Automated portfolio-based strategic asset management based on deep neural image classification: Fid-Bau Portal

Automated portfolio-based strategic asset management based on deep neural image classification

Fang, Zigeng / Tan, Tan / Yan, Jiayi / Lu, Qiuchen / Pitt, Michael / Hanna, Sean

Abstract Despite the popularity of image-based classification techniques in identifying building materials and elements in building and construction sites, the real feasibility of it accommodating the needs of operation and maintenance (O&M) inspection-repair processes in the real project scale is still awaiting testing due to the vast number of project assets, hundreds of asset categories, and requirements of portfolio-based strategic asset management (SAM) tasks. The building's O&M phase does not yet have a large-scale inspection-repair dataset created on images based on the actual workflow of building and infrastructure projects. This paper described a MobileNet1.0-based image classification method for optimising and automating a series of portfolio-based SAM service processes, including condition surveying, data validation, standardisation and integration. By examining the performance of image classification in the building O&M phase's SAM tasks, the paper examines whether the constructed MobileNet1.0 model can achieve satisfactory built assets category classification performance based on commercial surveying data with the enhancement of the online image training dataset. The constructed MobileNet1.0 model achieved satisfactory built assets category classification results for both building asset type category classification (with 85.6% level-1 test accuracy) and different building data attributes (e.g., built assets' condition, activity cycle, failure type, etc.). The experiment results proved the online enhanced image training dataset can further boost the on-site image classification performance. The results of this paper demonstrate a broader application of image-based technologies in portfolio-based SAM and other building and infrastructure projects' O&M phase applications. Future research can be conducted to exploit the performance of image classification methods over more building asset categories and building and building asset data attribute types.

Highlights • A MobileNet-based image classification method was proposed for optimising and automating SAM processes. • A large dataset based on on-site building surveying was built for training and testing. • The proposed method achieved satisfactory of built assets category classification based on BCIS code. • The proposed method can boost the on-site image classification performance based on the online image training dataset. • The proposed method can also predict a series of different building data attributes.