Scopus Index Publications
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This collection consists of all Scopus-indexed publications produced by SLIIT researchers. Scopus is recognized worldwide as a leading and reputable academic indexing database.
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Publication Open Access Sustainable Alternatives to Clay Bricks: A Review on PET-Based Masonry Units for Green Construction(Ontario International Development Agency, 2026) Wijesundara H; Perera S.V.T.JThe rapid escalation of global plastic consumption, particularly polyethylene terephthalate (PET), has created severe environmental challenges, while the conventional clay brick industry continues to generate significant greenhouse gas emissions and deplete nonrenewable resources. This paper reviews existing literature on two sustainable construction approaches aimed at addressing these dual issues: (i) the incorporation of melted PET in masonry blocks and (ii) the embedding of sand-filled PET bottles in masonry units. Findings indicate that melted PET-sand composite bricks, particularly at an optimal 1:3 plastic-to-sand ratio, exhibit superior performance compared to conventional clay bricks. These composites achieve compressive strength improvements of over 44% and reduce water absorption by up to 94.93%. They also demonstrate enhanced durability, with less than 2% strength loss under acid exposure, compared to over 15% in traditional bricks. Additionally, their production requires 79% less energy and reduces CO₂ emissions by a similar margin, underscoring their environmental advantages. The review also highlights the effectiveness of sand-filled PET bottles as structural masonry elements. Sand is a superior filler since it can hold up to 38.34 N/mm² of pressure, which is far more than bottles filled with dirt (8.99 N/mm²) or plastic bags (2.72 N/mm²). The review shows that both melted PET-sand bricks and sand-filled PET bottle masonry are good, eco-friendly substitutes for regular clay bricks. These methods have two benefits: they reduce plastic waste and encourage building techniques that are good for the environment. The results give an excellent justification to use PET-based masonry technologies as we shift toward building materials that are better for the environment.Publication Open 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, UGeopolymer 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.Publication Open 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, UGeopolymer 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.