Environmental impacts cost assessment model of residential building using an artificial neural network

• Published in: Engineering, construction, and architectural management construction, and architectural management, 2021-11, Vol.28 (10), p.3190-3215
• Main Authors: Hamida, Amneh, Alsudairi, Abdulsalam, Alshaibani, Khalid, Alshamrani, Othman
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Building information modeling; Carbon; Costs; Datasets; Decision making; Design; Energy conservation; Energy consumption; Energy costs; Energy modeling; Environmental impact; Environmental impact assessment; Error analysis; Glazing; Global warming; Green buildings; Greenhouse gases; Impact prediction; Learning theory; Machine learning; Neural networks; Prediction models; Regression analysis; Residential buildings; Simulation
• ISSN: 0969-9988; 1365-232X
• DOI: 10.1108/ECAM-06-2020-0450

PurposeBuildings are major contributors to greenhouse gases (GHG) along the various stages of the building life cycle. A range of tools have been utilised for estimating building energy use and environmental impacts; these are time-consuming and require massive data that are not necessarily available during early design stages. Therefore, this study aimed to develop an Environmental Impacts Cost Assessment Model (EICAM) that quantifies both energy and environmental costs for residential buildings.Design/methodology/approachAn Artificial Neural Network (ANN) was employed to develop the EICAM. The model consists of six input parameters, including wall type, roof type, glazing type, window to wall ratio (WWR), shading device and building orientation. In addition, the model calculates four measures: annual energy cost, operational carbon over 20 years, envelope embodied carbon and total carbon per square metre. The ANN architecture is 6:13:4:4, where the conjugate gradient algorithm was applied to train the model and minimise the mean squared error (MSE). Furthermore, regression analysis for the ANN prediction for each output was performed.FindingsThe MSE was minimised to 0.016 while training the model. Also, the correlation between each ANN output and the actual output was very strong, with an R2 value for each output of almost 0.998. Moreover, validation was conducted for each output, with the error percentages calculated at 0.26%, 0.25%, 0.03% and 0.27% for the annual energy cost, operational carbon, envelope materials embodied carbon and total carbon per square metre, respectively. Accordingly, the EICAM contributes to enhancing design decision-making concerning energy consumption and carbon emissions in the early design stages.Research limitations/implicationsThis study provides theoretical implications to the domain of building environmental impact assessment through illustrating a systematic approach for developing an energy-based prediction model that generates four environmental-oriented outputs, namely energy cost, operational energy carbon, envelope embodied carbon, and total carbon. The model developed has practical implications for the architectural/engineering (A/E) industries by providing a useful tool to easily predict environmental impact costs during the early design phase. This would enable designers in Saudi Arabia to make effective design decisions that would increase sustainability in the building life cycle.Originality/valueBy providing