This paper presents an innovative benefit‐evaluation framework integrating predictive analysis, construction risks, and environmental considerations to address the issues of inadequate project benefits, complex decision‐making risks, and inefficient optimization in long‐term highway network maintenance planning. First, this paper develops a two‐parameter prediction model for the following pavement condition indicators: pavement condition index (PCI), rut depth index (RDI), riding quality index (RQI), and skid resistance index (SRI). The paper uses the G3 Jing Tai Expressway as a case study. The validation results for the prediction model demonstrated an overall relative error within 3% which indicates that the model has high accuracy in predicting the pavement condition of this highway. Second, within the Bayesian network theoretical framework, a probabilistic risk assessment model for road infrastructure was developed using predetermined risk metrics through GeNIe software, and the model was demonstrated and validated using operational data from the G3 Expressway. Beyond this, this study extends the traditional cost‐benefit analysis model by incorporating environmental and risk factors, establishing a novel benefit‐assessment framework tailored to practical engineering needs. Finally, by conducting another case involving a specific highway in Shandong Province, the optimal maintenance strategy with the highest benefits over a long period is identified. This further validates the feasibility of the road maintenance decision model.
Huang et al. (Thu,) studied this question.