Journal of Civil and Environmental Engineering

Emission of greenhouse gases (GHGs) from mechanically ventilated deep pit manure storage was monitored in a swine gestation operation. Air samples were collected from pit exhaust fans at different times of the year (fall, summer, and spring) using a vacuum chamber and Tedlar bags. GHGs concentrations were measured with a greenhouse gas chromatograph (GC) within 24 hours of collection. Air flow rates from exhaust fans were measured using a 160 mm bi-directional Gill propeller anemometer and the ventilation rate was determined as the summation of air flow rates from all fans. The average methane (CH4) concentration was 88±61 ppm and CH4 concentration differences were statistically significant among sampling dates and seasons. The carbon dioxide (CO2) concentration followed the same trend as CH4. The average CO2 concentration was 1105±1063 ppm. Nitrous oxide (N2O) concentrations ranged from 0.02 to 0.66 ppm. Methane emissions varied between 115.94 to 572.18 g d-1 AU-1 and higher methane emission was observed during summer (480.28 g d-1 AU-1). The average carbon dioxide emissions varied from 5.35 to 15.83 kg d-11 AU-1, whereas average N2O emissions varied from 0.06 to 7.30 g d-1AU-1. Significant variation of GHG concentrations and emissions were observed among fall, summer and spring seasons.

This paper the findings of an environmental impact study aimed at determining traffic-generated noise pollution impact of new Township Redevelopment Project in Ahmadi, Kuwait. The specific objectives of the study were no measure traffic flow variables; Traffic-generated noise; examine and compare noise pollution compliance with the EPA standards; and recommend mitigation measures. Eight representative roadway locations were systematic – randomly selected for monitoring traffic flow and noise levels. At each monitoring site, the study variables were monitored during six daily peak periods. In addition, data were also collected on atmospheric conditions – temperature, wind velocity and direction and humidity for each monitoring day.

Problem statement: In the absence of long-range strategic plans, the urban infrastructural growth in Kuwait has been accompanied with significant adverse impacts on the urban environment, and has resulted in the deterioration of the quality of urban life. With this continuous growth, a growing percentage of urban population will be adversely affected by traffic noise pollution. Approach: Traffic-generated noise pollution was monitored at nearly four roadway locations in four districts in metropolitan Kuwait in 2007-2008. At each district, a sample of freeway, arterial, collector, and local residential streets were included in the noise and traffic flow monitoring plan. In addition to the analysis of noise, flow, and their interrelationships, three models – two neural network models and one regression model, were employed to predict traffic noise pollutions. Results: Five uncorrelated components of the noise pollution were used as the ANN model input to predict noise pollution using a back propagation neural network (BPNN), general regression neural network (GRNN) algorithm and a general regression model. The model inputs were the number of vehicles, the equivalent number of cars per hour, the heavy vehicle percentage, the width of road and the average height of buildings facing the road. The models optimum architectures were determined for BPNN model by varying the number of hidden layers, hidden transfer function, test set size percentages, and initial weights. Conclusion: Findings indicate that traffic noise is at or above, the standard outdoor limits in most locations, and especially at arterial roadways and freeways. Comparison of the two prediction results showed that GRNN had the ability to calibrate the multi-component traffic noise and yield reliable results close to that by direct measurements. It was concluded that the optimal BPNN model used in this study provided reasonable predictions of noise profiles for all the data sets employed in this study, with two parallel hidden layer back-propagation showing the best overall prediction. This research has demonstrated the great potential of GRNN modeling technique over BPNN techniques in predicting traffic noise.

In this paper, spruce-steel-spruce flitch joints connected by shot fired nails are subjected to short term loads. The failure modes and design resistance equations are compared with those detailed in Eurocode 5. The formation of a plastic hinge is observed in the nails at the steel-timber interface and Mode I (tensile opening) occurs longitudinally along the grain following the row of nails. The predictive equations of force in Eurocode 5 for dowel-type joints are modified with respect to yield moment and unequal fastener penetration depths to produce a significantly improved predictive equation for force.