The compressive strength of self-compacting concrete (SCC) needs to be determined during the construction design process. This paper shows that the compressive strength of SCC (CS of SCC) can be successfully predicted from mix design and curing age by a machine learning (ML) technique named the Extreme Gradient Boosting (XGB) algorithm, including non-hybrid and hybrid models. Nine ML techniques, such as Linear regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees (DTR), Random Forest (RF), Gradient Boosting (GB), and Artificial Neural Network using two training algorithms LBFGS and SGD (denoted as ANN_LBFGS and ANN_SGD), are also compared with the XGB model. Moreover, the hybrid models of eight ML techniques and Particle Swarm Optimization (PSO) are constructed to highlight the reliability and accuracy of SCC compressive strength prediction by the XGB_PSO hybrid model. The highest number of SCC samples available in the literature is collected for building the ML techniques. Compared with previously published works’ performance, the proposed XGB method, both hybrid and non-hybrid models, is the most reliable and robust of the examined techniques, and is more accurate than existing ML methods (R2 = 0.9644, RMSE = 4.7801, and MAE = 3.4832). Therefore, the XGB model can be used as a practical tool for engineers in predicting the CS of SCC.
. [J]. Frontiers of Structural and Civil Engineering, 2022, 16(7): 928-945.
Van Quan TRAN, Hai-Van Thi MAI, Thuy-Anh NGUYEN, Hai-Bang LY. Assessment of different machine learning techniques in predicting the compressive strength of self-compacting concrete. Front. Struct. Civ. Eng., 2022, 16(7): 928-945.
7 inputs: water to binder ratio, macro-synthetic polypropylene fibers, steel fiber, scoria, crumb rubber, natural fine aggregate, natural coarse aggregate
131
R2 = 0.9477
Azimi-Pour et al. [41]
Linear and nonlinear SVM
10 inputs: cement, water to cement, water to powder, water to binder, fine aggregate to powder, coarse aggregate to powder, high water range reducer to powder, viscosity modify admixture to powder, micro silica to binder.
340
R2 = 0.9388
Saha et al. [42]
Support vector regression approach
6 inputs: binder content, FA percentage, water to powder ratio, fine aggregate (f), coarse aggregate, superplasticizer dosage
115
SVR: R2 = 0.955ANN: R2 = 0.882
this study
LR, KNN, SVM, DTR, RF, GB, XGB, ANN_LBFGS and ANN_SGD, and the Hyperparameters tuning with PSO
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