A grey Bayesian inference framework for structural damage assessment

Author(s):  
Sheng‐en Fang ◽  
Shan Chen
Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1571 ◽  
Author(s):  
Jhonatan Camacho Navarro ◽  
Magda Ruiz ◽  
Rodolfo Villamizar ◽  
Luis Mujica ◽  
Jabid Quiroga

Geosciences ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 133
Author(s):  
Jérémie Sublime

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.


Author(s):  
Chin-Hsiung Loh ◽  
Min-Hsuan Tseng ◽  
Shu-Hsien Chao

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.


2004 ◽  
Vol 3 (2) ◽  
pp. 177-194 ◽  
Author(s):  
Lay Menn Khoo ◽  
P. Raju Mantena ◽  
Prakash Jadhav

2001 ◽  
Vol 17 (1) ◽  
pp. 89-112 ◽  
Author(s):  
Mauricio Sánchez-Silva ◽  
Libardo García

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.


2017 ◽  
Vol 842 ◽  
pp. 012016
Author(s):  
Yun-Lai Zhou ◽  
Cao Hongyou ◽  
Ni Zhen ◽  
Magd Abdel Wahab

2021 ◽  
Vol 8 ◽  
Author(s):  
Haibei Xiong ◽  
Lin Chen ◽  
Cheng Yuan ◽  
Qingzhao Kong

Early detection of timber damage is essential for the safety of timber structures. In recent decades, wave-based approaches have shown great potential for structural damage assessment. Current damage assessment accuracy based on sensing signals in the time domain is highly affected by the varied boundary conditions and environmental factors in practical applications. In this research, a novel piezoceramic-based sensing technology combined with a visual domain network was developed to quantitatively evaluate timber damage conditions. Numerical and experimental studies reveal the stress wave propagation properties in different cases of timber crack depths. Through the spectrogram visualization process, all sensing signals in the time domain were transferred to images which contain both time and frequency features of signals collected from different crack conditions. A deep neural network (DNN) was adopted for image training, testing, and classification. The classification results show high efficiency and accuracy for identifying crack conditions for timber structures. The proposed technology can be further integrated with a fielding sensing system to provide real-time monitoring of timber damage in field applications.


Author(s):  
Gomasa Ramesh ◽  

Damage may be assessed using several damage indices with values associated with different structural damage states. The usefulness of a variety of current response-based damage indices in seismic damage assessment is addressed and critically assessed. A novel rational damage assessment method is provided, which measures the structure’s physical reaction characteristics. A practical method based on various analyses is given to evaluate the damaged structures in earthquakes of different intensities. This paper provides an overview of previous research works on the damage assessment of the reinforced concrete structures. This study may be helpful for easy understanding about the damage assessment of reinforced concrete structures and reduce the impacts of disaster and surrounding structures.


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