Technology & Innovation Archives - Page 3 of 19

Online toolkits for collaborative and inclusive global research in urban evolutionary ecology

Savage, A. M., Willmott, M. J., Moreno‐García, P., Jagiello, Z., Li, D., Malesis, A., Miles, L. S., Román‐Palacios, C., Salazar‐Valenzuela, D., Verrelli, B. C., Winchell, K. M., Alberti, M., Bonilla‐Bedoya, S., Carlen, E., Falvey, C., Johnson, L., Martin, E., Kuzyo, H., Marzluff, J., … Gotanda, K. M. (2024). Online toolkits for collaborative and inclusive global research in urban evolutionary ecology. Ecology and Evolution, 14(6), e11633-n/a. https://doi.org/10.1002/ece3.11633

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Abstract

Urban evolutionary ecology is inherently interdisciplinary. Moreover, it is a field with global significance. However, bringing researchers and resources together across fields and countries is challenging. Therefore, an online collaborative research hub, where common methods and best practices are shared among scientists from diverse geographic, ethnic, and career backgrounds would make research focused on urban evolutionary ecology more inclusive. Here, we describe a freely available online research hub for toolkits that facilitate global research in urban evolutionary ecology. We provide rationales and descriptions of toolkits for: (1) decolonizing urban evolutionary ecology; (2) identifying and fostering international collaborative partnerships; (3) common methods and freely-available datasets for trait mapping across cities; (4) common methods and freely-available datasets for cross-city evolutionary ecology experiments; and (5) best practices and freely available resources for public outreach and communication of research findings in urban evolutionary ecology. We outline how the toolkits can be accessed, archived, and modified over time in order to sustain long-term global research that will advance our understanding of urban evolutionary ecology.

From Tweets to Energy Trends (TwEn): An exploratory framework for machine learning-based forecasting of urban-scale energy behavior leveraging social media data

Abbasabadi, N., & Ashayeri, M. (2024). From Tweets to Energy Trends (TwEn): An exploratory framework for machine learning-based forecasting of urban-scale energy behavior leveraging social media data. Energy and Buildings, 317, 114440-. https://doi.org/10.1016/j.enbuild.2024.114440

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Abstract

TwEn framework links tweet frequency with urban energy use patterns. AI models forecast energy from social media data sourced from X. Framework predicts NYC electricity use with high accuracy. Tweet frequency’s seasonal stability enhances energy research. Real-time social data can aid sustainable urban energy policy. Understanding energy behavior is crucial in addressing climate change, yet the accuracy of energy predictions is often limited by reliance on oversimplified occupancy data. This study develops an exploratory framework, from Tweets to Energy Trends (TwEn), leveraging machine learning and geo-tagged social media data to investigate the social dynamics of urban energy behavior. TwEn explores the relationship between social media interactions, specifically the frequency of tweets using data from the X Platform, and energy use patterns on an hourly basis. Employing various machine learning models, including artificial neural networks (ANN), decision tree (DTREE), random forest (RDF), and gradient boosting machine (GBM), the study evaluates their efficiency in both static and time-series forecasting of energy use trends and investigates the capability of social media data in predicting urban energy patterns. In addition, the study carries out a series of sensitivity analyses to provide an examination of the data and models. Furthermore, comprehensive data acquisition methods are developed and implemented. Tested on New York City using actual hourly electricity consumption data, the framework demonstrates significant predictive power of tweet frequency on urban electricity use. The framework also exhibits significant seasonality in X data, identifying patterns and trends that can inform time series urban building energy models (UBEM). The results offer new insights into the determinants of urban energy behavior and provide crucial perspectives for augmenting UBEMs, ensuring they are closely aligned with the complex social dynamics of contemporary urban environments. By integrating both digital and physical data, this study sheds light on urban energy behavior, supporting the formulation of effective and sustainable energy policies for urban futures.

Keywords

Occupancy; Social Media; Time Series Forecast; Urban Building Energy Modeling (UBEM); Urban Energy Behavior

2024 Innovation in the Construction Industry

Prof. Dossick’s CM515 Spring 2024 Class. (2024). 2024 Innovation in the Construction Industry (Sturts Dossick, C., & Ray, L., Eds.). UW Libraries Pressbooks.

