A minimum-of-maximum relative error support vector machine for simultaneous reverse prediction of concrete components

•A variant SVM that uses PSO to minimize the maximum relative error is proposed.•A novel learning strategy whereby the maximum relative error is used in the SVM.•The PSO-RE-MIMO SVM for simultaneous reverse prediction of concrete components.•The proposed method performs better than the other three methods with the dataset.•The proposed method is suitable in multiple-input multiple-output (MIMO) scenarios.

The simultaneous reverse prediction of multiple concrete components is very difficult, but very important in practical engineering applications. Thus, this paper presents a variant support vector machine (SVM) that uses particle swarm optimization (PSO) to minimize the maximum relative error (RE) in a multiple-input multiple-output (MIMO) scenario. This PSO-RE-MIMO SVM uses a novel learning strategy whereby the maximum relative error is used as a constraint in the optimization problem. Experimental results demonstrate that the PSO-RE-MIMO-SVM performs well for reverse prediction of multiple concrete components compared with the least-squares SVM, back propagation neural network, and radial basis function neural network approaches.