Beyond development: Challenges in deploying machine learning models for structural engineering applications

arXiv:2404.12544v1 Announce Type: new
Abstract: 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. …

abstract applications arxiv beyond challenges concept cs.ce cs.lg development engineering fields landscape machine machine learning machine learning models paper performance proof-of-concept solutions stat.ml type world