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Abstract

This book contains a series of case studies authored by graduate students in CM515 Virtual Construction Management Spring 2024. We explored how people, teams, and companies change practices with a variety of new technologies in the workplace. You will find cases of people who are innovators, teams who took on innovation, and specific design and construction projects that realized these innovation practice changes.

Keywords

Technology; Engineering; Agriculture; Industrial processes

Awareness, adoption readiness and challenges of railway 4.0 technologies in a developing economy

Awodele, I. A., Mewomo, M. C., Municio, A. M. G., Chan, A. P. C., Darko, A., Taiwo, R., Olatunde, N. A., Eze, E. C., & Awodele, O. A. (2024). Awareness, adoption readiness and challenges of railway 4.0 technologies in a developing economy. Heliyon, 10(4), e25934–e25934. https://doi.org/10.1016/j.heliyon.2024.e25934

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Abstract

The railway industry has witnessed increasing adoption of digital technologies, known as Railway 4.0, that is revolutionizing operations, infrastructure, and transportation systems. However, developing countries face challenges in keeping pace with these technological advancements. With limited research on Railway 4.0 adoption in developing countries, this study was motivated to investigate the awareness, readiness, and challenges faced by railway professionals towards implementing Railway 4.0 technologies. The aim was to assess the level of awareness and preparedness and identify the key challenges influencing Railway 4.0 adoption in Nigeria's railway construction industry. A questionnaire survey (was distributed to professionals in the railway construction sector to gather their perspectives on awareness of, preparation for, and challenges associated with the use of Railway 4.0 technologies. The results revealed that awareness of Railway 4.0 technologies was moderate, while readiness was low among the professionals. Using exploratory factor analysis, 10 underlying challenge constructs were identified including lack of technical know-how, resistance to change, infrastructure limitations, and uncertainty about benefits, amongst others. Partial Least Square Structural Equation Modelling (PLS-SEM) confirmed these constructs, with reliability and availability, lack of technical know-how, lack of training and resources, and uncertainties in benefit and gains having significant influence on awareness and readiness. The study concludes that focused efforts in training, infrastructure improvement, supportive policies, and communicating the advantages of Railway 4.0 are critical to drive adoption in Nigeria and other developing economies. The findings provide insights into tailoring Railway 4.0 implementation strategies for developing contexts.

Keywords

Railway 4.0; Awareness; Readiness; Challenges; Technologies

The impact of penalties, incentives, and monitoring costs on the stakeholders’ decision-making behaviors in non-compliance drone operations

Wang, X., Yang, Y., Darko, A., Chan, A. P. C., & Chi, H.-L. (2024). The impact of penalties, incentives, and monitoring costs on the stakeholders’ decision-making behaviors in non-compliance drone operations. Technology in Society, 77. https://doi.org/10.1016/j.techsoc.2024.102589

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Abstract

As an automated assistive tool, drones have revolutionized industrial activities and brought numerous potential benefits to society. However, irresponsible drone users often disregard compliance with regulations, leading to new challenges in drone usage. Although governments have implemented punishment and incentive mechanisms to prevent non-compliant drone operations, the extent to which they can effectively deter such activities remains unclear. To address this gap, the study employed evolutionary game theory to assess the impacts of penalties for non-compliance, incentives for public monitoring, and monitoring costs for the government on the multiple stakeholders' decision-making behaviors (SDBs). The study also used the Chinese construction market data to simulate how penalties, incentives, and monitoring costs influence SDBs. The numerical simulations reveal that penalties and incentives could reduce drone users' non-compliant operations, but this effect is useful only if the penalties and incentives exceed a certain value. In China, drone users' non-compliant operations can be controlled when penalties for drone users exceed 12,000 yuan, and incentives for the public's monitoring exceed 170 yuan/day. The current Chinese government's penalties that were administered for non-compliant drone operations have not achieved a deterrent effect, but the incentive is feasible. These findings provide a fresh insight into the decision-making behaviors of stakeholders in non-compliant drone operations. Additionally, the tripartite evolutionary game model developed in this study can assist other countries in determining optimal values for penalties, incentives, and monitoring costs to mitigate non-compliant drone operations effectively.

