skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Land coverage prediction using convolutional neural network for enhanced wind loading estimation
The wind loading on a building is likely to deviate further from the known wind loading due to the complexity of the real-world land coverage. To address this issue, research is needed in two separate areas. First, wind tunnel testing needs to be conducted for more complex terrains. Second, research is needed to classify real-world land coverage with high accuracy, specifically for wind engineering applications. This paper deals with this second area of research. The machine learning-based land cover prediction is a promising technique because it can remove subjectivity in human interpretation of upwind terrain. Design/methodology/approachThis paper presents a new deep neural network for land coverage prediction that can distinguish low- and mid-rise buildings in the built environment to enhance the estimation of surface roughness necessary in wind engineering. For the dataset, Landsat 8 satellite images were used. A patch-based convolutional neural network was employed and improved. The network predicted the land coverage at the center of the patch. Two different label schemes were used where the proposed network either achieved better accuracy than the conventional model or recognized additional building types while maintaining a similar level of accuracy. FindingsCompared to the validation accuracy of 78% in a previous study, the proposed method achieved the validation accuracy of 90% thanks to the improvements made in this study as well as the consolidation of labels with similar surface roughness. When additional building categories were added, the validation decreased to 80%, which is comparable to the previous study but is now able to predict different building types. Originality/valueThe improvement of the proposed method will depend on the site characteristics. For the sites tested in this paper, the error reduction in wind speed and pressure was up to about 55%. In addition to more accurate wind speed and pressure, better identification of buildings will benefit wind engineering research, as different building types cause different downwind effects. An example application would be automated recognition of areas that have a certain distance from the target building type to identify downwind areas affected by high winds.  more » « less
Award ID(s):
1856205
PAR ID:
10567634
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Emerald Publishing
Date Published:
Journal Name:
Smart and Sustainable Built Environment
ISSN:
2046-6099
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; (, International Journal of Sustainability in Higher Education)
    PurposeThis study aims to investigate the use of a sociotechnical case study as a means of integrating social and technical dimensions into an undergraduate engineering sustainability technical elective course. Design/methodology/approachThe “Big Wind Project” case study used a microhistory approach to engage students in the complexities of sustainable engineering, aiming to facilitate their exploration of the sociotechnical nature of engineering sustainability projects. Focused on a controversial wind energy project in Hawaii, the Big Wind Project case study served as a pedagogical tool in the course for engaging engineering students in complex sustainability challenges. FindingsThirty-nine students who engaged in the case study lesson responded to questions about their perceptions of the case and the role of stakeholders and other social dimensions in engineering decision-making and agreed that we could use their responses in this research. While many students acknowledged the importance of accounting for social dimensions, their discussions frequently reflected a persistent tendency of engineering work to view outcomes through a dualistic technical-vs-social lens rather than an integrated sociotechnical lens. Originality/valueThis study examined how a case study reveals and supports students’ navigation of the complexities of sociotechnical engineering sustainability work. 
    more » « less
  2. ; ; ; (, Rapid Prototyping Journal)
    PurposeThe purpose of this study is to develop a deep learning framework for additive manufacturing (AM), that can detect different defect types without being trained on specific defect data sets and can be applied for real-time process control. Design/methodology/approachThis study develops an explainable artificial intelligence (AI) framework, a zero-bias deep neural network (DNN) model for real-time defect detection during the AM process. In this method, the last dense layer of the DNN is replaced by two consecutive parts, a regular dense layer denoted (L1) for dimensional reduction, and a similarity matching layer (L2) for equal weight and non-biased cosine similarity matching. Grayscale images of 3D printed samples acquired during printing were used as the input to the zero-bias DNN. FindingsThis study demonstrates that the approach is capable of successfully detecting multiple types of defects such as cracks, stringing and warping with high accuracy without any prior training on defective data sets, with an accuracy of 99.5%. Practical implicationsOnce the model is set up, the computational time for anomaly detection is lower than the speed of image acquisition indicating the potential for real-time process control. It can also be used to minimize manual processing in AI-enabled AM. Originality/valueTo the best of the authors’ knowledge, this is the first study to use zero-bias DNN, an explainable AI approach for defect detection in AM. 
