Genetic algorithm hybridized with multilayer perceptron to have an economical slope stability design

The present work aimed to evaluate and optimize the design of an artificial neural network (ANN) combined with an optimization algorithm of genetic algorithm (GA) for the calculation of slope stability safety factors (SF) in a pure cohesive slope. To make datasets of training and testing for the developed predictive models, 630 finite element limit equilibrium (FELE) analyses were performed. Similar to many artificial intelligence-based solutions, the database was involved in 189 testing datasets (e.g., 30% of the entire database) and 441 training datasets; for example, a range of 70% of the total database. Moreover, variables of multilayer perceptron (MLP) algorithm (for example, number of nodes in any hidden layer) and the algorithm of GA like population size was optimized by utilizing a series of trial and error process. The parameters in input, which were used in the analysis, consist of slope angle (β), setback distance ratio (b/B), applied stresses on the slope (F_y) and undrained shear strength of the cohesive soil (C_u) where the output was taken SF. The obtained network outputs for both datasets from MLP and GA-MLP models are evaluated according to many statistical indices. A total of 72 MLP trial and error (e.g., parameter study) the optimal architecture of 4 × 8 × 1 were determined for the MLP structure. Both proposed techniques result in a proper performance; however, according to the statistical indices, the GA–MLP model can somewhat accomplish the least mean square error (MSE) when compared to MLP. In an optimized GA–MLP network, coefficient of determination (R²) and root mean square error (RMSE) values of (0.975, and 0.097) and (0.969, and 0.107) were found, respectively, to both of the normalized training and testing datasets.

     

Nonlinear evolutionary swarm intelligence of grasshopper optimization algorithm and gray wolf optimization for weight adjustment of neural network

Engineering with Computers - The advent of new data-mining techniques and, more recently, swarm-based optimization algorithms have antiquated traditional models in the field of energy performance...     

Effect of different levels of fat and inulin on the microbial growth and metabolites in probiotic yogurt containing nonviable bacteria

Effects of different levels of fat and inulin on bacterial cell counts, degree of proteolysis and concentrations of organic acids in the yogurt containing inactivated cells of probiotic strains Bifidobacterium animalis and Lactobacillus acidophilus were investigated. Results showed that both L. acidophilus and B. animalis grew well in the yogurt samples reaching cell counts higher than 10⁶ CFU mL^?1 at the final pH of 4.5. Inulin at the concentration of 1% had no significant effects on the production of organic acids and cell counts of L. acidophilus, but promoted the growth of B. animalis with a reduction in the degree of proteolysis. Generally, different fat levels showed significant effects on the production of organic acids and nonsignificant effects on the cell counts of probiotic bacteria and degree of proteolysis. In case of lactic acid, the ratio of L‐ (+)to D‐ (?) isomer ranged from 50/50 to 80/20 in yogurt samples.     

Linear set-membership state estimation with unknown but bounded disturbances

In this article, we consider the problem of discrete-time linear state estimation when at every discrete instant Δ the Euclidean norm of the discrete-time disturbance ‖w(Δ)‖₂ is bounded within some known value. Specifically, given a hypersphere that contains the uncertain disturbance signal w(Δ) and an ellipsoid containing the uncertain system state x(Δ) at time step Δ, a sub-optimal approach to computing a linear minimax filter which constructs a minimal ellipsoid to contain x(Δ?+?1) is derived. A distinct feature of our approach when compared to earlier solutions is that both the filter and the performance bound can be pre-computed off-line.     

A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications

Artificial neural network (ANN) aimed to simulate the behavior of the nervous system as well as the human brain. Neural network models are mathematical computing systems inspired by the biological neural network in which try to constitute animal brains. ANNs recently extended, presented, and applied by many research scholars in the area of geotechnical engineering. After a comprehensive review of the published studies, there is a shortage of classification of study and research regarding systematic literature review about these approaches. A review of the literature reveals that artificial neural networks is well established in modeling retaining walls deflection, excavation, soil behavior, earth retaining structures, site characterization, pile bearing capacity (both skin friction and end-bearing) prediction, settlement of structures, liquefaction assessment, slope stability, landslide susceptibility mapping, and classification of soils. Therefore, the present study aimed to provide a systematic review of methodologies and applications with recent ANN developments in the subject of geotechnical engineering. Regarding this, a major database of the web of science has been selected. Furthermore, meta-analysis and systematic method which called PRISMA has been used. In this regard, the selected papers were classified according to the technique and method used, the year of publication, the authors, journals and conference names, research objectives, results and findings, and lastly solution and modeling. The outcome of the presented review will contribute to the knowledge of civil and/or geotechnical designers/practitioners in managing information in order to solve most types of geotechnical engineering problems. The methods discussed here help the geotechnical practitioner to be familiar with the limitations and strengths of ANN compared with alternative conventional mathematical modeling methods.

     

CFD analysis of employing a novel ecofriendly nanofluid in a miniature pin fin heat sink for cooling of electronic components: Effect of different configurations

This research aims to study the thermal and hydraulic attributes as well as energy efficiency of a new ecofriendly nanofluid including functionalized graphene nanoplatelets in a mini heat sink with three different pin fins. The circular, triangular and drop-shaped pin fins are investigated and compared with each other. The effects of nanoparticle fraction and flow velocity on the thermal resistance, temperature uniformity, convective heat transfer coefficient, maximum surface temperature, average surface temperature, pressure loss and pumping power are assessed. Increasing the concentration or velocity reduces the temperature on the heated wall, and also improves the temperature distribution uniformity. At both constant velocity and invariant pumping power, the heat sink fitted with the circular pin fins leads to the best performance while that equipped with the triangular pin fins results in the worst efficiency. In addition, the Figure of Merit (FoM) is greater than 1 for all conditions, which proves that the nanoparticle suspension possesses a greater merit to be employed as the coolant in the heat sinks compared to the base fluid.     

Optimizing an ANN model with genetic algorithm (GA) predicting load-settlement behaviours of eco-friendly raft-pile foundation (ERP) system

Engineering with Computers - Eco-friendly raft-pile foundation (ERP) system is one of the most recent developed types of pile foundations that the original materials can be provided from local...     

Prediction of ultimate bearing capacity through various novel evolutionary and neural network models

Engineering with Computers - In the current study, various evolutionary artificial intelligence and machine learning models namely, optimized artificial neural network (ANN), genetic algorithm...     

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.