Web-Based Management of Public Buildings: A Workflow Based on BIM and IoT Sensors Integration with a Web-GIS Portal

Energy and environmental challenges are a major concern across the world and the urban residential building sector, being one of the main stakeholders in energy consumption and greenhouse gas emissions, needs to be more energy efficient and reduce carbon emissions. While it is easier to design net zero energy homes, existing home stocks are a major challenge for energy retrofitting. Two key challenges are determining the extent of retrofitting required, and developing knowledge-based effective policies that can be applied en-masse to housing stocks and neighborhoods. To overcome these challenges, it is essential to gather critical data about qualities of existing buildings including their age, geo-location, construction type, as well as electro-mechanical and occupancy parameters of each dwelling. The objective of this study was to develop a GIS-based model embedded with critical data of residential buildings to facilitate evidence-based retrofit programs for urban neighborhoods. A model based on a bottom-up approach was proposed in which information gathered from all stakeholders was inputted into one database that can be used for decision-making. A geo-located case study to validate a proposed GIS-based residential retrofitting model sample size of 74 residential buildings in the city of Riyadh was statistically analyzed and used. The results indicate behavior-based patterns, with a strong positive correlation (r = 0.606) between the number of occupants and number of household appliances, while regression analysis showed high occupancy rates do not necessarily result in high utility costs at the end of the month, and there is no statistical difference in the average monthly cost of gas between partial and fully occupied houses. Furthermore, neither the type of building, height, age, nor occupancy status play a significant role in the average energy consumed. Additionally, the GIS-based model was validated and found to be effective for energy-use mapping and gathering critical data for analyzing energy consumption patterns at neighborhood scale, making it useful for municipalities to develop effective policies aimed at energy efficient and smart neighborhoods, based on a recommended list of most effective energy-saving retrofit measures.

A GIS-Based Top-Down Approach to Support Energy Retrofitting for Smart Urban Neighborhoods

Ahmed,

Al-Ramadan,

Asif

et al. 2024

Enhancing Urban Safety: Optimal Patrol Route Strategies for Volunteer Security Squads Based on Integrated BIM-GIS Data

Lee,

et al. 2024

Urban safety is becoming an increasingly crucial issue due to rising crime rates and urbanization. The concept of a “Safe City” aims to ensure citizen safety through effective crime prevention and rapid response strategies. Volunteer security teams play a vital role in supplementing police efforts to maintain community safety. However, current patrol routes are often set unsystematically, relying on experience, leading to inefficient resource use and reduced effectiveness in crime prevention. This study optimized patrol routes for volunteer security teams using advanced data analysis techniques and route optimization algorithms. By integrating various data sources and applying advanced algorithms, the study systematically improved patrol efficiency and effectiveness. By analyzing security facility locations, crime data, and weak areas in Gangseo-gu, this study identified gaps between infrastructure and vulnerable areas. The novelty of this research lies in its comprehensive approach to deriving a security vulnerability index and designing optimal patrol routes based on integrated BIM-GIS data. This optimized approach ensures effective coverage of critical zones, significantly enhancing the operational efficiency of volunteer security teams.

An Open Disaster Information Platform, Methodology, and Visualization for High-Rise and Complex Facilities

Hong,

Park,

et al. 2024

The growing complexity of urban environments and high-rise facilities presents new challenges for disaster preparedness and response, particularly when managing multiple hazards. Traditional systems that focus on single hazards are insufficient for complex facilities that are prone to cascading disasters. This study develops an open disaster information platform that integrates Building Information Modeling (BIM), Geographic Information Systems (GIS), and real-time monitoring tools to enhance situational awareness and multi-hazard response coordination. The platform combines data from the internet of things’ sensors, structural models, and environmental systems to provide responders and facility managers with real-time access to critical information. Simulation tests and real-world deployments have confirmed the platform’s ability to optimize evacuation routes, improve response times, and minimize risks during emergencies. Integration with GIS further supports risk mapping and post-disaster recovery efforts. This study proposes a scalable disaster management framework that promotes real-time data sharing and collaboration across stakeholders. Aligned with the trend toward smart, resilient cities, the platform provides practical solutions for improving disaster preparedness and response in high-rise and complex urban environments.