• Smart Structures and Systems
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• v.14 no.4
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• pp.679-697
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• 2014

The strain data acquired from structural health monitoring (SHM) systems play an important role in the state monitoring and damage identification of bridges. Due to the environmental complexity of civil structures, a better understanding of the actual strain data will help filling the gap between theoretical/laboratorial results and practical application. In the study, the multi-scale features of strain response are first revealed after abundant investigations on the actual data from two typical long-span bridges. Results show that, strain types at the three typical temporal scales of $10^5$, $10^2$ and $10^0$ sec are caused by temperature change, trains and heavy trucks, and have their respective cut-off frequency in the order of $10^{-2}$, $10^{-1}$ and $10^0$ Hz. Multi-resolution analysis and wavelet shrinkage are applied for separating and extracting these strain types. During the above process, two methods for determining thresholds are introduced. The excellent ability of wavelet transform on simultaneously time-frequency analysis leads to an effective information extraction. After extraction, the strain data will be compressed at an attractive ratio. This research may contribute to a further understanding of actual strain data of long-span bridges; also, the proposed extracting methodology is applicable on actual SHM systems.

• Smart Structures and Systems
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• v.10 no.2
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• pp.131-154
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• 2012

In this paper, it is aimed to determine the seismic behaviour of highway bridges by nondestructive testing using ambient vibration measurements. Eynel Highway Bridge which has arch type structural system with a total length of 216 m and located in the Ayvaclk county of Samsun, Turkey is selected as an application. The bridge connects the villages which are separated with Suat U$\breve{g}$urlu Dam Lake. A three dimensional finite element model is first established for a highway bridge using project drawings and an analytical modal analysis is then performed to generate natural frequencies and mode shapes in the three orthogonal directions. The ambient vibration measurements are carried out on the bridge deck under natural excitation such as traffic, human walking and wind loads using Operational Modal Analysis. Sensitive seismic accelerometers are used to collect signals obtained from the experimental tests. To obtain experimental dynamic characteristics, two output-only system identification techniques are employed namely, Enhanced Frequency Domain Decomposition technique in the frequency domain and Stochastic Subspace Identification technique in time domain. Analytical and experimental dynamic characteristic are compared with each other and finite element model of the bridge is updated by changing of boundary conditions to reduce the differences between the results. It is demonstrated that the ambient vibration measurements are enough to identify the most significant modes of highway bridges. After finite element model updating, maximum differences between the natural frequencies are reduced averagely from 23% to 3%. The updated finite element model reflects the dynamic characteristics of the bridge better, and it can be used to predict the dynamic response under complex external forces. It is also helpful for further damage identification and health condition monitoring. Analytical model of the bridge before and after model updating is analyzed using 1992 Erzincan earthquake record to determine the seismic behaviour. It can be seen from the analysis results that displacements increase by the height of bridge columns and along to middle point of the deck and main arches. Bending moments have an increasing trend along to first and last 50 m and have a decreasing trend long to the middle of the main arches.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.647-652
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• 2008

This paper focused on the application of finite element model updating technique to evaluate the structural properties of the reinforced concrete specimen using the data collected from shaking table tests. The specimen was subjected to six El Centro(NS, 1942) ground motion histories with different Peak Ground Acceleration(PGA) ranging from 0.06g to 0.50g. For model updating, flexural stiffness values of structural members(walls and slabs) were chosen as the updating parameters so that the converged results have direct physical interpretations. Initial values for finite element model were determined from the member dimensions and material properties. Frequency response functions(i.e. transfer functions), natural frequencies and mode shapes were obtained using the acceleration measurement at each floor and given ground acceleration history. The weighting factors were used to account for the relative confidence in different types of inputs for updating(i.e. transfer function and natural frequencies). The constraints based on upper/lower bound of parameters and sensitivity-based constraints were implemented to the updating procedure in this study using standard bounded variable least-squares(BVLS) method. The veracity of the updated finite element model was investigated by comparing the predicted and measured responses. The results indicated that the updated model replicates the dynamic behavior of the specimens reasonably well. At each stage of shaking, severity of damage that results from cracking of the reinforced concrete member was quantified from the updated parameters(i.e. flexural stiffness values).

