English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Building Information Modeling (BIM) has been used in various industries for a long time. The railway system is another industry where BIM plays an important role. Since BIM can contain project information in different stages, a pool of information is involved and included in BIM. To use this information efficiently, machine learning, as a branch of artificial intelligence, is one of the tools widely applied nowadays. However, integrating BIM and machine learning in the railway system is new. This study is thus the world&#x2019;s first to integrate BIM and machine learning to localize defects in the railway infrastructure. In this study, wheelburns are used as case studies. Machine learning techniques used to localize defects are Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). From the study, the developed BIM model can be fully integrated with machine learning to localize defects in the railway infrastructure using the developed workflow. It is found that the CNN model provides the best outcome when Mean Absolute Error (MAE) is used as the main indicator. The MAE of the CNN model is 0.03 m and the Max Error (ME) is 0.3 m. The results of the study show that the integration of BIM and machine learning can be achieved and provide advantages to the railway industry. The developed machine learning models provide satisfactory performance and will be beneficial for the railway industry for better asset management and cost-effective maintenance.

Integration of Building Information Modeling and Machine Learning for Railway Defect Localization