Department of Civil Engineering

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Start date: 2024Has files: Yes

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Now showing 1 - 5 of 5
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    PublicationOpen Access
    Feasibility of Sediment Budgeting in an Urban Catchment with the Incorporation of an HEC—HMS Erosion Model: A Case Study from Sri Lanka
    (Springer, 2024-09-16) Abeysiriwardana, H. D.; Pattiyage, I. A. Gomes
    This study aimed at studying the feasibility of using a sediment model built in HEC – HMS incorporating Modified Universal Soil Loss Equation (MUSLE) in aiding the separation of sediment contribution as point and non-point, an important aspect in sediment pollution control. The model was developed and verified using a representative sub-catchment and a canal reach of a tropical climate. The field observations and model developed had a good agreement and indicated about 16% and 35% of total sediments in the canal may be from nonpoint sources for the dry and wet seasons, respectively. Results suggested that a major fraction of eroded sediment ended up in the main canal through the dense drainage network across the catchment. This meant sediment trapping should focus tributary drainage ditches or at point source inputs to canal rather than the main canal banks. The study recognized that HEC – HMS is also capable of simulating sediment generation with acceptable errors. Being a free software package, HEC – HMS would be an effective sediment modelling tool for jurisdictions where sediment analysis has been constrained by cost.
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    PublicationOpen Access
    Waste-based composites using post-industrial textile waste and packaging waste from the textile manufacturing industry for non-structural applications
    (Elsevier, 2024-09-26) Sulochani, R.M.N.; Jayasinghe, R.A.; Priyadarshana, G.; Nilmini, A.H.L.R.; Ashokcline, M.; Dharmaratne, P.D.
    The textile industry significantly contributes to environmental pollution, generating substantial amounts of waste. The prevailing linear model exacerbates this issue, accumulating a significant portion of the waste in landfills. This research aimed to tackle these challenges by developing value-added composites from postindustrial textile waste and packaging materials, for non-structural building applications. To achieve this, shredded polyester textile waste fibers served as the reinforcement, while waste packaging was used as the matrix. Varying fiber-matrix weight percentages seven composite types were developed. The physical, mechanical, and thermal properties of the composites were evaluated. The findings indicated that these composites exhibited properties comparable to those of commercial partition boards. Notably, composites with fiber weight percentages of 7.5 % and 10 % demonstrated the most favorable performance among the tested variations. Emphasizing the application of sustainable chemistry, this study highlights the potential of these composites to develop substitute materials for non-structural building applications. Moreover, it presents a promising solution to address the textile waste management challenge and value-added materials for the construction industry in a developing context.
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    PublicationOpen Access
    Eco-friendly mix design of slag-ash-based geopolymer concrete using explainable deep learning
    (Elsevier, 2024-09) Ranasinghe, R.S.S.; Kulasooriya, W.K.V.J.B; Perera, U.S; Ekanayake, I.U.; Meddage, D.P.P.; Mohotti, D; Rathanayake, U
    Geopolymer concrete is a sustainable and eco-friendly substitute for traditional OPC (Ordinary Portland Cement) based concrete, as it reduces greenhouse gas emissions. With various supplementary cementitious materials, the compressive strength of geopolymer concrete should be accurately predicted. Recent studies have applied deep learning techniques to predict the compressive strength of geopolymer concrete yet its hidden decision-making criteria diminish the end-users’ trust in predictions. To bridge this gap, the authors first developed three deep learning models: an artificial neural network (ANN), a deep neural network (DNN), and a 1D convolution neural network (CNN) to predict the compressive strength of slag ash-based geopolymer concrete. The performance indices for accuracy revealed that the DNN model outperforms the other two models. Subsequently, Shapley additive explanations (SHAP) were used to explain the best-performed deep learning model, DNN, and its compressive strength predictions. SHAP exhibited how the importance of each feature and its relationship contributes to the compressive strength prediction of the DNN model. Finally, the authors developed a novel DNN-based open-source software interface to predict the mix design proportions for a given target compressive strength (using inverse modeling technique) for slag ash-based geopolymer concrete. Additionally, the software calculates the Global Warming Potential (kg CO2 equivalent) for each mix design to select the mix designs with low greenhouse emissions.
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    PublicationEmbargo
    Applicability of machine learning techniques to analyze Microplastic transportation in open channels with different hydro-environmental factors
    (Elsevier Ltd, 2024-09-15) Fazil, A. Z; Gomes, P. I.A.; Sandamal, R.M. K
    This research utilized machine learning to analyze experiments conducted in an open channel laboratory setting to predict microplastic transport with varying discharge, velocity, water depth, vegetation pattern, and microplastic density. Four machine learning (ML) models, incorporating Random Forest (RF), Decision Tree (DT), Extreme Gradient Boost (XGB) and K-Nearest Neighbor (KNN) algorithms, were developed and compared with the Linear Regression (LR) statistical model, using 75% of the data for training and 25% for validation. The predictions of ML algorithms were more accurate than the LR, while XGB and RF provided the best predictions. To explain the ML results, Explainable artificial intelligence (XAI) was employed by using Shapley Additive Explanations (SHAP) to predict the global behavior of variables. RF was the most reliable model, with a coefficient of correlation of 0.97 and a mean absolute percentage error of 1.8% after hyperparameter tuning. Results indicated that discharge, velocity, water depth, and vegetation all influenced microplastic transport. Discharge and vegetation enhanced and reduced microplastic transport, respectively, and showed a response to different vegetation patterns. A strong linear positive correlation (R2 = 0.8) was noted between microplastic density and retention. In the absence of dedicated microplastic transport analytical models and infeasibility of using classical sediment transport models in predicting microplastic transport, ML proved to be helpful. Moreover, the use of XAI will reduce the black-box nature of ML models with effective interpretation enhancing the trust of domain experts in ML predictions. The developed model offers a promising tool for real-world open channel predictions, informing effective management strategies to mitigate microplastic pollution.
  • Thumbnail Image
    PublicationOpen Access
    Eco-friendly mix design of slag-ash-based geopolymer concrete using explainable deep learning
    (Elsevier, 2024-09) Ranasinghe, R.S.S.; Kulasooriya, W.K.V.J.B.; Perera, U S; Ekanayake, I.U.; Meddage, D.P.P.; Mohotti, D; Rathanayake, U
    Geopolymer concrete is a sustainable and eco-friendly substitute for traditional OPC (Ordinary Portland Cement) based concrete, as it reduces greenhouse gas emissions. With various supplementary cementitious materials, the compressive strength of geopolymer concrete should be accurately predicted. Recent studies have applied deep learning techniques to predict the compressive strength of geopolymer concrete yet its hidden decision-making criteria diminish the end-users’ trust in predictions. To bridge this gap, the authors first developed three deep learning models: an artificial neural network (ANN), a deep neural network (DNN), and a 1D convolution neural network (CNN) to predict the compressive strength of slag ash-based geopolymer concrete. The performance indices for accuracy revealed that the DNN model outperforms the other two models. Subsequently, Shapley additive explanations (SHAP) were used to explain the best-performed deep learning model, DNN, and its compressive strength predictions. SHAP exhibited how the importance of each feature and its relationship contributes to the compressive strength prediction of the DNN model. Finally, the authors developed a novel DNN-based open-source software interface to predict the mix design proportions for a given target compressive strength (using inverse modeling technique) for slag ash-based geopolymer concrete. Additionally, the software calculates the Global Warming Potential (kg CO2 equivalent) for each mix design to select the mix designs with low greenhouse emissions.