Skip to Main Content
 

Global Search Box

 
 
 

ETD Abstract Container

Abstract Header

Computer Vision and Building Envelopes

Anani-Manyo, Nina K
http://rave.ohiolink.edu/etdc/view?acc_num=kent1619539038754026

Abstract Details

2021, MS, Kent State University, College of Architecture and Environmental Design.
Computer vision, a field that falls under artificial intelligence (AI), is increasingly establishing grounds in many disciplines as the demand for automated means to solve real-world problems gradually grows. AI is progressively simplifying and speeding up the processes of day-to-day tasks. The application of computer vision within the field of architecture has the potential to increase efficiency as well. Building envelope is an important component of a building and requires regular assessment and inspection. The application of deep learning techniques reveals itself as an innovative way of carrying out a task that is typically performed by humans. Hence, this research discusses the explorations of using computer vision as a tool to classify building materials, evaluate the details, and potentially identify distresses of building envelopes. This is done using a collection of existing digital images and algorithms that help train the computer to produce efficient and reliable results. Deep learning techniques such as convolutional neural network algorithms and Google’s Teachable Machine are utilized to classify two sets of base data. The successes produced prove the models have the capability of classifying the dataset given to them. These approaches gradually introduce new methods and techniques that can and will revolutionize the industry of Architecture, Engineering, and Construction.
Rui Liu (Advisor)
Elwin Robison (Committee Member)
Ruoming Jin (Committee Member)
Mirian Velay-Lizancos (Committee Member)
Bill Lucak (Committee Member)
Architecture
Computer vision, architecture, building envelope, deep learning, algorithm, convolutional neural network, image classification

Recommended Citations

Citations

  • Anani-Manyo, N. K. (2021). Computer Vision and Building Envelopes [Master's thesis, Kent State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=kent1619539038754026

    APA Style (7th edition)

  • Anani-Manyo, Nina. Computer Vision and Building Envelopes. 2021. Kent State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=kent1619539038754026.

    MLA Style (8th edition)

  • Anani-Manyo, Nina. "Computer Vision and Building Envelopes." Master's thesis, Kent State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=kent1619539038754026

    Chicago Manual of Style (17th edition)

kent1619539038754026
1,274
© 2021, all rights reserved.
This open access ETD is published by Kent State University and OhioLINK.
Release 3.2.12