The construction sector is increasingly recognized as a critical domain in the pursuit of a circular economy (CE) (Adams et al. 2017). In the Netherlands, the landfilling of construction and demolition waste (CDW) is already minimized, underlining the transition outcome to CE, but most of the CDW is currently only processed low level recycling or backfilling (Azcárate- Aguerre 2023). Among the various strategies to circularity, reuse of construction products has shown particular promise, where materials and components are recovered, refurbished if necessary, and reintroduced into new or ongoing building projects(Tsui 2023; Van Uden et al. 2025). Yet, despite its potential, this approach faces a series of practical challenges that impede mainstream adoption: fragmented stakeholder networks, inconsistent or incomplete data about available used materials, and a general lack of robust, integrative logistics mechanisms (Tjahjono 2010). […]

The maintenance of buildings, underpinned by the digital twin technique, becomes integral to heritage conservation efforts. To achieve efficient modelling with minimal manual intervention, automated component recognition based on semantic segmentation of point clouds is imperative. Confronted by the challenges of the paucity of requisite datasets and the inherent geometric diversity of historical buildings, a two-step strategy including feature extraction and classification is proposed. First, an improved SHOT descriptor is proposed to extract discriminative features by defining a specific local reference system and concatenating support fields at different scales. The extracted features are then classified with a learning-based network, avoiding a feature learning process that relies on sufficient data. Experiments on real-world heritage point clouds yield 93.7% accuracy and an 80.0% mean-intersection-over-union (mIoU) when descriptors with radii of 0.3 m and 0.9 m are combined, surpassing computationally expensive deep learning networks and data-intensive unsupervised learning. A slight decrease in segmentation performance with random removal of points indicates the high robustness of the proposed method against data missing and sampling density changes. Additionally, a geometric modelling process with an error of less than 10% is introduced to achieve a direct transition from point cloud to model, contributing to the establishment of digital twins for heritage structures.

Automatic design tools are being developed to assist designers handle tedious work at scale. However, knowledge gaps still exist in harnessing deep learning models to learn from human experience for more efficient design generation while keeping the data understandable and interoperable. Moreover, human-in-the-loop approach is largely neglected, which are essential for more user-centered design. This research utilizes graph data to parametrically represent housing designs and graph-representative deep generative models for design generation, which provides an interactive design approach for the users at every step. This method would facilitate the human-centered design process by returning feasible and parametric housing design alternatives.

In recent years, monitoring the health condition of existing bridges has become a common requirement. By providing an information management system, Bridge Information Model (BrIM) can highly improve the efficiency of health inspection and the reliability of condition evaluation. However, the current modeling processes still largely rely on manual work, where the cost outweighs the benefits. The main barrier lies in the challenging step of semantic segmentation of point clouds. Efforts have been made to identify and segment the structural components of bridges in existing research. But these methods are either dependent on manual data preprocessing or need big training dataset, which, however, has rendered them unpractical in real-world applications. This paper presents a combined local descriptor and machine learning based method to automatically detect structural components of bridges from point clouds. Based on the geometrical features of bridges, we design a multi-scale local descriptor, which is then used to train a deep classification neural network. In the end, a result refinement algorithm is adopted to optimize the segmentation results. Experiments on real-world reinforced concrete (RC) slab and beam-slab bridges show an average precision of 97.26%, recall of 98.00%, and intersection over union (IoU) of 95.38%, which significantly outperforms PointNet. This method has provided a potential solution to semantic segmentation of infrastructures by small sample learning and will contribute to the fulfillment of the automatic BrIM generation of typical highway bridges from the point cloud in the future.