Department of Civil Engineering

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Open Access
COMPARATIVE STUDY ON THE STORMWATER RETENTION OF ORGANIC WASTE SUBSTRATES BIOCHAR, SAWDUST AND WOOD BARK RETRIEVED FROM PSIDIUM GUAJAVA L. SPECIES
(University of Montenegro, 2023) Kader, S.A; Jaufer, L; Bashir, O; Raimi, M. O
This research compares the stormwater retention performances of an organic waste growing medium extracted from the widely available Psidium guajavala L species in Sri Lanka. Rainfall gauges were manually constructed to outsource accurate precipitation data, and the study was conducted throughout the entire month of January 2023. A stormwater retention curve was constructed for the Biochar, Sawdust and Wood bark substrates and the hotspots were compared. Furthermore, the results were validated using a volumetric comparison of water retention. The experimental outcomes have shown that Biochar exhibits strong water retention ability and enables the overlaying vegetation to acquire nutrients without external obstacles. The main reason for this exceptional performance was biochar's low evaporation levels and high porosity. In contrast, Sawdust was found to be the worst performer in terms of water retention due to its high thermal conductivity. These experimental studies were rationalised by outsourcing the specimen from the same tree. Our recommendations suggest that the biochar manufacturing industry needs to be improved in the future since it provides a sustainable and effective alternative in terms of eco-friendly substrates.
Open Access
Influence of Laboratory Synthesized Graphene Oxide on the Morphology and Properties of Cement Mortar
(MDPI, 2023-01) Ganesh, S; Thambiliyagodage, C; Perera, S. V. T. J; Rajapakse, R.K.N.D
The introduction of Graphene Oxide (GO), a nanomaterial, has shown considerable promise in improving the mechanical properties of cement composites. However, the reasons for this improvement are not yet fully understood and demand further research. This study aims to understand the effect of laboratory-produced GO, using Tour’s method, on the mechanical properties and morphology of cement mortar containing GO. The GO was characterized using Fourier-transform infrared spectroscopy, X-ray Photoelectron Spectroscopy (XRD), X-ray powder diffraction, and Raman spectroscopy alongside Scanning electron microscopy (SEM). This study adopted a cement mortar with GO percentages of 0.02, 0.025, 0.03, 0.035, and 0.04 with respect to the weight of the cement. The presence of GO in cement mortar increased the density and decreased the consistency and setting times. At the optimum of 0.03% GO viscous suspension, the mechanical properties such as the 28-day compressive strength, splitting tensile strength, and flexural strength were enhanced by 41%, 83%, and 43%, respectively. In addition, Brunauer–Emmett–Teller analysis indicates an increase in surface area and volume of micropores of GO cement mortar, resulting in a decreased volume of mesopores. The improvement in properties was due to increased nucleation sites, calcium silicate hydrate (CSH) density, and a decreased volume of mesopores.
Embargo
An exact stiffness matrix method for nanoscale beams
(CRC Press/Balkema, 2023-01) Wijesinghe, R.A.R; De Silva, K. K. V.; Sapsathiarn, Y.; Rajapakse, N.
Conventional continuum theories are inapplicable to nanoscale structures due to their high surface- to-volume ratios and the effects of surface energy and inter-atomic forces. Although atomistic simulations are more realistic and accurate for nanostructures, their use in practical situations is constrained by the high computational cost. Modified continuum methods accounting for the surface energy are therefore considered computationally efficient engineering approximations for nanostructures. The modified continuum theory of Gurtin and Murdoch accounting for the surface energy effects has received considerable attention in the literature. This paper focuses on developing an exact stiffness matrix method for nanoscale beams based on the Gurtin-Murdoch theory. Past research has presented a classical finite element formulation to analyze nanoscale beams using the Galerkin weighted residual method. The proposed approach is based on the analytical solutions to the governing partial differential equations of nanobeams. These governing equations are established by using the Gurtin-Murdoch continuum theory. The general analytical solutions are used to derive the exact stiffness matrix and mass matrix of a beam finite element in closed form. The study examines the static and time-harmonic dynamic response of thin nanoscale beams. Normalized deflections and bending moments under concentrated and distributed loads are obtained for aluminum and silicon thin beams subjected to simply supported, cantilevered and clamped-clamped edges. Our results were compared with the available solutions in the literature, and close agreement was observed. Therefore, the method presented in this study serves as an efficient and accurate scheme to analyze nanobeams under static and dynamic loading compared to the conventional finite element schemes
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Consolidation settlement of vertically loaded pile groups in multilayered poroelastic soils
(Elsevier Ltd, 2023-01) Senjuntichai, T; Sornpakdee, N; Keawsawasvong, S; Phulsawat, B; Rajapakse, R.K.N.D.
