Concrete is one of the most used materials in buildings today; yet, predicting the accurate concrete compressive strength remains challenging because of the highly complex relationship between its mixture. An accurate method of predicting concrete compressive strength can provide a significant advantage to the construction material industry, particularly within the concrete material industry. Many methods can be used to build the prediction model of concrete compressive strength. However, the traditional methods have so many shortcomings, including expensive experimental costs and the inability to formulate an accurate complex relationship between the components of a concrete mixture with the compressive strength. To overcome this issue, this study applies multiple artificial intelligence (AI) methods to find the most accurate input and output relationships within concrete mixtures. The three types of AI methods that will be used in this study are artificial neural networks (ANN), support vector machine (SVM), and linear regression (LR). This study uses 1030 data samples from concrete compressive strength tests obtained from University of California, Irvine, to demonstrate the use of AI prediction models. The obtained results of the simulation show that these artificial intelligence methods can build predictive models without conducting any expensive experiments in the laboratory with good accuracy.

混凝土是当今建筑中使用最多的材料之一。然而，由于其混合物之间的高度复杂的关系，预测准确的混凝土抗压强度仍然具有挑战性。预测混凝土抗压强度的准确方法可以为建筑材料行业提供显着优势，特别是在混凝土材料行业中。很多方法可以用来建立混凝土抗压强度的预测模型。然而，传统方法存在许多缺点，包括昂贵的实验成本以及无法准确地制定混凝土混合物组分与抗压强度之间的复杂关系。为了克服这个问题，本研究应用多种人工智能 (AI) 方法来找到混凝土混合物中最准确的输入和输出关系。本研究将使用的三种人工智能方法是人工神经网络 (ANN)、支持向量机 (SVM) 和线性回归 (LR)。本研究使用来自加州大学欧文分校的混凝土抗压强度测试中的 1030 个数据样本来展示 AI 预测模型的使用。获得的仿真结果表明，这些人工智能方法无需在实验室中进行任何昂贵的实验即可建立预测模型，并且具有良好的准确性。和线性回归 (LR)。本研究使用来自加州大学欧文分校的混凝土抗压强度测试中的 1030 个数据样本来展示 AI 预测模型的使用。获得的仿真结果表明，这些人工智能方法无需在实验室中进行任何昂贵的实验即可建立预测模型，并且具有良好的准确性。和线性回归 (LR)。本研究使用来自加州大学欧文分校的混凝土抗压强度测试中的 1030 个数据样本来展示 AI 预测模型的使用。获得的仿真结果表明，这些人工智能方法无需在实验室中进行任何昂贵的实验即可建立预测模型，并且具有良好的准确性。