Representation of damage information for post-earthquake damage assessment of reinforced concrete frames

Anıl, Engin Burak
Akıncı, Burcu
Garett, James
Kurç, Özgür
Current practice of post-earthquake damage assessment is based on visual inspections by experts. Research has shown that visual inspection and reports produced as a result of inspections can be subjective, incomplete, and vary in terms of the level of detail and thoroughness. Critical data that provides information about damage can be captured using laser scanners and processed for a more objective and information driven damage assessment practice. In addition to damage information, the damage severity identification process requires reasoning about the structural behavior and topology. Building Information Modeling can be used as the underlying information source to streamline the damage assessment process. This research aims at representing crack information using a Building Information Modeling approach. In order to support reasoning for damage assessment, three activities were performed: 1) requirements were identified based on accepted damage assessment codes and standards; 2) capabilities of Industry Foundation Classes were explored for representing crack information; and 3) suggestions were provided for using existing concepts in IFCs for representing cracks.


Characterization of laser scanners for detecting cracks for post-earthquake damage inspection
Anil, Engin Burak; Akinci, Burcu; Garrett, James H.; Kurç, Özgür (2013-01-01)
Objective, accurate, and fast assessment of damage to buildings after an earthquake is crucial for timely remediation of material losses and safety of occupants of buildings. Laser scanners are promising sensors for collecting geometrical data regarding the damaged states of buildings, as they are able to provide high coverage and accuracy at long ranges. Yet, we have limited knowledge on the performance of laser scanners for detecting earthquake damage, and requirements of such data collection. This paper ...
Integration of practical supplemental measurements into bridge condition visual inspection grading
Shabana, Nefize; Avsar, Ozgur; Caner, Alp (2020-01-01)
The reliability of condition assessment of bridges obtained from analysis of visual inspection data is always a big concern among structural engineers. It has been known that the condition grading of a bridge is very subjective and can convey limited information to the end user. To finalize and verify the reported condition grading, inspectors and bridge owners have mainly been relying on images. It has been known that the image observation may not be sufficient to address all kinds of problems associated w...
Prediction of potential damage due to severe earthquakes
Yücemen, Mehmet Semih; Pay, AC (2004-01-01)
A statistical model is developed to estimate the seismic vulnerability of low- to mid-rise reinforced concrete buildings. The model is based on a novel utilization of the discriminant analysis technique of multivariate statistics. Number of stories above the ground level (N), soft story index (SSI), overhang ratio (OHR), minimum normalized lateral stiffness index (MNLSTFI), minimum normalized lateral strength index (MNLSI) and normalized redundancy score (NRS) are selected as the basic estimation variables....
Prediction of potential seismic damage using classification and regression trees: a case study on earthquake damage databases from Turkey
Yerlikaya-Ozkurt, Fatma; Askan Gündoğan, Ayşegül (Springer Science and Business Media LLC, 2020-09-01)
Seismic damage estimation is an important key ingredient of seismic loss modeling, risk mitigation and disaster management. It is a problem involving inherent uncertainties and complexities. Thus, it is important to employ robust approaches which will handle the problem accurately. In this study, classification and regression tree approach is applied on damage data sets collected from reinforced concrete frame buildings after major previous earthquakes in Turkey. Four damage states ranging from None to Seve...
Performance Comparison of Pretrained Convolutional Neural Networks on Crack Detection in Buildings
Özgenel, Çağlar Fırat; Sorguç, Arzu (2018-07-01)
Crack detection has vital importance for structural health monitoring and inspection of buildings. The task is challenging for computer vision methods as cracks have only low-level features for detection which are easily confused with background texture, foreign objects and/ or irregularities in construction. In addition, difficulties such as inhomogeneous illumination and irregularities in construction present an obstacle for fully autonomous crack detection in the course of building inspection and monitor...
Citation Formats
E. B. Anıl, B. Akıncı, J. Garett, and Ö. Kurç, “Representation of damage information for post-earthquake damage assessment of reinforced concrete frames,” 2013, Accessed: 00, 2020. [Online]. Available: