Bridge Ontology Architecture for Knowledge Management in Bridge Maintenance

Kuhaneswaran, Banujan; Vasanthapriyan, Shanmuganathan

doi:10.1109/mercon50084.2020.9185335

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Compton et al [20] presented an OWL2 ontology named SSN, designed to describe sensors and observations concerning capabilities, measurement processes, observations, and deployments. Banujan and Vasanthapriyan [21] created a bridge maintenance ontology aimed at facilitating the sharing of bridge maintenance knowledge. Ontologies play a central role in structuring data and facilitating efficient knowledge management and retrieval in these studies.…”

Section: B Ontologies In the Bridge Domainmentioning

confidence: 99%

Machine Learning-Driven Ontological Knowledge Base for Bridge Corrosion Evaluation

Jiang,

Li,

Yang

et al. 2023

IEEE Access

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In bridge maintenance, assessing structural performance requires adherence to rules outlined in safety and regulatory standards which can be effectively and formally represented in both human and machine-readable formats using ontologies. However, ontology-based semantic inference alone falls short when faced with the complicated mathematical operations required for structural analysis. The increasing digitization of bridge engineering has opened doors to data-driven prediction methods. Machine learning (ML)-based models, in particular, have the capacity to learn from historical data and forecast future structural performance with remarkable accuracy. This paper introduces an innovative approach that integrates ML models with an ontological knowledge base for evaluating bridge corrosion. Web Ontology Language and Semantic Web Rule Language are combined to develop the knowledge base. Random forest algorithm is used to train the ML model with a good agreement (coefficient of determination of 0.989 and root mean square error of 1.200). A Python-based module is designed to seamlessly integrate ML predictions with ontologybased semantic inference. The proposed approach not only infers the corrosion ratings based on the rules defined in the Network Rail standard, but also infers the structural safety performance based on predicted structural response under the action of corrosion. To demonstrate the effectiveness of the developed method in enabling accurate and rational evaluations, a real bridge in the UK is showcased as a practical application.

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Section: B Ontologies In the Bridge Domainmentioning

confidence: 99%

Machine Learning-Driven Ontological Knowledge Base for Bridge Corrosion Evaluation

Jiang,

Li,

Yang

et al. 2023

IEEE Access

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“…In the field of bridge engineering, some studies about ontology application [10][11][12]25] have been conducted. However, these studies mainly focused on knowledge management [26], and seldom focused on information retrieval [10].…”

Section: Ontology Application In the Construction Industrymentioning

confidence: 99%

An Enhanced Information Retrieval Method Based on Ontology for Bridge Inspection

et al. 2022

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Bridge management systems (BMSs) are widely used to assist an inspector in performing element-level bridge inspection. Retrieving and determining target elements to be inspected becomes an important factor in the efficiency of bridge inspection. This paper presents an enhanced information retrieval (IR) method based on ontology to predict the target elements. The novelty of this method is that an improved seven-step method based on automatic mapping technology is proposed to construct a new bridge inspection ontology (BIontology), which provides a knowledge base for the present IR method. A further novelty is that a new software architecture is designed for integrating ontology, and a promising prototype system based on the software architecture is developed to realize the present IR method using SPARQL query. In addition, a novel prediction algorithm based on the present IR method is proposed to automatically recommend the target elements. A case study of ontology construction is performed to demonstrate that the improved seven-step method can accelerate the construction of the BIontology compared with the manual method. A case study of bridge inspection is implemented to verify that the proposed algorithm outperforms an existing method, thereby validating the effectiveness of the present IR method.

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Bridge Ontology Architecture for Knowledge Management in Bridge Maintenance

Cited by 2 publications

References 17 publications

Machine Learning-Driven Ontological Knowledge Base for Bridge Corrosion Evaluation

Machine Learning-Driven Ontological Knowledge Base for Bridge Corrosion Evaluation

An Enhanced Information Retrieval Method Based on Ontology for Bridge Inspection

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