Design and implementation of a new tuned hybrid intelligent model to predict the uniaxial compressive strength of the rock using SFS-ANFIS

This study proposes a novel design to systematically optimize the parameters for the adaptive neuro-fuzzy inference system (ANFIS) model using stochastic fractal search (SFS) algorithm. To affirm the efficiency of the proposed SFS-ANFIS model, the predicting results were compared with ANFIS and three hybrid methodologies based on ANFIS combined with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO). Accurate prediction of uniaxial compressive strength (UCS) is of great significance for all geotechnical projects such as tunnels and dams. Hence, this study proposes the use of SFS-ANFIS, GA-ANFIS, DE-ANFIS, PSO-ANFIS, and ANFIS models to predict UCS. In this regard, the fresh water tunnel of Pahang–Selangor located in Malaysia was considered and the requirement data samples were collected. Different statistical metrics such as coefficient of determination (R²) and mean absolute error were used to evaluate the models. Referring to the efficiency results of SFS-ANFIS, it can be found that the SFS-ANFIS (with the R² of 0.981) has higher ability than PSO-ANFIS, DE-ANFIS, GA-ANFIS, and ANFIS models in predicting the UCS.

     

Strength evaluation of granite block samples with different predictive models

Over the last decade, application of soft computing techniques has rapidly grown up in different scientific fields, especially in rock mechanics. One of these cases relates to indirect assessment of uniaxial compressive strength (UCS) of rock samples with different artificial intelligent-based methods. In fact, the main advantage of such systems is to readily remove some difficulties arising in direct assessment of UCS, such as time-consuming and costly UCS test procedure. This study puts an effort to propose four accurate and practical predictive models of UCS using artificial neural network (ANN), hybrid ANN with imperialism competitive algorithm (ICA–ANN), hybrid ANN with artificial bee colony (ABC–ANN) and genetic programming (GP) approaches. To reach the aim of the current study, an experimental database containing a total of 71 data sets was set up by performing a number of laboratory tests on the rock samples collected from a tunnel site in Malaysia. To construct the desired predictive models of UCS based on training and test patterns, a combination of several rock characteristics with the most influence on UCS has been used as input parameters, i.e. porosity (n), Schmidt hammer rebound number (R), p-wave velocity (V_p) and point load strength index (Is(50)). To evaluate and compare the prediction precision of the developed models, a series of statistical indices, such as root mean squared error (RMSE), determination coefficient (R²) and variance account for (VAF) are utilized. Based on the simulation results and the measured indices, it was observed that the proposed GP model with the training and test RMSE values 0.0726 and 0.0691, respectively, gives better performance as compared to the other proposed models with values of (0.0740 and 0.0885), (0.0785 and 0.0742), and (0.0746 and 0.0771) for ANN, ICA–ANN and ABC–ANN, respectively. Moreover, a parametric analysis is accomplished on the proposed GP model to further verify its generalization capability. Hence, this GP-based model can be considered as a new applicable equation to accurately estimate the uniaxial compressive strength of granite block samples.

     

Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances

Uniaxial compressive strength (UCS) of rock is crucial for any type of projects constructed in/on rock mass. The test that is conducted to measure the UCS of rock is expensive, time consuming and having sample restriction. For this reason, the UCS of rock may be estimated using simple rock tests such as point load index (I _s(50)), Schmidt hammer (R _n) and p-wave velocity (V _p) tests. To estimate the UCS of granitic rock as a function of relevant rock properties like R _n, p-wave and I _s(50), the rock cores were collected from the face of the Pahang–Selangor fresh water tunnel in Malaysia. Afterwards, 124 samples are prepared and tested in accordance with relevant standards and the dataset is obtained. Further an established dataset is used for estimating the UCS of rock via three-nonlinear prediction tools, namely non-linear multiple regression (NLMR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). After conducting the mentioned models, considering several performance indices including coefficient of determination (R ²), variance account for and root mean squared error and also using simple ranking procedure, the models were examined and the best prediction model was selected. It is concluded that the R ² equal to 0.951 for testing dataset suggests the superiority of the ANFIS model, while these values are 0.651 and 0.886 for NLMR and ANN techniques, respectively. The results pointed out that the ANFIS model can be used for predicting UCS of rocks with higher capacity in comparison with others. However, the developed model may be useful at a preliminary stage of design; it should be used with caution and only for the specified rock types.     

Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles

Neural Computing and Applications - Rock-socketed piles are commonly used in foundations built in soft ground, and thus, their bearing capacity is a key issue of universal concern in research,...     

