###### Abstract
[PERSON] et al. [7] established five machine-learning models using linear regression, nonlinear regression, multi-logistic regression (MLR), and MSP tree, and an ANN based on 450 data points for predicting the compressive strength of concrete containing a high volume of fly ash (HVFA) and performed a comparative study on the techniques based on the predicted values. [PERSON] et al. [8] established an artificial intelligence (AI) model that grafted gray wolf optimizer (GWO) and classical optimization algorithms (COAs) onto an ANN and an adaptive neuro-fuzzy inference system (ANFIS). To predict the compressive strength of the normal concrete and high-strength concrete, 2817 data points were utilized. [PERSON] et al. [9] predicted the compressive strength of high-strength concrete using the k-nearest neighbor algorithm trained with 104 data points. This model was compared with the results of regression neural network, stepwise regression, and modular neural network models. [PERSON] et al. [10] predicted the compressive strength of normal concrete and high-strength concrete using the M5P model tree algorithm trained with 1912 data points. This algorithm was compared with the results of other machine-learning techniques, such as ANN, classification and regression trees, and ANFISs. [PERSON] et al. [11] studied the important factors for strength, stiffness, and the drift ratio of steel plate shear walls, as well as reinforced concrete shear walls utilizing meta-models developed with ANN, trained under 4300 data points. [PERSON] et al. [12] predicted two-phase flows independent of the oil pipeline's scale layer thickness based on 162 cases. Regiment identification was performed using the support vector machine (SVM), and the void fraction was predicted through the use of the multilayer perceptron with the Levenberg-Marquardt algorithm (MLP-LM). [PERSON] et al. [13] looked into determining the type and amount of four different petroleum by-products using gamma attenuation technique combined with ANN. [PERSON] et al. [14] predicted control chart pattern recognition (CCPR) employing a convolutional neural network (CSCNN) trained with 7194 data points. [PERSON] et al. [15] predicted gas-dol-water volume fractions of a three-phase flow using the group method of data handling (GMDH), a neural network trained with 108 data points. [PERSON] et al. [16] predicted the compressive strength of concrete using boosting smooth transition regression trees (BooST) based on 2456 data points. In addition, compared to other technologies (multilayer perceptron, support vector machine, etc. ), BooST exhibited good in complex model analysis. [PERSON] et al. [17] predicted the compressive strength of high-strength concrete using an extreme learning machine (ELM), a new method for an artificial neural network (ANN), trained with 324 data points. [PERSON] et al. [18] introduced a convolutional neural network (CNN) topology using wavelet kernels to detect and identify single or multiple partial discharges (PD). This study aims to predict the compressive strength of low-to-high-strength concrete using an MLA based on linear regression and to evaluate the accuracy of MLA when it was trained with a different range and/or amount of data. The open-source library TensorFlow, a representative machine-learning algorithm, was used to develop an algorithm for predicting the compressive strength of concrete. For testing MLA, 4279 data points were prepared. This is more data than previous studies. Among them, A total of 2991 training data were employed for the model training, and a total of 1288 data points were used to test the algorithm, and the measured compressive strength in data ranged from 7 to 100 MPa. First, the errors of the predicted values obtained from the MLA trained with all the data (2991 ea.) were examined (Case-1). Second, it is investigated how the predicted values were affected in the case where the number of training data points (1080 = 180 \(\times\) 6 ranges) in each compressive strength's range was the same (Case-2).