Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Explainable Machine Learning using Real, Synthetic and Augmented Fire
  Tests to Predict Fire Resistance and Spalling of RC Columns   This paper presents the development of systematic machine learning (ML)
approach to enable explainable and rapid assessment of fire resistance and
fire-induced spalling of reinforced concrete (RC) columns. The developed
approach comprises of an ensemble of three novel ML algorithms namely; random
forest (RF), extreme gradient boosted trees (ExGBT), and deep learning (DL).
These algorithms are trained to account for a wide collection of geometric
characteristics and material properties, as well as loading conditions to
examine fire performance of normal and high strength RC columns by analyzing a
comprehensive database of fire tests comprising of over 494 observations. The
developed ensemble is also capable of presenting quantifiable insights to ML
predictions; thus, breaking free from the notion of 'blackbox' ML and
establishing a solid step towards transparent and explainable ML. Most
importantly, this work tackles the scarcity of available fire tests by
proposing new techniques to leverage the use of real, synthetic and augmented
fire test observations. The developed ML ensemble has been calibrated and
validated for standard and design fire exposures and for one, two, three and
four-sided fire exposures thus; covering a wide range of practical scenarios
present during fire incidents. When fully deployed, the developed ensemble can
analyze over 5,000 RC columns in under 60 seconds thus, providing an attractive
solution for researchers and practitioners. The presented approach can also be
easily extended for evaluating fire resistance and spalling of other structural
members and under varying fire scenarios and loading conditions and hence paves
the way to modernize the state of this research area and practice.

23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc