iosr-JMCE   Volume-3 ~ Issue-1

Abstract:Reliability centered maintenance (RCM) methodology was originally developed by the U.S. commercial airlines industry in the year of 1960 for identifying applicable and effective preventive maintenance tasks. Safety systems are usually stand by systems and they are periodically to reveal and repair failures that may have occurred since the previous activation or inspection.RCM approach has been applied to the goliath crane system of transmission tower industry for research work reported in this paper.RCM methodology can be applied to Indian industry for reduction of breakdowns as well as optimization of preventive maintenance cost. In the present study preventive maintenance task suggested for power transmission subsystem, guiding and transportation subsystem, hydraulic subsystem and supply current subsystem in goliath train.
Keywords-Goliath crane system, optimization ,rcm, transmission tower

[1] Deshpande V. S. & Modak J.P. "Reliability Engg. & System safety",Elsevier Sci Ltd. 80 (2003) 1-18
[2] Society of automotive engineers (SAE), and Potential Failure Mode and Effects Analysis machinery (Machinery FMEA), June
2000.
[3] Sushil Kumar Shrivastava , "Maintenance Engineering and Management", S.chand and co. ltd new Delhi, 1998
[4] Jones Richard B. "Risk based management", Mumbai jaico publishing house ,1997

Abstract: This paper presents the results of comparative study conducted to evaluate the strength properties of high volume flyash concrete(HVFAC) using a cement replacement level of 50%, 55% and 60% in M20 and M25 grades of concrete. In this investigation an attempt has been made to study the strength properties of HVFAC specimens in comparison with control OPC concrete specimens of same grade. It is observed that the compressive strength of HVFAC is improved by about 50% cement replacement by fly ash with 1.5% of plasticizer was equal to control concrete.
Keywords: Flyash, plasticizer, Compressive strength, Splitting Tensile strength, Flexural Strength

[1] Dhir RK, Hubbard FH, Munday JGL, Jones MR, Duerden SL. Contribution of PFA to concrete workability and strength
development. Cement Concrete Research 1988;18(2):277–89.
[2] Mark Reivier and Kevins Rens, high volume fly ash concrete; analysis and application, practice, periodical on structural design and
construction @ ASCE, 2006.
[3] Ravina.D, Methta.P., "Properties of fresh concrete containing large amounts of fly ash" cement and concrete research,
vol,16,PP227-238,1986.
[4] IS-12269-1987, "Specification for 53 grade OPC" BIS New Delhi.
[5] IS – 3812-1981, "Specification for fly ash for use as pozzolanic and admixture' BIS New Delhi.
[6] IS 10262:2009, "Concrete Mix Proportioning-Guidelines"
[7] IS:516 – 1959, Methods of tests for strength of concrete, edition 1.2(1991-07)

Abstract: As widely known, concrete is an essential material in civil engineering. However, its properties can vary considerably, depending on the nature and proportions of its constituents, the construction methods applied to create it, and the loading and environmental conditions to which it will be subjected over time. Therefore, the development of control methods to determine the condition and ascertain the quality of concrete is critical. Ultrasonic methods can play an important role in this area, since they allow us to monitor the density and homogeneity of concrete, providing information about the strength development and the existence of internal flaws and defects. To ensure that the information provided is reliable, expert knowledge is needed for interpreting the ultrasonic data. Computational tools that support the interpretation of ultrasonic (and other NDT) data might facilitate the job, reducing bias and helping specialists to analyze, in a consistent way, the great amount of data generated by the test. In this context, the use of Artificial Neural Networks (ANN) techniques is seen as a viable and adequate strategy to develop such tools. This study is focused on the evaluation of the feasibility of developing a specialist ANN tool. This kind of technique allows the emulation of the processes of specialists when dealing with uncertainty. Using a neural model, it is Possible to establish a non-linear correlation between known input data, such as ultrasonic readings and a certain output in this case, compressive strength, because this is the most used parameter to determine concrete quality.
Keywords: Compressive strength, Non-destructive testing, Ultrasonic pulse velocity, Regression analysis, Artificial neural network.

[1] Qasrawi H. Y., "Concrete strength by combined nondestructive methods simply and reliablityy‟, Cement and Concrete Research, 30 (Civil), 739-746, 2000.
[2] Tapkin S., Tucan M., Arioz O., Tucan A., "Estimation of Concrete Compressive Strength by Using Ultrasonic Pulse Velocities and
Artificial Neural Network‟, 1-7.
[3] Erdal M., "Prediction of the compressive strength of vacuum processed concretes using artificial neural network and regression
techniques‟, Scientific Research and Essay 4 (10), 1057-1065, 2009.
[4] Kewalramani M., Gupta R., "Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural
networks‟, Automation in Construction 15 , 374 – 379, 2006.
[5] Noorzaei J., Hakim S., Jaafar M., Thanoon W., "Development of Artificial Neural Network For Predicting Concrete Compressive
Strength‟, International Journal of Engineering and Technology, 4( 2), 141-153, 2007.
[6] Hola J., Schabowicz K., "Application of Artificial Neural Netwoks To Determine Concrete compressive Strength Based On Non -
destructive Test‟, Journal of Civil Engineering And Management, XI (1), 23-32, 2005