Pile groups are commonly used as the foundations of many structures including those used in transportation infrastructures. Consolidation settlement of a pile foundation is an important design parameter. A theoretical model is developed in this study to estimate the consolidation settlement and axial load transfer of vertically loaded pile groups in multilayered poroelastic soils. The multilayered saturated soil is modeled according to Biot's poroelasticity theory. In order to determine quasi-static response of pile groups, the interaction problem is first formulated in the Laplace transform domain. Vertical displacement compatibility is enforced at the pile-soil interface to simulate the pile group-soil interaction. Axial deformation of each pile is represented by an exponential series with undetermined coefficients, which are obtained from a variational approach. Vertical displacement influence functions due to a buried uniform vertical load applied to the layered soil are required in the formulation. The application of an exact stiffness matrix method yields the required influence functions. Time-domain solutions are obtained by employing a numerical Laplace inversion method. Numerical results for time-dependent vertical stiffness and consolidation settlement are presented for different pile group configurations, layer profiles, pile elastic moduli and pile lengths.
Open Access
Wetland Water Level Prediction Using Artificial Neural Networks—A Case Study in the Colombo Flood Detention Area, Sri Lanka
(MDPI, 2023-01) Jayathilake, T; Sarukkalige, R; Hoshino, Y; Rathnayake, U
Historically, wetlands have not been given much attention in terms of their value due to the general public being unaware. Nevertheless, wetlands are still threatened by many anthropogenic activities, in addition to ongoing climate change. With these recent developments, water level prediction of wetlands has become an important task in order to identify potential environmental damage and for the sustainable management of wetlands. Therefore, this study identified a reliable neural network model by which to predict wetland water levels over the Colombo flood detention area, Sri Lanka. This is the first study conducted using machine learning techniques in wetland water level predictions in Sri Lanka. The model was developed with independent meteorological variables, including rainfall, evaporation, temperature, relative humidity, and wind speed. The water levels measurements of previous years were used as dependent variables, and the analysis was based on a seasonal timescale. Two neural network training algorithms, the Levenberg Marquardt algorithm (LM) and the Scaled Conjugate algorithm (SG), were used to model the nonlinear relationship, while the Mean Squared Error (MSE) and Coefficient of Correlation (CC) were used as the performance indices by which to understand the robustness of the model. In addition, uncertainty analysis was carried out using d-factor simulations. The performance indicators showed that the LM algorithm produced better results by which to model the wetland water level ahead of the SC algorithm, with a mean squared error of 0.0002 and a coefficient of correlation of 0.99. In addition, the computational efficiencies were excellent in the LM algorithm compared to the SC algorithm in terms of the prediction of water levels. LM showcased 3–5 epochs, whereas SC showcased 34–50 epochs of computational efficiencies for all four seasonal predictions. However, the d-factor showcased that the results were not within the cluster of uncertainty. Therefore, the overall results suggest that the Artificial Neural Network can be successfully used to predict the wetland water levels, which is immensely important in the management and conservation of the wetlands
Open Access
Trends and Variabilities in Rainfall and Streamflow: A Case Study of the Nilwala River Basin in Sri Lanka
(MDPI, 2023-01) Panditharathne, R; Gunathilake, M. B; Chathuranika, I.M; Rathnayake, U; Babel, M. S; Jha, M. K
Rainfall is one of the dominating climatic parameters that affect water availability. Trend analysis is of paramount significance to understand the behavior of hydrological and climatic variables over a long timescale. The main aim of the present study was to identify trends and analyze existing linkages between rainfall and streamflow in the Nilwala River Basin (NRB) of Southern Sri Lanka. An investigation of the trends, detection of change points and streamflow alteration, and linkage between rainfall and streamflow were carried out using the Mann–Kendall test, Sen’s slope test, Pettitt’s test, indicators of hydrological alteration (IHA), and Pearson’s correlation test. Selected rainfall-related extreme climatic indices, namely, CDD, CWD, PRCPTOT, R25, and Rx5, were calculated using the RClimdex software. Trend analysis of rainfall data and extreme rainfall indices demonstrated few statistically significant trends at the monthly, seasonal, and annual scales, while streamflow data showed non-significant trends, except for December. Pettitt’s test showed that Dampahala had a higher number of statistically significant change points among the six rainfall stations. The Pearson coefficient correlation showed a strong-to–very-strong positive relationship between rainfall and streamflow. Generally, both rainfall and streamflow showed non-significant trend patterns in the NRB, suggesting that rainfall had a higher impact on streamflow patterns in the basin. The historical trends of extreme climatic indices suggested that the NRB did not experience extreme climates. The results of the present study will provide valuable information for water resource planning, flood and disaster mitigation, agricultural operations planning, and hydropower generation in the NRB.
