Building Energy Modelling (BEM) extends performance evaluation beyond direct monitoring. Integrating BEM with Building Information Models (BIM), called BIM2BEM, streamlines energy performance analyses. However, strict one-to-one geometric correspondence often results in unnecessary complexity, inflating computational costs without significantly improving accuracy. This study proposes an HVAC-informed thermal zoning approach within BIM2BEM workflow, integrating geometry processing, HVAC topology establishment, zoning scenario generation, and model simplification, thereby automating simplified BEM generation. Its effectiveness is demonstrated by applying it to a complex building. The results indicate that different zoning routines can minimise the risk of overestimating building energy requirements and oversizing HVAC systems.
Safety management and monitoring are critical challenges in the Architecture, Engineering, Construction and Operations (AECO) industry, which is characterized by a high incidence of workplace injuries and fatalities, as well as limited process digitization. On-site safety inspections rely on inspectors' expertise and standardized checklists, making the process susceptible to human error and bias. This research aims to introduce a more effective paradigm for safety monitoring, leveraging technologies such as Augmented Reality (AR) and Building Information Modeling (BIM) to enhance the accessibility of safety plans on construction sites and to document the progression of safety equipment layouts faster.
Software for building performance analysis sees little use in early stage architectural design, yet research applying a practice perspective to tool integration remains limited. To understand how tool learning activities are linked to their perceived usefulness and ease-of-use, we applied interviews and questionnaires during the learning process of a daylight simulation software in a mid-sized architectural office in Sweden. We found that combining learning activities is beneficial, and that members throughout the organisation hierarchy need to learn about the tool capabilities for integration to be successful. These findings can guide training approach focus from ”user” to ”practice”.
In construction, human-robot collaboration (HRC) has primarily focused on advancing robotics technology, yet considering human factors can be critical, as worker responses have the potential to influence HRC performance. This study examines how robotic factors—specifically task pace and failure—affect cognitive, emotional, and behavioral responses during a controlled lab experiment. Eleven participants supervised a manipulator robot performing a line-drawing task under varying conditions, with cognitive and emotional responses assessed through questionnaires and behavioral responses measured using vision-based pose estimation. The results indicate that failure rate and task pace influenced both cognitive and emotional states, while behavioral responses varied across individuals.
The integration of automation and robotics in construction can address critical challenges such as safety hazards, inefficiencies, and cost overruns. This paper explores the evolving role of human-robot collaboration (HRC) in digital fabrication for architecture (DFAB). With a focus on robotic agency and shared authorship, three case studies—Interactive Robotic Plastering, Tie-a-Knot, and Autonomous Dry Stone Wall Construction—are analyzed to examine dynamic workflows, varying levels of robotic autonomy, and the implications of collaboration across different task phases. The findings contribute to a framework for construction practices that merge human intuition with robotic precision, enabling both efficiency and adaptability.
Large-scale building projects rely on extensive information exchange, often through file-based formats. However, many use cases require only a small subset of this data. To address over- and underfetching, element-level access is essential. GraphQL, a flexible query language for object-oriented data, offers a web-based alternative. This paper proposes a mapping from the IFC data model (EXPRESS) to a GraphQL schema, enabling structured, API-based access to building models. A prototypical implementation demonstrates the feasibility of this approach, supporting automatic API generation and querying of IFC models over the web.
The performance evaluation is crucial in comparing multiple house plans during the early stages. However, simulation-based methods often face challenges such as time consumption and complex modeling processes. Therefore, this paper extracted the features from house plans and proposed an image-based algorithm for automatic geometric simulation modeling. The algorithm successfully modeled 16,000 house plans and obtained simulation results for their energy consumption, indoor thermal comfort, and daylighting. This paper further compares the MARS-based and XGBoost-based surrogate models for performance evaluation using the house plan dataset. The results indicate that XGBoost performed better on all metrics.
A hydropower plant digital twin is a virtual model that integrates real-time data and simulations to optimize performance, monitoring, and maintenance. Its development enhances efficiency, reduces downtime, and enables predictive maintenance. Geometry generation is key, providing precise 3D models for simulations. However, creating geometries from laser scans is challenging due to complex structures, occlusions, and noisy data. This research presents a robust geometric segmentation approach, improving digital twin creation. It outlines a pipeline for point cloud segmentation, component relationships, and mesh generation, enhancing accuracy and efficiency for better decision-making and predictive capabilities in hydropower management.
