Narrow Results

With the wide application of building information modeling (BIM) technology in the field of architectural design, this study aims to explore the planning and design method of public space internal environment based on BIM topological relationship. Through in-depth analysis of the data structure and spatial topological relationships of BIM models, this study proposes a public space design framework that combines user needs and environmental standards. Collect and convert relevant data of public spaces into BIM models, use BIM technology to establish detailed models of spatial elements and their topological relationships, combine spatial connectivity, line of sight, and flow simulation analysis, and consider key factors such as spatial layout, lighting, and ventilation. Realize the planning and design of indoor environments in public spaces. The experimental results indicate that the method can effectively improve the quality of public space design and promote the sustainable development of the environment. This study not only highlights the application potential of BIM in public space design, but also provides a new design methodology for the internal environmental planning of public space.

In order to intuitively present the layout and design of the solar energy utilization system, improve the integrity, and consistency of the design. Design and analyze the solar energy utilization system of green buildings based on Building Information Model (BIM). The data acquisition layer collects relevant data of green buildings and solar energy utilization system in RFID and WSN mode and transmits them to the data processing layer; the data processing layer filters the data related to green buildings and solar energy utilization system according to IPC construction standards, and transmits them to the model layer through the Internet of Things; the model layer uses the REVIT software combined with the filtered data to build and update the BIM model of green buildings and solar energy utilization system, intuitively present the layout and design of the solar energy utilization system, and improve the integrity and consistency of the design; the application layer uses the fuzzy comprehensive evaluation model, combined with the green building and solar energy utilization system BIM model, to simulate and analyze the solar energy utilization system environment of green buildings; the user layer provides users with human–computer interaction functions to view the design and analysis process of the solar energy utilization system of green buildings. Experiments show that this method can effectively collect relevant data of green buildings and establish a BIM model of solar energy utilization system; this method can effectively design the solar energy utilization system of green buildings; the solar energy utilization system designed by this method can effectively reduce the electric heating capacity of green buildings, and has an excellent energy-saving effect.

Building Information Modeling (BIM) technology has revolutionized the architectural and construction industries by providing detailed digital representations of buildings, enhancing design efficiency and project management. Despite its widespread application, the utilization of BIM in optimizing interior space planning, particularly in boutique accommodations like guesthouses, remains underexplored. This research addresses the gap by introducing a novel method for interior environment space planning of guesthouses based on the topological relationships derived from BIM data. This study developed a new algorithm that utilizes detailed topology information from BIM in order to make space planning decisions more efficiently, thereby improving space utilization efficiency and guest experience. Through a comprehensive analysis of BIM topological relationships and the application of advanced optimization techniques, our method aims to optimize the use of space while considering the unique constraints and requirements of guesthouse environments. The proposed algorithm demonstrates significant improvements in spatial efficiency and design quality when tested against traditional planning methods on real-world BIM datasets. This research not only contributes a novel approach to the field of architectural design and planning but also offers practical implications for the enhancement of interior spaces in guesthouses, potentially influencing future applications of BIM technology in the hospitality industry.

The intelligent design of complex transportation nodes is an important way to improve urban transportation efficiency and safety. However, currently, there are still problems with low data acquisition and processing accuracy and high-technology application costs in complex transportation nodes. Therefore, the research attempts to achieve the intelligent design of complex traffic node organizations based on digital analogue-driven and integrated building information models and optimization graph convolution algorithms. It aims to achieve real-time monitoring and analysis of traffic information to improve the overall performance of the transportation system. These experiments confirmed that the studied BIM could achieve full lifecycle management of urban interchanges, and could achieve strain analysis and trend prediction based on time series. When the organizational members were 122, the information channels for smart stations based on research technology were 241, which improved information efficiency by more than 90%. When the sensor node was 48, the predicted values of the research model based on the optimization graph convolution algorithm were consistent with the actual values, with a prediction accuracy of 95%. Therefore, the technology studied in this study can provide reliable solutions for the design of complex traffic nodes and support traffic management decisions.

This paper presents the method and findings of a visual knowledge domain map used to explore the development and evolution of Building information modeling (BIM) technology. Metadata taken from 806 literature records is used to visualize the state of BIM research. The records were published between 1998 and 2017, and Citespace software is employed to visualize key data about the research contained on the Web of Science platform, including nationality of research institutions, research themes, co-citations of periodicals, authorship, literature sources, and the development of the discipline over time. The distribution of power in this field is also examined by interrogating the BIM knowledge base, identifying research hotspots to provide a reference for assessing the current state-of-the-art.

