Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Computers and Concrete Volume 33, Number 4, April 2024 (Special Issue) pages 409-423 DOI: https://doi.org/10.12989/cac.2024.33.4.409 |
|
|
|
A sensitivity analysis of machine learning models on fire-induced spalling of concrete: Revealing the impact of data manipulation on accuracy and explainability |
||
Mohammad K. al-Bashiti and M.Z. Naser
|
||
| Abstract | ||
| Using an extensive database, a sensitivity analysis across fifteen machine learning (ML) classifiers was conducted to evaluate the impact of various data manipulation techniques, evaluation metrics, and explainability tools. The results of this sensitivity analysis reveal that the examined models can achieve an accuracy ranging from 72-93% in predicting the fire-induced spalling of concrete and denote the light gradient boosting machine, extreme gradient boosting, and random forest algorithms as the best-performing models. Among such models, the six key factors influencing spalling were maximum exposure temperature, heating rate, compressive strength of concrete, moisture content, silica fume content, and the quantity of polypropylene fiber. Our analysis also documents some conflicting results observed with the deep learning model. As such, this study highlights the necessity of selecting suitable models and carefully evaluating the presence of possible outcome biases. | ||
| Key Words | ||
| concrete; deep learning; feature importance; fire; machine learning; sensitivity analysis; spalling | ||
| Address | ||
| Mohammad K. al-Bashiti: School of Civil and Environmental Engineering & Earth Sciences (SCEEES), Clemson University, Clemson, SC 29634, USA M.Z. Naser: 1) School of Civil and Environmental Engineering & Earth Sciences (SCEEES), Clemson University, Clemson, SC 29634, USA, 2) Artificial Intelligence Research Institute for Science and Engineering (AIRISE), Clemson University, Clemson, SC 29634, USA | ||