Journal: Automation in Construction

Abbreviation

Autom. constr.

Publisher

Elsevier

Journal Volumes

ISSN

0926-5805

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Publications1 - 10 of 63
  • Uncertainty propagation in satellite InSAR data analysis for structural health monitoring
    Item type: Journal Article
    Giordano, Pier Francesco; Kamariotis, Antonios; Giardina, Giorgia; et al. (2025)
    Automation in Construction
    Structural Health Monitoring (SHM) supports the management of the integrity and functionality of critical infrastructure like bridges. Interferometric Synthetic Aperture Radar (InSAR) offers a scalable, non-intrusive alternative to conventional methods, ideal for large-scale and automated monitoring. However, spatial and temporal resampling, commonly used to reconstruct structural displacements from InSAR data, introduces uncertainty into final estimates. This paper presents a framework to quantify and reduce this uncertainty. A case study of a bridge in Rome, Italy, demonstrates that uncertainty metrics can effectively guide improvements in Persistent Scatterer (PS) clustering and grid configuration. Specifically, removing off-bridge PSs from a single grid cell reduced displacement uncertainty by 45 %, while replacing a fixed grid with a tailored layout for the entire bridge resulted in a 12 % average reduction in uncertainty. These findings provide valuable guidance for optimising InSAR data processing in SHM and underscore the critical role of PS clustering in improving measurement precision.
  • Building information model based energy/exergy performance assessment in early design stages
    Item type: Journal Article
    Schlueter, Arno; Thesseling, Frank (2009)
    Automation in Construction
  • Automated Retrieval of 3D CAD Model Objects in Construction Range Images
    Item type: Journal Article
    Bosché, Frédéric N.; Haas, Carl T. (2008)
    Automation in Construction
  • Productivity of digital fabrication in construction: Cost and time analysis of a robotically built wall
    Item type: Journal Article
    García de Soto, Borja; Agustí-Juan, Isolda; Hunhevicz, Jens Juri; et al. (2018)
    Automation in Construction
    Although automation has been actively and successfully used in different industries since the 1970s, its application to the construction industry is still rare or not fully exploited. In order to help provide the construction industry with an additional incentive to adopt more automation, an investigation was undertaken to assess the effects of digital fabrication (dfab) on productivity by analyzing the cost and time required for the construction of a robotically-fabricated complex concrete wall onsite. After defining the different tasks for the conventional and robotically fabricated concrete wall, data was collected from different sources and used in a simulation to describe the distribution of time and cost for the different construction scenarios. In the example, it was found that productivity is higher when the robotic construction method is used for complex walls, indicating that it is possible to obtain significant economic benefit from the use of additive dfab to construct complex structures. Further research is required to assess the social impacts of using dfab.
  • Structural stay-in-place formwork for robotic in situ fabrication of non-standard concrete structures: A real scale architectural demonstrator
    Item type: Journal Article
    Hack, Norman; Dörfler, Kathrin; Walzer, Alexander N.; et al. (2020)
    Automation in Construction
  • Early-stage design support combining machine learning and building information modelling
    Item type: Journal Article
    Singh, Manav Mahan; Deb, Chirag; Geyer, Philipp (2022)
    Automation in Construction
    Global energy concerns necessitate designing energy-efficient buildings. Many important decisions affecting energy performance are made at early stages with little information. Dynamic simulations support informed decision-making; however, uncertainty, high computational time, and expensive modelling efforts impair their use at early stages. This article develops an approach using building information modelling and machine learning that provides quick energy performance information. This approach has been implemented into a web tool, p-energyanalysis.de. It allows design space exploration, assesses the energy performance of design options, compares multiple options, performs sensitivity analysis, and tracks changes. Twenty-one participants (researchers and architects) used it as a support tool for designing an energy-efficient building. Their feedbacks are discussed as part of the tool development. The study found that the tool supports early-stage design decisions by quickly providing relevant information. The limitations, such as the bias in the results towards training data population and implementation issues, are also discussed.
  • Parametric archetype: An incremental learning model based on a similarity measure for building material stock aggregation
    Item type: Journal Article
    Pei, Wanyu; Stouffs, Rudi (2025)
    Automation in Construction
    To reduce reliance on virgin resources, the building material stock (BMS) serves as a source for material recycling and reuse. However, quantifying BMS in urban areas with scarce material data remains challenging. This paper addresses this challenge by proposing a “parametric archetype” method, which integrates similarity measures in BMS modelling. The similarity in material content between buildings is quantified using an Euclidean distance measure based on multidimensional building feature parameters. By mapping material data to similar buildings, a cohesive dataset can be formed and further enriched, enabling incremental larger-scale BMS aggregation. This model is trained using a dataset with 52 Singapore buildings, achieving a 20.24% error rate in material predictions for all urban buildings. The finding highlights the feasibility of conducting BMS aggregation with quantifiable accuracy even with limited material data points. The proposed model can be integrated with environmental impact analysis of material circularity and support sustainable urban resource management.
  • Damage-augmented digital twins towards the automated inspection of buildings
    Item type: Journal Article
    Pantoja-Rosero, Bryan G.; Achanta, Radhakrishna; Beyer, Katrin (2023)
    Automation in Construction
    Current procedures for the rapid inspection of buildings and infrastructure are subjective, time-consuming, and cumbersome to document, necessitating new technologies to automate the process and eliminate these shortcomings. Fortunately, recent developments in imaging devices and artificial intelligence, such as computer vision, provide the necessary tools for this, though they are not yet integrated into infrastructure applications. In this paper, we propose an end-to-end pipeline that generates damage-augmented digital twins for buildings at LOD3, including geometrical information as well as data pertaining to damage condition and its characterization. Our framework incorporates multiple-view images to (1) create a level of detail model, (2) segment damage information, and (3) characterize damage. The core of the method is the structure from motion, which is used to reconstruct the building scene, and machine-learning models that segment and characterize damage. In contrast to current practices, our method does not require manual intervention, generates lightweight models, and can be applied to a wide range of assets. The results generated with our pipeline represent a significant step towards an automated infrastructure damage assessment. We intend to expand our work in the future to include real-time applications and applications to other types of infrastructure.
  • Surface finish classification using depth camera data
    Item type: Journal Article
    Frangez, Valens; Salido Monzú, David; Wieser, Andreas (2021)
    Automation in Construction
    We propose a novel approach for surface finish classification of digitally fabricated structures using an industrial depth camera. Data collected at different viewpoints are jointly processed to derive the spatial distribution of features describing the reflectance, which is in turn related to the surface finish. The features can be used to classify the surfaces according to their finish e.g., for assessing the homogeneity or conformance. We apply the method to four sprayed plaster specimens of similar visual appearance but different roughness. Using nearest neighbor classification we achieve an accuracy of 97% for the plaster samples. The approach is a contribution towards real-time quality inspection in digital fabrication.
  • Feature-based topology finding of patterns for shell structures
    Item type: Journal Article
    Oval, Robin; Rippmann, Matthias; Mesnil, Romain; et al. (2019)
    Automation in Construction
Publications1 - 10 of 63