Research Papers - Department of Civil Engineering
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Publication Embargo Long-term recovery from the 2004 Indian ocean tsunami in two Sri Lankan east coast municipalities(Elsevier Ltd, 2026-01) Thamboo, J; Josiah, R; Saja, A; Salah, P; Rossetto, T; Dias, PSri Lanka was the second most affected country after Indonesia, in the 2004 Boxing Day Indian Ocean tsunami (IOT). A study mission was therefore carried out twenty years after the 2004 IOT to assess the recovery of the affected regions, especially in the Eastern region of Sri Lanka, focusing on two of the most affected municipalities, i.e. Kalmunai and Batticaloa. The social and infrastructure characteristics of resettlements/relocations/new settlements in the affected regions, presence of critical infrastructure, preparedness and early warning systems installed have been assessed. It was observed that similar approaches have been adopted to plan the community relocation in both of these municipalities, while the significant reemergence of residential and commercial developments in the coastal stretches of Kalmunai municipality have been noted. Exposure analyses have revealed that there are still some critical infrastructure situated in the tsunami hazard zones. It can be construed that these municipalities have recovered from the physical losses incurred, and spatial planning is in place for future developments considering the tsunami risk. Challenges and opportunities from their differing geographical contexts appear to have been judiciously handled. However, shortcomings are noted in actual implementation due to various reasons, such as limited resources, availability of funding and preference of communities to live close to their original lands. Improving the resilience of infrastructure by designing against the expected tsunami hazard and multi-hazards, regular verification of the early warning systems and evacuation procedures are emphasized to mitigate the impacts from future tsunami.Publication Open Access Profiling Microplastic Pollution in Surface Water Bodies in the Most Urbanized City of Sri Lanka and Its Suburbs to Understand the Underlying Factors(Springer, Cham, 2023-02-23) Bandara, R. M. L. S.; Perera, M. D. D.; Gomes, Pattiyage I. A.; Yan, Xu-FengThis study investigated the microplastic pollution of surface waters in and around the most populated and urbanized city in Sri Lanka from 2019 to 2022. The sampling regime was designed to cover the rainfall-driven hydrology and varying levels of urbanization approximated by the built area fraction. Mass and particle concentrations of microplastics ranged from undetected to 0.01 g/L (average ± standard deviation: 0.00464 ± 0.00528 g/L) and from 2 to 36 particles/L (5.3 ± 6.9), respectively. The highest microplastic pollution was observed in the lake; however, in many cases it was without a statistically significant (P < 0.05) difference with canals. Concentrations in the dry state (i.e., at least 30 days after no rainfall) were about 1.5 times more than the wet state (i.e., at least 50 mm/day rainfall for 10 days) in the lake and in the semi-urban canal, but again, the differences were not significant; however, in urban canals, the concentrations were similar in both states. Over 80% of the microplastics were fibre and fragments. Mass concentrations of microplastics showed moderately positive (Pearson’s r > 0.6) correlations with the built area fraction of the contributing catchment in both states but was significant (P < 0.1) only in the dry state. In the case of particle concentrations, none showed even a weak correlation. The independence of microplastic content against built area fraction and rainfall, as well as twice the concentrations found in point source inputs against the surface waters, gave the following insights. Microplastic content in our study area was governed mostly by the modified catchment hydrology spearheaded by stormwater drainages (some cases trans-catchment) and diffusion factors such as non-residential population.Publication Open Access Spatio-Temporal Rainfall Variability and Concentration over Sri Lanka(Hindawi, 2022-09-28) Pawar, U; Karunathilaka, P; Rathnayake, UChanges in precipitation patterns significantly affect flood and drought hazard management and water resources at local to regional scales. Therefore, the main motivation behind this paper is to examine the spatial and temporal rainfall variability over Sri Lanka by Standardized Rainfall Anomaly Index (SRAI) and Precipitation Concentration Index (PCI) from 1990 to 2019. The Mann–Kendall (MK) trend test and Sen’s slope (SS) were utilized to assess the trend in the precipitation concentration based on PCI. The Inverse Distance Weighting (IDW) interpolation method was incorporated to measure spatial distribution. Precipitation variability analysis showed that seasonal variations are more than those of annual variations. In addition, wet, normal, and dry years were identified over Sri Lanka using SRAI. The maximum SRAI (2.27) was observed for the year 2014 for the last 30 years (1990–2019), which shows the extremely wet year of Sri Lanka. The annual and seasonal PCI analysis showed moderate to irregular rainfall distribution except for the Jaffna and Ratnapura areas (annual scale-positive changes in Katugastota for 21.39% and Wellawaya for 17.6%; seasonal scale-Vavuniya for 33.64%, Trincomalee for 31.26%, and Batticaloa for 18.79% in SWMS). The MK test, SS-test, and percent change analyses reveal that rainfall distribution and concentration change do not show a significant positive or negative change in rainfall pattern in Sri Lanka, despite a few areas which experienced significant positive changes. Therefore, this study suggests that the rainfall in Sri Lanka follows the