Machine Learning for Sustainable Structures: A Call for Data

Abstract

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 andmost 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.

Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era

“…There are many jobs where you need to request data, due to a stark data environment. The study carried out in [71] used data on materials, types of buildings and their geometry, as well as various data in the field of structures. In [72], data were taken from a business database to assess the environmental impact of roads, and in [73], mobility studies were carried out in the city of Chicago.…”

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

“…LCA criteria could be used for design structures. This was explored in a review article on the design of concrete structures [66] or in a call for data for the evaluation of structures [69]. Environmental criteria can be used for the design of buildings in the early design stage [71] or for the estimation of total energy consumption from environmental life cycle analysis (LCA) and in cost life cycle analysis (LCC) [68].…”

“…This justifies the need for call for data. For example, D'Amico made a request for data geometry, material, type of building and other aspects in the field of structures [69].…”

“…In the study of the state of the art carried out as part of the process of construction of the proposed conceptual framework, various work approaches have been shown. For example, several of these works [63,66,68,69,71] use the LCA life cycle analysis technique to improve the design. There are also works that focus on the exploitation phase [67,70,72,73], while others focus on the study of the methodology followed in the LCA [61,62,65].…”

“…In the field of sustainability applied to engineering projects, neural networks have been used to predict energy consumption [68], as a surrogate model to replace simulation software. [69], to obtain LCA results from product characteristics [64], to obtain toxicity with fewer parameters [61], to evaluate energy consumption and environmental impact of agricultural activities [67,73] and to evaluate the energy consumption in order to evaluate environmental impacts of roads [72], among many other applications.…”