Application of support vector regression for the prediction of concrete strength
The compressive strength of concrete is a commonly used criterion in producing concrete. However, the test on the compressive strength is complicated and time-consuming. More importantly, since the test is usually performed 28 days after the placement of the concrete at the construction site, it is too late to make improvements if unsatisfactory test results are incurred. Therefore, an accurate and practical strength estimation method that can be used before the placement of concrete is highly desirable. In this study, the estimation of the concrete strength is performed using support vector regression (SVR) based on the mix proportion data from two ready-mixed concrete companies. The estimation performance of the SVR is then compared with that of neural network (NN). The SVR method has been found to be very efficient in estimation accuracy as well as computation time, and very practical in terms of training rather than the explicit regression analyses and the NN techniques.
concrete strength support vector regression (svr) strength prediction concrete mix proportion data kernel function