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Machine Vision Based Concrete Surface Quality Assessment

Zhu z and brilakis i 2010 machine vision based concrete surface quality assessment. In terms of quality safety and efficiency.

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Machine vision based concrete surface quality assessment. The federal highway administration fhwa provides high quality information to serve. Also it is labor intensive. The method presented in this paper was implemented in c and a database of concrete surface images was tested to validate its performance. Development of a. Also it is labor. Damaged west facade right. Reference object office building with concrete surface left. This way for a given concrete surface image its quality in terms of air pockets and discoloration can be automatically measured by judging whether their virs are below the threshold values or not. Machine vision based concrete surface quality assessment. Request pdf machine vision based concrete surface quality assessment manually inspecting concrete surface defects eg cracks and air pockets is not always reliable. Existing methods either rely on complete 3d surface reconstruction which comes along with high equipment and computation costs or make use of acceleration data which can only provide preliminary and rough condition surveys. Development of a shape based pavement crack detection approach abstract state highway agencies shas routinely employ semi automated and automated image based methods for network level pavement cracking data collection and there are different types of pavement cracking data. In this paper we present a method for automated pothole detection in asphalt pavement images. In order to overcome these limitations automated inspection using image processing techniques was proposed. Machine vision based concrete surface quality assessment journal of construction engineering and management american society of civil engineers 1362.

The paper shows case studies of uas in condition monitoring based on. Health monitoring and assessment. Vision based monitoring of ageing structures case studies. High resolution orthophoto mosaic of the west facade. Journal of construction engineering and management 136. Reliable detection and classification of cracks from acquired two dimensional 2d concrete and asphalt pavement surface images. Machine vision based techniques have gained a lot of momentum during the last two decades which can be exposed in their multiple applications in the industry as well as in academia specifically for the detection and evaluation of distresses in civil infrastructure. Manually inspecting concrete surface defects eg cracks and air pockets is not always reliable. Brilakismachine vision based concrete surface quality assessment. Full text not available from this repository.