One of the most commonly used materials in civil engineering is concrete; not only is it cheap and strong, but it is also efficient and convenient. The efficiency of concrete is based on the easiness to place and to compact, which is usually known as workability. However, concrete strength and workability works in different ways; hence it is important to divide concrete into two groups: concrete with low workability and concrete with high workability, in order to achieve a more accurate prediction. Since there is a lot of variations of concrete mix designs, the relationship between each mixture is complex and, thus, requires advanced prediction methods in order to find the most accurate relationships between concrete mix proportion and its compression test result.–Recently, many studies have been conducted on applying multiple artificial intelligence (AI) methods in building different complex and challenging prediction models. Thus, this research employs ensemble machine learning methods to precisely forecast compression strength of concrete mix proportion. The accuracy of the proposed method was calculated using two performance measurements. Subsequently, the study has successfully built the prediction model that can accurately map the relationship between concrete mix proportion and compressive strength.

土木工程中最常用的材料之一是混凝土。它不仅便宜且坚固，而且高效便捷。混凝土的效率是基于易于放置和压实的，这通常被称为和易性。然而，混凝土强度和和易性以不同的方式起作用。因此，重要的是将混凝土分为两组：低和易性混凝土和高和易性混凝土，以实现更准确的预测。由于混凝土配合比设计有很多变化，每种配合比之间的关系很复杂，因此，需要先进的预测方法才能找到混凝土配合比与其压缩试验结果之间最准确的关系。-最近，已经进行了许多关于应用多种人工智能 (AI) 方法来构建不同的复杂和具有挑战性的预测模型的研究。因此，本研究采用集成机器学习方法来精确预测混凝土配合比的抗压强度。使用两个性能测量来计算所提出方法的准确性。随后，该研究成功建立了能够准确映射混凝土配合比与抗压强度关系的预测模型。