Load-Capacity Rating of Bridge Populations through Machine Learning: Application of Decision Trees and Random Forests

Author:

Alipour Mohamad1ORCID,Harris Devin K.2,Barnes Laura E.3,Ozbulut Osman E.4,Carroll Julia5

Affiliation:

1. Graduate Research Assistant, Dept. of Civil and Environmental Engineering, Univ. of Virginia, 351 McCormick Rd., Charlottesville, VA 22904-4742 (corresponding author). ORCID: .

2. Associate Professor, Dept. of Civil and Environmental Engineering, Univ. of Virginia, 351 McCormick Rd., Charlottesville, VA 22904-4742.

3. Assistant Professor, Dept. of Systems and Information Engineering, Univ. of Virginia, 151 Engineer’s Way, Charlottesville, VA 22904-4747.

4. Assistant Professor, Dept. of Civil and Environmental Engineering, Univ. of Virginia, 351 McCormick Rd., Charlottesville, VA 22904-4742.

5. Graduate Research Assistant, Dept. of Civil and Environmental Engineering, Univ. of Virginia, 351 McCormick Rd., Charlottesville, VA 22904-4742.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Building and Construction,Civil and Structural Engineering

Reference48 articles.

1. Alipour M. Gheitasi A. Harris D. K. Ozbulut O. E. and Barnes L. E. (2016). “A data-driven approach for automated operational safety evaluation of the national inventory of reinforced concrete slab bridges.” Transportation Research Board (TRB) 95th Annual Meeting Transportation Research Board Washington DC.

2. Pattern extraction for high-risk accidents in the construction industry: A data-mining approach;Amiri M.;Int. J. Inj. Control Saf. Promotion,2016

3. ASCE. (2013). “Report card for America’s infrastructure.” 〈http://www.infrastructurereportcard.org/〉 (July 25 2015).

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