Embodied carbon optimisation of singly reinforced concrete beams - Chew, Sean (Nanyang Technological University, 2022)
The built environment is one of the largest contributors of carbon emissions due to the rapid pace of urbanisation. This contribution can be further categorised into operating carbon (OC) and embodied carbon (EC). With recent advancements and maturity in building technologies, further improvements on operating carbon have gradually reduced over the years. Governments and institutions are now putting more emphasis on reducing EC emissions in the design of new buildings. This paper was therefore conceived to establish a guideline for structural engineers and other relevant industry professionals to develop small structural design guides, such that the environmental impact of construction with reinforced concrete elements, specifically singly-reinforced beams could be reduced. Normalisation approach based on embodied carbon per unit of moment capacity, per unit of length, was used to identify critical parameters within the study. A parametric study was carried out and results indicate that under Eurocode 2 design restrictions, bending members designed with k=0.167 gives the optimal EC values. It was also observed that lower concrete grades would reduce EC emissions. Subsequently, case studies on 18m and 30m long concrete slabs with constant thickness were carried out. Results indicate that, besides k=0.167, the other critical parameter to reduce EC is to adopt the largest beam spacings (Lx) allowable by the particular slab thickness. These case studies also found that in conjunction with wider beam spacings (Lx), lower concrete grades (fck) also contributed to much lower EC values. Further investigations indicate that such a method of reducing EC emissions is economically viable with relatively large reductions in costs for a concrete grade of 40N/mm2 and beams with similar breadths. It is hoped that such findings, could make it easier for engineers achieving sustainable design solutions without the need for large-scale trial and error exercises.