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Damage and Defects Assessment

Current Projects

DAMAGE DETECTION FOR BRIDGE CONDITION ASSESSMENT October 2014 – September 2018

Trimble Europe BV Grant, PI: Ioannis Brilakis, £99,858

 

Past Projects

COLLABORATIVE RESEARCH: MACHINE VISION ENHANCED POST EARTHQUAKE INSPECTION AND RAPID LOSS ESTIMATION August 2010 – August 2013

NSF Grant #1000700, PI: Reginald DesRoches, $360,032

This project combined infrastructure objects and damage recognition from video with structural engineering to enable quantitative assessments of buildings damaged by earthquakes. The purpose was to create the missing link between measured damage data and the condition of the building as a whole, so as to assist structural specialists in making an assessment decision grounded on measurements. The proposed automated procedure classified component damage per the ATC-20 guidelines using empirically based models. Component damage was compiled to determine the damage state of the building, recommend red, yellow, or green tagging of the building, and estimate repair time and cost. Building damage state, configuration and type were used to query a set of fragility curves defining the likelihood of building collapse during an aftershock and, thereby, provided an improved understanding of risk. The validation of this work was based on structural tests from NEESR and other sources. Leading student researcher: Stephanie German and Jong Su Jeon.

 

RAPID: URGENT COLLECTION OF PERISHABLE CONDITION DATA FROM STRUCTURES AFFECTED BY THE HAITI EARTHQUAKE April 2010 – July 2010

NSF Grant #1034845, PI: Laura Lowes, $40,000

This Rapid Response Research (RAPID) grant provided the opportunity to a team of researchers to travel to Haiti and collect damage data and design information for concrete buildings damaged during the 2010 earthquake. These data were used to validate a rapid, image-based, semi-automated method for assessing damage and collapse risk for reinforced concrete structures to both reduce the time needed for, and to improve the reliability of, post-event inspection. The aftermath of recent earthquakes in the United States suggests that for even a moderate intensity earthquake affecting a metropolitan area, it could take weeks or months to inspect, and thereby grant access to, damaged buildings. The research team sought to both reduce the time needed for and improve the reliability of post-event inspection by using the collected data to validate rapid methods for assessing damage and collapse risk for reinforced concrete structures. Preliminary results from this work can be foundhere. Leading student researchers: Zhenhua Zhu and Stephanie German.

 

REAL TIME CONCRETE DAMAGE VISUAL ASSESSMENT FOR FIRST RESPONDERS

The objective of this research was to test the following hypothesis: The risk of structural collapse after an earthquake in reinforced concrete frame structures due to column failure can be reasonably estimated by automatically recognizing these columns and the type of damage inflicted on them through a video camera mounted in a first responders hardhat. If this hypothesis is validated, it will assist in automating the prediction of potential structural collapse of concrete buildings for first responders (firemen, policemen and medics) who must enter these damaged buildings to perform essential emergency response functions after earthquakes. Preliminary results from this work can be found here. Leading student researcher: Zhenhua Zhu.

 

QUANTITATIVE SURFACE DEFECTS ASSESSMENT FOR CONCRETE INSPECTION

The objective of this research was to design an automated, quantitative model for locating and identifying defects, such as air pockets and discoloration, on concrete surfaces. Under this model, image processing techniques were used to locate the defects and determine their number, size and degree of impact. This information was then used to quantify the concrete surface quality and provide a recommendation for setting specification standards for cast in place and architectural concrete. Results from this work can be found here. Leading student researcher: Zhenhua Zhu.