Open 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.
Open Access
Minimizing Liability of the COVID-19 Pandemic on Construction Contracts—A Structural Equation Model for Risk Mitigation of Force Majeure Impacts
(MDPI, 2023-01) Chadee, A. A; Gallage, S; Martin, H. H; Rathnayake, U; Ray, I; Kumar, B; Sihag, P
A pandemic is a force majeure event, and contracting parties can invoke conditions under force majeure to minimize liability for unforeseen, uncontrollable, and unavoidable circumstances. This study develops a conceptual model to assist in the management of delays and cost overruns due to force majeure events arising from the construction sector in Small Island Developing States (SIDS). A critical case study analysis of past epidemics and pandemics was conducted to develop a survey questionnaire for administration to construction professionals in Trinidad and Tobago. Based on the empirical data of 65 construction professionals, the structural equation model shows that there are strong causal effects from the implications of COVID-19 and force majeure events, which in turn have a dire impact on the construction industry. The leading implication of COVID-19 is the drastic increases in the cost of materials. Also, granting an extension of time to contractors was the main risk variable under the force majeure conditions. From the results, the measurement model verifies that events under force majeure and its perceived implications strongly influence the construction industry, and proposes that force majeure contractual clauses require explicit treatment of the periodic reoccurrence of pandemics to avoid conflicts among contracting parties. This research explores and builds on new avenues from the latest COVID-19 scholarship to better understand existing impacts on the construction industry, and consequently add to the novel body of knowledge on the implications of pandemics on construction contracts. Overall, this research provides a risk-guidance framework for construction professionals and academia to mitigate unforeseen, uncontrollable, and unavoidable risks on construction projects
Open Access
Evaluation of the Impact of Land Use Changes on Soil Erosion in the Tropical Maha Oya River Basin, Sri Lanka
(MDPI, 2023-01) Palliyaguru, C; Basnayake, V; Makumbura, R. K; Gunathilake, M. B; Muttil, N; Wimalasiri, E. M; Rathnayake, U
Soil degradation is a serious environmental issue in many regions of the world, and Sri Lanka is not an exception. Maha Oya River Basin (MORB) is one of the major river basins in tropical Sri Lanka, which suffers from regular soil erosion and degradation. The current study was designed to estimate the soil erosion associated with land use changes of the MORB. The Revised Universal Soil Loss Equation (RUSLE) was used in calculating the annual soil erosion rates, while the Geographic Information System (GIS) was used in mapping the spatial variations of the soil erosion hazard over a 30-year period. Thereafter, soil erosion hotspots in the MORB were also identified. The results of this study revealed that the mean average soil loss from the MORB has substantially increased from 2.81 t ha−1 yr−1 in 1989 to 3.21 t ha−1 yr−1 in 2021, which is an increment of about 14.23%. An extremely critical soil erosion-prone locations (average annual soil loss > 60 t ha−1 yr−1) map of the MORB was developed for the year 2021. The severity classes revealed that approximately 4.61% and 6.11% of the study area were in high to extremely high erosion hazard classes in 1989 and 2021, respectively. Based on the results, it was found that the extreme soil erosion occurs when forests and vegetation land are converted into agricultural and bare land/farmland. The spatial analysis further reveals that erosion-prone soil types, steep slope areas, and reduced forest/vegetation cover in hilly mountain areas contributed to the high soil erosion risk (16.56 to 91.01 t ha−1 yr−1) of the MORB. These high soil erosional areas should be prioritized according to the severity classes, and appropriate land use/land cover (LU/LC) management and water conservation practices should be implemented as recommended by this study to restore degraded lands.
