## 2 Related Work  
Road infrastructure is crucial for nations' economic and social well-being, providing numerous benefits. However, it is essential to note that road construction, maintenance, and usage have substantial environmental impacts [14]. In light of recent warnings from the UN's International Panel on Climate Change (IPCC) about climate change, there is a growing consensus on the need for sustainable development of infrastructure systems, particularly transportation networks [15]. The focus has shifted towards finding sustainable methods to maintain and repair eroding pavement networks while minimizing costs, greenhouse gas emissions, and the use of non-renewable resources [16]. Traditional assessments of pavement repair options have primarily considered economic and technical factors, overlooking the environmental implications [17]. Air pollution and climate change are two significant environmental challenges, with human-made greenhouse gas emissions contributing to global warming and posing severe consequences for the environment, society, and economy [18]. Since the transportation industry is a significant contributor to air pollution, special attention must be given to its impact [19]. Furthermore, the projected increase in natural disasters due to global warming necessitates a greater emphasis on Maintenance and Rehabilitation (M&R) procedures, which may increase pollutant emissions [18]. Creating a viable long-term maintenance framework is essential for policymakers to avoid significant capital waste while maintaining an acceptable level of service, especially considering financial constraints [20]. To achieve this, prediction models are required to develop effective maintenance strategies. Numerous budget allocation models have been proposed to address project-level and network-level concerns [21]. Recent studies have focused on pavement performance forecasting using local datasets or Long-Term Pavement Performance (LTPP) data [22; 23]. The cost-effectiveness of methods is crucial for both data collection and maintenance frameworks. Collecting data should be economically feasible for small businesses, and cost-effectiveness is necessary to prevent budget waste in maintenance frameworks. Engineers have been investigating which maintenance methods yield the best results, often employing cost-effectiveness or cost-benefit analysis to assess their techniques [24; 25]. Maintaining pavements over a long period allows for more cost-effective and environmentally friendly options to be explored, leading to extended service life and reduced capital costs. Combining treatments and developing reliable schedules can significantly contribute to these outcomes [26; 27; 28]. Additionally, the timing of maintenance treatments is crucial, and developing maintenance schedules based on long-term deterioration research has been the focus of extensive studies [29; 30; 31; 32]. Machine learning has shown great potential in civil engineering by utilizing models created from data to mimic human intelligence. Many studies have tried integrating machine learning with civil engineering to tackle diverse engineering challenges [33; 34; 35; 36]. Deep learning, a popular technique, has been widely used for damage recognition, crack detection, and prediction [37; 38; 39; 40]. Reinforcement learning has emerged as a recent method to maximize cost-effectiveness in determining ideal treatment schedules [32; 41]. Reinforcement learning (RL) can be applied to overcome decision-making challenges in long-term maintenance. Pavement authorities face difficulties stemming from the intricate nature of pavement deterioration, the presence of alternative treatments, and the utilization of diverse pavement performance indicators.