Brook Waldman is a research engineer at the Carbon Leadership Forum, where he investigates the life cycle of building materials — their manufacture, use, and end-of-life — and the environmental impacts that accompany those processes. He also studies and aims to improve the methodologies and data behind the measurement and communication of those environmental impacts. At the CLF, he has been particularly involved in supporting the EC3 tool and developing the CLF Material Baselines.
Research Theme: Technology & Innovation
Includes both scholarly methodologies utilizing technology and innovation, as well as products or results related to the topics
Yang Shen
Yang Shen is a research engineer for the Carbon Leadership Forum at the University of Washington. Before joining CLF, he was a Postdoctoral Research Fellow in George Mason University focusing on multidisciplinary research such as Computer Vision/Deep Learning applications in the Built Environment. Yang got his PhD in Civil Engineering (Structural Engineering) from Texas A&M University. His Ph.D. research was tightly associated with building science, embodied carbon quantification/optimization, building operational energy simulation, parametric modeling, structural analysis, data analytics, and machine learning. He is passionate about using interdisciplinary studies to achieve climate change adaptation and mitigation.
Applicability of Smart Construction Technology: Prioritization and Future Research Directions
Ahn, H., Lee, C., Kim, M., Kim, T., Lee, D., Kwon, W., & Cho, H. (2023). Applicability of smart construction technology: Prioritization and future research directions. Automation in Construction., 153. https://doi.org/10.1016%2Fj.autcon.2023.104953
Abstract
The potential for facilitating faster, safer, and more sustainable construction processes through the adoption of smart construction technologies is widely recognized. However, the limited adoption of these technologies in construction projects highlights the significance of identifying the technological needs of major stakeholders and the prioritization of research and development investment. In this study, the quality function deployment technique is employed to extract and prioritize the required technologies (RTs) from various stakeholders, while a thematic literature review is conducted to identify challenges and future research directions. The findings improve the efficiency of resource allocation, allowing policymakers to strategically address pressing issues. This can facilitate collaboration and communication among researchers, stakeholders, and the wider community, fostering a shared vision and understanding of future research goals and outcome. Prioritizing smart construction technologies can enhance their applicability. The top nine of technologies were prioritized by using quality function deployment. Thematic review was conducted for each of the top nine technologies. The challenges and future research directions were presented by review.
Keywords
Fourth industrial revolution (4IR); Prioritization; Quality function deployment (QFD); Smart construction technologies; Technology innovation
Detecting Subpixel Human Settlements in Mountains Using Deep Learning: A Case of the Hindu Kush Himalaya 1990–2020
Chen, T.-H. K., Pandey, B., & Seto, K. C. (2023). Detecting subpixel human settlements in mountains using deep learning: A case of the Hindu Kush Himalaya 1990–2020. Remote Sensing of Environment, 294, 113625–. https://doi.org/10.1016/j.rse.2023.113625
Abstract
The majority of future population growth in mountains will occur in small- and medium-sized cities and towns and affect vulnerable ecosystems. However, mountain settlements are often omitted from global land cover analyses due to the low spatial resolution of satellite images, which cannot resolve the small scale of mountains settlements. This study demonstrates, for the first time, the potential of deep learning to detect human settlements in mountains at the sub-pixel level, based on Landsat satellite imagery. We hypothesized that adding spatial and temporal features could improve the detection of mountain settlements since spectral information alone led to inaccurate results. For spatial features, we compared a U-shaped neural network (U-Net), a deep learning algorithm that automatically learns spatial features, with a simple random forest (RF) algorithm. Then, we assessed whether temporal features would increase accuracy by comparing two input datasets, multispectral imagery and temporal features from the Continuous Change Detection and Classification (CCDC) algorithm. We evaluated each method by calculating the accuracies of (1) the binary settlement footprint, (2) the subpixel estimates of impervious surfaces, and (3) urban growth. We tested the accuracies using visually interpreted datasets from time-series Google Earth images across the Hindu Kush Himalaya that were not used for training to evaluate model transferability. The U-Net successfully improved mountain settlement mapping compared to the random forest, with a substantial discrepancy in small settlements. The time-series results from the U-Net successfully captured long-term urban growth but fewer short-term changes. Contrary to expectations, the CCDC temporal features reduced the accuracy of mountain settlement mapping due to frequent cloud cover in hilly areas. Our subpixel analysis reveals that the built-up area of the Hindu Kush Himalaya has expanded at a rate of 61 km2 per year from 1990 to 2020, which is about twice the estimate of the Global Human Settlement Layer using binary urban/non-urban classifications.