Open Access
Modelling the Implications of Delayed Payments on Contractors’ Cashflows on Infrastructure Projects
(Salehan Institute of Higher Education, 2023-01-01) Chadee, A; Ali, H; Gallage, S; Rathnayake, U
The consideration of payments to contractors is not only a legal obligation but a necessity for assuring the continuity and completion of a construction project. However, consistent payments to facilitate project cash flows are uncommon in the construction industry. Within the context of a small island developing state, this paper aims to uncover leading risks factors contributing to implications of delayed payments, on contractors’ cash flows and uncover causalities and effects on relationships among these factors. A two-tiered quantitative approach was adopted. Firstly, a compiled list of delay factors was collated from the literature review. Semi-structured interviews were conducted with experienced construction professionals to determine the factors’ relevance and applicability in Trinidad and Tobago. A closed-ended survey questionnaire was subsequently developed and administered to primary construction stakeholders. Secondly, the responses obtained were collated, validated, and ranked using the relative importance index. A confirmatory factor analysis (CFA) was carried out using SPSS, and thereafter, SPSS Amos was used to determine the best-fit Structural Equation Model (SEM). The results strongly indicate that the issue of delayed payments is very prevalent within public sector projects. Unstable political climates and the delay in employers’ issuance of variation orders were found to be the main causes of delayed payments within the industry. Delays in sub-contractor and supplier payments as well as an increase in the contractor’s debt were the leading effects of delayed payments on the contractor’s cash flows. Based on these findings, a risk response framework was outlined to assist small to medium-contracting enterprises to cope with payment delays, both locally and internationally. This research contributes to the advancement of construction management knowledge by informing construction professionals and policy makers of the implications of delaying approved payments, the consequential causes and effects, and a risk response technique to mitigate the negative effects on contractors’ cash flows.
Open Access
Reducing Cost Overrun in Public Housing Projects: A Simplified Reference Class Forecast for Small Island Developing States
(MDPI, 2023-04-10) Chadee, A; Martin, H; Gallage, S; Rathnayake, U
Inaccuracies in cost estimation on construction projects is a contested topic in praxis. Among the leading explanations for cost overrun (CO), factors accounting for large variances in actual cost are shown to have psychological or political roots. The context of public sector social housing projects (PSSHPs) in Small Island Developing States (SIDS) is positioned with similar CO challenges. This study is the fifth phase of a series of research projects on the vulnerability of PSSHPs to COs, and the need to de-risk cost estimates. The aim of this study is to present a simple and practical application of Reference Class Forecasting (RCF), a promising solution utilizing an “outside view” approach, as an effective control to reduce the variance of forecasted cost inaccuracies. Using a sample set of 82 housing projects, a reference class of 23 projects was selected based on properties such as design-build procurement type and local contractor involvement. A probability distribution was then established for this reference class, and required cost uplifts to be applied were based on the level of risk a housing agency is willing to accept for PSSHPs. Finally, the accuracy of the reference class was tested using a recently completed project. The results showed that the RCF method, based on a 50th percentile risk acceptance of CO, provides a closer estimate to the actual costs of the project as compared to the contracted costs. This empirical study is the first to undertake and implement RCF in the 52 SIDS and presents the first instance of practical RCF in public housing projects worldwide, thus providing a platform for improvement in future PSSHPs’ budget forecasting. The research can be applied to lessen societal and economic welfare losses as well as significant financial risks for governments. The implementation of practical safeguards, such as RCF, together with contemporary standard project controls, provides immediate advantages for enhancing accuracy in present forecasting approaches against financial risks. It allows for improved value derived from social infrastructure projects, improved supply of public housing, and consequently progress for these nations towards achieving their sustainable development goals.
