Design ideation requires both creative and divergent thinking as well as collaboration and exchange to bring forth new insights and design possibilities. In this paper, we investigate such a collaboration with the use of emerging generative AI tools, and we explore how they may help in achieving agreement and semantic convergence between designers. We conducted workshops using Text-to-Image AI with design students and found that in addition to known advantages of brainstorming, the text prompts required by the AI system engaged students in verbal articulations that aided their design process. In collaborative contexts, the generated images shifted the centre of attention for team members to reach convergence through sharing their interpretations. We conclude that text-to-image AI can be beneficial for students’ design processes as a catalyst for brainstorming rather than a tool for generating design imagery and presentations.

The field of construction informatics is fragmented and lacks clarity in understanding the interconnection of different data management strategies. This makes it challenging to address industry-specific data management issues. Using a critical interpretive synthesis, this study reviews and integrates both present and emerging data management approaches in construction informatics. The review is meant to be comprehensive, encompassing technologies and concepts such as Open Schema, Information Container, Common Data Environments, Linked Data, as well as cutting-edge Web3 technologies such as blockchain and decentralized data protocols. The different approaches are identified and classified into five categories and mapped into a two-dimensional framework that considers data storage and data processing modes. The systematic categorization provides a simple, but comprehensive understanding of data management strategies in construction informatics. Moreover, the framework allows to identify the state of the art and trends of data management approaches, providing guidance for future research perspectives, especially in the intersection with Web3 technologies.

While progressing through the different stages of a project in the architecture, engineering, and construction (AEC) sector, a project must continuously comply with several requirements. Yet, human skills are often essential when it comes to validating these requirements, which can be error-prone and contributes to the loss of information in the information stream of a project. As has been indicated before, higher levels of automation can be achieved by describing, querying, and validating AEC project data using the technology stack offered by the Semantic Web (SW). Particularly the logical basis of these technologies (semantic rule-checking) is promising, as it allows a more consistent representation and checking of such requirements. As part of the development of a decentralized rule-checking system using SW open standards, this study investigates particularly to what extent the more challenging requirements can be checked, such as 3D geometric requirements and more ambiguous or vague requirements. Tangible and clear results described in this report are (1) a concise classification of rule types that apply to BIM models, (2) a system architecture for a semantic rule-checking system, and (3) a demonstration of this system through three use cases. The results show how 66% of a document containing human-readable requirements can be validated automatically. Furthermore, a clear indication is given of how the semantic compliance check can be combined with 3D geometric compliance checks into a scalable web-friendly architecture.

Despite decades of the digitization of the construction industry, interoperability challenges prevail. One of which is the lack of sufficient integration between domain-specific BIM models. Therefore, the paper proposes a novel methodology for federating cross-domain BIM-based knowledge graphs. The approach leverages the integration of multiple models into a single knowledge graph, aiming to bridge the existing gaps in data exchange and enhance the semantic richness of building information models. Through a detailed exploration of existing data exchange mechanisms, popular ontologies, and schema limitations, this research addresses the interoperability dilemma. A practical demonstration, encapsulated in the “LBD-Online-Merge” demo application, validates the methodology's efficacy in real-world scenarios, illustrating its potential to revolutionize building operations management and data preparation for the operational phase of a building's lifecycle. This study significantly contributes to the academic and practical discourse by providing a robust framework for achieving a more integrated, intelligent building information management ecosystem, setting a new benchmark for future research and applications in constructing semantically rich digital twins of the built environment.

Increasing reliance on automation and robotization presents great opportunities to improve the management of construction sites as well as existing buildings. Crucial in the use of robots in a built environment is their capacity to locate themselves and navigate as autonomously as possible. Robots often rely on planar and 3D laser scanners for that purpose, and building information models (BIM) are seldom used, for a number of reasons, namely their unreliability, unavailability, and mismatch with localization algorithms used in robots. However, while BIM models are becoming increasingly reliable and more commonly available in more standard data formats (JSON, XML, RDF), they become more promising and reliable resources for localization and indoor navigation, in particular in the more static types of existing infrastructure (existing buildings). In this article, we specifically investigate to what extent and how such building data can be used for such robot navigation. Data flows are built from BIM model to local repository and further to the robot, making use of graph data models (RDF) and JSON data formats. The local repository can hereby be considered to be a digital twin of the real-world building. Navigation on the basis of a BIM model is tested in a real world environment (university building) using a standard robot navigation technology stack. We conclude that it is possible to rely on BIM data and we outline different data flows from BIM model to digital twin and to robot. Future work can focus on (1) making building data models more reliable and standard (modelling guidelines and robot world model), (2) improving the ways in which building features in the digital building model can be recognized in 3D point clouds observed by the robots, and (3) investigating possibilities to update the BIM model based on robot feedback.

