International Journal of Engineering and Technology Innovation

Comparative Analysis Between Fly Ash Geopolymer and Reactive Ultra-Fine Fly Ash Geopolymer

2021-06-24T02:44:44+00:00

This study investigates novel geopolymers by combining Reactive Ultra-fine Fly Ash (RUFA) with 4M sodium hydroxide as an alkali activator. Comparing with general fly ash geopolymers, RUFA geopolymer pastes are characterized in terms of compressive strength, microstructure, and crystalline phases. The RUFA geopolymer is successfully obtained as alumina-silicate bonding materials with the same properties as the general fly ash-based geopolymer. The high compressive strength of the RUFA-based geopolymer samples (13.33 MPa) can be attributed primarily to Ca-based alumino-silicate hydration products and Na-based alumino-silicate complexes. This research presents an innovative application for geopolymers using RUFA. In the follow-up study, the influence of synthesis and concentration of alkali activator can be considered in RUFA-based geopolymers.

Tunable Lossy and Lossless Grounded Inductors Using Minimum Active and Passive Components

2021-06-24T02:44:44+00:00

In this contribution, nine new Grounded Inductance Simulators (GISs) using a single Multiple-Output Current Controlled Current Conveyor Transconductance Amplifier (MO-CCCCTA) and one grounded capacitor are proposed. Among them, two are lossless types and seven are lossy types. The use of a single grounded capacitor makes the circuits suitable for fabrication. All the proposed circuits are electronically tunable through the bias currents of MO-CCCCTA. Furthermore, no component matching conditions are needed for realizing them. The designed circuits are verified through PSPICE simulator with ± 0.9 V power supply. The simulation results show that for all the proposed circuits: maximum operating frequencies are about 12 MHz, power dissipation is less than 0.784 mW, Total Harmonic Distortions (THDs) are under 8.09%, and maximum output voltage noise at 1 MHz frequency is 14.094 nV/√Hz. To exhibit the workability of the proposed circuits, they are used to design band-pass, low-pass filter, parallel RLC resonator, and parasitic inductance cancelator.

Numerical and Experimental Study on the Grinding Performance of Ti-Based Super-Alloy

2021-06-24T02:44:44+00:00

The experiments of the surface grinding of Ti-6Al-4V grade 5 alloy (Ti-64) with a resin-bonded cubic Boron Nitride (cBN) grinding wheel are performed in this research to estimate the influence of cutting parameters named workpiece infeed speed, Depth of Cut (DOC), cooling condition on the grinding force, force ratio, and specific energy. A finite element simulation model of single-grain grinding of Ti-64 is also implemented in order to predict the values of grinding forces and temperature. The experimental results show that an increase of workpiece infeed speed creates higher intensified cutting forces than the DOC. The grinding experiments under wet conditions present slightly lower tangential forces, force ratio, and specific energy than those in dry grinding. The simulation outcomes exhibit that the relative deviation of simulated and experimental forces is in the range of 1-15%. The increase in feed rate considerably reduces grinding temperature, while enhancement of DOC elevates the heat generation in the cutting zone.

Determination of the Compressive Strength of Concrete Using Artificial Neural Network

2021-06-29T03:13:46+00:00

The objective of the work is to estimate the compressive strength of concrete by means of the application of Artificial Neural Networks (ANNs). A database is created with design variables of mixtures of 175, 210, and 280 kgf/cm², which are collected from certified laboratories of soil mechanics and concrete of the city of Jaen. In addition, Weka software is used for the selection of the variables and Matlab software is used for the learning, training, and validation stages of ANNs. Five ANNs are proposed to estimate the compressive strength of concrete at 7^th, 14^th, and 28^th day. The results show that the networks obtain the average error of 4.69% and are composed of an input layer with eleven neurons, two hidden layers with nine neurons each, and the compressive strength of concrete as the output. This method is effective and valid for estimating the compressive strength of concrete as a non-destructive alternative for quality control in the construction industry.

A Framework for Crop Disease Detection Using Feature Fusion Method

2021-06-24T02:44:44+00:00

Crop disease detection methods vary from traditional machine learning, which uses Hand-Crafted Features (HCF) to the current deep learning techniques that utilize deep features. In this study, a hybrid framework is designed for crop disease detection using feature fusion. Convolutional Neural Network (CNN) is used for high level features that are fused with HCF. Cepstral coefficients of RGB images are presented as one of the features along with the other popular HCF. The proposed hybrid model is tested on the whole leaf images and also on the image patches which have individual lesions. The experimental results give an enhanced performance with a classification accuracy of 99.93% for the whole leaf images and 99.74% for the images with individual lesions. The proposed model also shows a significant improvement in comparison to the state-of-art techniques. The improved results show the prominence of feature fusion and establish cepstral coefficients as a pertinent feature for crop disease detection.

Effect of Zeolite on the Compaction Properties and California Bearing Ratio (CBR) of Cemented Sand

2021-06-24T02:44:45+00:00

This research investigates the impact of zeolite on the compaction properties and California Bearing Ratio (CBR) of cemented sand. For this purpose, firstly, sand, cement (2, 4, 6, and 8% by the sand dry weight), and zeolite (0%, 30%, 60%, and 90% of cement content, as a replacement material) are mixed. Then, various cylindrical samples with sizes of 101×116 mm and 119×152 mm are prepared for compaction and CBR tests, respectively. After curing for 28 days, the samples are tested according to the standards of compaction and CBR tests. The results depict that the use of zeolite reduces Maximum Dry Density (MDD) while it increases Optimum Moisture Content (OMC) of cemented sand. Furthermore, the inclusion of zeolite up to 30% of cement content contributes to the highest CBR values due to the pozzolanic and chemical reactions. Finally, some correlations with high correlation coefficients are proposed between the CBR and MDD of zeolite-cemented sand.