Abstract
The subgrade performance assessment and targeted maintenance of a highway during operation is very important and challenging. This paper focuses on the performance of the whole life-cycle of a highway subgrade during the operational period. Four roads with different traffic volume and geological conditions were selected; 20 test sections of these 4 roads were examined for a three-year distress survey, and 18 specific subgrade distresses of the 5 assessment objects were tracked and collected. First, based on the analytic hierarchy process (AHP), the subgrade performance of the selected section is evaluated, and the subgrade performance index (SPI) at different time periods is obtained. Then, based on the internal and external factors which affect the subgrade, three algorithms to determine the optimal support vector machine (SVM) model were proposed to train and predict the SPI. The results show that the SPI predicted results based on the data time series and particle swarm optimization–least squares SVM (PSO–LSSVM) model are better than those based on grid search (Grid-SVM) and genetic algorithm (GA-SVM) models. Finally, this paper provides a detailed idea for the rational layout of subgrade life-cycle assessment and decision-making by establishing a subgrade performance assessment–prediction–maintenance–management architecture system.
Highlights
By the end of 2019, the total mileage traveled on highways had reached 5.01 million kilometers inChina
The types of subgrade distresses vary across different regions, but generally include shoulder distresses, slope stability, damage to drainage facilities, and damage to facilities attached to reinforced structures [1,2,3,4]
The subgrade performance index (SPI) is regarded as the output parameter to establish the Particle swarm optimization (PSO)–Least Squares Support Vector Machine (LSSVM) model
Summary
By the end of 2019, the total mileage traveled on highways had reached 5.01 million kilometers in. Persistent rainfall accelerates the trend of slope sliding and stability deterioration, producing local uplift on the leading edge and obvious tensile deformation on the trailing edge It can affect the previously built retaining wall and intercepting ditch, causing small-scale toppling, faulting, and fracturing [8]. The SVM is a machine learning method established based on statistical learning theory for a small sample and the principle of structural risk minimization It looks for a non-linear relation between outputs and inputs by mapping the inputs to a high dimension space based on a kernel function [17,21,22]. By analyzing the response relationship between the SPI and the factors affecting subgrade performance, the particle swarm optimization–least squares support vector machine (PSO–LSSVM), based on the factors of time and precipitation, was proposed to predict the SPI in the near future. According to the predicted subgrade performances, the corresponding countermeasures were carried out
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