  Machine learning (ML)-based solutions are rapidly changing the landscape of
many fields, including structural engineering. Despite their promising
performance, these approaches are usually only demonstrated as proof-of-concept
in structural engineering, and are rarely deployed for real-world applications.
This paper aims to illustrate the challenges of developing ML models suitable
for deployment through two illustrative examples. Among various pitfalls, the
presented discussion focuses on model overfitting and underspecification,
training data representativeness, variable omission bias, and cross-validation.
The results highlight the importance of implementing rigorous model validation
techniques through adaptive sampling, careful physics-informed feature
selection, and considerations of both model complexity and generalizability.
