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首页> 外文期刊>Arabian Journal for Science and Engineering >Generalized Regression Neural Network and Empirical Models to Predict the Strength of Gypsum Pastes Containing Fly Ash and Blast Furnace Slag
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Generalized Regression Neural Network and Empirical Models to Predict the Strength of Gypsum Pastes Containing Fly Ash and Blast Furnace Slag

机译：广义回归神经网络和经验模型预测粉煤灰和高炉渣石膏浆料的强度

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摘要

Gypsum is widely used in constructions owing to its easy application, zero shrinkage, and excellent fire resistance. Severalparameters can affect the properties of gypsum pastes. To study the strength of the gypsum pastes experimentally by trying allthese parameters is time-consuming and costly. Therefore, artificial intelligence methods can be very useful to predict the pastestrength, which, in turn, can reduce the number of trial batches. Based on experimental data, the generalized regression neuralnetwork (GRNN) and empirical models were developed to predict strength of gypsum pastes containing fly ash (FA) andblast furnace slag (BFS). Gypsum content, pozzolan content, curing temperature, curing duration, and testing age constitutedthe input variables of the models while the paste strength was the target output. The trained and tested GRNN model wasfound to be successful in predicting strength. Sensitivity analysis by the GRNN model revealed that the curing durationand temperature were important sensitive parameters. In addition to the GRNN model, empirical models were proposed forthe strength prediction. The same input variables formed the input vectors of the empirical models. The same dataset usedfor the calibration of the GRNN model was employed to establish the empirical models by employing genetic algorithm(GA) method. The empirical models were successfully validated. The GRNN and GA_based empirical models were alsotested against the multi-linear regression (MLR) and multi-nonlinear regression (MNLR) models. The results showed theoutperformance of the GRNN and the GA_based empirical models over the others.
机译：石膏由于其易于使用，零收缩和优异的耐火性而被广泛用于建筑中。几个参数会影响石膏浆的性质。通过尝试所有这些参数来实验性地研究石膏糊的强度是耗时且昂贵的。因此，人工智能方法对于预测糊状强度可能非常有用，从而可以减少试验批次的数量。基于实验数据，建立了广义回归神经网络（GRNN）和经验模型来预测含粉煤灰（FA）和高炉矿渣（BFS）的石膏浆的强度。石膏含量，火山灰含量，固化温度，固化时间和测试年龄是模型的输入变量，而糊剂强度是目标输出。发现经过训练和测试的GRNN模型可以成功预测强度。 GRNN模型的敏感性分析表明，固化时间和温度是重要的敏感参数。除了GRNN模型外，还提出了强度预测的经验模型。相同的输入变量构成了经验模型的输入向量。采用遗传算法（GA），采用与GRNN模型校准相同的数据集建立经验模型。实证模型已成功验证。还针对多线性回归（MLR）和多非线性回归（MNLR）模型对基于GRNN和GA_的经验模型进行了测试。结果表明，GRNN和基于GA_的经验模型优于其他模型。

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