This research addresses the challenge of standardising and integrating information from laboratory tests in pavement engineering to enhance lifetime predictions. Data from laboratory tests is often scattered across various platforms, stored in disparate formats, and described using inconsistent terminologies. This fragmentation poses significant challenges in data sharing, retrieval, and interpretation, leading to inefficiencies and potential errors. The objective is to develop a comprehensive ontology that organises and connects extensive laboratory data from the Knowledge-based Pavement Engineering (KPE) project. This framework aims to facilitate enhanced data interoperability, retrieval, and application, thereby contributing to more efficient and effective pavement design, construction, and maintenance practices. This objective leads to the main research question: “How to effectively standardise and integrate laboratory information in pavement engineering to enhance lifetime predictions?”

The development of the ontology follows a structured methodology involving several steps. The process begins with identifying relevant stakeholders, such as pavement researchers, road owners, contractors, and third parties, and to understand their specific objectives and requirements. Following this, competency questions are formulated to define the scope and functional requirements of the ontology. These questions help in identifying the necessary classes, attributes, and relations within the ontology. The subsequent step involves specifying the structural requirements, followed by showing the alignments with the existing Dutch standard. This structured methodology ensures that the developed ontology is comprehensive, addresses the stakeholders' needs, and utilises existing knowledge.

The Lab Ontology focuses on standardising and structuring laboratory data related to pavement engineering. It categorises key concepts such as laboratory instrument, testing conditions, sample properties, and their relations. By establishing clear definitions and relations, the ontology enhances data consistency, comparability, and interoperability. Ultimately, the Lab Ontology answers the main research question by providing a standardised framework that improves data sharing and enhances decision-making processes in pavement engineering. This framework can significantly contribute to improving the lifetime predictions of pavement materials.

The Netherlands boasts an extensive road network that requires meticulous maintenance and preservation. Dutch asphalt pavements are assessed through functional properties such as mix stiffness and resistance to fatigue. However, current testing practice for these properties is intensive in time and resources, leading to the exploration of alternative methods for performance prediction. Recently, Artificial Intelligence (AI) has emerged as a tool for performance prediction in pavement engineering. Despite its potential, the application of AI is constrained by its limited interpretability and inconsistency with known physical laws. To enhance consistency and interpretability in AI predictive models, Physics Informed AI (PIAI) emerges as a promising approach.

This research develops a PIAI framework for physics infusion in pavement performance predictions. This infusion is accomplished through a Physics-Informed Loss Function balancing data and physics components in model training. The data component assures model predictions approximate the targets, whereas the physics component enforces a subset of features to follow a preset physical model. These components are also present during feature selection, where the physical model is used to guide the inclusion of important features in a PIAI model.

Using the developed framework, this research presents two PIAI prediction models based on the NL-LAB datasets. These models infuse homogenization theory and energy dissipation theory to enhance interpretability and consistency in stiffness and fatigue predictions. The results obtained on both models suggest that physics infusion is feasible without compromising prediction accuracy, balancing physical and statistical knowledge when predicting pavement performance. These findings also indicate that the PIAI framework is a promising approach for infusing physics into AI prediction models. Physics infusion can potentially enhance the acceptance and trust of AI within the pavement engineering community. Furthermore, the developed framework has the potential to accelerate pavement performance assessments by reducing the need for extensive material testing. Its flexibility also supports the incorporation of new physical models, fostering innovation and sustainability in pavement engineering.