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The amount of new knowledge about bridges, about the materials they are made of, deterioration processes for the different materials, typical defects, methods for early detection of them and related causes for damage increases day by day. Often the knowledge remains in the heads of ageing specialists leaving the inspection teams or universities. This paper presents a feasibility study about application of a new tool from artificial in-telligence, ontology, for modeling and organizing bridge engineering knowledge from a domain expert´s view. Exemplarily, knowledge and not only data from bridge testing in the field and in the laboratory were formalized for the presentation of the functionality of ontologies. The open source ontology editor Protégé was ap-plied to test the feasibility.
Bridges represent a crucial part of traffic infrastructure networks for both, freight transport and transport of people. None the less, sometimes it is not easy for infrastructure owners to manage or extract data and knowledge of interest about specific bridges from bridge management systems, e.g. along a line. In case of search for a special material parameter or for a recent strengthening measure, the selection of the wanted information may be time consuming. The search process depends on the complexity of the selected issue.
The paper presents the applicability of ontologies, a new tool from artificial intelligence transferred to bridge engineering issues. Current modern ontologies consider the rules from the World Wide Web consortium (W3C), use a defined Web Ontology Language (OWL) and the Dublin Core Metadata Initiative (DCMI®) to describe the members of a domain and the relations between them in a machine readable manor. Any information, data as well as text information is broken down to a simple triple (RDF) in a subject- predicate- object relation to organize the link between them. Advantage over any type of data base is processibility due to the use of restrictions and axioms. Visualization plug-ins, as OntoGraph or GraphViz, can visualize the structure of the ontology or parts of it and the described relations.
In the recent years, ontology editors like e.g. Protégé, were developed to enable domain experts to structure a specific domain. The editor is still under development. None the less, the study shows exemplarily, how an ontology for riveted steel bridges could consider results from testing in the laboratory and in the field. Besides typical materials, typical damage processes defects and structures, applicable methods are formalized, classified and described. Queries using e.g. DL-Query may address tasks of interest and extract it from the ontology.
In the field of non-destructive testing (NDT) in civil engineering, a large number of measurement data are collected. Although they serve as a basis for scientific analyses, there is still no uniform representation of the data. An analysis of various distributed data sets across different test objects is therefore only possible with high manual effort.
We present a system architecture for an integrated data management of distributed data sets based on Semantic Web technologies. The approach is essentially based on a mathematical model - the so-called ontology - which represents the knowledge of our domain NDT. The ontology developed by us is linked to data sources and thus describes the semantic meaning of the data. Furthermore, the ontology acts as a central concept for database access. Non-domain data sources can be easily integrated by linking them to the NDT construction ontology and are directly available for generic use in the sense of digitization. Based on an extensive literature research, we outline the possibilities that this offers for NDT in civil engineering, such as computer-aided sorting, analysis, recognition and explanation of relationships (explainable AI) for several million measurement data.
The expected benefits of this approach of knowledge representation and data access for the NDT community are an expansion of knowledge through data exchange in research (interoperability), the scientific exploitation of large existing data sources with data-based methods (such as image recognition, measurement uncertainty calculations, factor analysis, material characterization) and finally a simplified exchange of NDT data with engineering models and thus with the construction industry.
Ontologies are already the core of numerous intelligent systems such as building information modeling or research databases. This contribution gives an overview of the range of tools we are currently creating to communicate with them.
Due to the diversity of materials and the processes associated with their production and use, the complexity of the lifecycles of materials and the multitude of academic and industrial researchers participating in generation of data for material design impose a huge challenge. The topical goal of digitalizing materials and processes can only be adequately addressed by consolidating the efforts of all stakeholders in this field. There are many scattered activities, but there is a demand for an elimination of redundancies as well as an advance in acceptance and a common basis in the digitalization of materials. Furthermore, data analysis methods play an important role in both, the experimental and simulation-based digital description of materials, but they have been poorly structured so far.
Therefore, the two joint projects Platform Material Digital (PMD, materialdigital.de) and Materials open Laboratory (Mat-o-Lab, matolab.de) aim to contribute to a standardized description of data processing methods in materials research. Besides stimulating the formation of a collaborative community in this respect, their main technical goals are the quality assurance of the processes and the output data, the acquisition and definition of their accuracy as well as the interoperability between applications. In this regard, data management in accordance with the FAIR (findability, accessibility, interoperability, reuseability) principles is addressed. There is a common agreement in the scientific community following current discussions that data is supposed to be conform to these principles. This includes storage, processing and querying of data in a preferably standardized form.
To meet the challenge to contextualize material data in a way that is consistent with all stakeholders, all necessary information on the condition of the material including production and application-related changes have to be made available via a uniform, machine-readable description. For this purpose, ontologies are to be used since they allow for machine-understandable knowledge representations and conceptualizations that are needed for data management and the digitalization in the field of materials science.
