StructuresNet and FireNet: Benchmarking databases and machine learning algorithms in structural and fire engineering domains

Abstract

Machine learning (ML) continues to rise as an effective and affordable method of tackling engineering problems. Unlike other disciplines, the integration of ML into structural and fire engineering domains remains deficient. This is due in part to the lack of benchmark databases to compare the effectiveness of ML models. In order to bridge this knowledge gap, this paper presents a benchmark examination of common supervised learning ML algorithms that can be easily deployed into structural and fire engineering problems. The selected algorithms include; Decision Trees (DT), Random Forest (RF), Extreme Gradient Boosted Trees (ExGBT), Light Gradient Boosted Trees (LGBT), TensorFlow Deep Learning ..