An innovative software tool for enhanced building life cycle management and maintenance forecasting deployed via cloud

Community buildings are one of the largest infrastructure assets invested and managed by local governments in Australia. An optimised expenditure projection model which ensures building assets are maintained at a required level of service requires a reliable deterioration model for building components and a decision making algorithm which captures sustainability. Prediction of deterioration and decision making methods for community buildings have been explored by RMIT University in a research project conducted in collaboration with six Australian local councils and the Municipal Association of Victoria. A web-based software tool has been developed to enable buildings asset managers to make informed decisions for maintenance and rehabilitation activities of building assets. The software tool uses a probabilistic deterioration prediction model to reflect the stochastic nature of condition degradation to model variety of building components. Risk and expenditure forecasting based on Markov Chain deterioration prediction are generated within the program. Consequently, scenario analysis assists not only in organisational risk threshold identification based on levels of service, but also in funding allocation to attain required building performance. The software integrates a quadruple bottom line sustainability factors to enable decision makers in considering environment, social, economic and functional aspects of decisions for community buildings. The paper presents the algorithm of the software, challenges in integrating multiple modules, the analytical modules, input data and outcomes of forecasting for a data set from a City Council in Victoria, Australia.