AI and Machine Learning-Powered Predictive Maintenance for Structures and Mechanical Assets Using BIM

Abstract

While executing various management and maintenance techniques, relevant operational efficacy, output optimization, and the seamless operation of professional and residential areas are increasingly important. However, conventional maintenance approaches face substantial challenges due to rising costs associated with time-based preventative maintenance programs and the consequences of reactive repairs. This investigation investigates the use of Building Information Modelling (BIM) maintenance repositories as a foundation for creating machine learning tools for predictive maintenance. Our findings show that machine learning models can predict project vulnerabilities and equipment faults, allowing maintenance administrators to ensure that facilities continue to operate safely. The findings of this study support the use of such analytical methodologies in related disciplines. With a focus on cost-effectiveness, this research recommends examining various database options, from specialised repositories customised for particular industries and asset categories to comprehensive Computerised Maintenance Management Systems (CMMS). The research investigation also emphasises the increasing importance of mobile accessibility, the incorporation of AI and advanced analytical features, the growth of open-source distribution models, and the critical importance of carefully selecting solutions that meet the specific needs of various organisations and industry-specific imperatives. This study seeks to provide operators with a complete and analytical overview of the available options to assist them in making well informed judgements about which maintenance database solution best meets their specific operational demands. The research results showed that the average accuracy for the investigated datasets and machine learning models reaches around 90 percent. The study concludes that machine learning has strong predictive maintenance capabilities.