Comparison of dragonfly algorithm and Harris hawks optimization evolutionary data mining techniques for the assessment of bearing capacity of footings over two-layer foundation soils

Engineering with Computers - By assist of novel evolutionary science, the classification accuracy of neural computing is improved in analyzing the bearing capacity of footings over two-layer...     

A novel artificial intelligence technique for analyzing slope stability using PSO-CA model

This study aims to develop a new artificial intelligence model for analyzing and evaluating slope stability in open-pit mines. Indeed, a novel hybrid intelligent technique based on an optimization of the cubist algorithm by an evolutionary method (i.e., PSO), namely PSO-CA technique, was developed for predicting the factor of safety (FS) in slope stability; 450 simulations from the Geostudio software for the FS of a quarry mine (Vietnam) were used as the datasets for this aim. Five factors include bench height, slope angle, angle of internal friction, cohesion, and unit weight were used as the input variables for estimating FS in this work. To clarify the performance of the proposed PSO-CA technique in slope stability analysis, SVM, CART, and kNN models were also developed and assessed. Three performance indices, such as mean absolute error (MAE), root-mean-squared error (RMSE), and determination coefficient (R²), were computed to evaluate the accuracy of the predictive models. The results clarified that the proposed PSO-CA technique was the most dominant accuracy with an MAE of 0.009, RMSE of 0.025, and R² of 0.981, in estimating the stability of slope. The remaining models (i.e., SVM, CART, kNN) obtained poorer performance with MAE from 0.014 to 0.038, RMSE 0.030–0.056, and R² 0.917–0.974.

     

Computational modification of neural systems using a novel stochastic search scheme,namely evaporation rate-based water cycle algorithm: an application in geotechnical issues

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.

     

Quick integrative optimizers for minimizing the error of neural computing in pan evaporation modeling

To achieve an efficient methodology for approximating pan evaporation (E_P), this study offers two metaheuristic-integrated predictors. Shuffled complex evolution (SCE) and electromagnetic field optimization (EFO) are two of the fastest metaheuristic algorithms that are synthesized with artificial neural network (ANN). By doing this, the ANN is optimized in a noticeably shorter time compared to its integration with other metaheuristic techniques. Five-year climatic data of the Bakersfield station (California, USA) with an 80:20 ratio are used for developing and testing the methods. The proposed hybrids are implemented with appropriate population sizes (20 and 35 for the SCE and EFO, respectively) and their results are compared to a single ANN. Accuracy evaluation (correlation coefficients > 0.99) professed that the neural network with both conventional and sophisticated trainers is a competent approach for the E_P simulation. Besides, it was observed that the error of prediction by the ANN-SCE and ANN-EFO is 6.02 and 9.27% lower than the single ANN, respectively. Therefore, the used strategies can enhance the applicability of the ANN. The time elapsed in the optimization using SCE and EFO was 479.0 and 281.9 s, respectively. A comparison between these algorithms revealed that the EFO is both a faster and more accurate optimizer. The ANN-EFO is accordingly recommended as a new efficient model for predicting the E_P.

     

A novel constitutive modelling approach measured under simulated freeze–thaw cycles for the rock failure

In this study, we derived a computational modelling relation between the model parameters and characteristic parameters of rock deformation and failure via total differentials. We construct a damage model of rock under simulated freeze–thaw cycles and loading based on continuum damage mechanics theory that considers the influence of confining pressure and the random characteristics of rock material defects. This model reflects the variation in regulation between the internal mechanism of freeze–thaw damage and selected physical variables, making it more adaptable. We further analyze the evolution of microdamage and induced material mechanical properties of the rock using our proposed model, producing a total damage evolution curve under freeze–thaw cycles and loading that reflects the closure, initiation, propagation and coalescence of internal microcracks, as well as the subsequent appearance of macrocracks and rock failure. As the number of freeze–thaw cycles increases, rock damage intensifies, as demonstrated by the material’s deteriorating micromechanical properties. However, in later stages of deformation, both the strain and plasticity of the rock increase. With increasing confining pressure, rock damage and the damage accumulation rate, peak damage evolution ratio and descending segment after the peak decrease which manifest in the enhanced resistance of the rock to failure and increased macroscopic plastic deformation. Finally, we perform triaxial compression tests of rock under freeze–thaw cycles to validate our model. The macroscopic rock deformation and failure predicted by our model’s damage characteristics analysis are consistent with our experimental result.

