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Using the zone fire model CFAST as the simulation engine, time series data for building sensors, such as heat detectors, smoke detectors, and other targets at any arbitrary locations in multi-room compartments with different geometric configurations, can be obtained. An automated process for creating inputs files and summarizing model results, CData, is being developed as a companion to CFAST. An example case is presented to demonstrate the use of CData where synthetic data is generated for a wide range of fire scenarios. Three machine learning algorithms: support vector machine (SVM), decision tree (DT), and random forest (RF), are used to develop classification models that can predict the location of a fire based on temperature data within a compartment. Results show that DT and RF have excellent performance on the prediction of fire location and achieve model accuracy in between 93% and 96%. For SVM, model performance is sensitive to the size of training data. Additional study shows that results obtained from DT and RT can be used to examine the importance of each input feature. This paper contributes a learning-by-synthesis approach to facilitate the utilization of a machine learning paradigm to enhance situational awareness for fire fighting in buildings. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]