A New Decision-Making Optimization Approach for Sustainable Expressway Pavement Maintenance

Abstract

We use actual pavement disease data, pavement performance data and pavement maintenance data to develop a data envelopment analysis (DEA) model to evaluate the effectiveness of repairing each type of pavement disease. Next, an optimization model is proposed to improve the pavement maintenance performance while reducing the pavement maintenance cost. Results indicate that repairing transverse cracking and rutting pavement diseases is more effective in improving the pavement maintenance performance. We also find that the pavement maintenance performance is enhanced prominently if the cost constraint increases from 0.1 to 0.2, while only slight improvements are observed if the maintenance cost keeps increasing. When the cost constraint is set to 0.2, the number of expressway sections with excellent condition increases by 71 and the average pavement condition index (PCI) increases by 0.27, implying that the generated pavement maintenance strategy can achieve a good performance while using much lower maintenance cost.

Keywords

• data analysis
• decision-making optimization
• expressway pavement disease
• pavement condition index
• pavement maintenance strategy