Open Access
Risk Evaluation of Cost Overruns (COs) in Public Sector Construction Projects: A Fuzzy Synthetic Evaluation
(MDPI, 2023-04-22) Chadee, A.A; Martin, H.H; Gallage, S; Banerjee, K.S; Roopan, R; Rathnayake, U; Ray, I
In the Small Island Developing States (SIDS), public sector infrastructure projects (PSIPs) fail to both meet targeted performance metrics and deliver on the intended benefits to society. In terms of the cost performance metric, cost overruns (COs) beyond the initial contract value are more of a norm than a unique occurrence. Therefore, to ensure economic sustainability for SIDS, and value for money on PSIPs, there is a need to investigate and evaluate the risk impacts on COs. The purpose of this research was to identify and evaluate the perceived cost overrun risk factors that are within the primary project stakeholders’ sphere of control, and to reduce the ongoing ambiguities that exist in the prioritization of these risks. This was achieved by extracting critical risk factors from selected comparative studies in developing countries to formulate a closed-ended questionnaire to be administered to construction professionals in Trinidad and Tobago. Thereafter, the process of fuzzy synthetic evaluation (FSE) was used to develop a risk model based on three tiers of risks: 11 critical risk factors, 3 critical risk groupings (CRGs) and an overall risk level (ORL). The results showed that the two highest-ranked critical risks were project funding problems and variations by client. The leading critical risk grouping was client-related risk (5.370), followed by professionalrelated risk (4.815) and physical risk (4.870). The ORL was 5.068. Based on the FSE’s linguistic scaling, the CRGs and the ORL are perceived to be high risks in PSIPs. This research adds to the CO body of knowledge in primarily three ways. Firstly, the study extends the comparative assessment previously undertaken in scholarship into the context of SIDS to build on the generalizability of this context-specific phenomenon. Secondly, the FSE evaluation undertaken provides a practical tool to be promoted for use in SIDS’ construction industry among practitioners to focus and prioritize the critical risks in the planning phases and improve on contemporary risk practices in the execution phases of projects. Finally, this quantitative model approach is recommended to supplement the traditional qualitative risk management practices adopted in SIDS, thus contributing towards the overall improved economic sustainability and viability of PSIPs.
Open Access
Analysis of Multi-Temporal Shoreline Changes Due to a Harbor Using Remote Sensing Data and GIS Techniques
(MDPI, 2023-05-06) Zoysa, S; Basnayake, V; Samarasinghe, J. T.; Gunathilake, M.B.; Kantamaneni, K; Muttil, N; Muttil, U; Rathnayake, U
Coastal landforms are continuously shaped by natural and human-induced forces, exacerbating the associated coastal hazards and risks. Changes in the shoreline are a critical concern for sustainable coastal zone management. However, a limited amount of research has been carried out on the coastal belt of Sri Lanka. Thus, this study investigates the spatiotemporal evolution of the shoreline dynamics on the Oluvil coastline in the Ampara district in Sri Lanka for a two-decade period from 1991 to 2021, where the economically significant Oluvil Harbor exists by utilizing remote sensing and geographic information system (GIS) techniques. Shorelines for each year were delineated using Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager images. The Normalized Difference Water Index (NDWI) was applied as a spectral value index approach to differentiate land masses from water bodies. Subsequently, the Digital Shoreline Analysis System (DSAS) tool was used to assess shoreline changes, including Shoreline Change Envelope (SCE), Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR). The results reveal that the Oluvil coast has undergone both accretion and erosion over the years, primarily due to harbor construction. The highest SCE values were calculated within the Oluvil harbor region, reaching 523.8 m. The highest NSM ranges were recorded as −317.1 to −81.3 m in the Oluvil area and 156.3–317.5 m in the harbor and its closest point in the southern direction. The maximum rate of EPR was observed to range from 3 m/year to 10.7 m/year towards the south of the harbor, and from −10.7 m/year to −3.0 m/year towards the north of the harbor. The results of the LRR analysis revealed that the rates of erosion anomaly range from −3 m/year to −10 m/year towards the north of the harbor, while the beach advances at a rate of 3 m/year to 14.3 m/year towards the south of the harbor. The study area has undergone erosion of 40 ha and accretion of 84.44 ha. These findings can serve as valuable input data for sustainable coastal zone management along the Oluvil coast in Sri Lanka, safeguarding the coastal habitats by mitigating further anthropogenic vulnerabilities.
Embargo
Uncovering stress fields and defects distributions in graphene using deep neural networks
(Springer, Cham, 2023-05-19) Dewapriya, M. A. N.; Rajapakse, R. K. N. D.; Dias, W. P. S.
