IMPACT : International Journal of Research in Engineering & Technology

Number 6-9, 2018

1-8
Effect of Waste (PET) Bottle Fibers on the Properties of Concrete

Authors: Ajamu S. O., Ige J. A. & Oyinkanola T. M

Abstract

View in PDF

Number of views: 538

In this study, the effectiveness of waste PolyEthylene Terephthalate (PET) fibers in improving the properties of concretewas investigated. Recycling ofwaste PET bottles is a daunting task in developing countries due to inadequate recycling facilities. The addition of PET fibers in concrete has the potential of resisting the growth of minor internal cracks in the concrete, by preventing the crack width from widening under an applied load. The fibers were obtained by shredding the waste PET bottles into microfibers of 50 mm length and 5mm width, the fibers were then added to the concrete mix in fiber volume fractions of 0.5%, 1.0%, 1.5%, and 2.0%. The properties of the fresh concrete mix were measured using a slump test to evaluate the concrete workability. The hardened concrete properties were determined using a compressive strength test to determine the concrete strength at 7 and 28 days. The results of slump test showedthat the use of PET fibers in concrete mixtures slightly reduce the concrete workability. The addition of PET fibers to concrete increases the compressive strength of concrete at lower fiber volume contentand reduces the compressive strength of concrete at higher fiber volume content.The highest compressive strength of concrete was at 1.0% fiber volume addition, with a 15% increase in compressive strength at 28 days than the plain concrete. The study concludes that the addition of waste PET fibers to concrete serves as a means of utilizing the waste generated by PET bottles to increase the strength of concrete.

9-14
Remote Process Automation of Monitoring using Nagios

Authors: Venkata Rohit Gandikota & A. Mary Sowjanya

Abstract

View in PDF

Number of views: 496

With the advent of powerful computing machines and advances in technology, more and more machines are being used on a daily basis. With the increase in the infrastructure, monitoring of said infrastructure has been playing a key role in keeping the working of an organization streamlined and efficient. While modern machines are more than capable of running for days together without the risk of failing, there can be no shortcuts when coming to infrastructure as any of the machines can go down without warning if not properly looked after. This can cause delays which can sometimes extend to days or even months if not attended to immediately. However, the major gripe with the existing manual system is that problem identification has become a roadblock and also there are no ways to predict a failure of a system in advance. A person is always required to constantly keep looking at a screen in order to see if alerts are generated and when indeed they are, if that person is missing from his station then there will be a delay as to when the alert is generated and when it is actually notified to the concerned team. This human involvement becomes time-consuming as the alerts may or may not be generated. Since there is not a lot of work involved and the skills of the person can be put to better use at a different place. The aim of this work is to eliminate the human factor involved by automating the monitoring of infrastructure with the help of software tools and also integrate the alert generation and notification process by using machine learning algorithms and natural language processing. The monitoring tool can be configured according to the needs of the company and the generated alerts can be sent to the concerned team immediately without any delay. As the tool displays real-time data we can predict which system needs to be taken care of immediately and which machine is likely to go down in the future.