Research Papers - Department of Civil Engineering

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Start date: 2023Subject: search.filters.subject.Sri Lanka

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    PublicationEmbargo
    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, P
    Sri 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.
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    PublicationOpen 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-Feng
    This 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.
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    PublicationOpen Access
    Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka
    (IEEE, 2023-01) Rathnayake, N; Rathnayake, U; Chathuranika, I; Dang, T. L; Hoshino, Y
    The Rainfall-Runoff (R-R) relationship is essential to the hydrological cycle. Sophisticated hydrological models can accurately investigate R-R relationships; however, they require many data. Therefore, machine learning and soft computing techniques have taken the attention in the environment of limited hydrological, meteorological, and geological data. The accuracy of such models depends on the various parameters, including the quality of inputs and outputs and the used algorithms. However, identifying a perfect algorithm is still challenging. This study develops a fuzzy logic-based algorithm called Cascaded-ANFIS to accurately predict runoff based on rainfall. The model was compared against three regression algorithms: Long Short-Term Memory, Grated Recurrent Unit, and Recurrent Neural Networks. These algorithms have been selected due to their outstanding performances in similar studies. The models were tested on the Mahaweli River, the longest in Sri Lanka. The results showcase that the Cascaded-ANFIS-based model outperforms the other algorithms. The correlation coefficient of each algorithm’s predictions was 0.9330, 0.9120, 0.9133, 0.8915, 0.6811, 0.6811, and 0.6734 for the Cascaded-ANFIS, LSTM, GRU, RNN, Linear, Ridge, and Lasso regression models respectively. Hence, this study concludes that the proposed algorithm is 21% more accurate than the second-best LSTM algorithm. In addition, Shared Socio-economic Pathways (SSP2-4.5 and SSP5-8.5 scenarios) were used to generate future rainfalls, forecast the near-future and mid-future water levels, and identify potential flood events. The future forecasting results indicate a decrease in flood events and magnitudes in both SSP2-4.5 and SSP5-8.5 scenarios. Furthermore, the SSP5-8.5 scenario shows drought weather from May to August yearly. The results of this study can effectively be used to manage and control water resources and mitigate flood damages.