normal trend of precipitation with variations observed both annually and seasonally.Publication Open Access Global warming potential of English brick manufacturing in Sri Lanka: A cradle to gate analysis(IEEE, 2021-07-27) Herath, I; Karunaratne, S; Dharmarathna, DClay bricks are one of the most commonly used walling materials in Sri Lanka. The brick manufacturing process poses a notable impact on the environment. Clay excavation leads to resource depletion while diesel fuel utilized for excavation and clay transportation pollutes the air. Additionally, tree cutting for wood fuel contributes to deforestation whilst wood burning promotes air pollution. Thus, this research aimed to quantify the global warming potential due to English brick manufacturing using cradle to gate Life Cycle Assessment (LCA) approach. The functional unit used was one-meter square of the brick wall area. Data collection, analysis and interpretation of results were done by the ISO 14044/14040 standards. Primary life cycle inventory data was gathered via brick kiln owners and workers. The LCA assembly was modelled using the ‘OpenLCA’ software. Three product systems were defined according to the brick bond types and wall thicknesses. The global warming potential related to brick production was assessed using the ‘GWP 100a’ method. Clay excavation, clay transportation and wood chamber firing emerged as emission hotspots. The study revealed that the global warming potential of 225mm brick wall and 115mm brick wall as 11.9 and 5.9 kg CO2-eq/m 2 , respectively. Through this study, local clay brick industry can be driven to improve the overall sustainability of the brick manufacturing sector.Publication Open Access Forecasting Electricity Power Generation of Pawan Danavi Wind Farm, Sri Lanka, Using Gene Expression Programming(Hindawi, 2022-05) Herath, D; Jayasinghe, J.M.J.W; Rathnayake, Uis paper presents the development of a wind power forecasting model based on gene expression programming (GEP) for one of the major wind farms in Sri Lanka, Pawan Danavi. With the ever-increasing demand for renewable power generation, Sri Lanka has started harnessing electricity from wind power. ough the initial establishment cost of wind farms is high, the analyses clearly showcased the economic sustainability of wind power generation in long term. In this context, forecasting the wind power generation at Sri Lankan wind farms is important in many ways. However, limited research has been carried out in Sri Lanka to predict the wind power generation against the changing climate. erefore, to overcome this research gap, a model was developed to forecast wind power generation against two climatic factors, viz. on-site wind speed and ambient temperature. e results showcased the robustness and accuracy of the proposed GEP-based forecasting model (with R2 0.92, index of agreement 0.98, and RMSE 259 kW). Moreover, the results of the study were compared against three dierent forecasting models and found comparable in terms of the model accuracy. e GEP-based model is advantageous over machine learning techniques due to its capability in deriving a mathematical expression. As an acceptable relationship was found between wind power generation and climatic factors, the proposed model facilitates the future projection of wind power generations with forecasted climatic factors. ough the application of GEP in the eld of wind power generation is reported in a few research publications, this is the rst research in which GEP is employed to model the power generation with respect to weather indices. e proposed prediction model is advantageous than machine learning models as the relationship between the wind power and the weather indices can be expressed.Publication Open Access Multidecadal Land Use Patterns and Land Surface Temperature Variation in Sri Lanka(Hindawi, 2022-05-16) Samarasinghe, T; Rathnayake, U; Makumbura, R. KAgricultural land conversion due to urbanization, industrialization, and many other factors is one of the significant concerns to food production. Therefore, analyzing the temporal and spatial variation of agricultural lands is an emerging topic in the research world. However, an agrarian country like Sri Lanka was given weaker attention to the temporal and spatial variation of the land use, including the agricultural lands. This study presents an extended analysis of temporal and spatial variation of land use patterns in Sri Lanka, specifically looking at the agricultural land conversion and land surface temperature (LST) change. Remote sensing techniques and geographic information system (GIS) were used for the presented work. The satellite images from three Landsat’s were analyzed for 2000, 2010, and 2020 to identify the potential land use conversions. In addition, LSTs were extracted for the same period. Significant and continuous increases can be seen in the agricultural lands from 33.94% (of total area) in 2000 to 43.2% in 2020. In contrast, the forest areas showcase a relative decrease from 38.51% to 33.82% (of total area) during the analyzed period. In addition, the rate of conversion from agriculture to settlements is higher in the latter decade (2010–2020) compared to the earlier decade (2000–2010). Only general conclusions were drafted based on the LSTs results as they were not extracted in the same months of the year due to high cloud cover. Therefore, the results and conclusions of this study can be effectively used to improve the land use policies in Sri Lanka and lead to a sustainable land use culture.Publication Open Access Evaluation of Satellite Rainfall Products over the Mahaweli River Basin in Sri Lanka(Hindawi, 2022-04) Perera, H; Fernando, S; Gunathilake, M. B; Sirisena, J; Rathnayake, Ue availability of accurate spatiotemporal rainfall data is