By the end of 2019, the total mileage traveled on highways had reached 5.01 million kilometers in China. With the continuous improvement and use of the road network, the planning of roads began to transform from a large-scale construction period to a persistent maintenance stage. Due to the wide distribution of roads, the impact of environmental factors is extremely complex, and subgrade engineering will face severe challenges in the service process. With the increase in the service time of a subgrade, the performance of the subgrade structure will decline. More seriously, under the influence of rainfall, subgrade distresses will increase and cause subgrade performance to suffer.
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
]. Highway subgrade distresses in mountainous areas include subgrade subsidence, reinforced structure damage, slope landslides, subgrade cracks, and poor drainage [5
]. To address the different types of subgrade distresses, four assessment systems are established, which include shoulder, slope, drainage facilities, and retaining wall systems. Then, the assessment model of a highway subgrade is obtained by a linear regression method [6
During the operational period, rainfall is the main factor affecting the structure and function of a subgrade. The repeated failure of the railway embankment at a place called Malda, in the state of West Bengal in India, occurred after continuous heavy rainfall [7
]. 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 also affect the previously built retaining wall and intercepting ditch, causing small-scale toppling, faulting, and fracturing [8
]. Infiltration-induced landslides are a recurrent threat along many highway corridors and pose a challenge to safe operation and the maintenance of roadways [9
]. In addition, time not only plays an important role in the development of pavement cracks [10
], but also causes the aging of reinforced structures and a decrease in tensile strength [11
As there are different types of subgrade distresses in many areas during the operational stage, timely maintenance and treatment should be carried out to reduce the risk to driving safety. When there is a large-scale subgrade distress problem in the operational stage, it not only increases the difficulty of engineering treatment, but also increases the driving risk. Therefore, it is necessary to improve the service level of the highway life-cycle and reduce the economic cost of the project through the accurate evaluation and prediction of subgrade performance.
High precision prediction can provide a scientific guide for the early warning and forecasting of geotechnical activity [14
]. A machine learning method, the support vector machine (SVM), was proposed by Vapnik [20
]. 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
]. For better performance, an improved version of the SVM, the least square support vector machine, was proposed [23
]. This version runs faster and shows more adaptability. However, since the SVM is highly sensitive to the selection of model parameters, it is important to obtain the optimal parameters through an effective and intelligent algorithm [24
]. Here, intelligent algorithms such as grid search [27
], genetic algorithm [28
], and particle swarm optimization [15
] are the most widely adopted approaches. Particle swarm optimization (PSO) is a recently developed population-based global optimization technique [30
], which has been widely used by the optimization community due to its very good performance, wide applicability, and simplicity [15
]. Considering the excellent global search ability of the PSO algorithm, we use the PSO algorithm to obtain the model parameters.
In this paper, 20 test sites were selected from 4 typical national and provincial highways in Guangdong Province, China, to collect subgrade distresses over 3 years. Through the analytic hierarchy process, the assessment system of the Subgrade Performance Index (SPI) was established. 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. Then, according to the predicted subgrade performances, the corresponding countermeasures were carried out.