This paper focuses on the prediction of soil shear strength (SSS), which is one of the most fundamental parameters in geotechnical engineering. Consisting of 12 influential factors, namely depth of sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, dry density, void ratio, liquid limit, plastic limit, plastic Index, and liquidity index as input variables, as well as the shear strength as the desired output, the dataset is provided through a field survey in Vietnam. Thereafter, as for used intelligent techniques, the main focus of the current study is on evaluating the efficiency of three novel optimization techniques for optimizing an artificial neural network (ANN) in predicting the SSS. To this end, the dragonfly algorithm (DA), whale optimization algorithm (WOA), and invasive weed optimization (IWO) are synthesized with ANN to prevail its computational drawbacks. The complexity of the models is optimized by sensitivity analysis. The results confirmed the effectiveness of all three applied algorithms, as the learning error was reduced by nearly 17%, 27%, and 32%, respectively by functioning the DA, WOA, and IWO. As for the testing phase, the IWO and DA achieved a close prediction accuracy. Overall, due to the superiority of the IWO-ANN ensemble, this model could be a promising alternative to traditional methods of shear strength determination.
Today, energy conservation is more and more stressed as great amounts of energy are being consumed for varying applications. This study aimed to evaluate the application of two robust evolutionary algorithms, namely genetic algorithm (GA) and imperialist competition algorithm (ICA) for optimizing the weights and biases of the artificial neural network (ANN) in the estimation of heating load (HL) and cooling load (CL) of the energy-efficient residential buildings. To this end, a proper dataset was provided composed of relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution, as the HL and CL influential factors. The optimal structure of each model was achieved through a trial and error process and to evaluate the accuracy of the designed networks, we used three well-known accuracy criterions. As the result of applying GA and ICA, the performance error of ANN decreased respectively by 17.92% and 23.22% for the HL, and 21.13% and 24.53% for CL in the training phase, and 20.84% and 23.74% for HL, and 27.57% and 29.10% for CL in the testing phase. The mentioned results demonstrate the superiority of the ICA-ANN model compared to GA-ANN and ANN.
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.
Two novel hybrid predictors are suggested as the combination of artificial neural network (ANN), coupled with spotted hyena optimizer (SHO) and ant lion optimization (ALO) metaheuristic techniques, to simulate soil shear strength (SSS). These algorithms were applied to the ANN for counteracting the computational drawbacks of this model. As a function of ten key factors of the soil (including depth of the sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, liquid limit, plastic limit, plastic Index, and liquidity index), the SSS was considered as the response variable. Followed by development of the ALO–ANN and SHO–ANN ensembles, the best-fitted structures were determined by a trial and error process. The results demonstrated the efficiency of both applied algorithms, as the prediction error of the ANN was reduced by around 35% and 18% by the ALO and SHO, respectively. A comparison between the results revealed that the ALO–ANN (Error = 0.0619 and Correlation = 0.9348) performs more efficiently than the SHO–ANN (Error = 0.0874 and Correlation = 0.8866). Finally, an SSS predictive formula is presented for use as an alternative to the difficult traditional methods.
Employing league championship optimization (LCA) technique for adjusting the membership function parameters of the adaptive neuro-fuzzy inference system (ANFIS) is the focal objective of the present study. The mentioned optimization is carried out for better estimation of the soil compression coefficient (SCC) using twelve key factors of soil, namely depth of sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, dry density, void ratio, liquid limit, plastic limit, plastic Index, and liquidity index. This information is widely useable in designing high-rise buildings located in smart cities. Notably, the used data is collocated from a real-world construction project in Vietnam. The hybrid ensemble of LCA-ANFIS is developed, and the best structure is determined by a three-step sensitivity analysis process. The prediction accuracy of the proposed hybrid model is compared with typical ANFIS to examine the efficiency of the combined LCA. Based on the results, applying the LCA algorithm lead to a 4.88% and 6.19% decrease in prediction error, in terms of root mean square error and mean absolute error, respectively. Moreover, the correlation index rose from 0.7351 to 0.7539, which indicates the higher consistency of the hybrid model results. Due to the acceptable accuracy of the proposed LCA-ANFIS model, it can be a promising alternative to common empirical and laboratory methods.
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