An artificial neural network (ANN) model and response surface methodology (RSM) were established to estimate the compressive strength of concrete by using the combination of three non-destructive tests (NDT); rebound number, pulse velocity tests and resistance surface. These techniques are utilized in an attempt to increase the reliability of the non-destructive tests in detecting the strength of concrete. These methods were trained using a set of different mixes and at different ages of concrete specimens. In this case, 180 experimental specimens were conducted and their data are published. Then, different neural network topologies and algorithms besides RSM were examined using the given data. The published models are for two combination including the combination of UPV and RN and the combination UPV, RN and SR. The results show that the accuracy of the published models are increased by aging. In addition, it is showed that RSM don’t need calibration process, while its accuracy is enough. Hence, RSM is a promising method to conduct on NDTs and compressive strength prediction, while ANN needs to perform many times to find the best accuracy.

中文翻译：

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使用基于无损测试和自校准响应面方法的神经网络预测混凝土抗压强度

建立人工神经网络（ANN）模型和响应面法（RSM），结合三种无损检测（NDT）方法估算混凝土的抗压强度；回弹数、脉冲速度测试和阻力面。这些技术被用来尝试提高非破坏性测试在检测混凝土强度方面的可靠性。这些方法是使用一组不同的混合物和不同年龄的混凝土试样进行训练的。在这种情况下，进行了 180 个实验样本，并公布了它们的数据。然后，使用给定的数据检查了除 RSM 之外的不同神经网络拓扑和算法。公布的模型有两种组合，包括UPV和RN的组合以及UPV、RN和SR的组合。结果表明，已发布模型的准确性随着老化而增加。此外，表明RSM不需要校准过程，而其精度就足够了。因此，RSM 是进行无损检测和抗压强度预测的一种很有前途的方法，而 ANN 需要多次执行才能找到最佳精度。