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.     

K-Fold and State-of-the-Art Metaheuristic Machine Learning Approaches for Groundwater Potential Modelling

Groundwater being an essential resource is not easily available in some parts of the world. The present study, aimed at procuring better prediction maps for groundwater potential zones, is based on a novel approach combining the use of k-fold cross-validation method and the implementation of four scenarios, each comprising of six machine learning models, ANFIS (Adaptive Neuro Fuzzy Inference System) and five other ensembles of it, ANFIS-Firefly, ANFIS-Bees, ANFIS-GA, ANFIS-DE and ANFIS-ACO. Ada Boost Model has played a vital role in determining the collinearity among the fourteen conditioning factors, which are, Lithology, Slope, TST, TRI, LULC, HAND, Curvature, Distance to Stream, Distance to Fault, Rainfall, Fault Density, Drainage Density, Elevation and Aspect. The AUCROC (Area Under Curve – Receiver Operating Characteristics) approach was employed as a model evaluation metric along with Accuracy, Sensitivity and Specificity. Among the models, ANFIS-DE showed the most promising results, acquiring the highest average values among the four scenarios for AUC (0.934), Accuracy (0.987), Sensitivity (0.985) and Specificity (0.985). Promising results of this study gives the necessary incentive for further applying this approach for groundwater zonation of other areas of the world as well as other areas of hydrogeological studies.

     

A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model

Engineering with Computers - Recycled aggregate concrete is used as an alternative material in construction engineering, aiming to environmental protection and sustainable development. However, the...     

Optimizing ANN models with PSO for predicting short building seismic response

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...     

Proposing two new metaheuristic algorithms of ALO-MLP and SHO-MLP in predicting bearing capacity of circular footing located on horizontal multilayer soil

In this study, for the issue of shallow circular footing’s bearing capacity (also shown as F_ult), we used the merits of artificial neural network (ANN), while optimized it by two metaheuristic algorithms (i.e., ant lion optimization (ALO) and the spotted hyena optimizer (SHO)). Several studies demonstrated that ANNs have significant results in terms of predicting the soil’s bearing capacity. Nevertheless, most models of ANN learning consist of different disadvantages. Accordantly, we focused on the application of two hybrid models of ALO–MLP and SHO–MLP for predicting the F_ult placed in layered soils. Moreover, we performed an Extensive Finite Element (FE) modeling on 16 sets of soil layer (soft soil placed onto stronger soil and vice versa) considering a database that consists of 703 testing and 2810 training datasets for preparing the training and testing datasets. The independent variables in terms of ALO and SHO algorithms have been optimized by taking into account a trial and error process. The input data layers consisted of (i) upper layer foundation/thickness width (h/B) ratio, (ii) bottom and topsoil layer properties (for example, six of the most important properties of soil), (iii) vertical settlement (s), (iv) footing width (B), where the main target was taken F_ult. According to RMSE and R², values of (0.996 and 0.034) and (0.994 and 0.044) are obtained for training dataset and values of (0.994 and 0.040) and (0.991 and 0.050) are found for the testing dataset of proposed SHO–MLP and ALO–MLP best-fit prediction network structures, respectively. This proves higher reliability of the proposed hybrid model of SHO–MLP in approximating shallow circular footing bearing capacity.

     

An artificial neural network approach for under-reamed piles subjected to uplift forces in dry sand

The present study is about under-reamed pile subjected to uplift forces. They are known to be very effective especially against uplift forces. The objective is to develop a simple design formula based on an optimized artificial neural network (ANN) predictive approach model. This formula can calculate the ultimate uplift capacity of under-reamed piles (P_ul) embedded in dry cohesionless soil with excellent accuracy. The new generated ANN model was developed by taking into account the key factors such as under-reamed base diameter, angle of enlarged base to the vertical axis, shaft diameter, and embedment ratio. The proposed approach shows excellent agreement with a mean absolute error (MAE) less than 0.262, which is better than previous theories.

     

Swarm-based analysis through social behavior of grey wolf optimization and genetic programming to predict friction capacity of driven piles

Engineering with Computers - The advantage of new data mining-based solutions, and more recently, optimization algorithms (i.e., basically swarm-based solutions) have enhanced traditional...