Application of novel hybrid machine learning approach for estimation of ultimate bond strength between ultra-high performance concrete and reinforced bar

Predicting the Compressive Strength of Environmentally Friendly Concrete Using Multiple Machine Learning Algorithms

“…Zheng et al used decision tree, SVR and ANN to construct the performance prediction model of silica fume concrete, and they also used the bagging and boosting methods to assemble the above ML algorithms [19]. After reviewing the recent literature, it was found that the ANN-based modulus is the most wildly used machine learning algorithm for the performance prediction of concrete regarding recycled aggregate concrete [20,21], high-performance concrete [17,22,23], foamed concrete [24][25][26], metakaolin-based concrete materials [27], self-compacted concrete [28], rubberized concrete [29], concrete slabs [30] and other concrete accessories such as steel tubes [31,32], FRP bars [33][34][35][36][37], steel bars [38] and concrete blocks [39].…”

“…Although the above machine learning algorithms show a remarkable performance in terms of the specific database, the same ML algorithm may have a poor prediction performance in another database due to the specificity of material composition and regression problems [36]. At the same time, the experimental conditions vary between different research papers, which has a certain negative impact on the accuracy and generalization of ML algorithm prediction [40].…”

“…This method combines a neural network with fuzzy logic to classify the data. This fuzzy classification ability enables the computer to understand the input data, and then it has a higher ability to resist data error and excellent prediction performance [36].…”