     

Slope stability evaluation using neural network optimized by equilibrium optimization and vortex search algorithm

A dependable evaluation of the stability of slopes is a prerequisite in many construction projects. Although machine learning models have been satisfactorily used for this purpose, combining them with metaheuristic optimizers has resulted in a larger accuracy. This study, therefore, suggests the use of equilibrium optimization (EO) and vortex search algorithm (VSA) for optimizing a multi-layer perceptron neural network (MLPNN) employed to anticipate the factor of safety of a single-layer soil slope. Two hybrid models, as well as the regular MLPNN, are fed by a total of 630 data acquired from finite element simulations. The results, first, showed the applicability of artificial intelligence in this field. Next, reducing the training root mean square error (RMSE) of the MLPNN (from 0.4715 to 0.3891 and 0.4383 by the EO and VSA, respectively) revealed the efficiency of the used algorithms in remedying the computational weaknesses of this model. Moreover, the testing RMSE declined from 0.5397 to 0.4129 and 0.5155, which indicates a higher generalization ability of the hybrid models. Furthermore, due to the larger accuracy of the EO-based ensemble, this algorithm outperformed the VSA in optimizing the MLPNN.

     

Modification of landslide susceptibility mapping using optimized PSO-ANN technique

Engineering with Computers - In the present study, we applied artificial neural network (ANN) optimized with particle swarm optimization (PSO) for the problem of landslide susceptibility mapping...     

Machine learning predictive models for optimal design of building-integrated photovoltaic-thermal collectors

This research article aims to examine the feasibility of several machine learning techniques to forecast the exergetic performance of a building-integrated photovoltaic-thermal (BIPVT) collector. In this regard, it uses multiple linear regression, multilayer perceptron, radial basis function regressor, sequential minimal optimization improved support vector machine, lazy.IBK, random forest (RF), and random tree approaches. Moreover, it implements the performance evaluation criteria (PEC) to evaluate the system's performance from the perspective of exergy. The use of these approaches serves the identification process to realize the relationship between the input–output parameters of the BIPVT system. The novelty of this work is that it utilizes and compares multiple learning algorithms to predict the PEC of BIPVT through design parameters. Hence, the research considers the parameter (PEC) as the essential output of the BIPVT collector, while the input parameters are the length, width, and depth of the duct, located under the PV modules, as well as the air mass flow rate. The results of the research for the statistical indexes of mean absolute error, root mean square error, relative absolute error (%), and root relative squared error (%) show values of (0.2967, 0.3885, 1.8754, and 1.5237) and (0.4957, 0.8153, 2.9586, and 2.8289), respectively, for the training and testing datasets. While R² ranges (0.9997-0.9999) for those datasets. Therefore, to estimate the exergy performance of the BIPVT collector, the RF model is superior to other proposed models.     

Chemical Compositions of Oils from Several Wild Almond Species

Chemical compositions of oils extracted from three wild almond species [Amygdalus scoparia from Beyza, Iran (AZ); A. scoparia from Borazjan, Iran (AJ), and A. hausknechtii from the Firuzabad region, (AH)] and a domestic species, A. dulcis from Estahban, Iran (AD), as a reference, were investigated. Total oil content ranged from 44.4% in AJ to 51.4% in AD. Saponification numbers were in the range of 173.5 (for AJ) to 192.9 for AD. Oxidative stability, total phenolic contents and total wax contents were found to be within the ranges 11.7–14.0 h, 33.9–43.2 and 2.05–2.53%, respectively. The main monounsaturated fatty acid (MUFA) was oleic acid ranging from 66.7% for AH to 69.7% for AZ. The only polyunsaturated fatty acid (PUFA) was linoleic acid ranging from 18.2% for AZ to 23.0% for AD. The major saturated fatty acid was palmitic acid. MUFA contents and MUFA to PUFA ratio in the oils from wild almond species as well as those in the domestic one were found at higher levels than those in the common vegetable oils such as soybean, various nut oils, and also those from the seeds of pomegranate, grape, date and sesame. Oils from wild almond species investigated here are rich in oleic acid and can be considered as potential vegetable oils in the human diet.