According to the analysis and prediction of engineering cost, a BIM-aided analysis method based on GA network model is proposed. First, we improve the neural network by genetic algorithm; second, according to the engineering feature vector, BIM software is used to train the GA network model; finally, the GA network model reaches a steady state, given prediction of Engineering cost. According to the experimental study of 20 high-rise residential buildings in YJW area, the experimental results show that the proposed GA model combined with BIM auxiliary analysis method can accurately and easily complete the project cost prediction.

Global warming and other environmental problems are increasing the demand for high-performance buildings. The development of high-performance computers and building information models has a significant impact on buildings’ energy simulation. High-performance building design needs to comprehensively consider geography, climate, material, cost and other factors which is a highly complex multidisciplinary research problem. Therefore, it is urgent to use advanced modeling and simulation technology to realize it, involving Building Information Modelling (BIM), parametric design and cloud platform. Comprehensive simulation of building performance refers to a multidisciplinary collaborative design, and the correlation between research objects and parameters should be achieved by complex programming design. This study integrates BIM, computer, cloud computing and other technologies to simulate BIM-based building energy consumption performance. Based on project information, geometry information and physical properties exhibited by materials stored in BIM model, the energy analysis model is created. Revit–Dynamo API functions are employed to generate a novel BIM model in Revit after automatically changing and transferring user-defined parameters. BIM energy consumption model is converted into Green Building eXtended Markup Language (GBXML) file and uploaded to Green Building Studio (GBS) cloud server. The optimal project solution is yielded by retrieving the energy consumption simulation results of BIM models with a range of parameters. The case study shows that building volume, glass material, window-wall ratio and window height have significant influence on energy consumption targets of buildings. In hot-summer and cold-winter areas, the total energy consumption of glass materials with high insulation and reflection coefficient is small. The window size slightly impacts the annual lighting energy consumption, but it has significant influence on the annual air conditioning energy consumption, with a maximum increase of about 22%. Finally, the application advantages and limitations of the framework in high-performance building design and its application prospects in energy-saving building design are discussed.

Biologically inspired model (BIM) for image recognition is a robust computational architecture, which has attracted widespread attention. BIM can be described as a four-layer structure based on the mechanisms of the visual cortex. Although the performance of BIM for image recognition is robust, it takes the randomly selected ways for the patch selection, which is sightless, and results in heavy computing burden. To address this issue, we propose a novel patch selection method with oriented Gaussian–Hermite moment (PSGHM), and we enhanced the BIM based on the proposed PSGHM, named as PBIM. In contrast to the conventional BIM which adopts the random method to select patches within the feature representation layers processed by multi-scale Gabor filter banks, the proposed PBIM takes the PSGHM way to extract a small number of representation features while offering promising distinctiveness. To show the effectiveness of the proposed PBIM, experimental studies on object categorization are conducted on the CalTech05, TU Darmstadt (TUD) and GRAZ01 databases. Experimental results demonstrate that the performance of PBIM is a significant improvement on that of the conventional BIM.

Many cities in the Philippines are situated near fault systems that can generate large magnitude of earthquakes. This paper describes the development of a city seismic response analysis approach for Metro Manila’s low- to mid-rise RC structures using frame models which are generated from GIS feature or BIM data. To create the three-dimensional (3D) models, features and structural details from BIM are used. Finite element method was used to discretize the models with mesh of line elements. Validations of generated models were conducted by comparing the results with those obtained using solid finite element model, commercial software and experimental test. The developed approach was applied to a scenario earthquake analysis wherein the causative fault is the West Valley Fault. Two cities within Metro Manila, that vary in distribution of low- and mid-rise building and site condition, were analyzed. The results of statistical analysis show that the variations in distribution of maximum interstory drift (ISD) between cities and between floor levels, are influenced by the height and floor plan area of the structures. Visualizations in both city-level and building-level reveal the areas that are critical for the considered scenario earthquake. Analysis of the computation costs shows that using frame models for response analysis of each city in Metro Manila leads to million-order degrees-of-freedom (DoF) to solve, and necessitates the implementation of data partitioning and high performance computing techniques.