Selection of the number of neurons in different layers of an artificial neural network (ANN) is a key decision-making step involved in its successful training. Although the number of neurons in the input layer is decided by the number of input parameters, and similarly, in the output layer the number of neurons is fixed by the output parameters, however, the number of neurons in the hidden layer is not fixed, whereby the overall efficiency of the ANN depends upon the correct modeling of the realistic scenario in the form of a neural network. Concrete can be mixed in a huge variety of methods and compositions to achieve a specific or a combination of specific results. These results have been observed to repeat and follow patterns. Furthermore, for several types of concrete mixes, only actual physical trial data are available with no mix design method; this makes achieving different required results very complex and expensive task. Having an ANN to be able to predict a concrete mix to handle such variations will save a lot of time and money and will prove to be a universal mix design tool. A thorough analysis for deciding the number of neurons for mix design of concrete has been carried out in this research. The results of neural networks for a varying number of neurons and layers have been correlated. The data for a number of concrete mixes were sorted and normalized. Properties of concrete constituents like specific gravity of cement, coarse and fine aggregates, dry density of coarse and fine aggregates, cement type, type of mineral admixture, water-to-cement ratio, type and temperature of curing and hardened properties like compressive strength, modulus of elasticity and tensile strength were taken as input parameters. The contents of constituents of concrete such as water content, cement content, coarse aggregates content, fine aggregates content and content of mineral admixtures are regarded as output parameters. For 17 inputs and 5 outputs, it has been discovered that simple neural network with single or double hidden layers performed better that 3 or more such layers.

人工神经网络（ANN）不同层中神经元数量的选择是成功训练的关键决策步骤。尽管输入层中神经元的数量由输入参数的数量决定，并且类似地，在输出层中神经元的数量由输出参数固定，但是隐藏层中神经元的数量并不固定，因此，人工神经网络的整体效率取决于以神经网络形式对现实情景进行正确建模。可以用多种方法和组合物来混合混凝土，以达到特定或特定结果的组合。已经观察到这些结果重复并遵循模式。此外，对于几种类型的混凝土混合物，没有混合设计方法，只能获得实际的物理试验数据；这使得获得不同的所需结果非常复杂且昂贵。使用人工神经网络能够预测处理此类变化的具体混合料将节省大量时间和金钱，并且将被证明是通用的混合料设计工具。这项研究已经进行了彻底的分析，以确定用于混凝土配合比设计的神经元数量。神经网络对于不同数量的神经元和层的结果已经相关。对许多混凝土混合物的数据进行了分类和标准化。混凝土成分的特性，例如水泥的比重，粗骨料和细骨料，粗骨料和细骨料的干密度，水泥类型，矿物掺合料的类型，水灰比，固化的类型和温度以及抗压强度，弹性模量和拉伸强度等硬化性能均作为输入参数。将混凝土成分的含量，例如水含量，水泥含量，粗骨料含量，细骨料含量和矿物掺合料含量视为输出参数。对于17个输入和5个输出，已经发现具有单个或两个隐藏层的简单神经网络的性能要优于3个或更多此类层。