Conference Agenda

Advancing the level of digitalization and automation in concrete manufacturing can substantially contribute to lowering CO2 emissions associated with the concrete production. This work introduces a new methodology for predicting the time-dependent properties of fresh concrete during mixing. For the prediction, a deep learning network is created which uses stereoscopic image sequences of the flowing material together with tabular data as input. Besides mix design parameters and process state data, like energy consumption, moisture and fresh concrete temperature, temporal information is included in the tabular data. The temporal information represents the time interval between image acquisition and the time for which the properties should be predicted. During training, this interval corresponds to the difference between the image acquisition and the time at which reference measurements are taken, allowing the network to implicitly learn the temporal evolution of the material properties, namely the slump flow diameter, yield stress, and plastic viscosity. Incorporating time-dependent prediction enables the forecasting of property changes throughout the mixing process, offering a valuable tool for real-time process control. This capability allows timely adjustments whenever deviations from the desired material behavior are detected. The experimental investigations presented in this paper demonstrate the feasibility of this method under realistic conditions.

Effective integration of geometric and semantic data within Building Information Models (BIM) is essential for the efficient life cycle management of modern facilities. However, maintaining accurate as-is BIM models for existing buildings remains a significant challenge, as manual updates are labour-intensive and full 3D reconstruction is often impractical for incremental changes. In such cases, image-based approaches offer a fast and flexible alternative, but require reliable alignment of 2D imagery with existing BIM geometry. To address this challenge, this study introduces a streamlined pipeline for semantic enrichment that uses a single-image visual localisation approach to directly align 2D imagery with existing BIM geometry. The proposed method integrates transformer-based panoptic segmentation (Mask2Former) with a closed-form Perspective-n-Line solver to estimate 6-degrees-of-freedom (6-DoF) camera poses. The novelty of the proposed approach lies in the explicit use of semantic information as a geometric constraint to guide the selection of 2D–3D correspondences for pose estimation. Semantic labels are utilised to filter line correspondences, ensuring that only stable architectural boundaries (e.g., walls, floors, and ceilings) are used in the registration process. Such semantic filtering stabilises correspondence selection, effectively mitigating pose ambiguity in repetitive indoor layouts or scenes where structural elements are partially obscured by furniture and clutter. Experimental results confirm high accuracy, achieving a median position error of 9.84 cm and an orientation error of 1.05° in complex indoor environments. This robust registration framework provides a reliable foundation for the downstream semantic enrichment and digital twin updates.

Road markings are vital for HD maps and autonomous driving, yet LiDAR-based extraction is difficult due to missing RGB information, severe class imbalance, and thin, elongated geometry under sparse and noisy returns (Ma et al., 2020). We propose LSTNet, which performs local-shape tokenization by grouping points on tangent planes and encoding tokens from relative coordinates, normals, curvature, and intensity contrast. A geometry-aware transformer aggregates these tokens across multiple scales with attention biased by relative position and normal similarity, capturing long and thin structures while preserving edges. Our contributions can be summarized as follows: (1) We present LSTNet, which directly segments road marking from 3D point clouds, avoiding image conversion and preserving geometric fidelity. (2) We introduce a dedicated point-cloud dataset for road marking extraction to enable training and fair evaluation. (3) We design a task-specific and boundary-aware training objective that improves thin road marking recall and robustness under class imbalance.

The concept of Digital Twins (DTs) in Architecture, Engineering and Construction (AEC) domain encompasses a wide range of applications and scales, from single buildings to entire cities, spanning monitoring, simulation, energy management and operational control. Regardless of the specific application, a valid Digital Twin (DT) is a dynamic, data-driven model that stays continuously synchronized with its physical counterpart in both time and state via sensors and the Internet of Things (IoT). It must receive real-world input and provide feedback for analysis or control, ultimately progressing toward a self-operational DT. In the energy domain, an Energy Digital Twin (EDT) must be designed to (i) include sufficient geometric information (ii) support continuous monitoring, (iii) assist scenario-based simulation and (iv) enable operational maintenance and decision support. To achieve these objectives, the EDT’s geometry should be managed through two complementary representations: (i) a watertight solid volumetric model for physics-based simulation and (ii) a boundary representation (B-Rep) model for precise topology, semantics and data exchange. A mapping layer keeps the two representations consistent, preserving identity and topology across states and linking to the graph. Consequently, the EDT should adopt a multi-level architecture defining both geometric and data structures. This work introduces a robust Scan-to-Energy Digital Twins (Scan-to-EDTs) framework that generates multi-level building EDTs by integrating geometric, semantic and simulation layers to enable interoperable energy analyses.

