Solving constraints within a graph based dependency model by digitising a new process of incrementally casting concrete structures

The mechanisation of incrementally casting concrete structures can reduce the economic and environmental cost of the formwork which produces them. Low-tech versions of these forms have been designed to produce structures with cross-sectional continuity, but the design and implementation of complex adaptable formworks remains untenable for smaller projects. Addressing these feasibility issues by digitally modelling these systems is problematic because constraint solvers are the obvious method of modelling the adaptable formwork, but cannot acknowledge the hierarchical relationships created by assembling multiple instances of the system. This thesis hypothesises that these opposing relationships may not be completely disparate and that simple dependency relationships can be used to solve constraints if the real procedure of constructing the system is replicated digitally. The behaviour of the digital model was correlated with the behaviour of physical prototypes of the system which were refined based on digital explorations of its possibilities. The generated output is assessed physically on the basis of its efficiency and ease of assembly and digitally on the basis that permutations can be simply described and potentially built in reality. One of the columns generated by the thesis will be cast by the redesigned system in Lyon at the first F2F (file to factory) continuum workshop

Machine Learning for Sustainable Structures: A Call for Data

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 ef ciency and GHG emissions mitigation is to be found in ef cient 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 bene ts due to the high number of parameters and uncer- tainty 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 as- sessment methods with arti cial intelligence approaches such as machine learning and neural networks.This short communication will brie y present previous attempts to employ such techniques in civil and struc- tural engineering. It will present likely outcomes of machine learning and neural network applications in the eld 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