Research Papers - Department of Civil Engineering

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/598

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    PublicationOpen Access
    Experience of Automatic Traffic Data Collection for Development of Adjustment Factors in Colombo Suburban
    (Eastern Asia Society for Transportation Studies, 2019-12-31) AHAMED, R. W; LANKATHILAKE, T. N; AMARASINGHA, N
    Annual Average Daily Traffic (AADT) is one of the key parameters in the field of transportation. It is traditionally used for planning and designing purposes in road sector. This research was carried out for development of adjustment factors for AADT estimation two-way two-lane road of Colombo suburban. Malabe-Kaduwele roadway was selected to conduct the research. Data were collected using automatic traffic counter (Metro-Count device) at Malabe-Kaduwele road in front of SLIIT Malabe campus for the period of five and half months. From the data, hourly expansion factors (HEF) and daily expansion factors (DEF) were estimated. The data collection period was not sufficient to develop monthly expansion factors (MEF) but an attempt was made to develop factors for months fall in data collection period. The experience obtained in this study could be used for developing adjustment factors in future.
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    PublicationOpen Access
    Development of a Formula to Quantity Emlsslons Generated from Dlesel Vehicles in Sri Lanka
    (ACEPS-2017, 2017) Konara, K. M. T. N; Samarasekara, G. N; Chaminda, G. G. T; Dissanayaka, A. W; Perera, S. V. T
    Using the combination of optical properties of diesel exhaust and Beer Lambart law, particulate concentration was derved. Major component of the particulate matter of diesel exhaust was elė carbon which was derived from the optical properties of diesel exhaust. Characteriza emission composition was done through literature. According to the Spaciate 4.0 databa state environmental agency, characterization of diesel emission was finalized. Spaciate 4 the diesel exhaust is a primary combination of Organic carbon (31.80%), Elemental ca Sulphate (0, 67%), Nitrate (0.19%) and others including metallic components and etc.(6 that, a balanced chemical equation was formed for the incomplete combustion of the di air. Calculation of CO2, CO and PM was derived based on the stoichiometric ratio of the bäjä chemical equation.
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    PublicationOpen Access
    Development of wind power prediction models for Pawan Danavi wind farm in Sri Lanka
    (Hindawi, 2021-05) Peiris, A. T; Jayasinghe, J. M. J. W.; Rathnayake, U. S
    This paper presents the development of wind power prediction models for a wind farm in Sri Lanka using an artificial neural network (ANN), multiple linear regression (MLR), and power regression (PR) techniques. Power generation data over five years since 2015 were used as the dependent variable in modeling, while the corresponding wind speed and ambient temperature values were used as independent variables. Variation of these three variables over time was analyzed to identify monthly, seasonal, and annual patterns. The monthly patterns are coherent with the seasonal monsoon winds exhibiting little annual variation, in the absence of extreme meteorological changes during the period of 2015–2020. The correlation within each pair of variables was also examined by applying statistical techniques, which are presented in terms of Pearson’s and Spearman’s correlation coefficients. The impact of unit increase (or decrease) in the wind speed and ambient temperature around their mean values on the output power was also quantified. Finally, the accuracy of each model was evaluated by means of the correlation coefficient, root mean squared error (RMSE), bias, and the Nash number. All the models demonstrated acceptable accuracy with correlation coefficient and Nash number closer to 1, very low RMSE, and bias closer to 0. Although the ANN-based model is the most accurate due to advanced features in machine learning, it does not express the generated power output in terms of the independent variables. In contrast, the regression-based statistical models of MLR and PR are advantageous, providing an insight into modeling the power generated by the other wind farms in the same region, which are influenced by similar climate conditions.