Over time, after a highway is completed and operational, its pavement performance gradually deteriorates due to increased traffic volume and other external factors. Various issues and defects inevitably arise, affecting driving comfort and safety, and increasing the likelihood of traffic accidents. Therefore, timely maintenance is essential to address minor damages, restore pavement performance, and ensure safe driving conditions. To enhance maintenance decision-making, Building Information Modeling (BIM) technology can be effectively employed for road maintenance planning.
1. Project Overview
This highway engineering project spans a total length of 112.35 km and is designed to high-grade highway standards. It features a two-way six-lane road with a speed limit of 100 km/h and a roadbed width of 34.5 meters. The pavement is constructed from asphalt concrete.
The highway was completed and opened to traffic at the end of July 2015. Since May 2018, micro surfacing technology has been gradually applied as a preventive maintenance measure. This study focuses on heavily trafficked sections that have not yet received preventive maintenance, covering approximately 8.75 km.
The pavement structure consists of a 15 cm thick surface layer, comprising one 4 cm layer of fine-grained asphalt concrete and two layers of medium-grained asphalt concrete measuring 5 cm and 6 cm respectively. The base layer is stabilized soil with lime and fly ash, topped by a water-stable gravel base layer.
To ensure effective implementation of preventive maintenance, a road maintenance database module has been developed using advanced BIM technology. This system provides a reliable foundation for informed maintenance decisions.
2. Road Maintenance Decision-Making Using BIM Technology
The BIM-based road maintenance database includes four key modules: data management, pavement leveling condition evaluation, performance prediction, and maintenance decision-making. This article emphasizes the maintenance decision-making module.
2.1 Purpose of Maintenance Decision-Making
Maintenance decisions rely on predicted or measured data to plan preventive actions aimed at achieving the optimal maintenance strategy and guiding maintenance operations. As highways age under the stresses of traffic loads and environmental factors, pavement performance declines, compromising driving comfort and safety, and increasing accident risks.
Timely maintenance is crucial to restore pavement usability. However, due to the extensive highway length and limited maintenance budgets, decision-making aims to allocate funds efficiently to maximize pavement performance improvements through optimal solutions.
2.2 Selection of Decision Methods
Pavement maintenance decision-making was initially developed through AASHO (American Association of State Highway Officials) road tests, primarily based on pavement performance data and maintenance experience. As decision-making requirements have evolved, traditional methods have become inadequate.
Modern approaches include Artificial Neural Networks (ANN) and Genetic Algorithms (GA). ANN, an artificial intelligence technique, is a powerful information processing system with interconnected adaptive units. GA is an algorithm inspired by natural evolutionary processes, designed to find optimal solutions through simulated natural selection. Both are widely applied for pavement maintenance decisions and can be selected according to specific project needs.
2.3 Maintenance Decision-Making Process
2.3.1 Maintenance Requirement Analysis
Unlike routine maintenance, preventive maintenance does not follow a fixed schedule, requiring maintenance projects to be identified before decisions are made. Comprehensive inspections are conducted on selected road sections, assessing smoothness, skid resistance, strength, and general conditions.
Inspection data such as International Roughness Index (IRI), Rutting Depth (RD), Skid Resistance Coefficient (SFC), and deflection values are collected and converted into performance indicators like Pavement Condition Index (PCI), Rutting Depth Index (RDI), Slip Resistance Index (SRI), Structural Strength Index (PSSI), and overall Performance Quality Index (PQI).
These indicators are evaluated and predicted against maintenance standards. If values exceed maintenance thresholds, no action is needed; otherwise, maintenance is required.
2.3.2 Optimal Maintenance Timing
Preventive maintenance is ineffective against severe pavement deterioration and does not improve structural strength. Therefore, it should be performed when defects are still mild. Determining the optimal timing balances early maintenance, which preserves good performance but may increase unnecessary costs, against delayed maintenance, which risks severe damage beyond preventive repair.
Maintenance standards define upper and lower limits for pavement indicators. When measured values fall between these limits, it indicates the ideal timeframe for preventive maintenance.
2.3.3 Selection of Maintenance Measures
Effective preventive maintenance depends on selecting appropriate, feasible methods. This requires analyzing engineering, technical, and economic factors. Engineering considerations such as driving comfort, skid resistance, and waterproofing are qualitative and evaluated through expert scoring on a 0-5 scale, where higher scores indicate better improvement.
Among commonly used preventive techniques, on-site thermal regeneration technology scores highly across all criteria. It is mature, offers easy quality control, low cost, and strong economic benefits, making it the preferred preventive maintenance method.
2.4 Optimization Plan Based on BIM Maintenance Decision-Making
2.4.1 Economic Analysis
While the demand for highway maintenance continues to grow, available funding remains limited. Maximizing maintenance tasks within budget constraints requires analyzing both technical feasibility and economic viability to achieve the best results at the lowest cost.
Economic analysis focuses on daily maintenance costs, excluding initial construction expenses. When multiple maintenance plans exist, their cost-effectiveness must be compared, using methods such as cost-benefit analysis and net present value calculations. The plan offering the greatest benefit is selected as optimal.
2.4.2 Prioritizing Road Sections by Performance
The asphalt concrete pavement exhibits varying degrees of distress across sections. Due to the volume of maintenance work, completing all repairs simultaneously is impractical, necessitating prioritization.
Traditional methods prioritize the worst sections first but lack scientific rigor. BIM-based maintenance decision-making applies grey relational analysis to sort sections objectively, ensuring rational prioritization.
2.4.3 Multi-Objective Optimization Decision-Making
Highway asphalt pavement maintenance techniques are diverse. After determining the maintenance sequence, selecting the most cost-effective and efficient plan is essential. However, many evaluation indicators are fixed values influenced by variables like traffic load and pavement structure, causing variation in effectiveness across sections.
BIM technology enables multi-objective optimization by normalizing raw data and importing it into the BIM model. This process calculates the relative closeness of each solution to the ideal. The solution closest to the ideal is deemed optimal and guides preventive maintenance decisions.
3. Conclusion
Maintaining highway pavement is critical for safety and service life. To enhance maintenance decision-making, improve outcomes, and reduce costs, BIM technology offers a powerful tool to identify optimal maintenance plans and ensure the longevity of highways.
Source: Transportation World, Issue 04/05/06, 2022 (February)
Author: Wang Shaoran (China Construction Road and Bridge Group Co., Ltd.)
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