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Thang Dao

University of Alabama

101 Paul W. Bryant Dr.

Box 870382

Tuscaloosa, AL 35487

United States

  • University of Alabama

SCHOLARLY PAPERS

3

DOWNLOADS

75

TOTAL CITATIONS

0

Scholarly Papers (3)

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1.

What Really Matters for Automated Tornado Damage Assessment? Evidence from 2,300 Experiments Across 79 Open-Source Architectures with Cross-Event Generalization

Number of pages: 49 Posted: 09 Mar 2026
Robinson Umeike, Thang Dao, Shane Crawford, John W. van de Lindt, Blythe Johnston, Wanting (Lisa) Wang, Trung Do, Ajibola Mofikoya, Sarbesh Banjara and Cuong Pham
The University of Alabama, University of Alabama, The University of Alabama, affiliation not provided to SSRN, Government of the United States of America - Argonne National Laboratory, Old Dominion University, University of South Alabama, The University of Alabama, The University of Alabama and University of South Alabama
Downloads 36 (1,332,057)

Abstract:

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Deep Learning, Automated Damage Assessment, Vision transformers, Convolutional Neural Networks, Cross-Event Generalisation, Disaster Management

2.

Light Frame Wood Sheathing-to-Rafter Connections: Experimental Capacity and Cyclic Behavior Analysis

Number of pages: 64 Posted: 08 Jan 2026
Afeez Badmus, Elaina J. Sutley, William Collins, Thang Dao, Pedro Marquez, Ioannis Zisis, Amal Elawady and Arindam Gan Chowdhury
University of Kansas, University of Kansas, University of Kansas, University of Alabama, University of Kansas, affiliation not provided to SSRN, Florida International University and Florida International University
Downloads 26 (1,385,945)

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roof sheathing connection, experimental capacity, cyclic analysis, uplift loads, Energy dissipation

3.

TornadoNet: Real-Time Building Damage Detection with Ordinal Supervision

Number of pages: 33 Posted: 27 Apr 2026
Robinson Umeike, Cuong Pham, Ryan Hausen, Thang Dao, Shane Crawford, Tanya Brown-Giammanco, Gerard Lemson, John W. van de Lindt, Blythe Johnston, Arik Mitschang and Trung Do
The University of Alabama, University of South Alabama, Johns Hopkins University, University of Alabama, The University of Alabama, Government of the United States of America - National Institute of Standards and Technology, Johns Hopkins University Institute of Data Intensive Engineering and Science, affiliation not provided to SSRN, affiliation not provided to SSRN, Johns Hopkins University and University of South Alabama
Downloads 13 (1,541,665)

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Post-disaster Damage Assessment, Building damage detection, Ordinal Classification, object detection, Deep learning, Disaster resilience