zaker esteghamati
Beyond development: Challenges in deploying machine learning models for structural engineering applications
Esteghamati, Mohsen Zaker, Bean, Brennan, Burton, Henry V., Naser, M. Z.
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.
2404.12544
Country:
- North America > United States > California > Los Angeles County > Los Angeles (0.14)
- Europe > Austria > Vienna (0.14)
- North America > United States > Utah > Cache County > Logan (0.04)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)