This paper explores different building maintenance strategies in commercial buildings in Sydney, Australia, focusing on corrective maintenance (CM) and preventive maintenance (PM). While CM involves rectifying issues after they occur, PM aims to enhance productivity by anticipating potential issues. Although PM seems more logical, the decision to implement this type of maintenance strategy are typically made based on item reliability, failure frequency, and downtime cost, commonly found in manufacturing facilities or critical environments. However, as found in the selected/surveyed commercial real estate buildings, CM was more frequently adopted in aged facilities with older infrastructure, and PM was favoured for buildings without structural deficiencies; however, operating equipment failures were common. However, in many cases, decision makers did not consider the broader effects of downtime beyond direct financial losses, costs associated with customer satisfaction, worker efficiency, rent abatements, and reputation damage. While each building is unique and may require a bespoke maintenance schedule, this study’s insights may help managers select the most appropriate maintenance strategy. Nonetheless, further research is needed to investigate the role of innovative technologies (such as machine learning and artificial intelligence) in enhancing maintenance efficacy and explore the influences of economic shifts, corporate and financial objectives, and the availability of technical resources.

The Building Condition Index (BCI) is a widely adopted quantitative metric for assessing various aspects of a building’s condition, as it facilitates decision-making regarding maintenance, capital improvements and, most importantly, the identification of investment risk. In practice, longitudinal BCI scores are typically used to identify maintenance liabilities and trends and proactively provide indications when maintenance strategies need to be altered. This allows for a more efficient resource allocation and helps maximise the lifespan and functionality of buildings and their assets. Given the historical ambiguity concerns because of the reliance on visual inspections, this research investigates how AI and using ANN, DNN and CNN can improve the predictive accuracy of determining a recognisable Building Condition Index. It demonstrates how ANN and DNN perform over asset classes (apartment complexes, education and commercial buildings). The results suggest that DNN architecture is adept at dealing with diverse and complex datasets, thus enabling a more versatile BCI prediction model over various building categories. It is envisaged that with the expansion and maturity of ANN, DNN and CNN, the BCI calculation methodologies will become more sophisticated, automated and integrated with traditional assessment approaches.

BCA methodically assesses the state of a building’s deterioration to support Maintenance, Safety, Function, and Compliance purposes. Originally used to assist in identifying urgent repair requirements, it has evolved and become one of the most used tools for assessing a building’s outstanding maintenance liability when a building is transacted or acquired. Nevertheless, current practices involve several conflicts; for example, high costs are associated with inspections, inconsistent building component registers, and ambiguity and consistency regarding reporting parameters, all of which lead to compounding errors that reduce reliability. To address these gaps, the current research, involving one hundred and eighteen (118) active facilities managers and asset inspectors, suggests the development of an extension of the deterioration scale (0–7) and methodologies to reduce errors and ambiguity. Furthermore, it suggests using weighted indices to focus on crucial building components, thus improving condition assessment. As was found, these tools improve the accuracy of BCA, facilitate better management of the asset’s life cycle, and provide support in decision-making. This study adds consistency, limits subjectivity, and provides a framework applicable to different building types, assisting future management for sustainability. It, therefore, stands to serve the field by providing detailed and concise best practices for conducting condition audits on built assets.