Life Cycle Lab

The Life Cycle Lab at UW’s College of Built Environments leads research to advance life cycle assessment (LCA) data, methods and approaches to enable optimization of materials, buildings and infrastructure. Our work is structured to inform impactful policies and practices that support global decarbonization efforts. We envision a transformed, decarbonized building industry – better buildings for a better planet.

Our group is led by Professor Kate Simonen. Since arriving at UW in 2009, she has conducted research and spearheaded initiatives focused on accelerating the transformation of the building sector to radically reduce the greenhouse gas emissions attributed to materials (also known as embodied carbon) used in buildings and infrastructure. From June 2010 until April 2024 she directed the Carbon Leadership Forum (CLF) as it was hosted in UW’s College of Built Environments. The core of CLF’s work has been to lay essential foundations for understanding embodied carbon: a framework for comprehensive strategy, rigorous analysis, and transparent reporting that can support design tools, effective policy, and collective action.

In April 2024, two new entities were created to expand the program’s influence and impact: the Carbon Leadership Forum launched as an independent nonprofit organization and the newly named Life Cycle Lab was created to support the next generation of researchers and pursue critical embodied carbon research with an increased focus on academic publications. Learn more about this transition via this announcement.

Life Cycle Lab members include professional research staff, research assistants, students advised by Prof. Simonen, undergraduate interns and student assistants. Many of our members are formally affiliated with the Carbon Leadership Forum and the two organizations continue to actively collaborate developing strategies and executing aligned initiatives.

Projects associated with Life Cycle Lab include:

Automating building environmental assessment: A systematic review and future research directions

T.A.D.K. Jayasanka, Amos Darko, D.J. Edwards, Albert P.C. Chan, Farzad Jalaei, Automating building environmental assessment: A systematic review and future research directions, Environmental Impact Assessment Review, Volume 106, 2024, 107465, ISSN 0195-9255, https://doi.org/10.1016/j.eiar.2024.107465.

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Abstract

Building environmental assessment (BEA) is critical to improving sustainability. However, the BEA process is inefficient, costly, and often inaccurate. Because automation has the potential to enhance the efficiency and accuracy of the BEA process, studies have focused on automating BEA (ABEA). Updated until now, a comprehensive analysis of prevailing literature on ABEA remains absent. This study conducts the first comprehensive systematic analysis appraising the state-of-the-art of research on ABEA. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guided to systematically analyse 91 relevant studies. Results uncover that only 29.7% of BEA systems worldwide have automated their processes, with the US LEED residing at the vanguard of automation efforts. The New Buildings scheme was mostly focused on, while largely ignoring other schemes, e.g., Existing Buildings. Five key digital approaches to ABEA were revealed, namely building information modelling (BIM) and plug-in software, BIM-ontology, data mining and machine learning, cloud-BIM, and digital twin-based approaches. Based on identified gaps, future research directions are proposed, specifically: using data mining and machine learning models for ABEA; development of a holistic cloud-based approach for real-time BEA; and digital twin for dynamic BEA. This study generates a deeper understanding of ABEA and its theoretical implications, such as major constructs and emerging perspectives, constitute a basis for holistic, and innovation in, BEA.

Artificial Intelligence in Performance-Driven Design: Theories, Methods, and Tools

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Abstract

Artificial Intelligence in Performance-Driven Design: Theories, Methods, and Tools explores the application of artificial intelligence (AI), specifically machine learning (ML), for performance modeling within the built environment. This work establishes the theoretical foundations and methodological frameworks for utilizing AI/ML, with an emphasis on multi-scale modeling encompassing energy flows, environmental quality, and human systems.

The book examines relevant practices, case studies, and computational tools that harness AI's capabilities in modeling frameworks, enhancing the efficiency, accuracy, and integration of physics-based simulation, optimization, and automation processes. Furthermore, it highlights the integration of intelligent systems and digital twins throughout the lifecycle of the built environment, to enhance our understanding and management of these complex environments.

