind-driven optimization (WDO) are synthesized with artificial neural network (ANN) to create neural ensembles. The results indicated the efficiency of metaheuristic science for dealing with the non-linear analysis of the SSS and influential soil parameters. Also, a comparison between the models revealed that the SSA-MLP (Error = 0.0386 and Correlation = 0.8219) presents the most efficient prediction, followed by WDO-MLP (Error = 0.0403 and Correlation = 0.8025), SFLA-MLP (Error = 0.0408 and Correlation = 0.7559), and EHO-MLP (Error = 0.0436 and Correlation = 0.7195). Therefore, the proposed SSA-MLP can function as a reliable substitute for traditional approaches in prediction of the SSS. [ABSTRACT FROM AUTHOR]