Deep learning provides a new route for developing computationally efficient predictive models for some complex engineering problems by eliminating the need for establishing exact governing equations. In this work, we used conditional generative adversarial networks (cGANs) to identify defects in graphene samples and to predict the complex stress fields created by two interacting defective regions in graphene. The required data for developing deep learning models was obtained from molecular dynamics simulations, where the numerical results of the simulations were transformed into image-based data. Our results demonstrate that the neural nets can accurately predict some complex features of the interacting stress fields. Subsequently, we used cGANs to predict defect distributions; this revealed that a cGAN could predict the existence of a crack even though it had never seen a cracked sample during the training stage. This observation clearly demonstrates the remarkable generalizability of cGANs beyond the training samples, suggesting that deep learning can be a powerful tool for solving advanced nanoengineering problems.
Open Access
Predicting adhesion strength of micropatterned surfaces using gradient boosting models and explainable artificial intelligence visualizations
(Elsevier, 2023-06-27) Ekanayake, I.U; Palitha, S; Gamage, S; Meddage, D.P.P.; Wijesooriya, K; Mohotti, D
Fibrillar dry adhesives are widely used due to their effectiveness in air and vacuum conditions. However, their performance depends on various factors. Previous studies have proposed analytical methods to predict adhesion strength on micro-patterned surfaces. However, the method lacks interpretation on which parameters are critical. This research utilizes gradient-boosting machine learning (ML) algorithms to accurately predict adhesion strength. Additionally, explainable machine learning (XML) methods are employed to interpret the underlying reasoning behind the predictions. The analysis demonstrates that gradient boosting models achieve a high correlation coefficient (R > 0.95) in accurately predicting pull-off force on micro-patterned surfaces. The use of XML methods provides insights into the importance of features, their interactions, and their contributions to specific predictions. This novel, explainable, and data-driven approach holds potential for real-time applications, aiding in the identification of critical features that govern the performance of fibrillar adhesives. Furthermore, it improves end-users’ confidence by offering human-comprehensible explanations and facilitates understanding among non-technical audiences
Open Access
Achieving near-zero carbon dioxide emissions from energy use: The case of Sri Lanka
(Elsevier, 2023-07-04) Fernando, G.L; Liyanage, M.H; Anandarajah, G; Attalage, R. A; Karunaratne, S
Signatories to the Paris Agreement are to achieve net zero Green House Gas (GHG) emissions during the half-century to pursue the efforts limiting global average temperature increase by 2 °C compared to pre-industrial levels. This study models ambitious to challenging scenarios involving energy demand and supply side actions for energy system transition toward net-zero for Sri Lanka. To analyze these scenarios a least cost optimization-based bottom-up type energy system model was developed from 2015 to 2050. A Business-as-usual (BAU) scenario and four countermeasure (CM) scenarios termed Plausible, Ambitious, Challenging, and Stringent were developed. Four different carbon tax rates were used to fathom the level of carbon tax needed to achieve net-zero emissions. The CM scenarios were formulated considering different technology options and policy measures such as the diffusion of efficient technologies, availability of renewable energy sources, use of cleaner fuels, the introduction of nuclear and carbon capture and storage technologies, and green hydrogen for power generation. The result of this study reveals that the stringent scenario which includes aggressive policy measures in both the energy supply and demand sectors, such as nuclear, and renewable energy for power generation, diffusion of efficient Enduse devices, fuel switching, including the introduction of electric cars, and increased share for public transport achieves the near carbon-neutral scenario at a carbon tax trajectory of 32 US$/tCO2 in 2020 and 562US$/tCO2 in 2050. The Net Energy Import Dependency (NEID) of the country decreases to 13 % in 2050 compared to that of the BAU scenario (65 %) under the near carbon neutral scenario, which is a positive sign from the energy security perspective.
