Comparison Studies Between Machine Learning Optimisation Technique on Predicting Concrete Compressive Strength

Abstract

In this research, a comparison study of the machine learning (ML) optimisation technique to predict the compressive strength of concrete is discussed. In previous studies, researchers focused on identifying the machine learning model by comparing, ensemble, bagging, and fusion methods in predicting the concrete strength. In this research, an ML model hyper-parameter optimisation is used to improve the prediction accuracy and performance of the model. Extreme gradient boosting (XGBoost) is used as the base model to perform the prediction, as the XGBoost has a built-in model ensemble, bagging, and boosting algorithms. Grid Search, Random Search, and Bayesian Optimisation are selected and used to optimise the hyperparameters of the XGBoost model. For this particular prediction study, the optimised models based on Random Search performed better than other optimisation methods. The Random Search optimisation method showed substantial improvements in prediction accuracy, modelling error and computation time.

A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete

“…The correlation between the input characteristics (independent variables) was analyzed to see whether or not there was a dependence between the different parts; this statistical analysis contributes to the optimization of the predictive model [95] because it maximizes the prediction of the results. For this purpose, the Pearson correlation matrix (heat map) was calculated (Figure 6), analyzing the correlation between the independent variables (input variables).…”

“…where y i = fst (output variable), ŷi = estimated fst, y i = mean experimental fst, and n = number of samples. Currently, the R 2 value is thought to be the best metric for assessing the model [95,97]. Table 4 shows the range of R 2 values for prediction model evaluations [54,98,99].…”

“…Since the R 2 metric is more intuitive and convenient for comparing the performance of different ML models [95,97], in the following analysis, we adopted it as the primary metric index. Prediction accuracy is reflected in the value of R 2 , and a significant value for this metric indicates that a model has exhibited high prediction accuracy.…”

“…Prediction accuracy is reflected in the value of R 2 , and a significant value for this metric indicates that a model has exhibited high prediction accuracy. Values for the RMSE and MAE metrics were also considered; values less than 0.05 indicate that the ML model presents a good fit [95,101] for predicting the splitting tensile strength of 28-day-old SCC made with RA. Table 5 shows the R 2 results for both the overall data set and the training and test data sets for the models: XG Boost, GB, CB, and ETR.…”

Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods

“…The correlation between the input variables-i.e., water, cement, admixtures, coarse aggregates, water, fine aggregates, and superplasticizers-and output-i.e., splitting tensile strength (TS)-was investigated to see whether there was a link between them; this statistical analysis assisted in the creation of the predictive model by increasing the accuracy of the outcome's prediction [89]. For this purpose, the Pearson correlation matrix (heat map) was generated, as shown in Figure 3, which analyzed the correlation between the independent input variables.…”