Campus Buildings Research Articles

Wandering campus footloose: Honoring and developing connections to campus buildings

Campus buildings have been part of our community conversations of late, usually as leaders determine whether or not those particular founders or donors should be honored. But, as writer Goldie Blumenstyk recently shared, sometimes a building's name offers a sense of inclusion. Other authors have also discussed of late how physical infrastructure can help maximize campus pride, publicity, and even investment.

Intelligent Monitoring and Visualization System for High Building Nighttime Utilization Based on Image Processing

The rise of complex high-rise buildings has made building management increasingly challenging, especially the nighttime supervision of university laboratories. Idle occupation increases the risk of accidents and undermines campus sustainability. Effective occupancy detection is essential for optimizing campus building safety and energy efficiency. Environmental sensors for occupancy detection offer limited coverage and are costly, making them unsuitable for campuses. Surveillance cameras, as part of campus infrastructure, provide wide coverage. On this basis, we designed a detection algorithm that uses light brightness to assess nighttime building use. Experimental results showed that the algorithm achieves an average accuracy of 98.67%, enabling large-scale nighttime occupancy detection without the need for installing additional sensors, significantly improving the efficiency of campus building management. In addition, to address the limitations of indoor space representation in geographic information system (GIS) management models, this paper developed a comprehensive 3D GIS model based on a “building–floor–room” hierarchical structure, utilizing oblique photogrammetry and laser scanning technology. This study combined the detection results with real-world 3D data for visualization, providing a new perspective for the 3D spatiotemporal refinement of complex high-rise buildings, and providing a reference framework for the detection and analysis of other types of building environments.

Green Campus Transformation in Smart City Development: A Study on Low-Carbon and Energy-Saving Design for the Renovation of School Buildings

In the context of increasingly deteriorating global ecological conditions and rising carbon emissions from buildings, campus architecture, as the primary environment for youth learning and living, plays a crucial role in low-carbon energy-efficient design, and green environments. This paper takes the case of Yezhai Middle School in Qianshan, Anhui Province, to explore wind environment optimization and facade energy-saving strategies for mountainous campus buildings under existing building stock renovation. In the context of smart city development, integrating advanced technologies and sustainable practices into public infrastructure has become a key objective. Through wind environment simulations and facade energy retrofitting, this study reveals nonlinear increases in wind speed with building height and significant effects of ground roughness on wind speed variations. Adopting EPS panels and insulation layers in facade energy retrofitting reduces energy consumption for winter heating and summer cooling. The renovated facade effectively prevents cold air intrusion and reduces external heat gain, achieving approximately 24% energy savings. This research provides a scientific basis and practical experience for low-carbon energy retrofitting of other campus and public buildings, advancing the construction industry towards green and low-carbon development goals within the framework of smart city initiatives.

Automated image-based fire detection and alarm system using edge computing and cloud-based platform

To tackle the increasing wildfire challenges, this paper presents an automated image-based fire detection and alarm system utilizing edge computing and a cloud-based platform, specifically designed for urban building fire detection. The system captures both RGB and infrared images from thermal cameras and employs existing computer vision techniques to detect fire characteristics such as flames and smoke. By integrating edge computing, the system minimizes latency to enhance the accuracy of fire detection and alarm activation. The cloud platform supports extensive data storage, analysis, and remote monitoring, which can ensure data accessibility and scalable data management. The proposed system descriptions include a detailed system architecture design, data collection, and the selection and application of detection algorithms that leverage both RGB and thermal image data for fire detection. Using the campus building and surrounding risk-prone areas as a testbed, the proposed system demonstrated desired fire detection capabilities and a robust solution to quickly identify and respond to fire incidents within the urban area. The proposed system functionalities can be scaled and adapted to other fire risk-prone areas.