of utmost importance for reliable predictions from hydroclimatological studies. Challenges and limitations faced due to the absence of dense rain gauge (RG) networks are seen especially in the developing countries. erefore, alternative rainfall measurements such as satellite rainfall products (SRPs) are used when RG networks are scarce or completely do not exist. Noteworthy, rainfall data retrieved from satellites also possess several uncertainties. Hence, these SRPs should essentially be validated beforehand. e Mahaweli River Basin (MRB), the largest river basin in Sri Lanka, is the heart of the country’s water resources contributing to a signi cant share of the hydropower production and agricultural sector. Given the importance of the MRB, this study explored the suitability of SRPs as an alternative for RG data for the basin. Daily rainfall data of six types of SRPs were extracted at 14 locations within the MRB. ereafter, statistical analysis was carried out using continuous and categorical evaluation indices to evaluate the accuracy of SRPs. Nonparametric tests, including the Mann-Kendall and Sen’s slope estimator tests, were used to detect the possibility of trends and the magnitude, respectively. Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) outperformed among all SRPs, while Precipitation Estimation from Remotely Sensed Information using Arti cial Neural Networks (PERSIANN) products showed dire performances. However, IMERG also demonstrated underestimations when compared to RG data. Trend analysis results showcased that the IMERG product agreed more with RG data on monthly and annual time scales while Tropical Rainfall Measurement Mission Multisatellite Precipitation Analysis–3B42 (TRMM-3B42) agreed more on the seasonal scale. Overall, IMERG turned out to be the best alternative among the SRPs analyzed for MRB. However, it was clear that these products possess signi cant errors which cannot be ignored when using them in hydrological applications. e results of the study will be valuable for many parties including river basin authorities, agriculturists, meteorologists, hydrologists, and many other stakeholders.Publication Open Access Self-reported habitual practices and perceptions of young drivers in Sri Lanka(Sri Lanka Society of Transport and Logistics, 2021-03) Amarasingha, N; Firdhaws, H. M. MCrashes on the road have become a significant socio-economic problem. Younger generations, who have lesser experience in driving, are at greater risks of facing road accidents. It is therefore important to identify driving practices and perceptions of young and inexperienced drivers at an early phase of exposure so that factors that improve safe driving can be identified. This study analysed 400 young and inexperienced drivers’ self-reported habitual practices and perceptions based on a questionnaire survey. The questionnaire focused on supervision of early driving, limitation made by parents, accident and traffic offenses by these youngsters as drivers as well as other habitual driving practices. Analyses were done using reliability statistics, inter-item correlation, likelihood ratio tests, and parameter estimates. The highest inter-item correlation value was 0.467 for the pair of overtaking vehicles in restricted areas and taking the chance to speed and run a yellow light when it is about to change to red. Driving after alcohol consumption, mobile phone usage, taking an illegal U-turn at restricted areas, non-use of signals when changing lanes and overtaking a slow driver from the left side were influenced non-use of seat-belts by young drivers. These habitual practices while driving highlighted the importance of early intervention to improve road safety. In conclusion, there is always one or more habitual driving practices that have affect the other driving habits of a young driver.Publication Open Access Regression-Based Prediction of Power Generation at Samanalawewa Hydropower Plant in Sri Lanka Using Machine Learning(Hindawi, 2021-07-31) Ekanayake, P; Wickramasinghe, L; Jayasinghe, J. M; Rathnayake, U. SThis paper presents the development of models for the prediction of power generation at the Samanalawewa hydropower plant, which is one of the major power stations in Sri Lanka. Four regression-based machine learning and statistical techniques were applied to develop the prediction models. Rainfall data at six locations in the catchment area of the Samanalawewa reservoir from 1993 to 2019 were used as the main input variables. The minimum and maximum temperature and evaporation at the reservoir site were also incorporated. The collinearities between the variables were investigated in terms of Pearson’s and Spearman’s correlation coefficients. It was found that rainfall at one location is less impactful on power generation, while that at other locations are highly correlated with each other. Prediction models based on monthly and quarterly data were developed, and their performance was evaluated in terms of the correlation coefficient (R), mean absolute percentage error (MAPE), ratio of the root mean square error (RMSE) to the standard deviation of measured data (RSR), BIAS, and the Nash number. Of the Gaussian process regression (GPR), support vector regression (SVR), multiple linear regression (MLR), and power regression (PR), the machine learning techniques (GPR and SVR) produced the comparably accurate prediction models. Being the most accurate prediction model, the GPR produced the best correlation coefficient closer to 1 with a very less error. This model could be used in predicting the hydropower generation at the Samanalawewa power station using the rainfall forecast.Publication Open 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. SThis 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.