Beyond development: Challenges in deploying machine learning models for structural engineering applications
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913996645335040 |
|---|---|
| author | Esteghamati, Mohsen Zaker Bean, Brennan Burton, Henry V. Naser, M. Z. |
| author_facet | Esteghamati, Mohsen Zaker Bean, Brennan Burton, Henry V. Naser, M. Z. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_12544 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 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 Computational Engineering, Finance, and Science 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. |
| title | Beyond development: Challenges in deploying machine learning models for structural engineering applications |
| topic | Machine Learning Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2404.12544 |