Awareness of Campus Building Users to Achieve Energy Efficiency in Facing the Implementation of Green Buildings (Case Study: Politeknik Negeri Pontianak Integrated Lecture Building)

Abstract Building users’ awareness is a very crucial factor in efforts to achieve efficient energy use in buildings, especially in the green building concept. However, research that examines the awareness of building users specifically in the campus environment is still very rarely conducted, especially in Indonesia. This research, which uses a case study at Politeknik Negeri Pontianak integrated lecture building, investigates the criteria for awareness of building users and their behavior in using energy in the building. This research tries to identify social parameters in achieving building energy efficiency using a quantitative approach. The series of statistical tools used includes ranking of average values. The results of this research show that the focus of improvement can be done by increasing awareness of several things, namely recognizing the problem of decreasing electrical resources, dividing resource use in order to reduce electricity consumption, and understanding efforts and making preparations in the face of reduced electrical energy sources.

Energy Efficiency of Educational Buildings Through Envelope Design Based on Field Observations and Sefaira Simulation (Case Study: FIP B Building)

Abstract The dense activity in the FIP B Building makes energy optimization in the building very important because it will impact faculty operations. This research shows deficiencies in the design of the existing FIP B building, which can be corrected through a number of aspects of change based on field observation and Sefaira Simulation that can increase the building’s energy efficiency in the form of adding skylights, creating an atrium, and increasing the window-to-wall ratio. The value of increasing the efficiency of the new model compared to the existing one is known by the Sefaira energy performance simulation for both model envelopes. The results of the simulation show that the greatest increase in efficiency is in the use of AHU fans, the second is in AHU cooling, and the lowest is in interior lighting and equipment. However, lighting usage has the potential to be more efficient with the relatively improved daylight accessibility in the new design model. The overall annual energy use and EUI level in the new model are slightly more efficient than the existing model. The result of this study provides aspects of consideration in the architectural design of campus buildings with large masses.

Application of PVT Coupled Solar Heat Pump System in the Renovation of Existing Campus Buildings

A photovoltaic thermal panel (PV/T) is an integrated module that harnesses both photovoltaic and solar thermal technologies to convert solar energy into electricity and heat, thereby enhancing overall energy efficiency. This paper aims to explore the suitability of PV/T solar heat pump systems across various climate zones and assess their potential for widespread application. By analyzing the operating principles of an indirect expansion PV/T solar heat pump system in conjunction with the climate characteristics of different regions, MATLAB R2019b/Simulink software was employed to evaluate the photoelectric performance of PV and PV/T systems in representative cities across five distinct climate zones in China during typical winter days. Key metrics, such as power generation, hot water storage tank temperature, indoor temperature, and system COP, were chosen to assess the heating performance of the PV/T solar heat pump system. The findings indicate that the winter ambient temperature significantly affects the photoelectric efficiency of both the PV and PV/T systems. While higher latitudes with lower ambient temperatures yield greater photoelectric efficiency, the southern regions exhibit higher power generation during winter. The winter heating effectiveness of the PV/T solar heat pump system is mainly influenced by indoor and water tank temperatures, with Harbin’s system performing the poorest and failing to meet heating demands, whereas Nanjing’s system shows the best results.

Advanced Machine Learning Techniques for Energy Consumption Analysis and Optimization at UBC Campus: Correlations with Meteorological Variables

Energy consumption analysis has often faced challenges such as limited model accuracy and inadequate consideration of the complex interactions between energy usage and meteorological data. This study is presented as a solution to these challenges through a detailed analysis of energy consumption across UBC Campus buildings using a variety of machine learning models, including Neural Networks, Decision Trees, Random Forests, Gradient Boosting, AdaBoost, Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression, and K-Neighbors. The primary objective is to uncover the complex relationships between energy usage and meteorological data, addressing gaps in understanding how these variables impact consumption patterns in different campus buildings by considering factors such as seasons, hours of the day, and weather conditions. Significant interdependencies among electricity usage, hot water power, gas, and steam volume are revealed, highlighting the need for integrated energy management strategies. Strong negative correlations between Vancouver’s temperature and energy consumption metrics are identified, suggesting opportunities for energy savings through temperature-responsive strategies, especially during warmer periods. Among the regression models evaluated, deep neural networks are found to excel in capturing complex patterns and achieve high predictive accuracy. Valuable insights for improving energy efficiency and sustainability practices are offered, aiding informed decision-making for energy resource management in educational campuses and similar urban environments. Applying advanced machine learning techniques underscores the potential of data-driven energy optimization strategies. Future research could investigate causal relationships between energy consumption and external factors, assess the impact of specific operational interventions, and explore integrating renewable energy sources into the campus energy mix. UBC can advance sustainable energy management through these efforts and can serve as a model for other institutions that aim to reduce their environmental impact.

