Preliminary Analysis Framework for State Sustainable Transportation system

Sustainable practices have become the cornerstone of the transportation sector, and widely adopted by many states' transportation agencies. The nerve center of the economic development today circles around resource utilization and energy use. The transportation sector is the bloodline of the U.S economy and sustainability of this sector affects the growth of the economy. Even though sustainable practices have now become the edifice of transportation sectors, the adoption of such practices cannot be quick enough to overcome the ever-increasing demand of resources from the global population. Benchmarking sustainability is the most appropriate method to determine the sustainability of transportation practices. There are numerous rating and benchmarking systems, and most of them follow similar approach and format that outline the sustainability factors (namely, energy, water, land use, air quality, pollution etc.). Such approaches and formats can be found on many sustainable standards and tools such as the Leadership for Energy and Environmental Design (LEED). The purpose of this research is to develop a framework that includes an alternative approach to benchmark the sustainable performances of state transportation systems. The framework focuses on measuring the actual sustainability rather than to develop standard compliance approach similar to LEED rating system. It also focuses on utilizing modified/adjusted quantitative data to determine the sustainability of transportation practices. Such an approach would allow transportation agencies and states to compare and compete with one another

Wildfire Predictions: Determining Reliable Models using Fused Dataset

Wildfires are a major environmental hazard that causes fatalities greater than structural fire and other disasters Computerized models have increased the possibilities of predictions that enhanced the firefighting capabilities in U S While predictive models are faster and accurate it is still important to identify the right model for the data type analyzed The paper aims at understanding the reliability of three predictive methods using fused dataset Performances of these methods Support Vector Machine K-Nearest Neighbors and decision tree models are evaluated using binary and multiclass classifications that predict wildfire occurrence and its severity Data extracted from meteorological database and U S fire database are utilized to understand the accuracy of these models that enhances the discussion on using right model for dataset based on their size The findings of the paper include SVM as the best optimum models for binary and multiclass classifications on the selected fused datase

Exploring the economic viability of virtual reality in architectural, engineering, and construction education

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

WELL building for developing countries: Critical design criteria for residential buildings in Malaysia

Purpose: This study aims to investigate the interrelationship between critical design criteria (CDC) that affect health, well-being and productivity (i.e. WELL) for residential buildings in developing countries, using Malaysia as a case study. To achieve the aim, the objectives are to identify CDC that affect WELL collectively; determine CDC that affect health, well-being, and productivity simultaneously; and analyze the interrelationship between the CDC. Design/methodology/approach: Data from the semi-structured interviews and a systematic review of the existing literature were gathered for survey development. Next, survey data was collected from 114 professionals living in multistory buildings. Finally, normalized mean analysis, analytic hierarchy process (AHP), agreement analysis and Spearman correlation analysis were used to analyze the collected data. Findings: Out of the 51 potential design criteria, 16 are critically affecting WELL collectively. Furthermore, six are critically affecting WELL collectively as well as health, well-being and productivity simultaneously: property price, water flow and supply, water treatment, pest management, management services and waste management. Finally, “water treatment” is highly correlated to “water management” and “water flow and supply.” In addition, “waste management” and “management services,” as well as “fire safety” and “emergency evacuation plans,” are highly correlated. Originality/value: This study's originality includes investigating the CDC of residential buildings for the first time, to the best of the authors’ knowledge, in a developing country. As a result, this study uncovers holistic design criteria for policymakers to establish holistic building assessment tools for residential buildings

Energy Savings Results from Small Commercial Building Retrofits in the US

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.</jats:p

Learning Energy Consumption and Demand Models through Data Mining for Reverse Engineering

AbstractThe 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

Building energy modeling (BEM) using clustering algorithms and semi-supervised machine learning approaches

Energy efficiency is a critical element of building energy conservation. Energy Information Administration (EIA) and International Electrotechnical Commission (IEC) estimated that over 6% of electrical energy was lost during transmission and distribution. Sensing and tracking technologies, and data-mining offer new windows to better understanding these losses in real-time. Recent developments in energy optimization computational methods also allow engineers to better characterize energy consumption load profiles. The paper focuses on developing new and robust data-mining techniques to explore large and complex data generated by sensing and tracking technologies. These techniques would potentially offer new avenues to understand and prevent energy losses during transmission. The paper presents two new concepts: First, a set of clustering algorithms that model the supply-demand characterization of four different substations clusters, and second, a semi-supervised machine learning and clustering technique are developed to optimize the losses and automate the process of identifying loss factors contributing to the total loss. This three-step process uses real-time data from buildings and the substations that supply electricity to the buildings to develop the proposed technique. The preliminary findings of this paper help the utility service providers to understand the energy supply-demand requirements

Learning Energy Consumption and Demand Models through Data Mining for Reverse Engineering

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

Optimizing Comfort and Sustainability: The Impact of Passive Cooling and Eco-Friendly Materials on Indoor Temperature Reduction&mdash;A Case Study

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