Open Access
Evaluating expressway traffic crash severity by using logistic regression and explainable & supervised machine learning classifiers
(Elsevier, 2023-07-09) Shashiprabha, M.J.P.S; Kelum, S.R.M; Meddage, D.P.P; Pasindu, H.R; Gomes, P.I.A
The number of expressway road accidents in Sri Lanka has significantly increased (by 20%) due to the expansion of the transport network and high traffic volume. It is crucial to identify the causes of these crashes for effective road safety management. However, traditional statistical methods may be insufficient due to their inherent assumptions. This study utilized explainable machine learning to investigate the factors that affect the severity of traffic crashes on expressways. The study evaluated two groups of traffic crashes: fatal or severe crashes, and other crashes that included non-severe injuries or only property damage. Five factors that contribute to crashes were analyzed: road surface condition, road alignment, location, weather condition, and lighting effect. Four machine learning models (Random Forest (RF), Decision Tree (DT), extreme gradient boosting (XGB), K-Nearest Neighbor (KNN)) were developed and compared with Logistic Regression (LR) using 223 training and 56 testing data instances. The study revealed that the machine learning algorithms provided more accurate predictions than the LR model. To explain the machine learning models, Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were used. These methods revealed that all five features decreased the possibility of occurrence of fatal accidents. SHAP and LIME explanations confirmed the known interactions between factors influencing crash severity in expressway operational conditions. These explanations increase the trust of end-users and domain experts on machine learning models. Furthermore, the study concluded that using explainable machine learning methods is more effective than traditional regression analysis in evaluating safety performance. Additionally, the results of the study can be utilized to improve road safety by providing accurate explanations for decision-making processes for black-box models. © 2023
Embargo
Relationships amongst water and sediment qualities, discharge, and allochthonous inputs of intermittent streams in tropical dry climates: Implications on stream management
(Elsevier, 2023-07-19) Gomes, P.I.A; Perera, M.D.D
The interrelationships amongst water and sediment physicochemistry, catchment hydrology, and allochthonous inputs are not well established for intermittent streams, especially in tropical climates. This remains a major concern in water resources management, and understanding these streams is vital in forming targeted frameworks for protection. A two-year comprehensive study showed spatially independent water quality variations, where similar temporal patterns were observed in different streams in close catchments for many variables (such as for electrical conductivity, pH, nitrogen species, and dissolved oxygen). This was not the case for sediment quality variables; in addition, in-stream variation was high. This gave an indication of the regulatory potential of intermittent stream sediment. Redundancy analysis models showed that stream water quality was significantly correlated to, and could be explained by discharge, rainfall parameters, litter, and sediment quality. Sediment quality was not influenced by litter inputs but by discharge and rainfall-related parameters. The study reported new insights into the unique physicochemistry of intermittent streams and proposes the fact that sediment quality needs comprehensive monitoring and management both spatially and temporally.
Open Access
Vertically constructed wetlands for greywater reuse: Performance analysis of plants
(Elsevier, 2023-10-02) Siriwardhana, K.D; Miguntanna, N; Jayaneththi, D. I.; Kantamaneni, K; Rathnayake, U
Vertical Flow constructed wetlands (VFCWs) are environmentally feasible engineered systems that mimic the functions of natural wetlands. They are alternative engineering systems that are economical, and simple in structure with reduced land area compared to Horizontal Flow Constructed Wetlands (HFCW). Thus provides a sustainable solution for greywater treatment to a considerable extent. However, VFCWs feasibility and plant performance were not tested in the context of Sri Lanka for the greywater treatment. Therefore, the purpose of this study is to evaluate the potential of household greywater treatment using a pilot-scale VFCW and examine the performance characteristics of different types of plants. Three types of plants, the Canna plant (Canna indica), Ferns plant (Matteuccia struthiopteris), and Cattail plant (Typha latifolia) were used as emergent plants and a retention tank was constructed to retain solid particles in the greywater as primary treatment. The experiments were carried out for two months using a Completely Randomized Design (CRD) for three replicates. The quality of the influent and effluent was tested fortnight for a number of water quality parameters. Results revealed that the removal efficiency of contaminants was increased. Cattail plants showed higher removal efficiency for dissolved oxygen (DO), chemical oxygen demand (COD), nitrates (NO3 1-), turbidity, and electrical conductivity. In addition, Canna plants had higher efficiencies for the removal of total dissolved solids (TDS) and phosphates (PO4 3-). Furthermore, Ferns plants presented higher efficiency only for removing sulphate (SO4 3-). Conclusively, Cattail plants presented the overall best performance in treating greywater. This can be attributed to the ability of the Cattail’s dense fibrous root system to absorb more contaminants from greywater. This research also discussed the importance of microplastic analysis in greywater treatment which is a vital part of the current day research. The results of this study will be helpful to the further advanced research. Furthermore, this methodology can be implemented to other similar plants across the globe irrespective of geographical area.