[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124732-en":3,"doc-seo-124732-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124732,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning-Driven Ontological Knowledge Base for Bridge Corrosion Evaluation","Bridge maintenance requires structural performance assessments that follow safety and regulatory standards, which are best represented in both human- and machine-readable forms through ontologies. Ontology-based semantic inference alone cannot handle the complex mathematical operations needed for structural analysis. This paper proposes an approach integrating machine learning with an ontological knowledge base for bridge corrosion evaluation by combining Web Ontology Language and rule language, training a random forest model, and using a Python module to fuse ML predictions with ontology inference. The method infers corrosion ratings per Network Rail rules and derives structural safety performance from predicted responses, validated on a real UK bridge.","Received 16 October 2023, accepted 15 December 2023, date of publication 18 December 2023, date of current version 27 December 2023.  \nDigital Object Identifier 10.1109/ACCESS.2023.3344320  \nMachine Learning-Driven Ontological Knowledge Base for Bridge Corrosion Evaluation  \nYALI JIANG1, HAIJIANG LI2, GANG YANG3, CHEN ZHANG4, AND KAI ZHAO5  \n1College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China  \n2 School of Engineering, Cardiff University, CF24 3AA Cardiff, U.K.  \n3College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China  \n4ZJYY (Dalian) Bridge Underwater Inspection Company Ltd., Dalian 116026, China  \n5 School of Engineering and Architecture, University College Cork, Cork, T12 K8AF Ireland Corresponding authors: Haijiang Li ([lih@cardiff.ac.uk](lih@cardiff.ac.uk)) and Chen Zhang ([zhangyu_zc@163.com](zhangyu_zc@163.com))  \nThis work was supported in part by the China Scholarship Council under Grant CSC 202006570025, and in part by the Building Information Modeling (BIM) for Smart Engineering Centre in Cardiff University, U.K.  \nABSTRACT 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 ontology-based 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.  \nINDEX TERMS Knowledge engineering, knowledge base, machine learning, ontology, corrosion evaluation, bridge maintenance, data-driven, random forest.  \nI. INTRODUCTION  \nBridges are a vital part of the architecture, engineering, and construction (AEC) industry, and effective maintenance is essential for ensuring good condition of their structures [1],[2]. Bridge maintenance tasks carry a profound responsibility, and they must strictly adhere to a complex web of safety and regulatory standards. Bridge maintenance standards are typically represented in a manner recognized by humans but then converted to a different format for storage  \nThe associate editor coordinating the review of this manuscript and  \napproving it for publication was Weiping Ding  .  \nin computers [3]. Although that format is computer-readable, computers cannot understand the content of the documents, which results in inefficiencies when the documents are used. Therefore, using the Semantic Web (SW) to facilitate the use of domain-specific knowledge has become the focus of intensive investigation.  \nThe SW is a group of languages or technologies, such as Resource Description Framework (RDF) and Web Ontology Language (OWL), that allow machines to understand the meaning or semantics information on the World Wide Web [4] . ","cbCaiuyY43jIoOiG","https://ap.wps.com/l/cbCaiuyY43jIoOiG","pdf",5606188,1,12,"English","en",105,"# Abstract\n## Introduction\n## Related Work\n## Proposed Method and Knowledge Base","[{\"question\":\"How does the proposed method combine machine learning with ontological reasoning?\",\"answer\":\"It builds an ontology-based knowledge base using Web Ontology Language and Semantic Web Rule Language, trains a machine learning model (random forest) on historical data, then uses a Python module to integrate ML predictions into ontology-based semantic inference for joint corrosion-rating and safety-performance evaluation.\"},{\"question\":\"What knowledge representations and rule standards are used to construct the knowledge base?\",\"answer\":\"Web Ontology Language (OWL) and Web Rule Language (Semantic Web Rule Language) are used together to develop the ontological knowledge base and enable rule-driven semantic inference.\"},{\"question\":\"How are evaluation results used to infer corrosion ratings and structural safety performance?\",\"answer\":\"Corrosion ratings are inferred according to rules defined in the Network Rail standard, and structural safety performance is inferred based on predicted structural response under corrosion-related actions.\"}]","Machine Learning-Driven Ontological Knowledge Base for Bridge Corrosion Evaluation | 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does the proposed method combine machine learning with ontological reasoning?","Question",{"text":75,"@type":76},"It builds an ontology-based knowledge base using Web Ontology Language and Semantic Web Rule Language, trains a machine learning model (random forest) on historical data, then uses a Python module to integrate ML predictions into ontology-based semantic inference for joint corrosion-rating and safety-performance evaluation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What knowledge representations and rule standards are used to construct the knowledge base?",{"text":80,"@type":76},"Web Ontology Language (OWL) and Web Rule Language (Semantic Web Rule Language) are used together to develop the ontological knowledge base and enable rule-driven semantic inference.",{"name":82,"@type":73,"acceptedAnswer":83},"How are evaluation results used to infer corrosion ratings and structural safety performance?",{"text":84,"@type":76},"Corrosion ratings are inferred according to rules defined in the Network Rail standard, and structural safety performance is inferred based on predicted structural response under corrosion-related actions.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & 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