Wandering campus footloose: How—and why—to foster connections to our buildings

Campus buildings have been part of our community conversations of late, usually as leaders determine whether those particular founders or donors should be honored. But, as writer Goldie Blumenstyk recently shared, sometimes a building's name offers a sense of inclusion. Other authors, including Biemiller, Buckman, and DeSantis, have also discussed of late how physical infrastructure can help maximize campus planning, pride, publicity, and even investment.

Analysis of Indoor Air Quality and Fresh Air Energy Consumption Based on Students’ Learning Efficiency under Different Ventilation Methods by Modelica

This paper aimed to explore a suitable ventilation method at a lower cost of energy to pursue a high learning efficiency based on the characteristics of a Chinese student group and campus building. Firstly, the model was established by Modelica and a good agreement between the numerical simulation and the results by CONTAM 3.4.0.3 was obtained. Secondly, the effects of the fixed window-opening ratio method (FWM), switch control window-opening ratio method (SCM), and automatic control window-opening ratio method (ACM) on CO2 concentration, indoor air temperature, and the heating capacity of air conditioning were investigated. The results showed that, when the FWM with 0% opening or 20% opening was adopted, the indoor CO2 concentration (ICC) was higher than the limit value of the classroom air quality standard, which was 1000 ppm. When the fixed window-opening ratio was greater than 40%, the indoor air temperature could not be controlled at the set value of 18 °C, which presented bad indoor thermal comfort. Meanwhile, when the ACM was adopted, the duration to meet good indoor thermal comfort was 57.17% higher than that of the SCM. However, both of them could maintain the average ICC below the set value in the class. Lastly, the fresh air energy consumption under different ventilation methods was compared. When the design temperature was 13.5 °C, it could be revealed that the fresh air energy consumption under the ACM, SCM, and FWM with 40% opening was 46.58%, 48.38%, and 51.26% lower than those at 18 °C. In summary, it was recommended to set the design temperature of the classroom at 13.5 °C, and the ACM was suggested as a suitable ventilation method to provide fresh air for the classroom.

Application of high-performance computing in sustainable energy campus building design

In high-performance computing (HPC) systems, energy and power considerations play an increasingly important role. This work aims to ensure the implementation of China’s green and ecological concepts, respond to China’s strategy of building an environment-friendly and resource-saving society, and actively promote the construction of sustainable development campuses. First, the theoretical basis of sustainable energy campus architecture is described. Next, the teaching feedback model under HPC is constructed. Finally, with the evaluation results of students’ task completion processes and students’ task works as measurement indexes, the corresponding data is collected and comprehensively evaluated and analyzed to verify the effectiveness of the model. The analysis results indicate that most students are able to complete tasks within two hours; however, their proficiency with multimedia technology is inadequate, and they lack initiative in the learning process. There is a correlation between the overall evaluation of task performance and the students’ level of understanding of the tasks. By implementing a teaching feedback model, students’ learning enthusiasm and the quality of their work improved, providing effective educational support for promoting sustainable development on campus. Overall, the awareness of using computers and other multimedia technologies among students is not strong, and their learning process is relatively closed, with insufficient enthusiasm and initiative. This model can achieve the acquisition, integration, and statistical analysis of teacher feedback information. The model can realize the acquisition, integration, and statistical analysis of teachers’ feedback information. This work hopes to use this learning and feedback mode in practical teaching to address specific problems in computer multimedia courses.

Comparative Analysis of 4G LTE Signal Quality Using Okumura Hatta's Calculations in ITP Campus Buildings

Comparative Analysis of 4G LTE Signal Quality Using Okumura Hatta's Calculations in ITP Campus Buildings

Exploring Campus Configuration and Patient Arrival Behaviors at Large Hospitals: An Integrated Post-Occupancy Evaluation (POE) Protocol.

