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New Tool Created by CBE Researchers in partnership with Charles Pankow Foundation

CBE researchers worked with the Charles Pankow Foundation to develop a new Building Owner Assessment Tool (BOAT). Team members included CBE Dean Renée Cheng, FAIA; Associate Dean for Research Carrie Sturts Dossick, Ph.D, P.E.; and Laura Osburn, Ph.D. Other team members included Lingzi Wu, Ph.D. Daniel Dimitrov, and Xianxiang Sean Zhao. The tool was developed in partnership with the American Institute of Architects and the Integrated Project Delivery Alliance. The Building Owner Assessment Tool (BOAT): Helping You Understand Your Culture and Its…

February 2022 Inspire Fund Awardees: Progress and Products

Five projects were awarded Inspire Fund awards in February 2022. They have completed various stages of work and have provided a report on their progress and products. Below, excerpts from these reports are highlighted to showcase the work that has been “Inspired” in 2022-23. Rick Mohler: “One Seattle: Leveraging Seattle’s Comprehensive Plan Update to advance housing diversity, affordability, livability and racial equity” This funding supported products from the Architecture 594 research seminar and Architecture 508 design studio, which tasked students…

Automated two-dimensional geometric model reconstruction from point cloud data for construction quality inspection and maintenance

Kim, Minju & Lee, Dongmin. (2023). Automated two-dimensional geometric model reconstruction from point cloud data for construction quality inspection and maintenance. Automation in Construction, 154. https://doi.org/10.1016/j.autcon.2023.105024.

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Abstract

Despite the availability of 3D digital models, 2D floor plans remain extensively used for quality inspection and maintenance as they offer firsthand information. While laser scanners enable efficient capture and reconstruction of real-world scenes, challenges arise in accurately extracting building geometry from laser scanning data due to the loss of geometric features. This paper describes a method for accurately reconstructing 2D geometric drawings of built facilities using laser scanning data. These techniques involve transforming the dimension of 3D data into 2D and displaying the registered data as pixels to extract solid lines that represent wall structures. By employing dimensionality transformation and pixelation techniques, the method supports reliable quality inspection and maintenance processes, overcoming the challenges of extracting precise geometry from laser scanning data. This paper contributes to the automated extraction of geometric features from point clouds and inspires the future development of fully automated 2D CAD and 3D BIM in alignment with Scan-to-BIM.

Building a more just and beautiful future: CBE’s new faculty cohort makes strides on campus

The new cohort of faculty have made a big impact in their initial time on campus. Please see the full story here. The cohort includes: Dr. Narjes Abbasabadi, an assistant professor in the Department of Architecture and affiliate data science faculty UW eScience Institute, studies computation and decarbonization of the built environment. Dr. Amos Darko, an assistant professor in Construction Management, studies how digital technologies can help people better monitor, assess, understand, and improve the sustainability performance of the built…

Amos Darko

Dr. Darko brings with him a wealth of expertise and experience in sustainability, sustainable built environment, sustainable construction, green building, modular construction, project management, and digital technologies including building information modeling and artificial intelligence.

Dr. Darko earned his Ph.D. degree from The Hong Kong Polytechnic University (PolyU) in 2019, and his BSc degree (First Class Honors) from Kwame Nkrumah University of Science and Technology (KNUST) in 2014. Before joining the University of Washington, Dr. Darko was a Research Assistant Professor at PolyU.

Dr. Darko has published numerous papers in leading international peer-reviewed journals, conferences, and books. His papers have been rated as highly cited and hot papers by the Web of Science. His paper is the most cited paper of all time in the International Journal of Construction Management. He has also been ranked among the world’s top 2% most cited scientists by Elsevier BV and Stanford University. Dr. Darko has received several awards for his outstanding work, including the Green Talents Award from the German Federal Ministry of Education and Research in 2020, the Global Top Peer Reviewer Award from the Web of Science Group in 2019, the Outstanding Overseas Young Scholars Award from Central South University in 2019, and the Best Construction Technology and Management Student Award from KNUST in 2014.

Dr. Darko’s work has been supported by the Research Grants Council of Hong Kong, Chief Secretary for Administration’s Office of Hong Kong, and several internal grants.

Dr. Darko is an Associate Editor of Green Building and Construction Economics, an Associate Editor of Humanities and Social Sciences Communications, and an Academic Editor of Advances in Civil Engineering.

“I am excited to collaborate with colleagues from diverse disciplines to tackle the pressing challenges of sustainability and climate change, and to contribute to shaping a more just and beautiful world,” said Dr. Darko.

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

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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

Minju Kim

Affiliate Instructor, Construction Management

Population Health Initiative awards multiple College of Built Environments teams planning grants

The Population Health Initiative announced 12 climate change planning grant awardees. Of those 12 teams, 4 include College of Built Environments researchers. Descriptions of their projects are below. Read the CBE News story here.   Linking Climate Adaptation and Public Health Outcomes in Yavatmal, Maharashtra Investigators Sameer H. Shah, Environmental and Forest Sciences Celina Balderas Guzmán, Landscape Architecture Pronoy Rai, Portland State University Project abstract This proposal collects primary interview data with landed and landless agriculturalists in Yavatmal district in…

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…

Statistical Analysis and Representation Models of Working-Days Liquidated Damages

Abdel Aziz, A. M. (2023). Statistical Analysis and Representation Models of Working-Days Liquidated Damages. Journal of Construction Engineering and Management, 149(7). https://doi.org/10.1061/JCEMD4.COENG-13330

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Abstract

Contractors tend to challenge the enforceability of liquidated damages (LDs), claiming they are unreasonable, excessive, penalty statements, or concurrently caused. States customarily assert that the LD rates are a genuine reflection of the expenses expected to be suffered when a project gets delayed due to noncompletion. While there are common practices among the states for articulating LD specifications, which generally follow the Federal Code of Regulations, there are no published studies that assist states in comparing their LD rates to those of other states so that the LD rates might be defended. Further, there are no studies that offer models that would uncover the relationship between the LD rates and the contract sizes so that the LD rates might be justified. This work addresses such gaps in the body of knowledge (BOK) in LDs. With emphasis on the working-days (WD) LD rate schedules, the objectives of this work are to characterize the LD rate schedules across the states and to model a formula(s) that would represent the relationship between the WD LD rates and the contract amounts. The data set for the work represents the LD schedules in the standard specifications of all departments of transportation in the United States. Descriptive and cluster statistical analyses were used for the LD rate characterization. For model development, several linear and nonlinear regression models were employed. The results highlighted considerable LDs variability in the smaller contract sizes and exceptional LD rates stability in the larger sizes. Despite the economic differences among the states, it is found that the LD rate is, on average, 0.02 ¢/$ for projects $20 million or above. Below that, the rate increases between 0.03 ¢/$ and 0.18 ¢/$ until the contracts reach $750,000. LD rates tend to decrease sharply with the increase in contract sizes, forming an L or reverse J shape. This pattern proved complex, and only nonlinear regression with transformed variables successfully modeled it. Credible models were obtained after satisfying the least-squares regression assumptions. The work contributes to the BOK by adding a new statistical dimension to understanding LDs and developing regression model(s) that explain the relationships between the LD rates and the contract sizes. The work should help SHAs create, evaluate, and justify their LD rates.