A Hybrid Model for Prediction in Asphalt Pavement Performance Based on Support Vector Machine and Grey Relation Analysis

Abstract

Pavement performance prediction is a crucial issue in big data maintenance. This paper develops a hybrid grey relation analysis (GRA) and support vector machine regression (SVR) technique to predict pavement performance. The prediction model can solve the shortcomings of the traditional model including a single consideration factor, a short prediction period, and easy overfitting. GAR is employed in selecting the main factors affecting the performance of asphalt pavement. The SVR is performed to predict the performance. Finally, the data collected from the weather station installed on Guangyun Expressway were adopted to verify the validity of the GRA-SVR model. Meanwhile, the contrast with the grey model (GM (1, 1)), genetic algorithm optimization BP[[parms resize(1),pos(50,50),size(200,200),bgcol(156)]]081%, −0.823%, 1.270%, and −4.569%, respectively. The study concluded that the nonlinear and multivariate prediction model established by GRA-SVR has higher precision and operability, which can be used in long-period pavement performance prediction.

Analysis of the Optimal Application of Blockchain-Based Smart Lockers in the Logistics Industry Based on FFD-SAGA and Grey Decision-Making

“…The simulated annealing method may get the chance to accept poor solutions through disturbances, and with the gradual decrease of temperature, the chance of the poor solution being accepted becomes smaller and smaller, until the better solution is accepted. Therefore, some hybrid genetic algorithms and simulated annealing methods are proposed to improve efficiency [3,15,16].…”

“…The simulated annealing-genetic algorithm, which is a hybrid of the genetic algorithm and the simulated annealing method, as shown in the Figure 2, is proposed in this paper [3,15,16]. It improves the ability of the genetic algorithm to search for regions by using the characteristics of the simulated annealing method, which can make small disturbances in adjacent regions of the solution [22,23]…”

“…This paper refers to it as fractional factorial design-simulated annealing (FFD-SA). FFD-SA improves the traditional SA without systematic random disturbances, but uses systematic disturbances to improve the search performance of the algorithm (subsection (3), steps 3-5, below) [3,15,16,22,23]. By combining the fractional factor design method with the mating operator of the simulated annealing-genetic algorithm, the global optimal solution can be obtained.…”

“…FFD-SAGA can be regarded as FFD-GA combined with SA because of the added fractional factorial design method (in the same way as FFD-GA). The steps of the simulated annealing-genetic algorithm by fractional factorial design are as follows [3,15,16,22,23]: Mon :…”

Probability Bankruptcy Using Support Vector Regression

“…Essentially, regression task is quite similar with classification task. The objective of SVM model is to manage a plane so that the support vectors from both classification sets are farthest from the classification plane, and the objective of SVR model is to find a regression plane so that all data will be closest to the plane (Wang et al, 2020). Some academician stated that Support Vector Regression Machine (SVR) is introduced and developed by Vapnik (Drucker et.al, 1997).…”