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The construction industry is still one of the least digitised sectors worldwide and digitisation in the road construction sector in particular is even more dire. To improve the efficiency of the sector, it is of the utmost importance that construction monitoring tasks, i.e. progress, quality and quantity analyses, are automated. The goal of the presented framework is the automation of the following three vital interconnected subjects: (1) The systematic capture of remote sensing data with low-cost UAVs, (2) the information extraction from survey data and (3) the interlinking and exchange of interoperable BIM data (design, survey and extracted information) using Linked Data technologies. In this paper, the needed data for proper road construction digitisation and the desired data structure is discussed. Additionally, the automation of Real Time Kinematic (RTK)-flight processing is presented. The feasibility of the proposed workflow is examined based on real-life test cases. As a consequence of automation, the homogeneity of data across projects will increase. This will form the basis for further automation in road construction. ispartof: pages:69-76 ispartof: Measurement, Visualisation and Processing in BIM for Design and Construction Management II vol:XLVI-5/W1-2022 issue:W1 pages:69-76 ispartof: MVP BIM 2022 location:Prague Czech Republic date:7 Feb - 8 Feb 2022 status: Published online

Building Information models created from laser scanning inputs are becoming increasingly commonplace, but the automation of the modeling and evaluation is still a subject of ongoing research. Current advancements mainly target the data interpretation steps, i.e., the instance and semantic segmentation by developing advanced deep learning models. However, these steps are highly influenced by the characteristics of the laser scanning technologies themselves, which also impact the reconstruction/evaluation potential. In this work, the impact of different data acquisition techniques and technologies on these procedures is studied. More specifically, we quantify the capacity of static, trolley, backpack, and head-worn mapping solutions and their semantic segmentation results such as for BIM modeling and analyses procedures. For the analysis, international standards and specifications are used wherever possible. From the experiments, the suitability of each platform is established, along with the pros and cons of each system. Overall, this work provides a much needed update on point cloud validation that is needed to further fuel BIM automation. ispartof: Remote Sensing vol:14 issue:3 status: Published online

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. ispartof: Isprs International Journal Of Geo-Information vol:9 issue:9 status: Published online