Ultimate bearing capacity of strip footing resting on clay soil mixed with tire-derived aggregates

This study investigated the use of recycled tire-derived aggregate (TDA) mixed with kaolin as a method of increasing the ultimate bearing capacity ( UBC) of a strip footing. Thirteen 1g physical modeling tests were prepared in a rigid box of 0.6 m × 0.9 m in plan and 0.6 m in height. During sample preparation, 0%, 20%, 40%, or 60% (by weight) of powdery, shredded, small-sized granular (G 1–4 mm) or large-sized granular (G 5–8 mm) TDA was mixed with the kaolin. A strip footing was then placed on the stabilized kaolin and was caused to fail under stress-controlled conditions to determine the UBC. A rigorous 3D finite element analysis was developed in Optum G-3 to determine the UBC values based on the experimental test results. The experimental results showed that, except for the 20% powdery TDA, the TDA showed an increase in the UBC of the strip footing. When kaolin mixed with 20% G (5–8 mm), the UBC showed a threefold increase over that for the unreinforced case. The test with 20% G (1–4 mm) recorded the highest subgrade modulus. It was observed that the UBC calculated using finite element modeling overestimated the experimental UBC by an average of 9%.     

A polar-based guided multi-objective evolutionary algorithm to search for optimal solutions interested by decision-makers in a logistics network design problem

In practical multi-objective optimization problems, respective decision-makers might be interested in some optimal solutions that have objective values closer to their specified values. Guided multi-objective evolutionary algorithms (guided MOEAs) have been significantly used to guide their evolutionary search direction toward these optimal solutions using by decision makers. However, most guided MOEAs need to be iteratively and interactively evaluated and then guided by decision-makers through re-formulating or re-weighting objectives, and it might negatively affect the algorithms performance. In this paper, a novel guided MOEA that uses a dynamic polar-based region around a particular point in objective space is proposed. Based on the region, new selection operations are designed such that the algorithm can guide the evolutionary search toward optimal solutions that are close to the particular point in objective space without the iterative and interactive efforts. The proposed guided MOEA is tested on the multi-criteria decision-making problem of flexible logistics network design with different desired points. Experimental results show that the proposed guided MOEA outperforms two most effective guided and non-guided MOEAs, R-NSGA-II and NSGA-II.     

Support vector regression methodology for prediction of input displacement of adaptive compliant robotic gripper

The prerequisite for new versatile grippers is the capability to locate and perceive protests in their surroundings. It is realized that automated controllers are profoundly nonlinear frameworks, and a faultless numerical model is hard to get, in this way making it troublesome to control utilizing tried and true procedure. Here, a design of an adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize specific shapes of the grasping objects. Since the conventional control strategy is a very challenging task, soft computing based controllers are considered as potential candidates for such an application. In this study, the polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) to estimate and predict optimal inputs displacement of the gripper according to experimental tests and shapes of grasping objects. Instead of minimizing the observed training error, SVR _poly and SVR _rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVR approach compared to other soft computing methodology.     

A survey on video content rating: taxonomy,challenges and open issues

Multimedia Tools and Applications - Rating a video based on its content is one of the most important solutions to classify videos for audience age groups. In this regard, Film content rating and TV...     

Self-generation of reward by logarithmic transformation of multiple sensor evaluations

Artificial Life and Robotics - Although the design of the reward function in reinforcement learning is important, it is difficult to design a system that can adapt to a variety of environments and...     

BASE: A bacteria foraging algorithm for cell formation with sequence data

A cellular manufacturing system (CMS) is considered an efficient production strategy for batch type production. A CMS relies on the principle of grouping machines into machine cells and grouping parts into part families on the basis of pertinent similarity measures. The bacteria foraging algorithm (BFA) is a newly developed computation technique extracted from the social foraging behavior of Escherichia coli (E. coli) bacteria. Ever since Kevin M. Passino invented the BFA, one of the main challenges has been employment of the algorithm to problem areas other than those for which the algorithm was proposed. This research work studies the first applications of this emerging novel optimization algorithm to the cell formation (CF) problem considering the operation sequence. In addition, a newly developed BFA-based optimization algorithm for CF based on operation sequences is discussed. In this paper, an attempt is made to solve the CF problem, while taking into consideration the number of voids in the cells and the number of inter-cell travels based on operational sequences of the parts visited by the machines. The BFA is suggested to create machine cells and part families. The performance of the proposed algorithm is compared with that of a number of algorithms that are most commonly used and reported in the corresponding scientific literature, such as the CASE clustering algorithm for sequence data, the ACCORD bicriterion clustering algorithm and modified ART1, and using a defined performance measure known as group technology efficiency and bond efficiency. The results show better performance of the proposed algorithm.     

Separation of acid–water mixtures by pervaporation using nanoparticle filled mixed matrix copolymer membranes

BACKGROUND: Low energy and less expensive membrane based separation of acetic acid‐water mixtures would be a better alternative to conventional separation processes. However, suitable acid resistant membranes are still lacking. Thus, the objective of the present study was to develop mixed matrix membrane (MMM) which would allow high flux and water selectivity over a wide range of feed concentrations of acid in water. RESULTS: Three MMMs, namely PANBA0.5, PANBA1.5 and PANBA3 were made by emulsion copolymerization of acrylonitrile (AN) and butyl acrylate (BA) with 5.5:1 comonomer ratio and in situ incorporation of 0.5, 1.5 and 3 wt%, sodium montmorilonite (Na‐MMT) nanofillers, respectively. For a feed concentration of 99.5 wt% of acid in water the membranes show good permeation flux (2.61, 3.19, 3.97 kg m^?2 h^?1 µm^?1, for PANBA0.5, PANBA1.5 and PANBA3 membrane, respectively) and very high separation factors for water (1473, 1370, 1292 for PANBA0.5, PANBA1.5 and PANBA3 membrane, respectively) at 30 °C. Similarly for a dilute acid–water solution, i.e. for 71.6 wt% acid the membrane showed a very high thickness normalize flux (8.67, 9.44, 11.56 kg m^?2 h^?1 µm^?1, for PANBA0.5, PANBA1.5 and PANBA3 membrane, respectively) and good water selectivity (101.7, 95.3, 79 for PANBA0.5, PANBA1.5 and PANBA3 membrane, respectively) at the same feed temperature. The permeation ratio, permeability, diffusion coefficient and activation energy for permeation of the membranes were also estimated. CONCLUSION: Unlike most of the reported membranes, the present MMMs allowed high flux and selectivity over a wide range of feed concentrations. These membranes may also be effective for separating other similar organic‐water mixtures. Copyright © 2012 Society of Chemical Industry