This study addresses the challenges in hospital navigation, particularly focusing on the sequence of arrival experiences through foot traffic or driving within a medical campus. While previous research has emphasized wayfinding in interiors, this study recognizes the importance of transition from outdoor to indoor spaces in the hospital arrival zone. Twofold research efforts include: (1) Exploring vehicular and pedestrian behaviors in a hospital arrival zone using Space Syntax Analyses (SSA), and (2) Validating the SSA results using empirical data collected from onsite observations and behavior mapping. A series of axial map analyses were conducted on the hospital campus and building arrival zone regarding spatial connectivity and integration, depth of spaces, and wayfinding intelligibility based on vehicular and pedestrian movement paths. Systematic behavioral data collection includes vehicle shadowing (10 h) and pedestrian mapping (20 h) in the building arrival zone. Space Syntax Analyses indicates high intelligibility scores for vehicles (R = .91) and pedestrian (R = .78) circulation patterns; traffic situations were visualized via axial maps. A dataset of 219 vehicles and 2,096 pedestrian behaviors was analyzed and visualized using diagrams and heatmaps, identifying different occupancy patterns within the space. Correlational analyses indicated strong correlations between the results: connectivity and integration scores are significantly correlated with both vehicular and pedestrian traffic volumes. Preliminary findings of the study validated an integrated protocol to evaluate the impacts of campus configuration and building approach design on patient arrival and spatial navigation at large hospitals, serving as a POE protocol involving SSA.

Implementation of Augmented Reality of Laboratory Building and Room Using Fast Corner Detection Algorithm

The Laboratory Buildsing is one of the buildings in Campus IV UINSU which is used to carry out practicum activities, many of the lecturers students and the community do not know the location and also the rooms in the Laboratory building. This research aims to make this application can provide convenience for users such as students, lecturers and the public to obtain information about laboratory buildings. In this research, the method used is Marker Based Tracking where application users must scan the marker first in order to see 3D objects from a floor plan and laboratory building. The results of this study in the form of Augmented Reality applications using Android as an operating system, on the marker added with the Fast Corner Detection algorithm aims to speed up real-time computing time with the consequence of reducing the level of accuracy of corner detection. The conclusion of AR and Fast Corner Detection can be implemented through a scan of the marker, the result obtained is that when scanning the marker a point from the FCD algorithm will appear indicating that the point on this marker is the result of the implementation of the FCD algorithm.

Workplace circulation design and movement: a case study of three Australian campus buildings

ABSTRACT This paper explores the interconnections between a building’s spatial qualities and office workers’ health behaviours, specifically focusing on physical activity, circulation route usage, and sitting time. A mixed methods approach was adopted to examine three workplace buildings, using spatial analysis, physical activity measurement, and a survey of 99 participants. The findings revealed statistical differences in movement patterns among workers in the three case studies. The analysis highlighted the predominantly sedentary nature of the workplaces, staff’s prioritisation of the quickest route, their willingness to incorporate personal physical activity into the workday, and the significant impacts of circulation space design on movement patterns. In response to the pressing need for increased physical activity and reduced sedentary behaviour at work, a greater understanding of the embodied movement of workers must be central in workplace briefing, design, management, and research. We argue that circulation spaces in workplaces should be designed as more than just enablers of workplace functionality and emergency egress.

Predictive Analysis of a Building’s Power Consumption Based on Digital Twin Platforms

Colleges and universities are large consumers of energy, with a huge potential for building energy efficiency, and need to reduce energy consumption to build a low-carbon, energy-saving campus. Predicting the energy consumption of campus buildings can help to accurately manage the electricity consumption of buildings and reduce the energy consumption of buildings. However, the electricity consumption of a building’s operation is affected by many factors, and it is difficult to establish a model for analysis and prediction. Therefore, in this study, the training building of the BIM education center on campus was selected as the research object, and a digital twin O&M platform was established by integrating IoT, digital twin technology (DDT), smart meter monitoring devices, and indoor environment monitoring devices. The O&M management platform can monitor real-time changes in indoor power consumption data and environmental parameters, and organize data on multiple influencing factors and power consumption. Following training, validation, and testing, the machine learning models (back propagation neural network, support vector model, and multiple linear regression model) were assessed and compared for accuracy. Following the multiple linear regression and support vector models, the backpropagation neural network model exhibited the highest accuracy. Consistent with the actual power consumption detection results in the BIM education center, the backpropagation neural network model produced results. Consequently, the BP model created in this study demonstrated its dependability and ability to forecast campus building power usage, assisting the university in organizing its energy supply and creating a campus that prioritizes conservation.

