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Wollastonite is abundantly available in Rajasthan, Tamil Nadu, Uttarakhand, and Andhra Pradesh states of the Indian Union as a low-cost material. In this study, investigations were made on pastes and mortars to evaluate its potential as a new material for admixing with ordinary portland cement with or without microsilica. Its physical and chemical properties were analyzed. Wollastonite consists of 45.6% of CaO and 48% of SiO2, mostly in amorphous form. It has an average specific surface area of 842.7 m2/kg and retention on 45-micron sieve of 3.20%. When ground to fine powder, it attains an average particle size of 4 microns which is about 4.5 times finer than ordinary portland cement. Scanning electron microscope images show that wollastonite particles were solid, acicular in shape, and have rough surfaces. Several cementitious mix proportions of ordinary portland cement, wollastonite, and microsilica were investigated for normal consistency, initial and final setting time of paste, and compressive strength of mortar. Test results indicate that the mortar, which contains 82.5% cement, 10% wollastonite, and 7.5% microsilica, as cementitious material attains the highest compressive strength. The mortar, which contains 77.5% cement, 15% wollastonite, and 7.5% microsilica, as cementitious material achieves compressive strength higher than the conventional OPC mortar along with rendering maximum cement replacement for better economy of concrete work. It was observed that the compressive strength of mortar varied logarithmically with the days of moist curing and linearly with the proportion of admixing. Suitable predictive models are presented accordingly. [ABSTRACT FROM AUTHOR]

The modeling of traffic noise is more debated around intersections due to traffic flow and road geometry complexity. The available intersection-specific traffic noise models cannot be transferred to predict the traffic noise at intersections in the mid-sized Indian cities due to traffic heterogeneity, variety in driving conditions, and vehicle compositions. This article aims to develop an intersection-specific traffic noise model by collecting data at 19 intersections in Kanpur, India. The data include a wide range of traffic, road, and weather-related variables. Furthermore, significant input variables are determined and used in the statistical regression model to develop an intersection-specific traffic noise model for the mid-sized Indian cities. This study develops a separate entrance and exit arm model based on the corresponding influencing variables. The coefficient of determination (R2) value is 0.74 and 0.69 for the developed model at the entrance and exit arms, respectively, whereas these models achieve R2 values of 0.73 and 0.67 in the validation step. Also, the performance of developed models is evaluated on the standard and mean absolute errors as performance metrics. This study finds that traffic volume and receiver distance are relatively the most important variables in the entrance and exit arm noise models. [ABSTRACT FROM AUTHOR]