The use of data driven models has been shown to be useful for simulating complex engineering processes, when the only information available consists of the data of the process. In this study, four data-driven models, namely multiple linear regression, artificial neural network, adaptive neural fuzzy inference system, and K nearest neighbor models based on collection of 207 laboratory tests, are investigated for compressive strength prediction of concrete at high temperature. In addition for each model, two different sets of input variables are examined: a complete set and a parsimonious set of involved variables. The results obtained are compared with each other and also to the equations of NIST Technical Note standard and demonstrate the suitability of using the data driven models to predict the compressive strength at high temperature. In addition, the results show employing the parsimonious set of input variables is sufficient for the data driven models to make satisfactory results.

中文翻译：

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数据驱动的高温混凝土抗压强度预测模型

当唯一可用的信息包括过程数据时，数据驱动模型的使用已被证明对模拟复杂的工程过程很有用。在这项研究中，四个数据驱动模型，即多元线性回归，人工神经网络，自适应神经模糊推理系统和K根据207个实验室测试的集合，对最近邻模型进行了研究，以预测高温下的混凝土抗压强度。此外，对于每个模型，还将检查两组不同的输入变量：完整的一组和所涉及变量的简约组。将获得的结果相互比较，并与NIST技术说明标准的公式进行比较，并证明使用数据驱动模型预测高温下的抗压强度的适用性。此外，结果表明，采用简约的输入变量集足以使数据驱动的模型获得令人满意的结果。