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Paper lc3-2017-329:
Asphalt Road Layer Detection for Construction Progress Monitoring

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Steven Vick and Ioannis Brilakis

Asphalt Road Layer Detection for Construction Progress Monitoring

Abstract: Transportation construction projects consistently underperform, with an estimated $82.6 billion globally in annual cost overruns. Current progress monitoring practices contribute to this poor performance thanks to their manual, subjective, inaccurate, and time-consuming nature. Automating this task could address these shortfalls and improve project performance. One way to accomplish this automation compares 3D Civil Infrastructure Model design surfaces to 3D point cloud reconstructions of the as-built scene. This requires automated detection of the design surfaces in the as-built data. Research in this area has focused on all-or-nothing detection of structural building components using methods that are a poor fit for large, complex, and closely-layered road design surfaces. These approaches ignore the kind of incremental progress detection needed on transportation projects. This paper proposes a method for detecting large road design surfaces in discrete regions (i.e. increments) of as-built point cloud data, contributing a novel model-guided and sparse hierarchical data structure ('layerTree') that addresses the limitations of existing state-of-the-art methods. The authors collected as-built and as-planned data during construction of a small residential road in Cambridge, UK. A total of 640 experiments on this data examined different combinations of layerTree parameters and classification rules, producing a peak accuracy of 86.62%, peak precision of 80.65%, and peak recall of 92.50%. The most balanced combination produced an accuracy of 86.50%, precision of 68.17%, and recall of 60.99%.

Keywords: Construction Progress Monitoring, Transportation, Drones

DOI: https://doi.org/10.24928/JC3-2017/0329

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