    more » « less
  3. ; ; ; ; ; ; (, Studies in Graduate and Postdoctoral Education)
    PurposeThis study aims to present the evaluation of a competency-based online professional development training program, PhD Progression, tied to a digital badge system, created to support PhD students across fields. Design/methodology/approachThis study took place at Boston University, a large, nonprofit, Carnegie Classified R1 research-intensive institution located in the northeastern region of the USA. Through internal campus collaborations, the authors developed a PhD core capacities framework. Building from this framework, the authors designed the first learning level of the program and ran a pilot study with PhD students from various fields and at different stages of their PhD. Using surveys and focus groups, the authors collected both quantitative and qualitative data to evaluate this program. FindingsThe quantitative and qualitative data show that the majority of the PhD student participants found the contents of the competency-based training program useful, appropriate for building skills and knowledge and therefore relevant for both their degree progress and their future job. Gaining digital badges significantly increased their motivation to complete training modules. Practical implicationsThis type of resource is scalable to other institutions that wish to provide self-paced professional development support to their PhD students while rewarding them for investing time in building professional skills and enabling them to showcase these skills to potential employers. Originality/valueThis study demonstrates, for the first time, that tying a digital badging system to a competency-based professional development program significantly motivates PhD students to set professional development goals and invest time in building skills. 
    more » « less
  4. ; ; ; ; (, Rapid Prototyping Journal)
    PurposeThis paper aims to assess the feasibility of additively manufactured wind tunnel models. The additively manufactured model was used to validate a computational framework allowing the evaluation of the performance of five wing models. Design/methodology/approachAn optimized fighter wing was additively manufactured and tested in a low-speed wind tunnel to obtain the aerodynamic coefficients and deflections at different speeds and angles of attack. The flexible wing model with optimized curvilinear spars and ribs was used to validate a finite element framework that was used to study the aeroelastic performance of five wing models. As a computationally efficient optimization method, homogenization-based topology optimization was used to generate four different lattice internal structures for the wing in this study. The efficiency of the spline-based optimization used for the spar-rib model and the lattice-based optimization used for the other four wings were compared. FindingsThe aerodynamic loads and displacements obtained experimentally and computationally were in good agreement, proving that additive manufacture can be used to create complex accurate models. The study also shows the efficiency of the homogenization-based topology optimization framework in generating designs with superior stiffness. Originality/valueTo the best of the authors’ knowledge, this is the first time a wing model with curvilinear spars and ribs was additively manufactured as a single piece and tested in a wind tunnel. This research also demonstrates the efficiency of homogenization-based topology optimization in generating enhanced models of different complexity. 
    more » « less
  5. ; ; (, Journal of Managerial Psychology)
    PurposeCentral to the fit concept is that congruence between individual and environmental attributes leads to improved outcomes. However, when discussing fit, researchers often describe congruence as alignment between distinctive or unique individual and environmental attributes. We suggest that current approaches to examining fit do not adequately account for this assumption of distinctiveness because they fail to consider normative expectations and preferences. As such, we propose an alternative theoretical and methodological approach to conceptualizing and measuring fit. Design/methodology/approachWe introduce the normative theory of fit, outline how researchers can decompose fit into distinctive and normative components and identify areas for future research. FindingsManagement researchers have largely ignored the importance of decomposing fit into distinctive and normative components. This shortcoming necessitates additional research to ensure a more accurate understanding of fit and its relationship with outcomes. Originality/valueWe provide a clarification and critical examination of a pervasive construct in the field of management by introducing the normative theory of fit, identifying areas where researchers can employ this theoretical lens and suggesting a reevaluation of the importance placed on differentiation that is traditionally employed in practice. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email