Polybutadiene/polyhedral oligomeric silsesquioxane nanohybrid: investigation of various reactants in polyesterification reaction

Nano‐sized polyhedral oligomeric silsesquioxane (POSS) diol or ethylene glycol (EG) as diol monomer was incorporated into hydroxyl‐terminated polybutadiene (HTPBD) chain in the presence of fumaryl or thionyl chloride as extenders. Using these polyesterification reactions, two fumarate‐based polyesters and two polyester sulfites were synthesized. Each couple of polyesters and polyester sulfites includes a linear (diol:EG) and a nanohybrid macromer (diol:POSS). Full structural characterization was performed using Fourier transform infrared, ¹H NMR and ¹³C NMR spectroscopies. Gel permeation chromatography was undertaken to study polyesterification mechanisms by deconvolution of the obtained traces. Finally, differential scanning calorimetry, thermogravimetric analysis and cell culture were performed to evaluate the structure–property relationship for the synthesized macromers in comparison with unreacted HTPBD. © 2016 Society of Chemical Industry     

Properties of waste‐poly(ethylene terephthalate)/acrylonitrile butadiene styrene blends compatibilized with maleated acrylonitrile butadiene styrene

Waste poly(ethylene terephthalate) (W‐PET)/acrylonitrile‐butadiene‐styrene (ABS) blends were prepared with a variety of compositions at several rotor speeds in an internal mixer, replacing ABS with different maleated ABS (ABS‐g‐MA) samples in compatibilized blends. A Box–Behnken model for three variables, with three levels, was chosen for the experimental design. ABS‐g‐MA‐based samples exhibited finer particles with a more uniform particle size distribution than ABS‐based ones, as a consequence of the compatibilizing process. Rheological results implied a greater elastic nature for compatibilized blends which increased in the presence of more ABS content; the same trend was observed for complex viscosity. With increasing ABS‐g‐MA or MA concentration, more shear thinning behavior was observed similar to that of ABS; whereas the uncompatibilized blends showed Newtonian behavior like that of W‐PET. The observed shifting in T_gW‐PET and T_gABS obtained from dynamic mechanical thermal analysis confirmed the good compatibility in W‐PET/ABS‐g‐MA blends in contrast with that in ordinary W‐PET/ABS blends. The mechanical properties were measured and modeled versus the various factors considered in a response surface methodology. The experimental data found a good fit with the obtained equation models. The mechanical properties of the compatibilized blends showed a large positive deviation from the mixing rule, while the uncompatibilized samples had lower properties, even compared to those predicted by the mixing rule. J. VINYL ADDIT. TECHNOL., 2010. © 2010 Society of Plastics Engineers     

Second-order data obtained from differential pulse voltammetry: Determination of tryptophan at a gold nanoparticles decorated multiwalled carbon nanotube modified glassy carbon electrode

Three-way data obtained from different pulse heights of differential pulse voltammetry (DPV) was analyzed using multivariate curve resolution by alternating least squares (MCR-ALS) algorithm. Differential pulse voltammograms of tryptophan were recorded at a gold nanoparticles decorated multiwalled carbon nanotube modified glassy carbon electrode (GCE/MWCNTs-nanoAu). The determination of tryptophan was performed even in the presence of unexpected electroactive interference(s). Both the simulated and experimental data were non-bilinear. Therefore a potential shift algorithm was used to correct the observed shift in the data. After correction, the data was augmented and MCR-ALS was applied to the augmented data. A relative error of prediction of less than 8% for the determination of the simulated analyte of interest and tryptophan in synthetic samples indicated that the methodology employing voltammetry and second-order calibration could be applied to complex analytical systems.     

An investigation on the microstructural evolution and mechanical properties of A380 aluminum alloy during SIMA process

Microstructural evolution and tensile properties of thixoformed near net shape parts of A380 aluminum alloy have been studied. The effect of plastic deformation and holding time in the appropriate semi-solid temperature has been investigated in the strain-induced melt activating process (SIMA). The results of image analysis showed that by increasing the deformation value from 5% to 11%, the recrystallized α-Al grains gradually refined as a result of the increasing the nucleation rate. At the value of 11% plastic deformation a dominant globular structure of α-Al grains was obtained. However, further increasing in the plastic deformation (14%) reduced considerably the shape factor. The equivalent diameter and the shape factor of the globular grains were 70.3 μm and 79%, respectively, after about 11% of plastic deformation. Holding time of the specimens at the semi-solid temperature has been studied from zero to 30 min. It was found that the holding time of 25 min is the best to prevent grain growth. The mechanical properties of thixoformed alloys were also investigated. The results showed that the yield, tensile strength and elongation have been increased considerably using thixoforming, compared with the as-cast condition.