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The coal based thermal power plant in Malaysia produces annually around 1.7 million tons of coal bottom ash (CBA) as a discarded material which poses great environmental problems. Hence, the objective of this study is to utilize ground CBA as a supplementary cementing material in concrete. The oven dried original CBA was grinded for 20hours to achieve required fineness. The mixture of concrete was prepared with CBA proportion of 10, 20 and 30% by weight of cement. For the evaluation ofdensity, water absorption, compressive and tensile strength of concrete, 48 specimens were cast. The workability of fresh mix concrete was also evaluated, and it was found to be decreased as the quantity of CBA increased in the mixture. However, the compressive and splitting tensile strength of concrete was also reduced with the addition of ground CBA but with 10% replacement has attended the targeted compressive strength at the age of 28days.Besides that, it was also observed that the influence of CBA on concrete is obvious in density as well as in water absorption. The density of concrete gradually decreased due to addition of ground CBA, concrete containing 10% ground CBA was likely to be similar to that of control mix and the opposite performance was noticed in water absorption.Hence, experimental findings of this study indicated potentiality of ground CBA as supplementary cementing material in concrete construction which will reduces the environmental concern.

Traffic Analysis has been a problem that city planners have dealt with for years. Smarter ways are being developed to analyze traffic and streamline the process. Analysis of traffic may account for the number of vehicles in an area per some arbitrary time period and the class of vehicles. People have designed such mechanism for decades now but most of them involve use of sensors to detect the vehicles i.e. a couple of proximity sensors to calculate the direction of the moving vehicle and to keep the vehicle count. Even though over the time these systems have matured and are highly effective, they are not very budget friendly. The problem is such systems require maintenance and periodic calibration. Therefore, this study has purposed a vision based vehicle counting and classification system. The system involves capturing of frames from the video to perform background subtraction in order detect and count the vehicles using Gaussian Mixture Model (GMM) background subtraction then it classifies the vehicles by comparing the contour areas to the assumed values. The substantial contribution of the work is the comparison of two classification methods. Classification has been implemented using Contour Comparison (CC) as well as Bag of Features (BoF) and Support Vector Machine (SVM) method.