A Systematic Review of Visualization in Building Information Modeling

BEMTrace: Visualization-Driven Approach for Deriving Building Energy Models from BIM

Building Information Modeling (BIM) describes a central data pool covering the entire life cycle of a construction project. Similarly, Building Energy Modeling (BEM) describes the process of using a 3D representation of a building as a basis for thermal simulations to assess the building's energy performance. This paper explores the intersection of BIM and BEM, focusing on the challenges and methodologies in converting BIM data into BEM representations for energy performance analysis. BEMTrace integrates 3D data wrangling techniques with visualization methodologies to enhance the accuracy and traceability of the BIM-to-BEM conversion process. Through parsing, error detection, and algorithmic correction of BIM data, our methods generate valid BEM models suitable for energy simulation. Visualization techniques provide transparent insights into the conversion process, aiding error identification, validation, and user comprehension. We introduce context-adaptive selections to facilitate user interaction and to show that the BEMTrace workflow helps users understand complex 3D data wrangling processes.

Identifying Visualization Opportunities to Help Architects Manage the Complexity of Building Codes

We report a study investigating the viability of using interactive visualizations to aid architectural design with building codes. While visualizations have been used to support general architectural design exploration, existing computational solutions treat building codes as separate from, rather than part of, the design process, creating challenges for architects. Through a series of participatory design studies with professional architects, we found that interactive visualizations have promising potential to aid design exploration and sensemaking in early stages of architectural design by providing feedback about potential allowances and consequences of design decisions. However, implementing a visualization system necessitates addressing the complexity and ambiguity inherent in building codes. To tackle these challenges, we propose various user-driven knowledge management mechanisms for integrating, negotiating, interpreting, and documenting building code rules.

Side-by-Side Comparison of Human Perception and Performance Using Augmented, Hybrid, and Virtual Reality

Alternative reality (XR) technologies, including physical, augmented, hybrid, and virtual reality, offer ways for engineered spaces to be evaluated. Traditionally, practitioners (such as those designing spacecraft habitats) have relied on physical mockups to perform such design evaluations, but digital XR technologies present several streamlining advantages over their physical counterparts. These digital environments vary in their level of virtuality, and consequently have different effects on human perception and performance, with respect to a completely physical mockup environment. To date, very little has been done to characterize and quantify such differences in human perception and performance across XR environments of equal fidelity for the same end application. Here, we show that perception and performance in the virtual reality environment most closely mirror those in the physical reality environment, as measured through volumetric assessment and functional task experiments. These experiments required subjects to judge the dimensions of 3D objects and perform operational tasks presented via checklists. Our results highlight the potential for virtual reality systems to accelerate the iterative design of engineered spaces relative to the use of physical mockups, while preserving the human perception and performance characteristics of a completely physical environment. These findings also elucidate specific advantages and disadvantages to specific digital XR technologies with respect to one another and the physical reality baseline. Practitioners may inform their selection of an XR modality for their specific end application based on this comparative analysis, as it contextualizes the niche for each technology in the realm of iterative design for engineered spaces.

Designing Interfaces to Display Sensor Data: A Case Study in the Human-Building Interaction Field Targeting a University Community

The increase of smart buildings with Building Information Modeling (BIM) and Building Management Systems (BMS) has created a large amount of data, including those coming from sensors. These data are intended for monitoring the building conditions by authorized personnel, not being available to all building occupants. In this paper, we evaluate, from a qualitative point of view, if a user interface designed for a specific community can increase occupants’ context-awareness about environmental issues within a building, supporting them to make more informed decisions that best suit their needs. We designed a user interface addressed to the student community of a smart campus, adopting an Iterative Design Cycle methodology, and engaged 48 students by means of structured interviews with the aim of collecting their feedback and conducting a qualitative analysis. The results obtained show the interest of this community in having access to information about the environmental data within smart campus buildings. For example, students were more interested in data about temperature and brightness, rather than humidity. As a further result of this study, we have extrapolated a series of design recommendations to support the creation of map-based user interfaces that we found to be effective in such contexts.

The Role of the Interface and Interface Management in the Optimization of BIM Multi-Model Applications: A Review

This review targets the BIM interface, the BIM multi-model approach, and the role of employing algorithms in BIM optimization to introduce the need for automation in the BIM technique, instead of complicating manual procedures in order to reduce possible errors. The challenge with adopting BIM lies in the limiting ability of computer-aided design (CAD) to generate a read-able and straightforward Revit by BIM, requiring the homogeneous data format to be generalized better and maintain a super data mod. Furthermore, the communication and management inter-face (CMI) faces some shortcomings due to limitations in its ability to recognize the role of the interface during the project construction phase. This review demonstrates several proposals to simplify the interface, in order to facilitate better communication amongst participants. The industry foundation class (IFC) model requires a new technique to unlock the potential future of intelligent buildings using the BIM multi-model approach integrated with the Internet of Things (IoT). Trials conducted to enhance the BIM model lack advanced methods for optimizing cost, energy consumption, labor, material movement, and the size of layout of the project, by utilizing heuristic, metaheuristic, and k-mean algorithms. The enhancement of BIM could involve algorithms to achieve better productivity, safety, cost, time, and construction frameworks. The review shows that some gaps and limitations still exist, especially considering the potential link between BIM and building management system (BMS) and the level of influence of the BIM-IoT prototype. Future work should find the best approach to solve facility management within the dynamic model, which is still under investigation.

Recommendation algorithm based on attributed multiplex heterogeneous network

In the field of deep learning, the processing of large network models on billions or even tens of billions of nodes and numerous edge types is still flawed, and the accuracy of recommendations is greatly compromised when large network embeddings are applied to recommendation systems. To solve the problem of inaccurate recommendations caused by processing deficiencies in large networks, this paper combines the attributed multiplex heterogeneous network with the attention mechanism that introduces the softsign and sigmoid function characteristics and derives a new framework SSN_GATNE-T (S represents the softsign function, SN represents the attention mechanism introduced by the Softsign function, and GATNE-T represents the transductive embeddings learning for attribute multiple heterogeneous networks). The attributed multiplex heterogeneous network can help obtain more user-item information with more attributes. No matter how many nodes and types are included in the model, our model can handle it well, and the improved attention mechanism can help annotations to obtain more useful information via a combination of the two. This can help to mine more potential information to improve the recommendation effect; in addition, the application of the softsign function in the fully connected layer of the model can better reduce the loss of potential user information, which can be used for accurate recommendation by the model. Using the Adam optimizer to optimize the model can not only make our model converge faster, but it is also very helpful for model tuning. The proposed framework SSN_GATNE-T was tested for two different types of datasets, Amazon and YouTube, using three evaluation indices, ROC-AUC (receiver operating characteristic-area under curve), PR-AUC (precision recall-area under curve) and F1 (F1-score), and found that SSN_GATNE-T improved on all three evaluation indices compared to the mainstream recommendation models currently in existence. This not only demonstrates that the framework can deal well with the shortcomings of obtaining accurate interaction information due to the presence of a large number of nodes and edge types of the embedding of large network models, but also demonstrates the effectiveness of addressing the shortcomings of large networks to improve recommendation performance. In addition, the model is also a good solution to the cold start problem.