2017 - NZ - Transition from visual condition rating of cracking, shoving and ravelling to automatic data collection.

NZTA Research Report 617.

The manual road condition survey method used in New Zealand (road asset maintenance management ((RAMM)) surveys) was developed during the early 1980s with the primary purpose of feeding into the treatment selection algorithm. For more than 20 years the rating system was adequate for this purpose but as more sophisticated asset management evolved into deterioration modelling and advanced trend monitoring, the data quality from the manual surveys came under scrutiny. Attempts to improve the robustness of the rating system included increasing the recommended sampling size from 10% to 20% of the treatment length plus increasing the requirements for accreditation during the training of raters. Yet, these steps still fall short in increasing the overall usability and repeatability of rated data for the new demands of asset management processes. Automated defect data collection has been undertaken since the mid-1990s with early technology relying on photographic imaging and processing of road surface data. The technology was particularly popular for application on busy asphalt and concrete motorways in the northern hemisphere but failed to deliver acceptable robustness on chipseal surfaces. This situation changed with the arrival of laser scanning technology, which has overcome the limitations of photo-imaging technology. The measurements now solely depend on laser scanning at a high resolution, which gives a comprehensive 3D image of the road profile. Any defects such as cracks, potholes or surface defects can be identified on the image. The benefits this technology offers to the sector include: • surveys of 100% of the road are possible • all aspects of the condition of the surface are captured simultaneously • the measurements take place at high speed (60 to 80km/h), providing significant safety and traffic management benefits • ‘removing’ the human element from the measuring allows for more repeat measurements. Despite the accuracy of the measurement, the constraining factor for the technology is the algorithms that interpret the digital image to identify and quantify specific defects. This has resulted in the main question posed for this project – is the measurement sufficiently robust and is the sector ready to adopt this technology on a wide scale?