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Title: Machine Learning Applications in Structural Engineering: Hope, Hype or Hindrance?

Author(s): Henry Burton

Publication: Web Session



Appears on pages(s):



Date: 4/1/2021

The recent (within the last decade) success of machine learning (ML) applications in areas such as bioengineering, medicine and advertising has been highly visible. This has created a domino effect where others have begun to ask whether their respective fields of practice, including structural engineering, can be transformed or “revolutionized” by ML. Structural engineering researchers began to explore ML applications in the field as early as the late 1980s. However, it is only within last five years that the community of structural engineering researchers and practitioners have begun to seriously explore ways in which ML can improve the efficiency and/or accuracy of specific tasks or solve previously intractable problems. As with other fields, some have expressed legitimate concern that the potential benefits of ML to our field are being overhyped, and in the worst case, exploited for marketing purposes. This paper will discuss the areas of current and potential machine learning applications in structural engineering while placing them into three categories: (1) improving the predictive accuracy of existing empirical/statistical models used in structural engineering practice (e.g., empirical drift capacity models for concrete shear walls), (2) increasing the efficiency of long-standing structural engineering tasks (e.g. performance-based seismic design) and (3) solving problems that, without the use of ML, would be otherwise intractable (e.g., near real-time post-earthquake damage assessment). The challenges and opportunities associated with utilizing ML within these three contexts will be interwoven into the discussion. The article concludes with some strategies on how the community can proceed with a “cautious exploration” of the usefulness of ML to our field.