The role of technology in education is becoming increasingly important, and the introduction of advanced technology and AI is transforming the way we learn. Virtual reality (VR) is an effective technology that enhances student engagement and improves learning outcomes. However, the cost of implementing VR is a significant concern for educational institutions, making integrating VR technology into education challenging. To address this challenge, this study aims to explore the costs associated with integrating VR into architectural, engineering, and construction (AEC) education. The study had three objectives: to identify relevant case studies that utilized VR in AEC education, to perform keyword analysis, and to conduct a cost analysis of the selected case studies. The thematic analysis identified VR applications in various categories, including VR platforms, construction safety training, design review simulators, civil engineering labs, building information modeling (BIM) integration, architectural design, and surveying engineering. The results revealed that the cost of VR varies based on the application, indicating that it is possible to implement VR in education even on a limited budget. This research provides valuable insights and recommendations for researchers and practitioners who want to adopt VR technology in AEC education effectively.

Small commercial buildings, or those comprising less than 50,000 square feet of floor area, represent 94% of U.S commercial buildings by count and consume approximately 8% of the nation’s primary energy; as such, they represent a largely unexploited opportunity for energy savings. Small commercial buildings also represent a large economic market—the National Institute of Building Sciences (NIBS) estimated the small commercial retrofit market at USD 35.6 billion. Despite the prominence of small commercial buildings and the economic opportunity for energy retrofits, many energy efficiency programs focus on large commercial buildings, and create efficiency solutions that do not meet the needs of the small commercial market. This paper presents an analysis of 34 small commercial case study projects that implemented energy efficiency retrofits. This paper contributes to the existing building retrofit body of knowledge in the following ways: (1) it identifies the decision criteria used by small commercial building stakeholders that decided to complete an energy retrofit; (2) it identifies the most commonly implemented efficiency measures in small commercial buildings, and discusses why this is the case; and (3) it provides empirical evidence about the efficacy of installing single energy efficiency measures (EEMs) compared to packages of EEMs in small commercial buildings by reporting verified energy savings. To the authors’ knowledge, this paper is the first to catalog decision criteria and energy savings for the existing small commercial buildings market, and this research illustrates that small commercial building decision-makers seem most motivated to retrofit their spaces by energy cost savings and operational concerns. Furthermore, small commercial building decision-makers opted to implement single-system retrofits in fifteen (15) of the thirty-four cases studied. Finally, this research documents the improved savings, in the small commercial buildings market, associated with a more integrated package of EEMs compared to a single-system approach, achieving approximately 10% energy savings for a single-system approach and more than 20% energy savings for integrated approaches. These savings translate to CO₂ savings of 1,324,000 kgCO₂/year to 2,647,000 kgCO₂/year, respectively, assuming small commercial buildings are retrofit at a rate of 0.95% of the stock annually.

As global energy consumption, climate change, and environmental degradation continue to escalate, the need for sustainable solutions has become more critical than ever. Passive cooling is emerging as a promising approach to improve energy efficiency in the built environment. This research investigates the potential of passive cooling in new buildings, focusing on its ability to lower indoor temperatures and utilize eco-friendly materials. By reviewing the existing literature, case studies, and technological innovations, this study explores the feasibility and effectiveness of passive cooling strategies, providing a basis for future research and practical implementation. Using quantitative methods, the research analyzes temperature variations under different ventilation scenarios to assess the impact of passive cooling. Additionally, it qualitatively examines the thermal properties of various interior finishing materials, including cement, wood, and loess bricks. Software simulations are employed to assess the temperature reduction effects of eco-friendly materials such as wood and clay bricks. The findings indicate that passive cooling effectively reduces indoor temperatures, irrespective of the interior materials used. While materials like wood, known for its thermal efficiency, or cost-effective red clay blocks may influence overall building performance, the core principles of passive cooling remain universally applicable across different material choices.

The estimation of energy demand (by power plants) has traditionally relied on historical energy use data for the region(s) that a plant produces for. Regression analysis, artificial neural network and Bayesian theory are the most common approaches for analysing these data. Such data and techniques do not generate reliable results. Consequently, excess energy has to be generated to prevent blackout; causes for energy surge are not easily determined; and potential energy use reduction from energy efficiency solutions is usually not translated into actual energy use reduction. The paper highlights the weaknesses of traditional techniques, and lays out a framework to improve the prediction of energy demand by combining energy use models of equipment, physical systems and buildings, with the proposed data mining algorithms for reverse engineering. The research team first analyses data samples from large complex energy data, and then, presents a set of computationally efficient data mining algorithms for reverse engineering. In order to develop a structural system model for reverse engineering, two focus groups are developed that has direct relation with cause and effect variables. The research findings of this paper includes testing out different sets of reverse engineering algorithms, understand their output patterns and modify algorithms to elevate accuracy of the outputs

Sustainable practices have become the cornerstone of the transportation sector and widely adopted by many states' transportation agencies. Sustainable practices have now become the edifice of the transportation sectors, but the adoption of such practices is not fast enough to overcome the ever-increasing global demand for resources. There are numerous sustainable policies and rating systems, and most of them follow similar approaches and formats that outline the sustainability factors. This paper proposes a quantitative approach to measure and evaluate sustainable performance of the transportation sector. The paper makes an extensive analysis on carbon emissions when treated by total population and gross domestic product (GDP). The evaluation focuses on the transparency of sustainability through measurement and quantification of sustainability rather than to develop standard compliance approaches. These measurement and quantification approaches would allow transportation agencies to measure and thus reduce their carbon footprints.