EMBODIED CARBON
; NEURAL-NETWORKS
; BUILT ENVIRONMENT
; BUCKLING LOAD
; PREDICTION
; BUILDINGS
; STRENGTH
; MODEL
WOS学科分类:
Engineering, Civil
WOS研究方向:
Engineering
英文摘要:
Buildings are the world's largest contributors to energy demand, greenhouse gases (GHG) emissions, resource consumption and waste generation. An unmissable opportunity exists to tackle climate change, global warming, and resource scarcity by rethinking how we approach building design. Structural materials often dominate the total mass of a building; therefore, a significant potential for material efficiency and GHG emissions mitigation is to be found in efficient structural design and use of structural materials.
To this end, environmental impact assessment methods, such as life cycle assessment (LCA), are increasingly used. However, they risk failing to deliver the expected benefits due to the high number of parameters and uncertainty factors that characterise impacts of buildings along their lifespans. Additionally, effort and cost required for a reliable assessment seem to be major barriers to a more widespread adoption of LCA. More rapid progress towards reducing building impacts seems therefore possible by combining established environmental impact assessment methods with artificial intelligence approaches such as machine learning and neural networks.
This short communication will briefly present previous attempts to employ such techniques in civil and structural engineering. It will present likely outcomes of machine learning and neural network applications in the field of structural engineering and - most importantly - it calls for data from professionals across the globe to form a fundamental basis which will enable quicker transition to a more sustainable built environment.