The Use of Machine Learning for the Prediction of fire Resistance of Composite Shallow Floor Systems

This paper is proposing an machine learning based expert system for preliminary prediction of the insulation fire resisting performance of shallow floor systems when subject to exposure to the ISO 834 Standard Fire Curve. The proposed system is a digital tool which incorporates a machine learning algorithm trained on the outcomes of pre-run two-dimensional finite element heat transfer analyses of shallow floor system details. The algorithm predicts the insulation performance of similar details with a measurable accuracy of 96% (i.e the insulation rating band was predicted correctly in 96% of the cases) without the requirement for an explicit deterministic analysis. The paper presents the challenges and prerequisites to build such an expert system and acts as a proof of concept for the use of machine learning for the assessment of the insulation performance of shallow floor system under the guidance of BS EN 1363-1:2012. A Support Vector Machine machine learning algorithm is adopted in this work. The required processes that were needed for the development of the expert system include the stages of data acquisition, exploratory data analysis, choice of machine learning algorithm, model training, tuning, and validation. This expert system is useful for practitioners to rapidly assess the feasibility of different construction details at early stages of the design process.

Automated summary

This paper introduces a machine learning based expert system for predicting the insulation performance of shallow floor systems under fire exposure, achieving an accuracy of 96%. The development of the system involves processes such as data acquisition, exploratory data analysis, algorithm selection, model training, tuning, and validation.