Abstract
Construction site monitoring is currently performed through visual inspections and costly selective measurements. Due to the small overhead in construction projects, additional resources are scarce to frequently conduct a metric quality assessment of the constructed objects. However, contradictory, construction projects are characterised by high failure costs which are often caused by erroneously constructed structural objects. With the upcoming use of periodic remote sensing during the different phases of the building process, new possibilities arise to advance from a selective quality analysis to an in-depth assessment of the full construction site. In this work, a novel methodology is presented to rapidly evaluate a large number of built objects on a construction site. Given a point cloud and a set of as-design BIM elements, our method evaluates the deviations between both datasets and computes the positioning errors of each object. Unlike the current state of the art, our method computes the error vectors regardless of drift, noise, clutter and (geo)referencing errors, leading to a better detection rate. The main contributions are the efficient matching of both datasets, the drift invariant metric evaluation and the intuitive visualisation of the results. The proposed analysis facilitates the identification of construction errors early on in the process, hence significantly lowering the failure costs. The application is embedded in native BIM software and visualises the objects by a simple color code, providing an intuitive indicator for the positioning accuracy of the built objects.
Highlights
Failure costs are prevalent in the construction industry and range from 5% to 20% of the total project cost [1,2]
A detailed explanation is presented of the metric quality assessment of the constructed objects on the construction site using point cloud data
A novel metric quality assessment method is proposed to evaluate whether built objects on a construction site are built within tolerance
Summary
Failure costs are prevalent in the construction industry and range from 5% to 20% of the total project cost [1,2]. It is vital to asses the quality of constructed elements during the execution phase. The emphasis is on the metric quality assessment and on the positioning errors of the objects. We discuss the works that focus on remote sensing data capture and those related to metric quality assessment on site. Kalyan et al [22] report that while construction sites can be captured, the resulting point cloud often lacks the accuracy to check for building tolerances. The lack of texture for photogrammetric processes and the lack of geometric data dispersion for lidar approaches cause errors in the registration procedures of both approaches. The registration errors of mobile sensors or photogrammetric processes can reach up to several centimeters in average sized construction sites [10]
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