The adoption of robotics is promising to improve the efficiency, quality, and safety of prefabricated construction. Besides technologies that improve the capability of a single robot, the automated assembly planning for robots at construction sites is vital for further improving the efficiency and promoting robots into practices. However, considering the highly dynamic and uncertain nature of a construction environment, and the varied scenarios in different construction sites, it is always challenging to make appropriate and up-to-date assembly plans. Therefore, this paper proposes a Deep Reinforcement Learning (DRL) based method for automated assembly planning in robot-based prefabricated construction. Specifically, a re-configurable simulator for assembly planning is developed based on a Building Information Model (BIM) and an open game engine, which could support the training and testing of various optimization methods. Furthermore, the assembly planning problem is modelled as a Markov Decision Process (MDP) and a set of DRL algorithms are developed and trained using the simulator. Finally, experimental case studies in four typical scenarios are conducted, and the performance of our proposed methods have been verified, which can also serve as benchmarks for future research works within the community of automated construction.Note to Practitioners—This paper is conducted based on the comprehensive analysis of real-life assembly planning processes in prefabricated construction, and the methods proposed could bring many benefits to practitioners. Firstly, the proposed simulator could be easily re-configured to simulate diverse scenarios, which can be used to evaluate and verify the operations’ optimization methods and new construction technologies. Secondly, the proposed DRL-based optimization methods can be directly adopted in various robot-based construction scenarios, and can also be tailored to support the assembly planning in traditional human-based or human-robot construction environments. Thirdly, the proposed DRL methods and their performance in the four typical scenarios can serve as benchmarks for proposing new advanced construction technologies and optimization methods in assembly planning.

This paper proposes a method to integrate the rich semantic data-set provided by Building Information Modeling (BIM) with robotics world models, taking as use case indoor semantic localization in a large university building. We convert a subset of semantic entities with associated geometry present in BIM models and represented in the Industry Foundation Classes (IFC) data format to a robot-specific world model representation. This representation is then stored in a spatial database from which the robot can query semantic objects in its immediate surroundings. The contribution of this work is that, from this query, the robot’s feature detectors are configured and used to make explicit data associations with semantic structural objects from the BIM model that are located near the robot’s current position. A graph-based approach is then used to localize the robot, incorporating the explicit map-feature associations for localization. We show that this explainable model-based approach allows a robot equipped with a 2D LiDAR and odometry to track its pose in a large indoor environment for which a BIM model is available.

To be able to design indoor wayfinding systems that adhere better to the needs of the users, user perception on complexity needs to be examined and linked to user characteristics and decision point characteristics. To identify how these characteristics influence perception, an online survey is executed in which participants had to indicate how complex they found a decision point, while interpreting a route instruction. The results show that complexity ratings depend both on user characteristics and on the function of the decision point. Decision points to change levels, start or end a route and to take turns each received significantly different complexity ratings. Isovist and visibility graph analysis characteristics of these decision points show that the first two actions were perceived as more complex when they took place in a narrow hallway, while the third action was perceived as more complex in a convex space. The results of this study can be used in the design of an adaptive wayfinding system that adapts the route instructions to the perceived decision point complexity. This adaptation will adhere better to the needs of the users compared to an adaptation based on solely theoretical complexity.

With the rapidly growing number of online product models in construction industry, there is an urgent need for developing effective domain-specific information retrieval methods. Explicit semantic analysis (ESA) is a method that automatically extracts concept-based features from human knowledge repositories for semantic retrieval. This avoids the requirement of constructing and maintaining an explicitly formalized ontology. However, since domain-specific knowledge repositories are relatively small, the available terminologies are insufficient and concepts have coarse granularity. In this paper, we propose an enhanced ESA method for product model retrieval in construction industry. The major enhancements for the original ESA method consist of two parts. First, a novel concept expansion algorithm is proposed to solve the problem caused by insufficient terminologies. Second, a reranking algorithm is developed to solve the problem caused by coarse granularity of concepts. Experimental results show that our method significantly improves the performance of product model retrieval and outperforms the state-of-the-art methods. Our method is also applicable to product retrieval in other engineering domain if a specific knowledge repository is provided in that domain.

The control logic implemented in building automation systems (BAS) has a significant impact on the overall energy demand of the building. However, information on the control logic, if documented, is often concealed from further data integration and reuse in heterogeneous information silos using disparate data formats. In particular, existing data formats and information models offer limited support to describe control logic explicitly. Ontology-based modeling of the control logic of BAS can potentially result in a versatile source of information for information-driven processes to further increase the performance of technical equipment in a building. Therefore, we present a novel information model, CTRLont, which allows to formally specify the domain of control logic in BAS. We demonstrate the usefulness of the novel information model by using it as a knowledge base for automating rule-based verification of designed control logic in BAS. We successfully apply the methodology to a simple control of an air handling unit and indicate a number of future steps.