Infrared signal-based implementation of model-based predictive control (MPC) for cost saving in a campus building

Model-based predictive control (MPC) is an advanced control method that is used to achieve energy and cost savings in building energy management. However, there are various challenges to implementing MPC in actual buildings, such as additional engineering costs, physical damage to systems, and security concerns. This study proposes a non-intrusive implementation method for MPC based on a low-cost communication system. Firstly, a grey-box building model was developed along with the heating, ventilation, and air conditioning (HVAC) models based on the actual measurements of the test building. A simulation case study of MPC was performed, along with baseline feedback control, using these constructed models. The MPC outperformed the feedback control in terms of comfort, electricity consumption, and cost. It was implemented in an actual test zone to demonstrate the feasibility of a low-cost and non-intrusive implementation method that was realized using Arduino-based infrared signal communication and to evaluate the cost-saving potential of the MPC when compared with the baseline control. This experiment was conducted under a confined schedule for occupancy and set-point temperature for three days for the MPC and two days for the baseline feedback control in the cooling season. The cost savings of the MPC are as high as 5.8 %. Additional feedback cases were considered with a non-confined occupancy schedule and set-point temperature. The resulting energy consumption and cost savings are 25.2 % and 33.7 %, respectively. In this study, the applicability of the Arduino-based low-cost and non-intrusive implementation method for MPC was demonstrated, and the actual saving percentage of the MPC was compared with the conventional control method in a real building.

Study on the Energy Consumption Characteristics and the Self-Sufficiency Rate of Rooftop Photovoltaic of University Campus Buildings

Study on the Energy Consumption Characteristics and the Self-Sufficiency Rate of Rooftop Photovoltaic of University Campus Buildings

Evaluating different CFD surrogate modelling approaches for fast and accurate indoor environment simulation

Indoor Environment Quality (IEQ) holds significant importance in building design and operation, and the simulation of indoor environments playing a crucial role in enhancing IEQ. Although Computational Fluid Dynamics (CFD) has been widely employed for simulating building environments, it is computationally demanding, particularly for large spaces. To tackle this challenge, we conducted a systematic evaluation of three surrogate models for accelerating CFD: Proper Orthogonal Decomposition (POD), Artificial Neural Networks (ANN), and a combined POD-ANN approach. Our evaluation criteria focused on assessing the model accuracy, the model size, computational time and extrapolation ability. A validated CFD case model and a real campus building are employed for model evaluation. The findings demonstrate that the top five modes can reconstruct the original data matrix accurately, and the POD-ANN significantly reduces model complexity and computation time by reducing the number of parameters in the neural network, the POD-ANN parameters is only 0.14 % of the ANN, and computation time is reduced by 63 %. In addition, the combination with ANN helps increase the extrapolation ability of POD significantly. In conclusion, this research proves that the POD-ANN can enhance the efficiency of CFD calculations with the advantages of both ANN and POD. By applying the POD-ANN to predict indoor temperature, we achieve faster predictions without compromising model accuracy, and an excellent extrapolation ability is achieved. This approach also reduces model complexity, highlighting its practical value for indoor environment prediction, particularly for large and complicated spaces.

Application Strategies of Modular Design in Assembled Teaching Buildings

As an efficient, environmentally friendly, energy-saving construction method, assembled buildings are now widely used in campus building construction. Modular design thinking is system-based design thinking, and its application to the design of an assembled teaching building project will comprehensively improve the rationality of the teaching building and component design. The paper focuses on the application of modular design thinking in assembled teaching building design, aiming to provide references for China’s architectural design units, giving full play to the advantages of modular design thinking in future teaching building design projects, and enhancing the level of design, for the construction of the teaching building and the basis of the technical guarantee.