A Bayesian updating approach for structural damage assessment using visual inspection data

In December 2020, a strong earthquake occurred in Northwestern Croatia with a magnitude of ML = 6.3. The epicenter of this earthquake was located in the town of Petrinja, about 50 km from Zagreb, and caused severe structural damage throughout Sisak-Moslavina county. One of the biggest problems after this earthquake was the structural condition of the bridges, especially since most of them had to be used immediately for demolition, rescue, and the transport of mobile housing units in the affected areas. Teams of civil engineers were quickly formed to assess the damage and structural viability of these bridges and take necessary actions to make them operational again. This paper presents the results of the rapid post-earthquake assessment for a total of eight bridges, all located in or around the city of Glina. For the assessment, a visual inspection was performed according to a previously established methodology. Although most of the inspected bridges were found to be deteriorated due to old age and lack of maintenance, very few of them showed serious damage from the earthquake, with only one bridge requiring immediate strengthening measures and use restrictions. These measurements are also presented in this paper.

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Features of Cross-Correlation Analysis in a Data-Driven Approach for Structural Damage Assessment

Sensors ◽

10.3390/s18051571 ◽

2018 ◽

Vol 18 (5) ◽

pp. 1571 ◽

Cited By ~ 4

Author(s):

Jhonatan Camacho Navarro ◽

Magda Ruiz ◽

Rodolfo Villamizar ◽

Luis Mujica ◽

Jabid Quiroga

Keyword(s):

Correlation Analysis ◽

Damage Assessment ◽

Structural Damage ◽

Cross Correlation ◽

Data Driven ◽

Cross Correlation Analysis ◽

Data Driven Approach

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The 2011 Tohoku Tsunami from the Sky: A Review on the Evolution of Artificial Intelligence Methods for Damage Assessment

Geosciences ◽

10.3390/geosciences11030133 ◽

2021 ◽

Vol 11 (3) ◽

pp. 133

Author(s):

Jérémie Sublime

Keyword(s):

Artificial Intelligence ◽

Case Studies ◽

Damage Assessment ◽

Structural Damage ◽

North East ◽

2011 Tohoku Tsunami ◽

Artificial Intelligence Methods ◽

The Future ◽

Tohoku Tsunami

The Tohoku tsunami was a devastating event that struck North-East Japan in 2011 and remained in the memory of people worldwide. The amount of devastation was so great that it took years to achieve a proper assessment of the economical and structural damage, with the consequences still being felt today. However, this tsunami was also one of the first observed from the sky by modern satellites and aircrafts, thus providing a unique opportunity to exploit these data and train artificial intelligence methods that could help to better handle the aftermath of similar disasters in the future. This paper provides a review of how artificial intelligence methods applied to case studies about the Tohoku tsunami have evolved since 2011. We focus on more than 15 studies that are compared and evaluated in terms of the data they require, the methods used, their degree of automation, their metric performances, and their strengths and weaknesses.

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Structural Damage Assessment Using Output-Only Measurement: Localization and Quantification

Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting ◽

10.1115/smasis2013-3008 ◽

2013 ◽

Author(s):

Chin-Hsiung Loh ◽

Min-Hsuan Tseng ◽

Shu-Hsien Chao

Keyword(s):

Damage Detection ◽

Damage Assessment ◽

Structural Damage ◽

Damage Identification ◽

Model Updating ◽

Null Space ◽

Singular Spectrum Analysis ◽

Damage Index ◽

Computation Efficiency ◽

Output Only

One of the important issues to conduct the damage detection of a structure using vibration-based damage detection (VBDD) is not only to detect the damage but also to locate and quantify the damage. In this paper a systematic way of damage assessment, including identification of damage location and damage quantification, is proposed by using output-only measurement. Four level of damage identification algorithms are proposed. First, to identify the damage occurrence, null-space and subspace damage index are used. The eigenvalue difference ratio is also discussed for detecting the damage. Second, to locate the damage, the change of mode shape slope ratio and the prediction error from response using singular spectrum analysis are used. Finally, to quantify the damage the RSSI-COV algorithm is used to identify the change of dynamic characteristics together with the model updating technique, the loss of stiffness can be identified. Experimental data collected from the bridge foundation scouring in hydraulic lab was used to demonstrate the applicability of the proposed methods. The computation efficiency of each method is also discussed so as to accommodate the online damage detection.

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Structural Damage Assessment Using Vibration Modal Analysis

Structural Health Monitoring ◽

10.1177/1475921704042680 ◽

2004 ◽

Vol 3 (2) ◽

pp. 177-194 ◽

Cited By ~ 38

Author(s):

Lay Menn Khoo ◽

P. Raju Mantena ◽

Prakash Jadhav

Keyword(s):

Modal Analysis ◽

Damage Assessment ◽

Structural Damage ◽

Vibration Modal

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Earthquake Damage Assessment Based on Fuzzy Logic and Neural Networks

Earthquake Spectra ◽

10.1193/1.1586168 ◽

2001 ◽

Vol 17 (1) ◽

pp. 89-112 ◽

Cited By ~ 23

Author(s):

Mauricio Sánchez-Silva ◽

Libardo García

Keyword(s):

Neural Networks ◽

Fuzzy Logic ◽

Damage Assessment ◽

Structural Damage ◽

Management Strategies ◽

Computer Software ◽

Good Representation ◽

Feed Forward Neural Network ◽

Risk Management Strategies ◽

Potential Damage

Potential damage assessment is fundamental for defining mitigation procedures and risk management strategies. Damage assessment involves the difficulties of defining, assessing, and modeling the variables involved, as well as handling uncertainty. Seismic damage estimation of structures does not only depend on the behavior of the structural system, but it involves other factors, which differ in nature. The paper presents a methodology for damage assessment of structures that combines systems theory, fuzzy logic, and neural networks. A feed-forward neural network supported on the systemic organization of information is used to assess the expected structural damage for a given earthquake. The methodology provides a very useful environment to consider the context of the building structure. The network has been trained using the damage observed in the recent earthquake that occurred in central Colombia. Several sets of structures were evaluated and the results compared to the damage observed. The model showed to be highly reliable and a good representation of experts' opinions. Computer software ERS-99 was developed and is currently being used for teaching and consulting purposes.

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