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.

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 creation of Building Information Models (BIM) is driven by cutting-edge software applications, plug-ins, and APIs that constitute the backbone of BIM authoring tools. While free tools and APIs offer visualization and customization options, geometric modelling remains largely restricted to interactive work and proprietary platforms, which sometimes limits flexibility and efficiency. There are still only a few comprehensive workflows that fully automate the reconstruction of building elements from reality-based surveyed data. This paper introduces an innovative reconstruction pipeline developed for the Scan-to-BIM Challenge at the CVPR 2024 Workshop, where it achieved second place in the competition. A deep learning (DL)-driven BIM Module for parametric IFC reconstruction is designed to accurately reconstruct both primary and secondary building elements within a BIM framework, starting from unstructured point cloud data captured via Terrestrial Laser Scanning (TLS). By leveraging DL techniques, particularly Convolutional Neural Networks (CNNs) and Transformers Networks (PTv3), our approach uses late fusion instance segmentation across both 2D and 3D modalities to accurately identify and reconstruct class-specific elements. The pipeline ultimately generates Industry Foundation Classes (IFC) elements, enhancing modelling accuracy, parameter estimation, and consistency in subsequent stages. Results highlight the pipeline’s strong performance on various datasets, underscoring the crucial role of DL in advancing Scan-to-BIM workflows.

The maintenance of railway infrastructure requires detailed inspection of track assets including the rails, sleepers, fasteners, and tie plates. Current methods using total stations and measurement trains are costly and the subsequent data processing is often manual. A potent alternative is the use of Unmanned Aerial Vehicles (UAV) to investigate track asset deviations. However, the potential degree of automation and the overall accuracy is still very much the subject of ongoing research. In this work, a potential pipeline is investigated for the planimetric inspection of rails using UAV photogrammetry. Specifically, state-of-the-art line detectors such as Holistically-Nested Edge Detection, DexiNed, Segment Anything Model, and Mobile Line Segment Detection are combined with logical filtering to assess the localisation and gauge of the rails. The experiments indicate that the accuracy and detection rate are promising. Overall, the proposed method is a promising step towards affordable and safe railway infrastructure inspection.

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.