###### Abstract  
This study aims to predict the compressive strength of concrete using a machine-learning algorithm with linear regression analysis and to evaluate its accuracy. The open-source software library TensorFlow was used to develop the machine-learning algorithm. In the machine-earning algorithm, a total of seven variables were set: water, cement, fly ash, blast furnace slag, sand, coarse aggregate, and coarse aggregate size. A total of 4297 concrete mixtures with measured compressive strengths were employed to train and testing the machine-learning algorithm. Of these, 70% were used for training, and 30% were utilized for verification. For verification, the research was conducted by classifying the mixtures into three cases: the case where the machine-learning algorithm was trained using all the data (Case-1), the case where the machine-learning algorithm was trained while maintaining the same number of training dataset for each strength range (Case-2), and the case where the machine-learning algorithm was trained after making the subcase of each strength range (Case-3). The results indicated that the error percentages of Case-1 and Case-2 did not differ significantly. The error percentage of Case-3 was far smaller than those of Case-1 and Case-2. Therefore, it was concluded that the range of training dataset of the concrete compressive strength is as important as the amount of training dataset for accurately predicting the concrete compressive strength using the machine-learning algorithm. compressive strength of concrete; TensorFlow; linear regression; concrete mixture; artificial neural network +
Footnote †: journal: Medical  
compressive strength. Therefore, in recent years, researchers have attempted to predict the compressive strength according to the mixture using a machine-learning algorithm (hereafter, MLA). [PERSON] et al. [1] utilized a machine-learning technique called the individuals and ensemble algorithm to predict the compressive strength of concrete containing fly ash. Among the ensemble algorithms, the begging method was used. An accurate prediction was achieved using the begging method with 20 submodels and a decision tree. [PERSON] et al. [2] predicted the concrete compressive strength at 28, 56, and 91 days. An artificial neural network (ANN) model based on a small amount of data, i.e., a total of 76 data points, was developed, and through Levenberg-Marquardt training, the concrete compressive strength was predicted. [PERSON] et al. [3] used a weak learner learning method, which has a low prediction error, along with the boosting method, a machine-learning technique that accelerates the learning to perform a strong learner with a good prediction for predicting the compressive strength of concrete. The machine learning was conducted with 1030 data points, and the algorithm was verified with 103 data points. [PERSON] et al. [4] predicted the compressive strength of high-strength concrete using four prediction algorithms: support vector regression (SVR), multilayer perceptron (MLP), gradient boosting regressor (GBR), and gradient boosting (XGBoost). A total of 1133 data points for the concrete compressive strength were used for the machine learning, and the hyperparameter tuning process was conducted to increase the accuracy of the algorithm. [PERSON] et al. [5] predicted the compressive strength through various machine-learning techniques, including a support vector machine (SVM), a decision tree-based model, linear regression, multivariate polynomial regression, Kernelized regression methods, and a regression tree based on 1681 fields and laboratory concrete data, and performed a comparative study on the techniques based on the predicted values. [PERSON] et al. [6] established the ANN model using the multiobjective slap swarm algorithm (MOSSA) and MSP model tree algorithm based on 624 data points and predicted the compressive strength of concrete containing blast furnace slag. This model exhibited a small error percentage, with mean absolute percentage errors (MAPEs) of 12.5% and 7.25%.