Evaluating uncertainty is critical for reliable use of Mobile Laser Scanning (MLS) point clouds in many high-precision applications such as Scan-to-BIM, deformation analysis, and 3D modeling. However, obtaining the ground truth (GT) for evaluation is often costly and infeasible in many real-world applications. To reduce this long-standing reliance on GT in uncertainty evaluation research, this study presents a learning-based framework for MLS point clouds that integrates optimal neighborhood estimation with geometric feature extraction. Experiments on a real-world dataset show that the proposed framework is feasible and the XGBoost model delivers fully comparable accuracy to Random Forest while achieving substantially higher efficiency (about 3 times faster), providing initial evidence that geometric features can be used to predict point-level uncertainty quantified by the C2C distance. In summary, this study shows that MLS point clouds' uncertainty is learnable, offering a novel learning-based viewpoint towards uncertainty evaluation research.

The automation of Scan-to-BIM remains a major challenge within the Architecture, Engineering, and Construction industry, particularly in the detection and geometric characterisation of architectural openings such as doors and windows. Although recent advances in 3D semantic segmentation have improved the classification of architectural elements, the effect of segmentation accuracy on downstream geometric detection and reconstruction is still under study. This work compares five state-of-the-art deep learning models, PointNeXt, PointMetaBase, Point Transformer V1, Point Transformer V3, and Swin3D, on opening detection in Scan-to-BIM. A unified evaluation framework integrating DBSCAN clustering with axis-aligned bounding box fitting is introduced to generate per-instance geometric representations. The models are assessed using semantic metrics and geometric reliability indicators, including centroid error, dimensional deviation and 3D IoU. Experiments on the S3DIS Area 5 dataset, reveal notable performance differences across models. Swin3D achieved the highest door detection rate of 96.9%, followed by PointMetaBase at 92.9%, PointNeXt at 87.4%, PTV3 at 85.0%, and PTV1 at 81.9%. Window detection proved more challenging, with Swin3D and PTV3 both achieving 75.0%, PTV1 at 71.2%, and PointNeXt and PointMetaBase at 67.3%. Notably, PointMetaBase produced strong geometric accuracy for doors despite lower semantic scores. These results suggest that high segmentation accuracy does not always lead to precise geometric reconstruction. To assess generalisation, the trained models were applied to 11 Matterport3D rooms, confirming that the observed patterns extend across different scanning environments. This study concludes that in Scan-to-BIM workflows, greater emphasis should be placed on geometric reconstruction algorithms than segmentation performance alone.

The digitalization of the architecture, construction and subsurface utility engineering sectors demands efficient, accurate and flexible 3D point surveying methods. Established ones based on Global Navigation Satellite System (GNSS) rovers or total stations suffer from significant limitations, such as requiring open-sky visibility, high costs and complex setups. This paper introduces a novel method for georeferencing 3D points using the PIX4Dcatch mobile application coupled with an external Real-Time Kinematic (RTK) GNSS receiver. The method enables to survey a point of interest by just aiming the smartphone and tapping on the screen during a capture. A lightweight, modified Bundle Adjustment algorithm runs on the device, delivering accurate 3D coordinates in seconds without any post-processing. We evaluated the method by surveying several known cadaster points for hundreds of times across diverse field conditions, achieving a mean planimetry error norm of approximately 3 cm and 97% of errors below 10 cm. Similar statistics are achieved with single-point measurements using an RTK rover. Although not intended to replace millimeter-precision instruments, the accuracy profile of our method is perfectly suited for many applications, such as subsurface utility mapping, which often have decimeter-level regulatory requirements. Given its high efficiency, low cost and ease of use, we believe that our method has the potential to transform as-built documentation workflows in diverse engineering sectors.