Estimation of Annual Routine Maintenance Cost for Highway Tunnels

Abstract

In highway management, the prediction of the routine maintenance cost of tunnels is an important issue in saving tunnel maintenance costs due to its uncertainty, and the influencing factors should be carefully selected because too many variables could not be involved in the model. The complicated relationship between variables may lead to the inconsistency of model coefficients with the actual situation even though the goodness of fit of the model constructed with more variables is higher. This paper presents an approach in which quantitative analysis is combined with qualitative analysis to quickly select the independent variables of the tunnel routine maintenance cost (TMC) model. Based on the routine maintenance data collection of nine highway tunnels in Shaanxi province from 2007 to 2016, the independent variables of the models are determined with one-way ANOVA, Pearson correlation, partial correlation, and hierarchical regression. Afterwards, a fixed-effect regression model which can reflect the overall regional features is developed. Results show that tunnel age (Age) and tunnel length proportion (PET) have less effect on TMC among the main influencing factors such as district, Age, annual average daily traffic volume (AADT), truck traffic volume proportion (PTT), PET, and number of ventilation facilities (NVF), while the NVF makes a positive contribution to the TMC. Compared with grouped regression models, the fixed-effect regression model has higher fitting accuracy and a better regression coefficient significance. The quick independent variable selection method can shorten the time of establishing the model and determine the influencing factors of the research object effectively. The established model is suitable for forecasting the TMC and budget arrangement. In addition, the elastic analysis results of regression coefficients are helpful to the decision of maintenance strategy and the allocation of maintenance funds.

Uncontrollable Factors Analysis on Sustainable Highway Routine Maintenance Management: A Case Study of Shaanxi Province in China

“…It adopts a hierarchical management model of groupoperation company-maintenance management subsidiary for HMM; the organizational structure and corresponding responsibilities are depicted in Figure 2. In a previous study conducted by the authors, Shaanxi Province can be typically divided into three regions based on topography [65]: the Shaanbei plateau (northern Shaanxi), the Guanzhong plain (central Shaanxi), and the Shaannan mountains (southern Shannxi). There are 23 maintenance management subsidiaries (23 DMUs) responsible for the maintenance management of different road sections, the distribution of which is illustrated in Figure 3.…”

“…The relative maintenance management data from 2020 to 2022 was provided by STHG, which involves the climate conditions, traffic, maintenance mileages, maintenance management resources, road conditions, etc., of each subsidiary, providing a strong foundation for the following research. In a previous study conducted by the authors, Shaanxi Province can be typically divided into three regions based on topography [65]: the Shaanbei plateau (northern Shaanxi), the Guanzhong plain (central Shaanxi), and the Shaannan mountains (southern Shannxi). There are 23 maintenance management subsidiaries (23 DMUs) responsible for the maintenance management of different road sections, the distribution of which is illustrated in Figure 3.…”