Sourav Bhattacharya1 , Saurav Ranjan Das
2
[Vol. 04 (01), June, 2023, pp. 01-07
]
Use of polymer modified concrete as a construction material for structural applications in recent years is becoming gradually popular. Among the Polymers, Styrene-Butadiene Rubber (SBR)-latex modified concrete has proven strength and healthiness properties. From various Literature survey, we came to know that Styrene contributes rigidity and chemical resistance to the polymer, while butadiene contributes flexibility and elasticity. The random copolymerization of these two monomers in SBR results in a polymer with a wide range of properties, including good abrasion resistance, low-temperature flexibility, and high tensile strength. In different ratio of Styrene-Butadiene Rubber (SBR) latex-cement shows that compressive strength and flexural strength were determined at different ages. It has been observed that SBR latex has negative effect at early age while at 28 days, the addition of SBR latex in concrete results in enhancement of compressive strength as well as Flexural Strength. It can be used as an additive in concrete repair mortars, helping to improve the bond between existing concrete surfaces and the repair material, and reducing the risk of cracks forming in the repaired area, increases compressive and Flexural Strength compared with samples having old and new concrete without bonding layer.
Rangan Mukherjee1, Ruma Ghosh2
[Vol. 04 (01), June, 2023, pp. 01-11
]
As toxic substances can affect living things as well as cause disease and death in humans, air pollution has recently become a serious social issue. Currently, a vast amount of data is available to get insightful knowledge into user profiles that contain dynamic contextual information due to the popularity of IoT devices and the accessibility of wireless networks. Massive amounts of data are continuously produced by IoT sensors. Raw sensor data must be understood and used in a context-aware computing platform in order to be valuable, though. In this work an array of heterogeneous sensors coupled to an Arduino 2560 board are used to measure the concentrations of CO, NO2 ,CO2, particulate matter like PM1.5, PM2.5 and PM10 along with temperature and humidity. Later the accumulated data analyzed for identification of different classroom events using automated technique.