This thesis investigates the integration of reinforcement learning (RL) techniques to enhance inspection and maintenance planning for timber structures, considering the increasing impact of climate change on their structural integrity. Timber, a critical material in historical construction, is vulnerable to environmental factors such as temperature, moisture, and biological degradation. These vulnerabilities are exacerbated by climate change, leading to significant alterations in mechanical properties and thereby challenging the longevity and safety of these structures.
Given the dynamic nature of these challenges, traditional inspection methods, which rely heavily on manual processes and individual expertise, are insufficient. This research employs machine learning to develop a predictive maintenance model that adapts to the evolving conditions affecting timber. The model aims to improve the accuracy and efficiency of inspections and maintenance planning, facilitating timely interventions to preserve the structural integrity of timber constructions.
This study addresses several key questions: identifying the principal factors influencing timber degradation, understanding the impact of climate change on these factors, evaluating current maintenance strategies, and determining the most suitable RL techniques for this application. Additionally, the research explores methods to optimize the RL model for reliability and accuracy, methods for validating and evaluating the model's planning capabilities, and strategies to enhance the interpretability of RL outputs for practical use in the field.
By leveraging advanced RL methodologies, this thesis contributes to the field of timber engineering and proposes a scalable and adaptable solution to enhance sustainable construction practices in response to evolving climate patterns.