4.4 Article

Automated Damage Index Estimation of Reinforced Concrete Columns for Post-Earthquake Evaluations

Journal

JOURNAL OF STRUCTURAL ENGINEERING
Volume 141, Issue 9, Pages -

Publisher

ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)ST.1943-541X.0001200

Keywords

Post-earthquake reconnaissance; Machine vision; Damage detection; Reinforced concrete columns; Damage index; Structural safety and reliability

Funding

  1. National Science Foundation [CMMI-1034845, CMMI-0738417]
  2. EPSRC [EP/I019308/1, EP/K000314/1, EP/L010917/1] Funding Source: UKRI
  3. Engineering and Physical Sciences Research Council [EP/I019308/1, EP/L010917/1, EP/K000314/1] Funding Source: researchfish

Ask authors/readers for more resources

The safety of buildings which have been affected by a natural disaster such as an earthquake is currently evaluated manually by certified inspectors who identify visible damage on the structural elements. This process has been proven to be time-consuming, costly, and can delay the response and recovery. In order to automate this type of assessment, it is necessary to automatically determine the damage state of the individual structural members. Previously, the writers have created novel methods for automated detection of RC columns as well as significant damage on the column surfaces (spalling and cracking). This paper presents a novel method of automatically determining the damage state of RC columns in RC frame buildings based only on the automatically detected damage and column information. In addition, the novel method automatically determines an associated engineering demand parameter, residual drift capacity. All of the methods previously developed by the writers are combined with the method presented in this paper and the results are compared with those of manual assessment procedures. The method was implemented using computer software and its effectiveness was confirmed with this comparison. (C) 2014 American Society of Civil Engineers.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.4
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available