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

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

Chapter 10 in Digital Built Asset Management, 2024, pp 240-275 from Edward Elgar Publishing

Abstract: Despite the popularity of image-based classification techniques in identifying building materials and elements in building and construction sites, the real feasibility of them accommodating the needs of operation and maintenance (O&M) phase inspection-repair processes at 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 chapter describes 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 chapter 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 that the online enhanced image training dataset can further boost the on-site image classification performance. The results of this chapter 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.

Keywords: Urban and Regional Studies (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.elgaronline.com/doi/10.4337/9781035321445.00014 (application/pdf)
Our link check indicates that this URL is bad, the error code is: 503 Service Temporarily Unavailable

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:elg:eechap:22792_10

Ordering information: This item can be ordered from
http://www.e-elgar.com

Access Statistics for this chapter

More chapters in Chapters from Edward Elgar Publishing
Bibliographic data for series maintained by Darrel McCalla ().