Browsing by Author "Perera, U.S"

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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
    Framework for generating high-resolution Hong Kong local climate projections to support building energy simulations
    (American Institute of Physics, 2025-03-07) Wang, J; Kudagama, B.J; Perera, U.S; Li, S; Zhang, X
    Finer resolution climate model projections are essential for designing regional building energy consumption and adaptation strategies under changing climate conditions. However, projections from Global Climate Models (GCMs) are typically coarse in resolution and subject to biases and uncertainty. To address this, the present study uses bilinear interpolation and morphing statistical downscaling to obtain high spatial (around 10 km) and temporal (hourly) resolution weather data, for more accurate estimations of future residential building energy consumption under climate change. An empirical quantile mapping bias-correction technique is applied to adjust the projection data from 44 GCMs under four representative Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The bias-corrected data are validated against meteorological observations from the Hong Kong Observatory's King's Park station. The hourly data are then converted to typical meteorological year data and used as input for EnergyPlus to predict future energy consumption patterns in public rental housing in Hong Kong. Case studies under the four SSPs show that climate change will significantly impact residential building energy use. Energy consumption is projected to increase by up to 14.0% for harmony-type buildings, 12.8% for trident-type buildings, and 12.4% for slab-type buildings by the end of the century under the SSP5-8.5 scenario, highlighting the urgent need for adaptive building design and energy policy measures.
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    PublicationEmbargo
    Selecting suitable passive design strategies for residential high-rise buildings in tropical climates to minimize building energy demand
    (Elsevier Ltd, 2025) Perera, U.S; Weerasuriya A.U; Zhang, X; Ruparathna R; Tharaka M.G.I; Lewangamage C.S
    Passive design strategies (PDS) are a fitting solution to reduce the ever-growing energy cost of residential high-rise buildings in tropical regions. However, PDSs’ building energy saving potential significantly varies with local climate conditions, but it has been sparsely investigated. Hence, this study investigated the energy-saving efficiency of eight common PDSs integrated into a typical residential high-rise building in three sub-climates: extremely hot humid (0 A), very hot humid (1 A), and warm humid (3 A) defined by ASHRAE for the tropical climate. This study developed a Building Performance Analysis (BPA) workflow with a BIM-based simulation framework and local and global sensitivity analyses for the building energy analysis. The global sensitivity analysis revealed that low e-coating on glasses is the most influential PDS for 0 A and 1 A climates, but it has a negative effect in the sub-climate zone 3 A. The low-conducting exterior walls are the most effective PDS in the sub-climate zone 3 A, but they are poorly performed in the other two sub-climate zones. Based on the energy calculation and sensitivity analysis, this study proposes the best PDS groups, saving up to 40.1 %, 63.5 %, and 31.7 % of average annual building consumption in the sub-climate zones, 0 A, 1 A, and 3 A climates.