[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118132-en":3,"doc-seo-118132-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},118132,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Integration of Machine Learning and Building Information Modelling - for Predictive Railway Maintenance","The thesis integrates machine learning or AI with building information modelling (BIM) to enable predictive railway maintenance. It begins with data collection from two sources: field data from a Brazilian railway authority and numerically generated data from simulations such as multi-body simulations and finite element models. Simulations are validated against field data, ensuring realistic numerical datasets and enabling machine-learning-ready diversity. The work prepares and trains supervised, unsupervised, and reinforcement learning models for defect detection, severity evaluation, deterioration prediction, and maintenance planning. BIM models are developed and integrated as a data management platform, yielding high performance and decision support that improves reliability, availability, maintainability, safety, and passenger comfort.","Integration of Machine Learning and Building Information Modelling  \nfor predictive railway maintenance  \nBy  \nJessada Sresakoolchai  \nA thesis submitted to the University of Birmingham for the degree of Doctoral of Philosophy  \nSchool of engineering  \nDepartment of civil engineering  \nUniversity of Birmingham  \nAugust 2023  \nUniversity of Birmingham Research Archive  \ne-theses repository  \nThis unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation.  \nAny use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder.  \n© 2023 University of Birmingham  \nAll rights reserved. This copyright is held by the author. No part of this thesis may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from the author.  \nP a g e | I  \nAbstract  \nThe aims of this thesis are to integrate machine learning or artificial intelligence (AI) and building information modelling (BIM) for predictive railway maintenance. The study process starts by collecting data. Data sources in this thesis consist of two main sources which are field data and numerical data. For field data, it is mainly from MRS Logística S.A. which is a railway authority in Brazil. Examples of field data are track design, track measurement, defect inspection report, maintenance report, and maintenance manual. For numerical data, data are numerically generated using simulations such as multi-body simulations and finite element models. To ensure that numerical data are reliable and realistic, every simulation and finite element model is validated using relevant field data. If differences between numerical data and field data are in the acceptable range, it demonstrates that numerical data can be used as representatives of field data. An advantage of using numerical data is it can create data diversity and variation which is required for machine learning model development. Then, data are prepared and processed to make them in forms that can be used to train machine learning models. Different machine learning models will be used in this thesis. The techniques can be grouped into three types which are supervised learning, unsupervised learning, and reinforcement learning. The application of these machine learning models is to develop predictive maintenance in the railway system such as defect detection, defect severity evaluation, deterioration prediction, and maintenance plan preparation. To fulfil the aim of the thesis, BIM models are developed and integrated with machine learning which can be conducted using different techniques. Results from the study  \nP a g e | II  \nshow that the developed machine learning models can fulfil their purposes with high performance and the developed BIM models can be fully integrated with machine learning as a data management platform. Contributions of the study are the developed approach can improve the maintenance efficiency and support predictive maintenance in the railway, machine learning models can support decision making and have high performance according to their functions, and the railway system can improve reliability, availability, maintainability, safety, including passenger comfort when applies the developed approach proposed in this thesis.  \nP a g e | III  \nAcknowledgement  \nI like to thank my supervisor, Dr Sakdirat Kaewunruen, for his support and guidance in my PhD. His expertise and wisdom are invaluable to me.  \nI am grateful for the financial support provided by the Thai government, w","cbCaicld7p94fJ2K","https://ap.wps.com/l/cbCaicld7p94fJ2K","pdf",12506069,1,314,"English","en",105,"# Abstract\n# Acknowledgement\n# List of publications","[{\"question\":\"What is the main aim of the thesis?\",\"answer\":\"To integrate machine learning/AI with building information modelling (BIM) for predictive railway maintenance.\"},{\"question\":\"What types of data are used for model development?\",\"answer\":\"The thesis uses field data from a Brazilian railway authority and numerical data generated via simulations such as multi-body simulations and finite element models.\"},{\"question\":\"How are numerical simulation results made reliable for machine learning?\",\"answer\":\"Each simulation and finite element model is validated using relevant field data, and numerical outputs are accepted when differences fall within an acceptable range.\"}]","Integration of Machine Learning and Building Information Modelling - 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