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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]

Balancing the exploration and exploitation in any nature-inspired optimization algorithm is an essential task, while solving the real-world global optimization problems. Therefore, the search agents of an algorithm always try to explore the unvisited domains of a search space in a balanced manner. The sine cosine algorithm (SCA) is a recent addition to the field of metaheuristics that finds the solution of an optimization problem using the behavior of sine and cosine functions. However, in some cases, the SCA skips the true solutions and trapped at sub-optimal solutions. These problems lead to the premature convergence, which is harmful in determining the global optima. Therefore, in order to alleviate the above-mentioned issues, the present study aims to establish a comparatively better synergy between exploration and exploitation in the SCA. In this direction, firstly, the exploration ability of the SCA is improved by integrating the social and cognitive component, and secondly, the balance between exploration and exploitation is maintained through the grey wolf optimizer (GWO). The proposed algorithm is named as SC-GWO. For the performance evaluation, a well-known set of benchmark problems and engineering test problems are taken. The dimension of benchmark test problems is varied from 30 to 100 to observe the robustness of the SC-GWO on scalability of problems. In the paper, the SC-GWO is also used to determine the optimal setting for overcurrent relays. The analysis of obtained numerical results and its comparison with other metaheuristic algorithms demonstrate the superior ability of the proposed SC-GWO. [ABSTRACT FROM AUTHOR]

Early assessment of the energy performance of buildings (EPB) is focused in this study. This task is carried out by predicting the cooling load (CL) in a residential building. To this end, due to the drawbacks of neural computing approaches (e.g., local minima), a novel metaheuristic technique, namely teaching–learning-based optimization (TLBO) is employed to modify a multi-layer perceptron neural network (MLPNN). The complexity of the proposed model is also optimized by a trial and error process. Evaluating the results revealed a high efficiency for this scheme. In this sense, the prediction error of the MLPNN was reduced by around 20%, and the correlation between the measured and forecasted CLs rose from 0.8875 to 0.9207. It was also deduced that the TLBO outperforms two benchmark optimizers of cuckoo optimization algorithm (COA) and league championship algorithm (LCA) in terms of both modeling accuracy and network complexity. Moreover, the TLBO-MLP emerged as the most time-effective hybrid as it required considerably lower computation time than COA-MLP and LCA-MLP. Regarding these advantages, the proposed model can be promisingly used for early assessment of EPB in practice. [ABSTRACT FROM AUTHOR]

In this article, thermal buckling and frequency analysis of a size-dependent laminated composite cylindrical nanoshell in thermal environment using nonlocal strain–stress gradient theory are presented. The thermodynamic equations of the laminated cylindrical nanoshell are based on first-order shear deformation theory, and generalized differential quadrature element method is implemented to solve these equations and obtain natural frequency and critical temperature of the presented model. The results show that by considering C–F boundary conditions and every even layers' number, in lower value of length scale parameter, by increasing the length scale parameter, the frequency of the structure decreases but in higher value of length scale parameter this matter is inverse. Finally, influences of temperature difference, ply angle, length scale and nonlocal parameters on the critical temperature and frequency of the laminated composite nanostructure are investigated. [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]

In this study, the pollution risks related to arsenic, fluorine, iron, physicochemical parameters, and various indices have been investigated in the Bazoft dam site. The proposed dam is a double-curvature arch dam with a height of 211 m in order to produce hydroelectric energy and the supply of agricultural and drinking water in the west of Iran. Chemical analysis of water samples obtained from springs, river, and boreholes in the dam site was conducted. Anions such as HCO3, Cl, SO4, CO3, and NO3, and cations including Ca, Na, Mg, K, F, Fe, and As had the highest amounts, respectively. The concentration of assessed variables was lower than the WHO permissible limit. The results of the principal factor analysis (PCA) showed that four factors control 82% of the water quality variations. The factors such as EC, TDS, Na, K, SO4−, CO3, HCO3, total hardness, and alkalinity with 43.10% were the most important factors in the dam site. The heavy metals of iron and arsenic have no meaningful relationship with any of the ions. The accuracy of the relations was investigated based on different statistical criteria. The main processes in controlling the water quality are weathering and the dissolution of the carbonates caused by the interaction of water and rock. Ionic ratios indicated that the water origin of the springs is the dolomitic aquifers and the main source of magnesium is carbonate dissolution. The water resources of the dam site are corrosive, and all indices indicate the appropriate quality of water for irrigation and drinking purposes. Filed studies and boreholes water level variations showed that karstification is limit in the dam site. [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]

Using single layer microchannels accompanied by nanofluids is one of the most practical solutions in thermal management of high power density devices. The main challenge in cooling systems of electronic devices is to provide a uniform temperature distribution. In the present study, fluid flow and heat transfer in a fractal microchannel heatsink have been simulated employing the computational fluid dynamics (CFD) method. The fractal microchannel is used to achieve uniform temperature distribution. Thermal performance of single-walled carbon nanotubes (SWCNT) and multi-walled carbon nanotubes (MWCNT) dispersed in the two base fluids of water and kerosene in a fractal microchannel at Reynolds (Re) numbers of 1500 to 3000 are investigated. It should be noted that the nanofluids have been simulated by the two-phase mixture model. The results indicated that the use of fractals silicon microchannel leads to having a uniform temperature distribution. Based on the results, at maximum Re number when the working fluid is water, Nu number and pumping power are 20.9 and 0.033 W whereas, in kerosene flow at the same condition, Nu number and pumping power are 6 and 0.054 W, respectively. According to the obtained results, using the SWCNT nanoparticle compared with the MWCNT nanoparticle leads to a significant enhancement in the Nusselt (Nu) number. This difference is more pronounced by increasing the Re number and nanoparticle volume fraction. In addition, the results indicated that at the same Re number and nanoparticle volume fraction, the performance evaluation criterion of the water-based nanofluid is 4 times higher than that of the kerosene-based nanofluid. So the use of the water as the working fluid with the SWCNT nanoparticle for cooling in the fractal silicon microchannel is recommended. [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]

The issue of leakage and spillage of light non-aqueous phase liquid (LNAPL) contribute to groundwater contamination, resulting in groundwater pollution and rendering the quality of groundwater unsafe for drinking and agriculture. Therefore, the objective of this study was to investigate LNAPL migration in the double-porosity soil under vibration effect, which has become important for sustainability of groundwater utilization and a comprehensive understanding of the behaviour of liquid migration into the groundwater. A laboratory experiment was conducted to observe the phenomena and characteristics of soil structure, and the pattern of liquid migration in deformable double-porosity by using digital image processing technique. The experiments show that the simulated results were reasonably concise with the visual observations. The gradual increase in vibration table excitation frequency yielded different vibration responses from the respective soils. This indicated that the soil surface acceleration depended significantly on the soil conditions, soil water content, soil structure, and the pattern of soil crack. Faster migration occurred at the cracked soil surface condition compared to other locations on the soil surface that were not cracked. Comparison between soils with 25% and 30% moisture content showed that the downward migration of LNAPL is faster when the soil moisture content is higher. This occurs in soil with higher moisture content due to greater capillary pressure exerted by the liquids and the inter-aggregate pores. This study proved that the digital image processing technique is capable to provide detailed migration flow information to facilitate the researchers to better understand contaminate migration patterns and to ensure sustainability of groundwater resources. [ABSTRACT FROM AUTHOR]