• Energy Research

Concrete is the world’s most used construction material. There are significant challenges with the decarbonisation of concrete, particularly cement, due to the release of carbon dioxide emissions during clinker production. Therefore, strategies to reduce embodied carbon in concrete buildings should aim to focus on material efficiency efforts first. Existing relationships between concrete and embodied carbon, such as increased concrete strength, increasing global warming potential (GWP), increased span length increasing GWP, and taller buildings generally having higher carbon have been investigated. However, some relationships are yet to be explored. These include the relationship of form and associated structural layouts to embodied carbon. One of the challenges of conducting embodied carbon assessments is the selection of material embodied carbon coefficients (ECCs). This is particularly true during early-stage design when the specific material selection is still unknown. A common early-stage technique for identifying embodied carbon reduction strategies is identifying carbon hotspots. However, these hotspots can be heavily influenced by the large variation in environmental product declarations (EPD) across manufacturers and differing product specifications. Without quantifying this uncertainty due to material ECC uncertainty, selecting the most likely lowest-impact design option is challenging. The most common approach for uncertainty propagation is to use Monte Carlo (MC) simulations. This propagation provides results as a distribution instead of a single, deterministic result. It is simple to compare single-value results to demonstrate the difference between design options and select the lowest-impact alternative. However, using uncertainty statistical methods to compare structural frame designs against each other and also against industry benchmarks is yet to be investigated. Therefore, comparative statistical methods are evaluated for their suitability in early-stage decision-making and, specifically, structural frame design comparisons. This thesis introduces a newly-proposed methodology that propagates uncertainties in material quantities and ECCs during early-design comparisons. The methodology incorporates a novel ranking step to identify key contributing materials, streamlining assessments by reducing time and focusing on material hotspots. A new uncertainty characterisation of construction materials is introduced, utilising statistical parameters from an industry material ECC database. The thesis later integrates quantity uncertainty by design stage with material ECC uncertainty for early-stage structural EC assessments, capturing incompleteness and variation due to quantity take-off methods and early-stage estimations. Additionally, the thesis introduces a new parametric tool for early-stage concrete frame designs. This tool incorporates form definition (for seven-shaped buildings), followed by a layout derivation for all possible solutions within a span range. Next, a C# script within the tool conducts structural RC design for multiple slab types, and finally, product-stage embodied carbon calculations with uncertainty are presented. An investigation into the influence of architectural form on structural frame layouts and resulting embodied carbon was conducted, considering seven equally-sized shaped forms for two plot sizes. In combining the uncertainty procedure and parametric form design tool, comparative statistical methods for evaluating uncertain results are tested for the first time in a building EC context. Lastly, the thesis concludes by proposing a novel application for comparing structural EC results against the SCORS rating system. By including uncertainty in early-stage building EC assessments, this thesis enables engineers to conduct more reliable and fair comparative EC studies. Relying on average material ECCs (with uncertainty) through early design stages also benefits practitioners by prioritising EC savings through demand reduction and material efficiency without relying on low-carbon products.

A pilot study run by the University of Bath in partnership with Bath & North East Somerset Council, Chapter2 Architects and the South West Net Zero Hub.

Abstract Decarbonisation of the energy industry and enforcement of strict targets for operational energy consumption means that non-operational greenhouse gases (GHG) emissions, also known as embodied carbon (EC), will soon represent the majority of whole life carbon associated with buildings. EC assessments are often presented as deterministic, single-point values but contain a high degree of variability which is typically unacknowledged. Common sources of uncertainty are variability, data gaps, measurement error and epistemic uncertainty such as absence of detailed material specification (e.g. manufacturer, concrete mix, recycled content etc). Particularly during early design stages when such information is unconfirmed, average material data is used by necessity. While some material databases and LCA software can provide ranges of embodied carbon coefficients (ECC) between some materials and/or the uncertainty within individual manufacturers’ carbon data, the practice of reporting this is uncommon and has limited practicality for whole building assessments. This paper presents a simple procedure that selects the highest impact materials of the EC of an asset and implements a Monte-Carlo simulation to estimate the uncertainty behind the product stage EC assessment. Material coefficients of variation (CoV) are obtained from database values where available, and interpolated values are used in the absence of such data. A product stage EC assessment of a UK educational building, initially undertaken using single data points for each material, gave an EC prediction of 525 kgCO2e/m2 GIFA. Two scenarios were then assessed using our proposed procedure: 1) the full building scope and 2) substructure and superstructure only. It was demonstrated that, for scenario one, the EC can range from 50 to 140% of the original result when considering the extreme results from the Monte-Carlo simulation. Scenario one (considering the full building scope) resulted in an average EC value (mean ± CoV) of 526 kgCO2e/m2 GIFA ± 10.0%. The second scenario (sub- and super-structure only) resulted in an average EC value of 312 kgCO2e/m2 GIFA ± 11.9% with a full range of 45–155% of the original result. This paper shows that a straightforward uncertainty analysis procedure can support designers in understanding the possible range of asset product-stage EC and, therefore, inform construction product selections at an early stage where detailed information is not known. The variation also gives a degree of confidence/caution in the average EC prediction in lieu of a single-point result. The construction product CoV results can be used to set target ECCs on projects to help ensure reliable low-carbon products are specified. If these target ECCs were met, a minimum of 29% and 33% (excl. EPD uncertainty) in product stage EC reductions could be achieved. Future work should extend this method to include additional life cycle assessment (LCA) stages and other uncertainty factors. And, the method could be applied to comparative life cycle assessments and optioneering exercises, as well as including more specific construction product variability data.