Explainable artificial intelligence for integrity assessment of civil engineering infrastructure: Insights from various engineering applications

The incorporation of Artificial Intelligence (AI) into civil engineering, especially structural engineering, has the potential to improve decision-making by making it more precise and data driven. However, relying on AI to make decisions without understanding its reasoning poses a serious risk: It undermines the essential concept of human judgment. This concern has led to global regulatory measures, including the European Union AI Act, which highlights the importance of explainability and human oversight in creating trustworthy AI. In response to these needs, eXplainable AI (XAI) has attracted significant attention from researchers over the past five years. It serves as a technical tool to shed light on the internal computing processes of AI systems, which are often seen as black boxes, addressing safety and ethical implications. Most research into the application of XAI in civil engineering is conducted under controlled and standardized conditions. However, real-world engineering problems often occur in complex and changing environments. These problems often span multiple domains, making their modeling challenging despite their importance. In addition, the presence of scarce and poor quality data and real-world behaviors that are difficult to replicate in laboratory experiments (e.g., in the presence of damage) add to these complications. These inherent issues challenge the fundamental assumptions of many AI models, which are usually trained and validated in controlled or synthetic environments. Consequently, both the predictive accuracy and the reliability of model explanations can deteriorate when these models are extended to real-world situations, a risk that is often underestimated or overlooked. In 2026, the European Union’s AI Act will come into force for critical infrastructures, such as the vast majority of civil engineering infrastructure, making explainability and human oversight a legal requirement. There is a significant technical gap, as well as pressing safety, ethical, and regulatory concerns, due to the lack of assessments of the robustness of XAI models in real-world applications. This thesis shifts the focus from presenting novel AI models that excel in standardized benchmark problems to understanding how cutting-edge but existing AI and XAI methods perform under the irregular and data-poor conditions typically found in real-world civil engineering systems. This research highlights the need for realistic domain-aware deployment strategies that prioritize robustness, interpretability, and practical insights over purely theoretical advances. Rather than relying on idealized datasets or conditions favorable to AI, such as clean, balanced, and well-annotated data, which do not accurately represent realworld engineering conditions, this thesis focuses on investigating, understanding, and improving the behavior of the AI model in complex environments using real data from various case studies. To achieve these objectives, the thesis begins by investigating a complex type of rail surface degradation problem known as corrugation. This phenomenon is affected by a combination of dynamic and static factors, including vehicle-track interaction, material properties, and environmental conditions. However, measuring or integrating these contributing variables presents significant challenges as they are often difficult to assess directly, expensive, or even unfeasible to acquire in real-world situations. In order to ensure a realistic and application-oriented approach, a minimal set of input parameters (acceleration and velocity), which are readily available and can be easily measured from the moving vehicle, have been selected to predict the rail corrugation profile based on time series data of these input parameters. To align with practical constraints in the field, multiple damaged zones were considered in order to capture the variability and complexity of actual conditions of the rail infrastructure. Despite the common practice of removing outliers as an initial step in data preprocessing, the research presented in this thesis intentionally refrains from doing so in this particular scenario. Retaining outliers is crucial for accurately representing the unpredictable nature of real-world engineering data, where noise, anomalies, and irregular patterns are inherent in reality. Excluding them could lead to an overly simplified understanding of the problem and diminish the relevance of AI models in practical applications. The XAI method, Grad-CAM, is applied to enhance transparency and increase human oversight in the prediction of rail corrugation under the aforementioned realistic conditions. This approach allows for visual interpretation of the model decision-making process, helping to bridge the gap between black-box predictions and domain-relevant understanding.