A Renewable-Energy Resource Management Framework for Low-Carbon Network-Level Pavement Maintenance Using Simulation-Based Pavement–Energy Modeling and Multi-Agent Deep Reinforcement Learning

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a novel multi-agent RL framework for infrastructure maintenance with carbon and energy constraints.

🏢実務担当者:Offers a decision-support tool for pavement maintenance planning that balances cost, carbon, and renewable energy use.

🏛政策担当者:Demonstrates how AI can support low-carbon infrastructure management, relevant for public works and climate policy.

Sustainable pavement maintenance increasingly requires coordinated management of infrastructure condition, renewable-energy availability, carbon emissions, financial resources, and operational capacity. This study proposes a renewable-energy resource management framework for low-carbon network-level pavement maintenance using simulation-based pavement-energy modeling and multi-agent deep reinforcement learning. The proposed framework develops an AnyLogic-based pavement-energy simulation environment in which road sections, deterioration states, work zones, maintenance crews, equipment resources, photovoltaic generation, battery storage, grid support, diesel backup, carbon tracking, and budget consumption are represented within one integrated decision environment. To support adaptive maintenance control, pavement sections are modeled as interacting agents, while road connectivity, dispatch dependency, traffic interaction, and maintenance-route relationships are encoded through graph structures. A graph-based multi-agent deep reinforcement learning model, named Graph-MAPPO, is then used as the decision controller. The model integrates multi-head graph attention for spatial dependency learning, GRU-based temporal memory for deterioration-history representation, finite-element-assisted structural-risk indicators for hidden damage characterization, and constraint-aware action masking to prevent infeasible decisions under budget, carbon, energy, crew, and equipment constraints. Two calibrated datasets were generated to support the framework: a pavement network and maintenance dataset containing 4437 records and 55 features, and a renewable energy-carbon-budget dataset containing 9875 records and 38 features. The decision controller jointly selects the pavement section, treatment type, intervention timing, crew, equipment, and energy mode. Results from 20 experimental configurations show that the balanced Graph-MAPPO policy improves average PCI from 69.4 to 78.9, achieves an RSL gain of 6.8 years, reduces emissions to 58.3 tCO2e, maintains a renewable-energy share of 74.6%, and limits the constraint-violation rate to 1.8%. Under high renewable-energy availability, the framework achieves the best overall performance, with an average PCI of 80.2, renewable-energy share of 84.6%, emissions of 50.8 tCO2e, and reward of 0.90. These findings demonstrate that integrating pavement-energy simulation, renewable-energy resource allocation, carbon-aware maintenance planning, structural-risk awareness, and multi-agent decision control can support more adaptive, low-carbon, and resource-efficient pavement maintenance management.

• openalex https://doi.org/10.3390/resources15070086first seen 2026-07-03 05:04:35