Wu, L., Mohamed, E., Jafari, P., & AbouRizk, S. (2023). Machine Learning–Based Bayesian Framework for Interval Estimate of Unsafe-Event Prediction in Construction. Journal of Construction Engineering and Management, 149(11). https://doi.org/10.1061/JCEMD4.COENG-13549
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Abstract
Construction safety is a critical concern for industry and academia, and numerous models and algorithms have been developed to predict incidents or accidents to facilitate proactive decision-making. However, previous studies have been limited due to the inability to account for uncertainties because predictions are given as a single value (i.e., Yes or No) and the failure to integrate subjective judgment. To address these limitations, this research proposes a machine learning–based Bayesian framework for predicting construction incidents using interval estimates. This framework combines a state-of-the-art machine-learning algorithm with a binary Bayesian inference model to develop an incident predictor that considers a range of project characteristics and conditions. Notably, this framework also is capable of incorporating historical or subjective judgment through prior selection and outputs the unsafe event prediction as an interval of possibilities, thus accounting for various uncertainties. The efficacy of our framework was demonstrated in a real-life case study, showcasing its practical implications for proactive decision-making and risk management in the construction industry and representing a valuable contribution to the field of construction safety.
Wang, Y., Hu, S., Lee, H. W., Tang, W., Shen, W., & Qiang, M. (2023). To Achieve Goal Alignment by Inter-Organizational Incentives: A Case Study of a Hydropower Project. Buildings (Basel), 13(9), 2258–. https://doi.org/10.3390/buildings13092258
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Abstract
Although the use of incentives has been widely recognized as an effective project management tool, its application still needs specific exploration. Existing research on incentives mainly focuses on intra-organizational incentives, lacking systematic research with empirical evidence from the perspective of the inter-organizational level. To fill this research gap, this study conducted an in-depth investigation into the application and impacts of inter-organizational incentives by studying a typical case of a hydropower project. In this case, a series of innovative inter-organizational incentives, involving a multiple contractual incentive scheme concerning schedule, quality, safety, as well as environmental performance, is applied. Using a mixed methodology that included a document review, a questionnaire survey, and interviews, this case study revealed that inter-organizational incentives could effectively help promote goal alignment, stimulate cooperative inter-organizational relationships, and improve project performance. This research developed a novel classification of inter-organizational incentives and emphasized the importance of non-contractual and informal incentives, which were ignored in previous research. The results further highlight that while incentivized organizations generally value incentives according to their monetary intensity, their prioritization of goals is determined by various factors. Therefore, to achieve project goal alignment, the optimization of incentive schemes should comprehensively consider a variety of influencing factors rather than merely focusing on monetary intensity. These findings will help both academic researchers and industrial practitioners design and execute effective inter-organizational incentives for superior project performance, especially for those projects that pursue high sustainable performance with safety and environmental performance included.
Keywords
inter-organizational incentive; inter-organizational relationship; multiple incentive; motivation; goal alignment; relational contracting; contractual incentive; environment incentive; environment performance; project performance
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
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…
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…
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.
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…
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.
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
Affiliate Instructor, Construction Management