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.

The construction industry is still one of the least digitized sectors in the world, and digitization in the road construction sector in particular is even more dire. To improve the efficiency of the sector, it is of utmost importance to automate construction monitoring tasks, i.e. progress, quality and quantity analyses. The goal of the presented framework is the automation of volume calculation in road construction, so the last branch, i.e. quantity analysis. This paper discusses volume calculations in road construction sites. These volume calculations will be done based on both as-design data and as-built data, where the former is modelled by considering the regulations formulated by the Agency of Roads and Traffic, Agentschap Wegen en Verkeer (AWV) of Flanders, and the latter is captured by using an RTK-UAV. The feasibility of the proposed workflow is examined using real test cases. As a result of the automation, the homogeneity of data across projects will increase and this forms the basis for further automation in road construction. By performing these checks on a regular basis at set intervals, one can obtain a clear overview of progress. In this way, possible delays or errors can be spotted quickly, resulting in higher quality and reduced failure costs.

The use of as-built Building Information Models (BIM) has become increasingly commonplace. This process of creating a BIM model from point cloud data, also referred to as Scan-to-BIM, is a mostly manual task. Due to the large amount of manual work, the entire Scan-to-BIM process is time-consuming and error prone. Current research focuses on the automation of the Scan-to-BIM pipeline by applying state-of-the-art techniques on its consecutive steps including the data acquisition, data processing, data interpretation and modelling. By automating the matching and modelling of window and door objects, a considerable amount of time can be saved in the Scan-to-BIM process. This is so because each window and door instance needs to be examined by the modeller and must be adapted to the actual on-site situation. Large object libraries containing predefined window and door objects exists but the matching to the best-fit predefined object remains time consuming. The aim of this research is to examine the possibilities to speed up the modelling of window and door objects. First, a literature review discussing existing methods for window and door detection and matching is presented. Second, the acquired data is examined to explore the capabilities of capturing window and door information for different remote sensing devices. Followed by tests of some commonplace features in the use for window and door occurrence matching and clustering.

The use of Building Information Models (BIM) during the entire life cycle of a building or facility requires an up-to-date and detailed digital representation. Nowadays the BIM focus mostly lies on the design and construction phase of the building and is rarely used for the rest of the life cycle. For further use the BIM must be updated after construction and enriched with appliance objects such as safety equipment, heating elements to enhance its usability. In this work a new approach is presented to detect and locate appliance objects in a three dimensional environment using object detection in images created by indoor mobile mapping devices. By detecting and locating these appliance objects in the three dimensional world they can be included in the BIM model. This approach enables the detection and localization needed for the placement of placeholders or detailed geometric models of those appliances in the BIM model or other digital representations. Resulting in an increased level of detail and usability of the digital representation of the building or facility during its further life cycle. Overall, this work demonstrates and examines the accuracy of the detection and localization of appliance objects.

The construction industry is experiencing a gradual shift towards digitization. Currently, this digitization is relatively limited, but the need for that digitization as well as for automation is large. Digitization in road construction in particular is even more dire than in the construction industry in general. It involves various tasks such as progress, quality and quantity analyses. In this paper a proof of concept is presented on the possible automation of accuracy analysis in road construction throughout the construction of a road. The used data is 3D as-design data on the one hand and multiple 3D as-built data on the other hand, with the former being modelled using the regulations formulated by the Flemish ”Departement van Mobiliteit en Openbare Werken” (MOW), and the latter being captured using a Real Time Kinematic Unmanned Aerial Vehicle (RTK-UAV). The feasibility of the proposed workflow is examined using a real test case. Conducting these quality checks regularly will provide a clear overview of the construction site progress. In this way, any delays or errors can be detected quickly, resulting in higher quality and reduced failure costs.