2024/04/18 by Mohsen Zaker Esteghamati, Brennan Bean, Esteghamati, Mohsen Zaker +5
Engineering · #BIM and Construction Integration #Computational Engineering #FOS: Computer and information sciences #Finance #Infrastructure Maintenance and Monitoring #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Structural Health Monitoring Techniques #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2404.12544
openalex publication_date 2024/04/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.