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.

     

The effects of a “distinct window” screen design on computer-mediated group decision making

An experiment was performed to test a distinct-window conferencing screen design as an electronic cue of social status differences in computer-mediated group decision-making. The screen design included one distinct window to symbolize high-status, and two nondistinct windows to symbolize low-status. The results indicated that the distinct-window screen design did produce status affects in groups of peers making decisions on judgmental problems. Randomly assigned occupants of the distinct window had greater influence on group decisions and member's attitudes than occupants of nondistinct windows.The authors would like to thank Shyam Kamadolli and Phaderm Nangsue, the programmers who developed the software used in this experiment. We would also like to thank the editor and our three anonymous reviewers for exceedingly helpful comments on an earlier draft of this article.     

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.     

Failure analysis of unidirectional polymeric matrix composites with two serial pin loaded-holes

The objective of this study is to investigate the effects of geometrical and physical parameters on failure modes and failure loads in unidirectional polymeric matrix composites with two serial pin loaded holes, analytically and experimentally. It is assumed that all of unidirectional fibers in the laminate lie in one direction while loaded by a load p ₀ at infinity, parallel to the direction of the fibers. To derive equilibrium equations based on a Shear-Lag theory, a rectangular arrangement of fibers is considered and with the proper use of boundary and boundness conditions, stress and displacement fields are computed within the laminate, along with the surrounding pinholes. Finally by using the Hashin criterion failure modes and failure loads are estimated. To validate analytical results based on shear-lag theory, an experimental program is carried out. A very good agreement is observed between two procedures. Based on results, in small sizes of two pins, the dominant failure mode is bearing and with the increasing of hole sizes, failure modes are changed to tension and shear modes.     

Automatic navigation of mobile robots in unknown environments

Online navigation with known target and unknown obstacles is an interesting problem in mobile robotics. This article presents a technique based on utilization of neural networks and reinforcement learning to enable a mobile robot to learn constructed environments on its own. The robot learns to generate efficient navigation rules automatically without initial settings of rules by experts. This is regarded as the main contribution of this work compared to traditional fuzzy models based on notion of artificial potential fields. The ability for generalization of rules has also been examined. The initial results qualitatively confirmed the efficiency of the model. More experiments showed at least 32 % of improvement in path planning from the first till the third path planning trial in a sample environment. Analysis of the results, limitations, and recommendations is included for future work.     

Synthesis of nano-structured La0.8Ba0.2MnO3 perovskite via a mechano-thermal route

In this study, barium-doped lanthanum manganite, La_0.8Ba_0.2MnO₃, was synthesized via a mechano-thermal route employing high energy ball milling and subsequent heat treatment. The structural evolution, morphology and thermal behaviour of the powders were evaluated using XRD, FESEM, and DTA/TGA, respectively. DTA/TGA results showed that the calcination temperature of the carbonates significantly decreased by increasing the milling time. The results revealed that single phase perovskite was formed at 900 °C in a milled sample for 2 h and this temperature decreased to 600 °C by increasing the milling time to 30 h. The mean crystallite size also decreased from 32 to 20 nm by increasing the milling time from 2 to 30 h. The reaction sequence of La_0.8Ba_0.2MnO₃ formation via the mechano-thermal route is proposed using XRD and DTA/TGA results. FESEM micrographs showed that the mean particle size of the perovskite phase is increased slightly from 30 to 40 nm by increasing the heat treatment temperature from 600 to 900 °C.