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.

本文提出了一种基于机器学习的专家系统，用于对暴露于ISO 834标准防火曲线下的浅层地板系统的隔热耐火性能进行初步预测。所提出的系统是一种数字工具，其中结合了机器学习算法，该算法对浅层地板系统细节的运行前二维有限元热传递分析的结果进行了训练。该算法可以以96％的可测量精度预测相似细节的绝缘性能（即，在96％的情况下可以正确预测绝缘等级），而无需进行明确的确定性分析。本文提出了建立这样一个专家系统的挑战和先决条件，并作为在BS EN 1363-1：2012指导下使用机器学习评估浅层地板系统隔热性能的概念证明。本文采用了支持向量机的机器学习算法。开发专家系统所需的必需过程包括数据采集，探索性数据分析，机器学习算法的选择，模型训练，调整和验证的阶段。该专家系统对于从业人员在设计过程的早期阶段快速评估不同施工细节的可行性很有用。本文采用了支持向量机的机器学习算法。开发专家系统所需的必需过程包括数据采集，探索性数据分析，机器学习算法的选择，模型训练，调整和验证的阶段。该专家系统对于从业人员在设计过程的早期阶段快速评估不同施工细节的可行性很有用。本文采用了支持向量机的机器学习算法。开发专家系统所需的必需过程包括数据采集，探索性数据分析，机器学习算法的选择，模型训练，调整和验证的阶段。该专家系统对于从业人员在设计过程的早期阶段快速评估不同施工细节的可行性很有用。