Keywords
Urban land cover; Land cover fraction; Peri-urban; Built-up area; Subpixel mapping; Machine learning; Time-series; Himalaya; CCDC
Urban Infrastructure Lab Report on High-Speed Rail
The Urban Infrastructure Lab researchers have released a report on a Cascadia region high-speed rail project. College of Built Environments faculty Jan Whittington and Qing Shen were authors on the report, along with 3 Interdisciplinary Ph.D. in Urban Design and Planning students (Siman Ning, Haoyu Yue, and Chin-Wei Chen), and a Master of Urban Planning candidate (Richard McMichael). This report examines the successes and lessons learned from existing high-speed rail projects in Europe and Asia, including 50 hours of interviews…
Campus Sustainability Fund selects College of Built Environments researchers for 2022-2023 work
The Campus Sustainability Fund selected College of Built Environments PhD student Daniel Dimitrov, along with Associate Dean for Research Carrie Sturts Dossick, to receive funding for the project described below. Energy, Information, and the New Work of Building Operations in the Digital Age Amount Awarded: $19,833 Funding Received: 2022-2023 Project Summary: The built environment industry is in the midst of a data revolution paired with a drive for sustainable campus operations. Innovation, information, communication access, and integration provide an opportunity…
Don’t take concrete for granite: the secret research life of CBE Department of Construction Management Assistant Professor and concrete materials researcher Fred Aguayo
Concrete can sequester carbon, and the cement that glues its components together has been used since antiquity. Now, CBE professor Fred Aguayo is introducing students to the complex world of concrete research.
Selection of Wearable Sensor Measurements for Monitoring and Managing Entry-Level Construction Worker Fatigue: a Logistic Regression Approach
Lee, Wonil; Lin, Ken-Yu; Johnson, Peter W.; Seto, Edmund Y.W. (2022). Selection of Wearable Sensor Measurements for Monitoring and Managing Entry-Level Construction Worker Fatigue: a Logistic Regression Approach. Engineering, Construction, and Architectural Management, 29(8), 2905–23.
Abstract
The identification of fatigue status and early intervention to mitigate fatigue can reduce the risk of workplace injuries. Off-the-shelf wearable sensors capable of assessing multiple parameters are available. However, using numerous variables in the fatigue prediction model can elicit data issues. This study aimed at identifying the most relevant variables for measuring occupational fatigue among entry-level construction workers by using common wearable sensor technologies, such as electrocardiogram and actigraphy sensors.
Keywords
Technology, management, construction safety, information and communication technology (ICT) applications
The Impact of Empowering Front-Line Managers on Planning Reliability and Project Schedule Performance
Kim, Yong-Woo, and Rhee, Byong-Duk. (2020). The Impact of Empowering Front-Line Managers on Planning Reliability and Project Schedule Performance. Journal of Management in Engineering, 36(3).
Abstract
This study applies empowerment theory to production planning at the level of frontline managers in a construction project. Using structural equation modeling, we investigate how empowering frontline managers impacts their planning performance. In contrast to prior studies, we find that although psychological empowerment of frontline managers has no direct effect on their production planning reliability or scheduling performance, it has an indirect effect on planning reliability and scheduling performance, as long as the organization supports the empowerment structurally during production planning. This implies that a project manager should provide frontline managers at the operational level with proper formal and informal authority over workflow development, shielding, and resource allocation when planning production in order to enhance job performance through psychological empowerment. This study contributes to the body of knowledge on construction management by exploring the impact of psychological and structural empowerment of frontline managers on their performance of production planning reliability and scheduling performance.
Keywords
Organizations, Managers, Structural models, Scheduling, Structural reliability, Construction management, Human and behavioral factors, Resource allocation
$2 Million Award from National Science Foundation Will Support Team to Develop 3D-printed Microorganisms for Sustainable Construction Materials
An interdisciplinary research team led by University of Washington Chemistry Professor Alshakim Nelson received $2 million in funding from the National Science Foundation’s Emerging Frontiers in Research and Innovation (EFRI) program. The funding will be used to combine engineered microorganisms with 3D printing to create materials for sustainable built environments. This grant will provide funding to researchers at UW, the University of Texas at Austin, and University of California Davis over four years. In addition to Nelson, the team also…