As first efforts in PMD and Mat-o-Lab, application ontologies are created to explicitly describe processes and test methods. Thereby, the well-known tensile test of metals at room temperature was described ontologically in accordance with the respective ISO standard 6892-1:2019-11.
The efforts in creating this tensile test application ontology are shown in this presentation. Especially, the path of ontology development based on standards to be pursued is focused, which is in accordance with the generic recommendations for ontology development and which is supposed to be exemplary for the creation of other application ontologies.
Data analysis methods play an important role in both the experimental and simulation-based digital description of materials but have so far been poorly structured. The platform Material Digital (PMD) is supposed to contribute to a standardized description of data processing methods in materials research. The goal is the quality assurance of the processes and the output data, the acquisition and definition of their accuracy as well as the interoperability between applications.
Therefore, application ontologies are created to explicitly describe processes and test methods. In this presentation, the first efforts within the joint project PMD in creating a tensile test application ontology in accordance with the ISO standard 6892-1:2019-11 are shown. Especially, the path of ontology development to be pursued based on standards was focused. Furthermore, the presentation includes a live demonstration of queries possibly performed to query data that was uploaded in the PMD triple store.
Data analysis methods play an important role in both the experimental and simulation-based digital description of materials but have so far been poorly structured. The platform Material Digital (PMD) should contribute to a standardized description of data processing methods in materials research. The goal is the quality assurance of the processes and the output data, the acquisition and definition of their accuracy as well as the interoperability between applications.
Therefore, application ontologies are created to explicitly describe processes and test methods. In this presentation, the first efforts in creating a tensile test application ontology in accordance with the ISO standard 6892-1:2019-11 are shown. Especially, the path of ontology development to be pursued based on standards was focused.
A large amount of publicly available data is reproduced every day in the field of materials science, while these kind of material data can have different formats and types like paper-type publications, standards, datasheets or isolated datasets in repositories. However, gathering a specific library from such extensive and diverse material data is always challenging for the materials scientists and engineers, since the time-related limitations are not allowed to fully access the large publicly available databases; search across these disparate databases, manage the large volumes of heterogeneous datasets, and integrate data from multiple sources. To address these challenges and make data findable, accessible, interoperable, and reusable (FAIR), an efficient data management system is necessary to build comprehensive, documented, and connected data spaces in the future. A formal standardized knowledge representation through an ontology can address such problems and make data more available and interoperable between related domains. Ontology can also rich machine processable semantic descriptions that increases the performance of scientific searches. In this regard, the Platform MaterialDigital (PMD) is currently working on developing a high-level ontology for the materials and material related processes. For example, in one of the PMD projects of “KupferDigital”, we will try to develop a data ecosystem for digital materials research based on ontology-based digital representations of copper and copper alloys. As a case study, this paper describes the methodology for ontology development of Brinell hardness, based on PMD core ontology. The methodology we describe includes the following steps; gathering the required domain terminology from different resources like standards (DIN EN ISO 6506-1) and test reports, representing the performance of a standard-conformant hardness test and the treatment of the recorded values up to a “reportable” hardness value for a material, designing the process chain according to the semantic technologies, and developing a domain-level ontology of Brinell hardness based on PMD ontology semantic formalization. Apart the mentioned methodology, some interesting tools and methods were introduced and ontology design challenges and possible solutions for modelling materials and processes were discussed. Furthermore, a dataset from the Brinell hardness measurement of cast copper samples is prepared for testing the query process.
Efforts towards digitalization in the material science and technology community have enhanced in the last years. In 2019 the German digitalization initiative platform „MaterialDigital“1 (MD) has been started. Numerous projects concerning digitalization, including the copper related project „KupferDigital“ (copper digital) have been initiated under the umbrella of MD. The initiative strives to
address numerous issues concerning data access, exchange, security, provenance and sovereignty. Heterogeneous data origin, storage and evaluation often result in problems concerning comparability and reproducibility of scientific and technological results. In many cases material data are recorded, but the methods of testing are insufficiently described, or such information is not communicated along with the raw data. The material data can also have numerous different formats such as paper printouts, pdfs, excel sheets or csv-files. Hence, gathering and integrating material data from different sources is challenging for potential users like materials scientists and engineers, especially if there are contradictory data where the reasons for contradictions is not clear due their vague description. In order to address these problems, data should comply to the so called „FAIR“ principle which calls for data to be findable, accessible, interoperable, and reusable (FAIR)2 and hence be accessible via so-called decentralized but interconnected data spaces. By using knowledge representation withontologies, data can be enriched with meaning and the methods of the testing procedures can be accurately provided. In this presentation we want to introduce our approach to such knowledge representation based on a high-throughput alloy development process for Cu-based alloys³ along with characterization techniques such as hardness testing and microstructural characterization (e.g. EBSD – Electron Backscattered Diffraction).