Mohammadreza Vafaei

Real-time Seismic Damage Detection of Concrete Shear Walls Using Artificial Neural Networks

Concrete shear walls are widely employed in buildings as a main resistance system against lateral loads. Early identification of seismic damage to concrete shear walls is vital for deciding post-earthquake occupancy in these structures. In this article, a method based on artificial neural networks for real-time identification of seismic damage to concrete shear walls was proposed. Inter-story drifts and plastic hinge rotation of concrete walls were used as the inputs and outputs of a MLP neural network. Modal Pushover Analysis was employed to prepare well-distributed data sets for training the neural network. The proposed method was applied to a five-story concrete shear wall building. The results from the network were compared with those obtained from Nonlinear Time History Analysis. It was observed that the trained neural network successfully detected damage to concrete shear walls and accurately estimated the severity of seismic-induced damage.

Seismic damage detection of tall airport traffic control towers using wavelet analysis

In this study, the applicability of continuous wavelet transform (CWT) and discrete wavelet transform (DWT) for seismic damage detection of tall airport traffic control (ATC) towers was investigated. Nonlinear finite element (NFE) model of Kuala Lumpur International Airport (KLIA) ATC tower with the height of 120 m was created using discrete moment-curvature hinges. Three different strong ground motions excited the tower and three different damage scenarios were then obtained. Response accelerations at four strategically selected locations were analysed by CWT and DWT to detect the damage scenarios. It was found that CWT successfully detects seismic-induced damage even when the signals are polluted by noises. On the other hand, DWT is quite sensitive to noisy signals and successful damage detection by DWT depends on noise level and sampling interval. Moreover, it was observed that DWT is more sensitive to the change in the stiffness of the tower structural elements than CWT.

A wavelet-based technique for damage quantification via mode shape decomposition

In this study, a neuro-wavelet technique was proposed for damage identification of cantilever structure. At first, damage localisation was accomplished through mode shape decomposition using discrete wavelet transforms. Subsequently, a damage indicator was defined based on the detail coefficients of the decomposed signals. It was found that distinct patterns relate the damage indicators to damage locations. Considering this property, a neural network ensemble was developed for damage quantification. Damage indicators and damage locations were selected as input parameters for the neural networks. Three individual neural networks were trained by input samples obtained from different combinations of decomposed mode shapes. Then, the outcomes of the individual neural networks were fed to the ensemble neural network for damage quantification. The proposed method was tested on a cantilever structure both experimentally and numerically. Six different damage scenarios including three different damage locations and three different damage severities were introduced to the structure. The results revealed that the proposed method was able to quantify different damage levels with a good precision.

A neuro-wavelet technique for seismic damage identification of cantilever structures

A method based on artificial neural networks and wavelet transform is proposed for identifying seismic-induced damage of cantilever structures. In the proposed method, response accelerations are measured at strategically selected locations. To extract damage-induced sharp transitions from the measured signals, they are decomposed by continuous wavelet transform. The size of the decomposed signals is reduced by principal component analysis (PCA). Principal components obtained from PCA are fed to a set of neural networks to identify damage. The proposed algorithm is applied to a tall airport traffic control tower by means of numerical simulations. The obtained results show that the proposed method effectively identifies seismic-induced damage, and the noise intensity has a negligible effect on the predicted results. Moreover, the trained neural network system is able to predict the seismic-induced damage of unseen samples well.

Seismic performance evaluation of an airport traffic control tower through linear and nonlinear analysis

Due to a lack of adequate information about seismic design and the performance of airport traffic control (ATC) towers, structural engineers often rely on building codes. However, seismic performance and the demands of ATC towers differ significantly from common structures. In this paper, the seismic performance of Kuala Lumpur International Airport traffic control tower, with a height of 120 m, was investigated. The results showed that in comparison to modal response spectrum analysis, equivalent static analysis overestimated overturning moments, drifts, maximum displacement and demand/capacity ratio. In addition, linear analysis underestimates base shear, drifts and overturning moments in comparison to the results of nonlinear time history and pushover analysis.

