Application of Machine Learning to Bridge Design

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The primary objective of the proposed research program is to develop an innovative methodology, based on machine learning, that will enable engineers to extract knowledge efficiently from previously constructed bridges and apply this knowledge seamlessly in support of an enhanced bridge design process that is adaptable not only to the design of familiar systems but also of systems that incorporate novel elements. The use of machine learning will create a more rational basis for the use of correlation in design and hence will provide a means for designers to determine, in function of the amount and quality of underlying data, the extent to which correlation can be relied on as a basis for validating design decisions. The first step in the methodology is to apply available methods of machine learning to support a replicative design process, i.e., one in which the designs generated by machine learning models reflect the features of the data used to train the models. Because a suitable dataset does not yet exist, one will be created as part of the work in this research program. The second step will be to adapt these models to support a creative design process, i.e., one in which the designs generated by machine learning models contain features that are not found in the original data. The strategy that will be employed in this step will be to augment the original data with a sufficient number of additional entries that reflect the design intent. The assumption to be confirmed through this work is that a relatively small number of additional entries will be sufficient to re-train the models to enable them to support the design of bridges with these new features. This assumption is based on the a high degree of consistency among the structural systems used for bridges, as well as the capacity for machine learning algorithms to fit highly complex data with great accuracy. Machine learning is intrinsically correlative yet the primary basis of bridge design, checks of demand and capacity, is based on causation, i.e., an application of physical principles. The third step in the methodology will be to investigate and characterize mathematically the relation between designs generated by machine learning on the basis of correlation and their validity as established on the basis of causation. The proposed research program is the first systematic study of the application of machine learning to bridge design. It is also one of the few studies of how to enable machine learning, an intrinsically replicative technique, to support a meaningful creative process. The proposed research thus has the potential to set the direction of future research and development in this field, which will has the potential to bring about a major transformation in the practice of bridge design.