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Robust condition data feeding into asset management processes is a key step towards having confidence in long-term strategies for renewals and replacements.

The manual condition rating system was originally developed as an input into the treatment selection algorithm; however, in later years the data has been used for pavement deterioration modelling and trend monitoring, which are outside the intended scope of the rating system. It was therefore not unexpected that both field inspectors and researchers highlighted shortcomings in the quality and repeatability of manually recorded data.

Automated scanning technologies promise to overcome many of the issues associated with manual condition data collection. However, before a wide-spread adoption of the scanning technology is possible, research had to prove the accuracy of the measurements and determine the impact of new data items in the asset management processes.

This research addressed both these items and has concluded the technology is ready for adoption in New Zealand. However, fully automated surveys yield less than desirable accuracy with a high portion of false negatives identified.

All scanning surveys must be supplemented by appropriate manual quality assurance processes.

Keywords: automated data collection, chip loss, crack detection, data quality, ravelling, scanning laser, shoving