iosr-JMCE   Volume-17 ~ Issue-5 ~ Sep. - Oct. 2020

Abstract: The use of steel, one of the essential materials for structural reinforcement in the construction industry, is plagued by rise in cost, degradation of non–renewable materials, pollution due to industrial production and sharp practices on quality. In the light of this, bamboo, being a common plant growing all over the world, strong, dense, flexible and elastic in nature, is being considered as an alternative reinforcement material in concrete structures. This work assessed the structural performance of bamboo relative to its ability to structurally support concrete in compression. Matured and seasoned bamboo culms of over 40 and 10 mm in diameter and thickness, respectively, were used. Bamboo splints of various width sizes of 12, 16, 20 and 25mm were cut, prepared and used.....

Keywords: steel, bamboo, compression stress, flexure and tensile strength

[1]. Adewuyi, A. P., Otukoya, A. A., Olaniyi, O. A. &Olafusi, O.S. (2015) Comparative Studies of Steel, Bamboo and Rattan as Reinforcing Bars in Concrete: Tensile and Flexural Characteristics. Open Journal of Civil Engineering, 5, 228-238. http://dx.doi.org/10.4236/ojce.2015.52023
[2]. Agarwal, A., &Maity, D., (2011) Experimental Investigation on Behaviour of Bamboo Reinforced Concrete Members. 16th International Conference on Composite Structures ICCS
[3]. Alade, G. A., Olutoge, F. A. &Alade, A. A. (2004) The Durability and Mechanical Strength Properties of Bamboo in Reinforced Concrete. Journal of Applied Science, Engineering and Technology, 1, 35-40.
[4]. Austin, R. & Ueda, K. (1972).Bamboo. New York: Weather Hill Publishing.
[5]. Chembi, A. &Nimityongskul, P. (1989) A Bamboo Reinforced Cement Water Tank. Journal

Abstract: The mismatch between solar energy availability and the cooling load energy demands for AC solar refrigeration systems in different geographical locations complicates the design and sizing of milk solar refrigeration systems components. This is caused by variation seasonal solar insolation and different levels of global solar insolation. In this study, three different sizes AC milk solar refrigeration systems, have been investigated for maximum cooling loads developed from the refrigeration systems when exposed to varying levels of solar insolation in Nakuru Kenya.Regression models were developed for predicting maximum cooling loads delivered from the milk solar refrigeration systems based on available mean daily solar insolation of the location. The predictive models developed is useful.....

Keywords: Cooling loads; Solar insolation; AC solar refrigeration system.

[1]. Coste,E., EftimieG. and Serban,P. (2007), Empirical model for estimating global radiation for Brasov urban area, Journal of Information Engineering and Application, 10(2),2224-5782
[2]. Pongsid,S, Santha,G. and Supachart.C. (2001), Review of Vapour Absorption Refrigeration Technologies. Renewable and sustainable Energy Journal, (5),
[3]. Lopez,G, Rubio,M. andButles.F. (2009), Estimation of hourly direct normal Radiation from measured lobal solar radiation in Spain, Renewable Energy, (21), 175-186.
[4]. Sendanayeke,S., Miguntana,N.P. and Jayasinghe.M. (2014), Estimating Incident Solar Radiation in Tropical Islands with Short Term Weather Data, European Scientific Journal, 3 (2014), 1857-2881
[5]. Lovell,S. and Vickers,L.E. (2005), Cogeneration of heat and electricity in spray drying plant, Progress in Photovoltaic: Research and Application, 12(6), 467-479.

Abstract: Companies in auto Industry are confronting a focused test to enhance and improve on quality of segments utilized as a part of a car. Nature of paint and welding on a vehicle are critical components which affects the general nature of the item. In the keep going few years, forms, supplies and apparatus have experienced quick mechanical changes, accordingly requesting even more particular devices to control and survey the quality parameters. This paper displays a definite writing audit to evaluate and enhance the nature of painting and welding in the auto business

Keywords: ..........

[1]. Akanksha Tiwari. (2013). Application of TQM and Engineering Techniques to Optimize Quality of Paint in automobile. International Journal of Mechanical Engineering and Research, 565-572.
[2]. Asakawa N. ( 1997). Robotics and Automation. International Journal of Electric and Electronic Engineering , 1875 - 1879 vol.3.
[3]. Carl Occhialini. (2013). Robotic Welding of Aluminum space Frames Speeds Introduction of Sports Car . Welding Journal, 4.
[4]. Daimler Benz Ag. (2014, March 29). Electrostatic spray arrangement. Patent Journal.
[5]. Dick Ghiselin. (2013, November 04). All-electric industrial robot aims to improve quality, efficiency, safety. Retrieved from Offshore: http://www.offshore-mag.com/articles/print/volume-73/issue-4/drilling-and-completion/all-electric-industrial-robot-aims-to-improve-quality-efficiency.html

Abstract: Background:The project cost is an inevitable step in the conceptual phase of the project, as it can influence or change the project scope. The cost estimate at this stage faces many difficulties due to the limited data at this stage. This research aims to develop a Regression models for estimating the cost of construction projects in the conceptual stage. The proposed model will serve as a tool that could be used by all parties involved in the project and will facilitate the cost estimation process at the conceptual phases of projects through more effective utilization of the limited available information. Materials and Methods: The regression analysis techniques are the mathematical methods which used widely in several fields. Seven factors affecting cost estimation..........

Key Word: construction cost; residential building; regression analysis;Fuzzy logic.

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[2]. A. Idri, A. Abran, A fuzzy logic based set of measures for software project similarity: Validation and possible improvements, Int. Softw. Metrics Symp. Proc., 2001: pp. 85–96. https://doi.org/10.1109/metric.2001.915518.
[3]. K.J. Kim, K. Kim, Preliminary cost estimation model using case-based reasoning and genetic algorithms, J. Comput. Civ. Eng. 24 (2010) 499–505. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000054.
[4]. G.L. Feng, L. Li, Application of genetic algorithm and neural network in construction cost estimate, Adv. Mater. Res., 2013: pp. 3194–3198. https://doi.org/10.4028/www.scientific.net/AMR.756-759.3194.
[5]. Y.R. Wang, C.Y. Yu, H.H. Chan, Predicting construction cost and schedule success using artificial neural networks ensemble and support vector machines classification models, Int. J. Proj. Manag. 30 (2012) 470–478. https://doi.org/10.1016/j.ijproman.2011.09.002.