{"id":23998,"date":"2026-01-24T13:12:36","date_gmt":"2026-01-24T13:12:36","guid":{"rendered":"http:\/\/141.23.68.248\/wp\/?page_id=23998"},"modified":"2026-02-09T03:52:20","modified_gmt":"2026-02-09T03:52:20","slug":"lca-group-g-2026","status":"publish","type":"page","link":"http:\/\/141.23.68.248\/wp\/?page_id=23998","title":{"rendered":"Climate Responsive Asphalt Pavement System (CRAPS)"},"content":{"rendered":"\n

1. System Overview<\/strong><\/h2>\n\n\n\n

The Climate-Responsive Asphalt Pavement System (CRAPS) is conceived as an advanced flexible pavement system designed to address the increasing impacts of climate change on road infrastructure. Rising average temperatures, more frequent heat waves, increased rainfall intensity, and greater variability in freeze\u2013thaw cycles accelerate traditional asphalt deterioration mechanisms such as rutting, thermal cracking, moisture damage, and binder aging. Conventional pavement systems, which were largely designed for historical climate conditions, are therefore increasingly exposed to premature failure and rising maintenance costs.<\/p>\n\n\n\n

CRAPS integrates climate-adaptive materials, improved drainage concepts, and data-driven maintenance strategies to enhance long-term performance and reliability. The system-level objective is not only to delay physical deterioration but also to reduce environmental burdens and life-cycle costs while maintaining high service availability. This merged system description combines risk-based condition assessment with life-cycle environmental and economic evaluation, providing a holistic whole-life perspective consistent with modern infrastructure systems engineering.<\/p>\n\n\n\n

The analysis focuses on the asphalt wearing course, as it is the most climate-exposed layer and plays a dominant role in both deterioration risk and life-cycle impacts<\/p>\n\n\n\n

2. Functional Unit and Geometry<\/strong><\/h2>\n\n\n\n

To ensure transparency and comparability, a clearly defined functional unit and fixed geometry are adopted. The functional unit represents the service provided by the pavement rather than just material quantities, aligning with ISO-based LCA principles.<\/p>\n\n\n\n

Functional unit:<\/strong> Asphalt wearing course for a 100 m long, two-lane roadway over a 30-year service period.<\/p>\n\n\n\n

Parameter<\/th>Value<\/th><\/tr>
Road length<\/td>100 m<\/td><\/tr>
Number of lanes<\/td>2<\/td><\/tr>
Lane width<\/td>3.5 m<\/td><\/tr>
Total pavement width<\/td>7.0 m<\/td><\/tr>
Wearing course thickness<\/td>0.05 m<\/td><\/tr>
Asphalt density<\/td>2400 kg\/m<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Keeping geometry constant across all design alternatives ensures that observed differences in environmental impact, cost, and performance are attributable to material choice and system behavior, not to volume effects.<\/p>\n\n\n\n

3. Design Alternatives<\/strong><\/h2>\n\n\n\n

Three wearing-course alternatives are evaluated, representing different responses to climate stress while remaining realistic and applicable in current engineering practice.<\/p>\n\n\n\n

Option<\/strong><\/td>Description<\/strong><\/td>Key Climate-Relevant Feature<\/strong><\/td><\/tr>
HMA<\/td>Conventional Hot Mix Asphalt<\/td>High heat absorption, faster aging<\/td><\/tr>
Cool Pavement<\/td>Reflective coating on HMA<\/td>Reduces surface temperature by 5\u201310 \u00b0C<\/td><\/tr>
PMA<\/td>SBS Polymer-Modified Asphalt<\/td>Higher rutting and cracking resistance<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

The conventional HMA option serves as a benchmark, while the Cool Pavement and PMA options represent climate-adaptive strategies focusing on thermal mitigation and material durability, respectively.<\/p>\n\n\n\n

4. Risk-Based Condition Assessment<\/strong><\/h2>\n\n\n\n

Risk-based assessment evaluates how pavement condition evolves over time under uncertainty caused by climate, traffic, material behavior, and maintenance actions. Instead of deterministic life estimates, probabilistic modeling captures the likelihood of being in different condition states at any point in time.<\/p>\n\n\n\n

4.1 Condition States<\/h2>\n\n\n\n

A five-state condition classification is adopted, consistent with PCI\/PASER-based pavement management systems. Each state reflects a distinct level of structural and functional performance.<\/p>\n\n\n\n

State<\/strong><\/td>Condition<\/strong><\/td>Typical Action<\/strong><\/td><\/tr>
1<\/td>Excellent<\/td>No action, monitoring<\/td><\/tr>
2<\/td>Good<\/td>Preventive maintenance<\/td><\/tr>
3<\/td>Fair<\/td>Surface treatment \/ thin overlay<\/td><\/tr>
4<\/td>Poor<\/td>Structural overlay \/ major rehab<\/td><\/tr>
5<\/td>Failed<\/td>Full reconstruction<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

4.2 Markov Chain Results<\/h3>\n\n\n\n

A discrete-time homogeneous Markov Chain is used to model transitions between condition states. Transition probabilities represent the combined effect of climate exposure, traffic loading, and material performance. Climate-responsive features reduce transition rates to worse states compared with conventional pavements.<\/p>\n\n\n\n

Over a 35-year horizon, the model shows a gradual shift from high-probability Excellent and Good states toward Poor and Failed states, reflecting natural aging and accumulated damage.<\/p>\n\n\n\n

Key findings from probabilistic deterioration modeling:<\/p>\n\n\n\n