|Author:||Ansah, Mark Kyeredey|
|Title:||Developing a BIM-based LCA approach for cost-effective lifecycle optimization of building energy and carbon emissions|
|Advisors:||Yang, Hongxing (BEEE)|
Lu, Lin (BEEE)
Lam, T. I. Patrick (BRE)
|Subject:||Buildings -- Energy conservation|
Buildings -- Environmental aspects
Environmental impact analysis
Hong Kong Polytechnic University -- Dissertations
|Department:||Department of Building Environment and Energy Engineering|
|Pages:||xxviii, 227 pages : color illustrations|
|Abstract:||This thesis aims to develop a Building information Modelling (BIM)-based design assessment approach with an application to the whole lifecycle design and optimization of the energy use, carbon emission, and economic performances of buildings. Current life cycle assessment (LCA) tools are criticized for complex assessment methods, intricate data requirements and incompatibility with conventional whole building energy simulation tools. Therefore, an LCA approach based on the BIM and optimization framework is proposed to improve the integration of a holistic life cycle assessment with the whole building energy simulation which can address the intrinsic synergy between energy, carbon, and economic performances throughout the entire lifecycle of buildings. This comprehensive design approach can account for the interactive effects between different design strategies in different life cycle phases of a building (i.e. material production, transportation, building construction, building operation, building maintenance, and end-of-life cycle phases) and enable decision-makers to comprehend the relative importance of each design strategy in order to deploy them for achieving the optimum energy, carbon and cost performance.|
The functional database, BIM module, and impact estimation module were determined as the fundamental components to develop the detailed whole lifecycle design assessment model. After designing the data structure and repository, impact estimation module and BIM module, BIM models were developed at various assessment levels specific to prefabricated and non-prefabricated buildings to assess its robustness for evaluating the energy and carbon performance of buildings using various energy and environmental indicators. The accuracy and robustness of the model was validated through a comparison with lifecycle assessment results of the same buildings with conventional tools. The results showed high levels of consistency and accuracy for various energy use and carbon emission indicators. Prediction accuracy and swiftness were improved through parametric modelling and data structure.
On top of the BIM-based model, a tier-hybrid uncertainty assessment method was developed to evaluate the uncertainties specific to the embodied impacts (i.e. material production, transportation, building construction, building maintenance, and end-of-life phases) of buildings. The parameter, model, and scenario uncertainties intrinsic to these lifecycle phases were determined through a comprehensive literature review and characterized using quantitative and qualitative analyses. LCA parameters with sufficient data from literature and manufactures were characterized using pure statistical distributions and Monte Carlo simulations whereas Data Quality Indicator (DQI) method was applied to LCA parameters without sufficient datasets. The tier-hybrid model was proved reliable given its consistency with a deterministic LCA. After uncertainty characterization, a propagation model was applied to understand the relative uncertainties specific to various lifecycle stages and building materials. After determining the parameter uncertainties, alternative statistical distributions (lognormal, triangular, normal and uniform) were explored to show the impacts of model uncertainties. The results of the model/analytical uncertainty imply that the final output uncertainty is highly correlated with defined probability distributions rather than the uncertainty characterization method. Hence integrating the pure statistical approach based on adequate data with the DQI method can reflect uncertainties more precisely. However, the proposed tier-hybrid approach can increase dispersion of LCA results as pure statistical distributions are collected from a wide range of sources.
Succeeding the above statistical analysis, a staged design optimization approach was proposed integrating embodied and operational impacts through the whole building energy simulation and LCA of passive design parameters as well as building materials and constructions with a multi-objective optimization. The NSGA-II optimization was conducted to obtain the Pareto front which demonstrates a trade-off between embodied and operational impacts. Following the post-optimization analysis and comparison of optimal solution selection methods, the optimal solution showed energy savings of up to 36.93% when compared with the baseline building. The BIM-based optimization method was further applied to three other tropical and subtropical climate cities.
Finally, a novel comprehensive BIM-based energy use, carbon emission and economic assessment and optimization model as a better alternative to the traditional whole building energy simulation and conventional lifecycle assessment was established and applied to a holistic lifecycle building design and assessment. The optimization approach is a single tiered integrated optimization process with an extensive evaluation of the embodied and operational impacts of buildings. The operational assessment includes energy use, carbon emission and economic implications of the building orientation, shape coefficient, window to wall ratio, external wall, roof and floor thermal resistance, windows U-value, infiltration rate, photovoltaic configuration. The embodied assessment also includes the corresponding implications of materials, constructions and energy systems. With the comprehensive BIM-based design model finalized, different optimization settings were examined to identify the most suitable settings that balance computational efficiency and optimization productivity. The most suitable settings showed up to 50% reduction in computational time. After a post evaluation of the optimization results, the final optimum BIM-based lifecycle design achieved 42%, 58% and 32% energy, carbon and cost reductions, respectively.
A post-optimization exploration is then conducted on confounding factors such as the lighting density, equipment load, ventilation rate, occupancy gains and occupancy density. In comparison to the optimized base case design, a low-level internal load scenario can reduce energy use, carbon emission and cost by 53%, 75% and 59%, respectively whereas a high-level internal load scenario can increase energy use, carbon emission by 63%, 91% and 68%, respectively. The BIM-based lifecycle optimization model was further applied to explore the influence of climate change representative concentration pathways (RCPs) in four scenarios: RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5. It is shown that global warming will lead to higher energy use and carbon emissions in tropical regions within the near future, while stringent mitigation strategies aligned with RCP 2.6 can reverse the trend after two decades. A further exploration of end-of-life strategies indicates that demolishing, transportation and sorting processes increase the energy use, carbon emission and cost, while recycling strategies can reduce such impact especially when extensively adopted. The BIM-based optimization model has been successfully applied to both typical mid-rise and high-rise residential buildings and its modularity allows for applications to other building architypes.
The proposed BIM-based optimization model can be used by researchers, developers, consultants and engineers to improve the overall lifecycle energy use, carbon emission and economic performance of buildings. The model bridges the segmentation between operational and embodied impacts of buildings and provides opportunities to explore the trade-off between design parameters from a lifecycle perspective. This comprehensive design approach curbs the surge in embodied impacts during the early design stage when it can be minimized. Furthermore, the design approach provides great opportunities for low carbon designs in a cost-effective approach and is therefore a pertinent step towards reducing the impact of the climate change. The proposed BIM-based optimization model can be further adapted and extended to other applications such as retrofitting of existing buildings.
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