Eleftheriadis, Efstathios; (2018) Decision-based and BIM-embedded optimisation framework for material, cost and carbon efficiency of RC building structures. Doctoral thesis (Eng.D), UCL (University College London).

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Abstract

The raising concerns in buildings life cycle sustainability complicate the optimisation of structural systems and thus trade-offs between material use, carbon and cost efficiencies are often hard to recognise. Additionally, Decision-Makers’ (DM) expertise can influence the final selection of structural systems. To better understand where trade-offs might occur both the engineering optimisation and DM preferences should be considered when specifying a structural system. Currently there are no effective ways to consolidate those aspects. However, opportunities to enrich the current sustainable engineering practices lie within Building Information Modelling (BIM). Key questions emerged: How optimised structural alternatives can be established based on explicit constructability constraints; what does the optimisation of the structure mean to the lifecycle carbon performance of the building; how the preferences of DM influence the selection of optimum structural designs? To address these challenges, a participatory group decision making model to specify project and engineering design requirements is first established using Quality Function Deployment (QFD). The QFD model uses Evidential Reasoning (ER) algorithms under uncertainty. Subsequently, a multilevel computational model for cost and carbon optimisation using NSGA-II and constructability functions is developed. Selected optimised solutions are then evaluated using TOPSIS in an a posteriori optimisation procedure that identifies solutions with the highest acceptance rankings based on the design priorities computed in the QFD model. Finally, to ensure that the selected optimised structural designs do not have any consequential impacts at building level a whole building assessment is carried out utilising Life Cycle Assessment (LCA). All the components in the new decision-based optimisation framework are applied and tested in real buildings. The study shows how structural engineers and other DM can effectively use the proposed framework to augment decision-making procedures towards more material efficient, sustainable and cost-effective building structures. The knowledge gained from the computational models are summarised in BIM-based applications that facilitate more informed structural design decisions.

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