In the comprehensive analysis of our integrated maintenance planning, design option 2 consistently emerged as the optimal choice, excelling in both life cycle cost and maintenance planning aspects. This substantiates the wisdom of designing this combination of subsystems within our integrated system.

Our venture into multi-objective optimization, utilizing a genetic algorithm, showcases a pragmatic approach to address intricate engineering challenges. This is particularly relevant in real-life scenarios where conflicting objectives, such as minimizing costs and maximizing performance, demand a nuanced strategy. The Pareto Front generated by the genetic algorithm provides a clear illustration of the trade-offs between cost and duration, highlighting alternatives marked in red as superior performers.

Implementing a genetic algorithm for a multi-objective optimization function that integrates life cycle impact and system usability during maintenance tasks is a distinctive feature of our approach. Recognizing the dual influence of maintenance on both Life Cycle Analysis (LCA) and system usability is crucial for informed decision-making. The function, of incorporating energy usage and emissions into maintenance tasks, ensures a holistic consideration of environmental impacts.

As we delve into the maintenance planning intricacies for our integrated system, we have visualized component interventions, frequency, lifespan, and duration, culminating in a detailed system maintenance timeline. Design option 2, consistently displaying the least downtime, has been identified as the preferred choice, and our subsequent analysis involves further refining the maintenance strategies based on preferences, as illustrated in Figures 4 and 5.

The Pareto Frontier, presented in Figure 6, plays a pivotal role in our decision-making process, offering a clear and efficient tool for selecting optimal solutions based on defined preferences and criteria. While it enhances strategic planning and decision-making, it’s important to acknowledge its limitations, including subjectivity, potential complexity in large scenarios, and the static nature of its representation.

In conclusion, our holistic approach to integrated maintenance planning, life cycle analysis, and multi-objective optimization ensures that our system not only operates efficiently but also aligns with sustainable and cost-effective practices. This strategic blend of engineering principles positions our project as a noteworthy contribution to the dynamic landscape of urban infrastructure development.

Discussion

During our life cycle analysis, the variations among pavement options stood out prominently. It’s essential to note that due to potential misalignment in data sources for subsystems, these differences could introduce distortions in data representation. Future research endeavors should focus on more integrative approaches to ensure better alignment and accuracy in the data.

Additionally, it’s worth highlighting that our study primarily focused on the pavement subsystem, and for comprehensive research, incorporating more diverse concrete maintenance tasks would provide a more nuanced understanding of the overall system’s dynamics. This expansion could offer valuable insights into the broader implications of maintenance planning on the integrated system’s performance and sustainability.