A fuzzy weighted relative error support vector machine for reverse prediction of concrete components. (1st April 2020) - Fan, Zongwen; Chiong, Raymond; Hu, Zhongyi; Lin, Yuqing
Highlights: Reverse prediction of concrete components is studied. An improved relative error support vector machine (RE-SVM) is proposed. A fuzzy weighted (FW) operation is applied to improve robustness. Results show excellent performance of the proposed FW-RE-SVM. Abstract: Concrete is one of the most commonly used construction materials in civil engineering. Being able to accurately predict concrete components based on concrete strength, slump and flow is crucial for saving manpower and financial resources. The reverse prediction nature of this task, however, makes it a very difficult problem to solve. Relative error support vector machines (RE-SVMs) have been successfully applied to tackle this problem using relative errors as equality constraints. Nevertheless, RE-SVMs are sensitive to noise, and their target values cannot be zero. In this paper, we present a fuzzy weighted RE-SVM (FW-RE-SVM) to address the limitations of RE-SVMs. A fuzzy weighted operation is first utilised to improve the robustness of RE-SVMs by assigning weights to the relative error constraints. A small value is further added to the denominators of the relative error constraints, in case their values are equal to zero. This helps to generalise the approach. Experimental results confirm that our proposed model has very good performance for reverse prediction of concrete components under both multi-input, one-output and multi-input, multi-output scenarios.