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.

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.

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.