IntroductionOver the last few decades, the advancement of sensor technology and the ability to analyze large datasets have enabled innovative data-driven maintenance functions within a number of industrial sectors. Whilst these advances have been theoretically tested, and practically imbedded in some service and industrial sectors, other sectors such as the built environment have failed to embrace these developments. For example, Randall, 2011; Heng, et al. 2009; and Al-Najjar and Alsyouf, 2004 review and comment on case studies in a range of industrial settings and sectors, but do not mention applications in the built environment sector. For example, airplane performance has been optimized through advanced statistical analyses of in service performance and lifecycle data. This analysis is subsequently applied to maintenance programs to identify the optimum maintenance intervals to ‘ensure safe, reliable, and costeffective airplane performance’. This is demonstrated practically and empirically by Boeings’ Statistical Analysis for Scheduled Maintenance Optimisation (SASMO) tool (McLoughlin etal, 2011)
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