Accurate prediction of the concrete compressive strength is an important task that helps to avoid costly and time-consuming experiments. Notably, the determination of the later-age concrete compressive strength is more difficult due to the time required to perform experiments. Therefore, predicting the compressive strength of later-age concrete is crucial in specific applications. In this investigation, an approach using a feedforward neural network (FNN) machine learning algorithm was proposed to predict the compressive strength of later-age concrete. The proposed model was fully evaluated in terms of performance and prediction capability over statistical results of 1000 simulations under a random sampling effect. The results showed that the proposed algorithm was an excellent predictor and might be useful for engineers to avoid time-consuming experiments with the statistical performance indicators, namely, the Pearson correlation coefficient (R), root-mean-squared error (RMSE), and mean squared error (MAE) for the training and testing parts of 0.9861, 2.1501, 1.5650 and 0.9792, 2.8510, 2.1361, respectively. The results also indicated that the FNN model was superior to classical machine learning algorithms such as random forest and Gaussian process regression, as well as empirical formulations proposed in the literature.

中文翻译：

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基于前馈神经网络的高龄期混凝土抗压强度预测

准确预测混凝土的抗压强度是一项重要的任务，有助于避免进行昂贵且费时的实验。值得注意的是，由于需要进行实验，确定较晚混凝土的抗压强度更加困难。因此，在特定的应用中，预测高龄混凝土的抗压强度至关重要。在这项研究中，提出了一种使用前馈神经网络（FNN）机器学习算法的方法来预测高龄混凝土的抗压强度。在随机抽样效应的作用下，对1000个模拟的统计结果在性能和预测能力方面进行了全面评估。结果表明，该算法是一种很好的预测器，对于工程师避免耗时的统计性能指标（皮尔逊相关系数（R），均方根误差（RMSE）和培训和测试部分的均方误差（MAE）分别为0.9861、2.1501、1.5650和0.9792、2.8510、2.1361。结果还表明，FNN模型优于经典的机器学习算法，例如随机森林和高斯过程回归，以及文献中提出的经验公式。8510、2.1361。结果还表明，FNN模型优于经典的机器学习算法，例如随机森林和高斯过程回归，以及文献中提出的经验公式。8510和2.1361。结果还表明，FNN模型优于经典的机器学习算法，例如随机森林和高斯过程回归，以及文献中提出的经验公式。