Volume 23, Issue 5 (2023)                   MCEJ 2023, 23(5): 195-209 | Back to browse issues page


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Pourgholi M, Ghanadi M, Mohammadzadeh Gilarlue M. Detecting Damage in Steel Buildings through Ambient Vibration Tests. MCEJ 2023; 23 (5) :195-209
URL: http://mcej.modares.ac.ir/article-16-69293-en.html
1- Assistant Professor, Department of Civil Engineering, Islamic Azad University, Sarab, Iran. , pourgholi@iausa.ac.ir
2- Assistant Professor, Department of Civil Engineering, Islamic Azad University, Sarab, Iran.
3- Assistant Professor, Department of Electrical Engineering, Islamic Azad University, Sarab, Iran.
Abstract:   (802 Views)
Infrastructures such as bridges, buildings, pipelines, marine structures, etc., play an important role in human life. Since major disasters in these structures, such as the collapse of bridges or buildings, often result in many casualties, damages, and social and economic problems, most industrialized countries allocate significant funds to monitor their health. Failure detection strategies and continuous monitoring of the structure's condition, especially after natural and manufactured disasters, make necessary measures to be taken in the early stages of failure and can reduce the cost of maintenance and the possibility of collapse. Structural health monitoring methods often provide an opportunity to reduce maintenance, repair, and retrofit costs during the structure's life cycle. Most of the structural health monitoring methods proposed and implemented to identify possible damages depend on the structure's dynamic characteristics. One of the most practical methods, which uses the results of time domain system identification to detect failure, is the damage locating vector (DLV) method. The DLV method aims to identify load combinations that result in zero strain fields for damaged members in both healthy and damaged structures. To accomplish this, we find a vector in the null space of the difference between the plasticity matrices of the two structures. The singular value analysis method is used on the plasticity difference matrix to calculate this space. The method involves applying the space vectors to the healthy structure and recording the internal stresses of the members, which are then converted into weighted normal stress (WSI) using statistical tools. The member with a lower WSI is more likely to be damaged. Since truss structures are usually used in bridges, long-span structures, as well, as a wide range of steel buildings with simple and braced frames, this research uses the covariance-based random subspace optimal method in identifying the modal characteristics, which is very efficient in low excitations, has been taken into consideration to check and monitor health during operation. To investigate the capability of the DLV method in the damage detection of these structures, a 5-story residential building with a simple steel frame was subjected to the Centro earthquake. According to the desired damage scenario, the second and fifth floors were introduced as the damaged floors in this earthquake by applying a 30 and 50% reduction in the cross-section. To account for uncertainty in the data collection, we included the mean root square of the second sensor's data in the results for sensors 3 and 5. As a result of this uncertainty, the damping error between 5 and 10% has been shown in the damaged and healthy structure. Using the method (SSI_ORT), it was observed that two DLV vectors were extracted. Further, with the increasing uncertainty of the random vibration test results, it was observed that the extraction DLVs could extract the possible damaged elements with high accuracy. Next, the effect of input and output noises on the results obtained from the DLV method was investigated. This study found that by increasing the SNR of the outputs by 15% while increasing the error of the extracted modal characteristics, the extracted DlVs also lose sufficient accuracy in diagnosing structural damage.
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Article Type: Original Research | Subject: Civil and Structural Engineering
Received: 2023/05/20 | Accepted: 2023/06/21 | Published: 2023/11/1

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