This highway engineering project spans a total length of 112.35 km and is built to high-grade highway standards. It features a six-lane, two-way road designed for speeds up to 100 km/h, with a roadbed width of 34.5 meters. The pavement consists of an asphalt concrete structure. The highway was completed and opened to traffic at the end of July 2015. Since May 2018, micro surfacing technology has been gradually introduced for preventive pavement maintenance. For this effort, heavy-duty traffic sections that have not yet undergone preventive maintenance were selected, covering approximately 8.75 km in total.
The pavement structure includes a 15 cm thick surface layer composed of one 4.0 cm layer of fine-grained asphalt concrete and two layers of medium-grained asphalt concrete—one 5.0 cm thick and the other 6.0 cm thick. The base layer is stabilized with lime fly ash soil, resting on a water-stable gravel foundation. To ensure the smooth and organized implementation of preventive maintenance, a road maintenance database module was developed using advanced Building Information Modeling (BIM) technology. This system provides a reliable foundation for maintenance decision-making.
2. Road Maintenance Decision-Making Using BIM Technology
The road maintenance database based on BIM technology comprises four key modules: a data management module, a road surface leveling condition evaluation module, a road performance prediction module, and a maintenance decision-making module. This study focuses primarily on the maintenance decision-making module.
2.1 Purpose of Maintenance Decision-Making
Maintenance decisions are based on predicted or measured data to determine the most effective preventive maintenance strategies for the pavement. The goal is to develop an optimal maintenance plan that guides road upkeep activities. After the highway enters service, its performance gradually declines due to vehicle loads and environmental factors, affecting driving comfort and safety, and increasing the risk of traffic accidents.
To address this, timely and effective maintenance measures are crucial to restore pavement usability. Given the highway’s extensive length and limited maintenance budget, the primary objective of maintenance decision-making is to allocate funds efficiently to maximize road performance improvements. Achieving this requires identifying the best maintenance solution.
2.2 Selection of Decision Methods
Pavement maintenance decision-making was first applied in AASHO (American Association of State Highway Officials) road tests, relying heavily on pavement performance data and maintenance experience. However, as decision requirements have evolved, traditional methods no longer suffice. New approaches such as Artificial Neural Networks (ANN) and Genetic Algorithms (GA) have emerged.
ANN is an advanced artificial intelligence system made up of interconnected processing units that adapt non-linearly, offering powerful information processing capabilities. GA, inspired by natural selection, is an algorithm designed to find optimal solutions through simulated evolutionary processes. Both ANN and GA are widely used in pavement maintenance decision-making and can be selected based on specific needs.
2.3 Maintenance Decision-Making Process
2.3.1 Maintenance Requirement Analysis
Unlike routine maintenance, preventive maintenance is not fixed and requires determining the specific maintenance needs beforehand. A thorough inspection of the selected road section is conducted, assessing factors such as smoothness, skid resistance, strength, and overall condition. Inspection data include the International Roughness Index (IRI), rutting depth (RD), skid resistance index (SFC), and deflection values.
These measurements are converted into evaluation indicators such as the Pavement Condition Index (PCI), Road Rutting Depth Index (RDI), Road Slip Resistance Index (SRI), Pavement Structural Strength Index (PSSI), and Pavement Quality Index (PQI). Based on established evaluation criteria, these indicators are assessed and predicted, then compared with maintenance standards. Values above the standards indicate no need for maintenance, while those below suggest maintenance is required.
2.3.2 Optimal Maintenance Timing
Preventive maintenance is ineffective for severe pavement damage and does not alter structural strength. Therefore, it should be applied early, when damage is mild. Early preventive maintenance maintains good pavement condition but may lead to unnecessary costs, whereas late maintenance risks worsening damage beyond repair scope.
Preventive maintenance standards define upper and lower limits of pavement indicators. When indicator values fall between these limits, it signals the optimal time to perform maintenance.
2.3.3 Selection of Maintenance Measures
To ensure effective preventive maintenance, the chosen methods must be feasible and appropriate. Prior to selection, engineering, technical, and economic factors must be carefully analyzed. Engineering factors—such as ride comfort, skid resistance, and waterproofing—are qualitative and evaluated through expert scoring on a 0-5 scale, where lower scores indicate poorer improvement effects.
After comprehensive analysis, on-site thermal regeneration technology scored highly across all factors. This method is mature, offers easy quality control, low cost, and strong economic benefits, making it the preferred option for preventive maintenance.
2.4 Optimization Plan Based on BIM Maintenance Decision-Making
2.4.1 Economic Analysis
While the demand for highway maintenance is increasing, available funds remain limited. It is essential to complete as many maintenance tasks as possible within budget constraints. Therefore, analyzing both the technical feasibility and economic viability of maintenance plans is crucial to achieving the best outcomes at minimal cost.
When assessing economic feasibility, initial road design and construction costs are excluded, focusing instead on ongoing maintenance expenses. When multiple maintenance plans are available, comparing their cost-effectiveness and benefits helps select the optimal plan. Methods such as the effective benefit-cost ratio and net present value analysis are commonly used.
2.4.2 Prioritizing by Road Performance
This project involves asphalt concrete pavement with varying degrees of distress. The volume of maintenance work is substantial, making it impractical to address all sections simultaneously. Therefore, maintenance sections are prioritized during decision-making.
Traditional prioritization often focused on the worst-performing sections first, but this approach lacks scientific rigor. Using BIM technology, grey relational analysis can be applied to rank sections more objectively, ensuring a rational maintenance sequence.
2.4.3 Multi-Objective Optimization Decision-Making
Highway asphalt pavement maintenance offers many technique options. After determining the maintenance sequence, the plan with the lowest cost and best results must be chosen. However, most evaluation indicators are fixed values influenced by factors like traffic load and pavement structure, leading to varying effectiveness in different sections.
BIM technology enables multi-objective optimization by standardizing data and integrating it into the BIM model. The model calculates the relative closeness of each solution to an ideal scenario. The plan closest to the ideal solution is considered optimal and guides preventive maintenance decisions.
Source: Transportation World, Issue 04/05/06, 2022 (February)
Author: Wang Shaoran (China Construction Road and Bridge Group Co., Ltd.)
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