Seismic Damage Detection Using Pushover Analysis

Inter-story drift ratio is a general damage index which is being used to detect damaged stories after severe ground motions. Since this general damage index cannot detect damaged elements also the severity of imposed damages on elements, a new real-time seismic damage detection method base on artificial neural networks was proposed to overcome this issue. This approach considers nonlinear behaviour of structures and not only is capable of detecting damaged elements but also can address the severity of imposed damages. Proposed algorithm was applied on a 3-story concrete building .The obtained results confirmed accuracy and robustness of this method.

Adequacy of first mode shape differences for damage identification of cantilever structures using neural networks

Damage identification of structures has attracted attention of researchers due to sudden collapse of in-service structures. Modal parameters and their derivatives have been widely employed in the proposed damage identification techniques. However, mode shape differences have been shown to be an ideal damage indicator when used as the input vector of neural networks. Since measurement of higher-order mode shapes is very difficult to be acquired reliably, this study investigated the adequacy of using only the first mode shape differences for damage identification using artificial neural networks. Results of numerical and experimental studies on a cantilever beam indicated that the first mode shape differences alone can accurately localize imposed damages. Damage intensity at the lower levels of cantilever beam was predicted with less than 15% error; however, prediction of damage intensity at the free end of the beam encountered large discrepancies. It was also found that damage localization was successful even when the first mode shape differences were measured at few points along the beam.

Assessment of seismic design response factors of air traffic control towers

Abstract Air Traffic Control (ATC) towers are among the most vital structures in each airport. Due to inadequate information regarding the seismic design and assessment of these types of structures, practicing engineers may refer to building codes. However, taking into account the special dynamic behavior of ATC towers, instructions and recommendations provided in building codes often do not comply with the required seismic performance levels of ATC towers. In this study, seismic behaviors of three in-service ATC towers with a dual concrete core lateral load resisting system were studied through pushover and incremental dynamic collapse analysis. Seismic design response factors of the reference towers were calculated. It was found that seismic design response factors adopted by the design code did not provide a uniform safety margin for all reference ATC towers. It was also observed that shorter towers have significantly higher response modification factors compared to taller towers. For the studied towers, a structural over-strength factor of 2.4 and a displacement amplification factor of 4 were obtained.

Seismic Performance Evaluation of an ATC Tower through Pushover Analysis

Abstract Air traffic control (ATC) towers are a vital form of infrastructure that controls the landing and taking off of airplanes at every airport. In spite of the significant role that ATC towers play during disasters like earthquakes, little attention has been paid to their seismic behavior. In this study, the seismic performance of an existing ATC tower with a dual lateral-load resistance system was evaluated through pushover analysis. The tower was pushed by two different lateral-load patterns and the performance points were calculated accordingly. It was observed that when the applied lateral load follows the pattern obtained from the response spectrum analysis, the maximum displacement demand is twice that of the mass-proportional lateral-load pattern. The drift values indicate that the tower suffers from low lateral stiffness at its higher levels. It was also found that at the target displacement the longitudinal reinforcements in the internal concrete core reached yield stress. The results of the plastic hinge formations show that the steel frame satisfies the requirements of the immediate occupancy (IO) performance level. The stress values in the concrete of the external walls were found to be less than the design values.

Seismic retrofit of masonry wall infilled RC frames through external post-tensioning

In this study, an external post-tensioning technique was employed to enhance the seismic performance of infilled RC frames through preventing premature failure of infill walls and increase in their engagement with RC frames. Totally, six 1/3-scale single-story single-bay RC frames were constructed and tested. The frames were divided into two groups based on their aspect ratios. Each group consisted of a bare frame, an infilled frame and a retrofitted infilled frame. All specimens were subjected to a similar quasi-static cyclic loading and their responses were measured through load cells, strain gauges and linear variable differential transformers. Results indicated that the retrofitted infilled frames had higher initial stiffness, ultimate lateral strength and ductility ratio when compared with the bare and un-retrofitted infilled frames. The retrofitted infilled frames also showed a prolonged failure mode and a lower stiffness degradation rate compared to other frames. It was also observed that, compared to other frames, the retrofitted frames had smaller strain values at their critical zones.