Hu, Y., & Dossick, C. S. (2023). Decoding the dynamics of BIM use practice in construction projects. Construction Management and Economics, 1–25. https://doi.org/10.1080/01446193.2023.2277925
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Abstract
Over the past two decades, thought leaders positioned Building Information Modeling (BIM) as a driver to change the Architecture, Engineering, and Construction (AEC) industry. However, instances of unexpected BIM use have surfaced, with projects often shifting from BIM to hybrid or even solely 2D practices midway. What technology use conditions cause these practice-based rejections of BIM use and how these happen have not been fully explored and make BIM cannot fully play its role in a project. To fill this gap, we use structuration theory as a theoretical lens to analyze the interactions between BIM and project teams and explore how three technology use conditions, (interpretive, technological, and institutional), impact the interactions, which finally shape technology use practices. Specifically, a case study method has been selected. The research team attended a project for two years, collected meeting observations, and conducted surveys and interviews to track the emergent and situated BIM use practice in an integrated project setting with technology use conditions that changed over the course of the project. We analyzed how the three technology use conditions impacted the interactions between BIM and project teams in different ways and how these impacted change in different project phases. We conclude that the sustained use of BIM requires the alignment of project organizations with BIM features and alignment with both top-down and bottom-up investment in practice change, which includes motivation for senior management investment in a sustained project team, in individual capability training, and in early planning.
Keywords
Building information modeling; structuration theory; technology-in-practice; practice lens
Cai, M., Acolin, A., Moudon, A. V., & Shen, Q. (2023). Developing a multi-criteria prioritization tool to catalyze TOD on publicly owned land areas. Cities, 143, 104606-. https://doi.org/10.1016/j.cities.2023.104606
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Abstract
Public agencies can take a leading role in catalyzing TOD by making land available to developers (selling or leasing land, potentially below market prices). In particular, park-and-ride areas that are publicly owned can be leveraged to support TOD uses, such as affordable housing, office space, small businesses, and mixed-use buildings given their convenient access to transit systems and often large land areas. However, few previous studies have discussed the use of publicly owned park-and-rides, which are an important component of publicly owned land, as a catalyst for TOD. To fill the gap in the literature and effectively support TOD planning, this research developed a multi-criteria prioritization tool to identify the most promising locations for TOD and tested it at three park-and-ride sites owned by the Washington State Department of Transportation. The tool was developed through the Delphi process, which is an effective and inexpensive approach to evaluate relevant indicators by synthesizing the opinions of experts from various backgrounds. Five categories with a total of 14 TOD indicators, including transit supportive land-use zoning, job accessibility, land price, land-use mix, and household income, were selected as measures of TOD suitability. The importance of these indicators varied with three different TOD scenarios: (1) emphasis on affordable housing, (2) emphasis on market-rate housing, and (3) emphasis on mixed-use development. Using the calculated suitability scores, this tool can prioritize potential TOD sites for further review.
Keywords
TOD; Delphi method; Multi-criteria planning tool; Multi-sources geospatial data; Publicly owned land
Population Health Initiative awarded a Climate Change Pilot Grant to two teams that includes CBE researchers. Projects will begin January 2024, and were awarded $50,000. Read the full story here. Project title: “Sustainable metamaterials for insulation applications.” Project team: Eleftheria Roumeli, Materials Science & Engineering Tomás Méndez Echenagucia, Architecture Project abstract: Amidst an urgent global shift towards a circular economy, the demand for sustainable materials has reached a critical juncture. This transformation requires materials sourced from renewable sources, processed via…
Research interests: project delivery methods and impact to project success; project control and construction contracts; privatization in construction and private-public partnerships; project cost management; sustainability.
M.S. Construction Management, University of Washington (2022)
B.S. Civil Engineering, Dedan Kimathi University of Technology, Kenya (2017)
Owner of RDF Consulting Services and consultant for Turner Construction, Renzo di Furia, is working with Associate Dean for Research Carrie Sturts Dossick in supporting student-industry collaboration. “Applied Research Consortium brings together an interdisciplinary group of built environment firms with faculty experts and graduate student researchers at the University of Washington’s College of Built Environments (CBE) to address the most vexing challenges that firms face today.” A case study in applied research is highlighted in the article. 3D modeling was…
Kim, M., Zhao, X., Kim, Y.-W., & Rhee, B.-D. (2023). Blockchain-enabled supply chain coordination for off-site construction using Bayesian theory for plan reliability. Automation in Construction, 155, 105061–. https://doi.org/10.1016/j.autcon.2023.105061
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Abstract
The potential of blockchain is being widely explored within the construction industry, particularly for transparent communication and information sharing. However, only limited research has focused on implementing blockchain to address the challenge of aligning conflicting interests among independent agents, specifically, supply chain coordination. This paper develops a blockchain-enabled supply chain coordination system that facilitates the alignment of diverse decisions made by stakeholders in an off-site construction supply chain. To achieve this goal, Bayesian updating is employed to estimate the probabilistic distribution of plan reliability, enabling the calculation of a supplier rebate that incentivizes the contractor to schedule deliveries aimed at minimizing joint supply chain costs. Additionally, the paper describes a blockchain-enabled system that allows practitioners to measure plan reliability. The research findings demonstrate that the blockchain-enabled supply chain coordination system fosters shared common knowledge among project stakeholders and facilitates real-time updates of changes in the contractor's plan reliability, aligning the interests of both the supplier and contractor.
Keywords
Supply chain coordination; Bayesian updating; Plan reliability; Rebate pricing; Blockchain; Smart contracts; Off-site construction
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.
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.
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
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
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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