This book also:
• Incorporates emerging technologies into practical ideas to improve performance analysis and sustainable design
• Presents data-driven methodologies and technologies that seamlessly integrate into modeling and design platforms
• Shares valuable insights for developing decarbonization pathways in urban buildings
• Includes contributions from expert researchers and educators across a range of related fields

Artificial Intelligence in Performance-Driven Design is ideal for architects, engineers, planners, and researchers involved in sustainable design and the built environment. It’s also of interest to students of architecture, building science and technology, urban design and planning, environmental engineering, and computer science and engineering.

Causal effects of place, people, and process on rooftop solar adoption through Bayesian inference

Min, Y., & Ko, I. (2023). Causal effects of place, people, and process on rooftop solar adoption through Bayesian inference. Energy (Oxford), 285, 129510-. https://doi.org/10.1016/j.energy.2023.129510.

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Abstract

While previous studies have established correlations between rooftop solar adoption and various factors, a comprehensive understanding of the underlying causal mechanisms has been limited by the intricate interrelationships among these variables. To address this gap, we propose a Bayesian causal inference approach that examines the interplay of various factors influencing rooftop solar adoption across multiple cities. By employing post-phenomenology, we uncover latent variables encompassing place, people, and process, shedding light on how they shape public responses to emerging energy technologies. We analyze the causal effects of these factors and highlight the significance of housing and built environment attributes in determining energy expenditure and rooftop solar adoption, emphasizing the need for policies that target energy equity. Additionally, we reveal the influence of neighborhood spillovers on adoption, indicating the role of social norms and information diffusion. The observed city-level variability underscores the importance of local contexts and location-specific factors in the adoption process. Furthermore, we highlight the need to consider causal relationships and the indirect effects of people-related attributes mediated through place-related attributes. Overall, these findings contribute to a deeper understanding of the factors shaping rooftop solar adoption via causal modeling and underscore the importance of tailored policies to promote adoption.

Keywords

Spillover effects; Energy equity; Post-phenomenology; Ignorability; Factor analysis; Clean energy; Photovoltaic systems; Overcoming barriers; Technology adoption; Decision-making; Energy justice; United-States; Vulnerability; Diffusion; Deployment; Responses

Can ChatGPT Evaluate Plans?

Xinyu Fu, Ruoniu Wang & Chaosu Li (2023). Can ChatGPT Evaluate Plans?, Journal of the American Planning Association, DOI: 10.1080/01944363.2023.2271893

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Abstract

Problem, research strategy, and findings
Large language models, such as ChatGPT, have recently risen to prominence in producing human-like conversation and assisting with various tasks, particularly for analyzing high-dimensional textual materials. Because planning researchers and practitioners often need to evaluate planning documents that are long and complex, a first-ever possible question has emerged: Can ChatGPT evaluate plans? In this study we addressed this question by leveraging ChatGPT to evaluate the quality of plans and compare the results with those conducted by human coders. Through the evaluation of 10 climate change plans, we discovered that ChatGPT’s evaluation results coincided reasonably well (with an average of 68%) with those from the traditional content analysis approach. We further scrutinized the differences by conducting a more in-depth analysis of the results from ChatGPT and manual evaluation to uncover what might have contributed to the variance in results. Our findings indicate that ChatGPT struggled to comprehend planning-specific jargon, yet it could reduce human errors by capturing details in complex planning documents. Finally, we provide insights into leveraging this cutting-edge technology in future planning research and practice.
Takeaway for practice
ChatGPT cannot be used to replace humans in plan quality evaluation yet. However, it is an effective tool to complement human coders to minimize human errors by identifying discrepancies and fact-checking machine-generated responses. ChatGPT generally cannot understand planning jargon, so planners wanting to use this tool should use extra caution when planning terminologies are present in their prompts. Creating effective prompts for ChatGPT is an iterative process that requires specific instructions.

Keywords

ChatGPT; large language model; natural language processing; plan evaluation; plan quality