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.7
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• pp.725-731
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• 2008

This paper focused on the application of finite element model updating technique to evaluate the structural properties of the reinforced concrete specimen using the data collected from shaking table tests. The specimen was subjected to six El Centre (NS, 1942) ground motion histories with different peak ground acceleration (PGA) ranging from 0.06 g to 0.50 g. For model updating, flexural stiffness values of structural members (walls and slabs) were chosen as the updating parameters so that the converged results have direct physical interpretations. Initial values for finite element model were determined from the member dimensions and material properties. Frequency response functions (i.e. transfer functions), natural frequencies and mode shapes were obtained using the acceleration measurement at each floor and given ground acceleration history. The weighting factors were used to account for the relative confidence in different types of Inputs for updating (j.e. transfer function and natural frequencies) The constraints based on upper/lower bound of parameters and sensitivity-based constraints were implemented to the updating procedure in this study using standard bounded variable least-squares(BVLS) method. The veracity of the updated finite element model was investigated by comparing the predicted and measured responses. The results indicated that the updated model replicates the dynamic behavior of the specimens reasonably well. At each stage of shaking, severity of damage that results from cracking of the reinforced concrete member was quantified from the updated parameters (i.e. flexural stiffness values).

• Journal of the Korea institute for structural maintenance and inspection
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• v.13 no.5 s.57
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• pp.169-179
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• 2009

The purpose of this paper is to find an efficient heat source to amplify the surface temperature of damaged concrete structures for infrared thermography. we compare two different heat sources of far-infrared lamp and halogen lamp each other for their efficiency. The two heat sources were applied to the concrete specimens. Two different concrete specimens were used: one was the concrete containing internal void and the other was wrapped with partially unbonded fiber reinforced polymer sheet. it was found that the far-infrared lamp was more efficient than the halogen lamp. In addition, we propose a new algorithm to make the damage zone displayed clear in the image obtained from the thermographic operation. The algorithm is a combination of Gauss filtering process and the Prewitt mask operation.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• Smart Structures and Systems
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• v.9 no.3
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• pp.287-301
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• 2012

It is well known that overloaded vehicles may cause severe damages to bridges, and how to estimate and evaluate the status of the overloaded vehicles passing through bridges become a challenging problem. Therefore, based on the monitored strain data from a structural health monitoring system (SHM) installed on a bridge, a method is recommended to identify and analyze the probability of overloaded vehicles. Overloaded vehicle loads can cause abnormity in the monitored strains, though the abnormal strains may be small in a concrete continuous rigid frame bridge. Firstly, the abnormal strains are identified from the abundant strains in time sequence by taking the advantage of wavelet transform in abnormal signal identification; secondly, the abnormal strains induced by heavy vehicles are picked up by the comparison between the identified abnormal strains and the strain threshold gotten by finite element analysis of the normal heavy vehicle; finally, according to the determined abnormal strains induced by overloaded vehicles, the statistics of the overloaded vehicles passing through the bridge are summarized and the whole probability of the overloaded vehicles is analyzed. The research shows the feasibility of using the monitored strains from a long-term SHM to identify the information of overloaded vehicles passing through a bridge, which can help the traffic department to master the heavy truck information and do the damage analysis of bridges further.

• Journal of the Korea Concrete Institute
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• v.24 no.6
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• pp.737-744
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• 2012

Hysteretic steel damper has been applied mainly to steel buildings. However, the usage in RC buildings is rapidly increasing recently. In order to apply the steel hysteretic damper in RC buildings, supporting elements of the damper should have sufficient strength and stiffness suitable for transferring damper forces to beams and walls. But due to the inevitable damage in reinforced concrete elements due to cracking, identification of the load transfer mechanism from damper to supporting element and hysteretic characteristics of the supporting element are extremely important in evaluating the damper behavior. Experiments were carried out on connection details between RC walls and supporting elements of the steel plate typed damper such as EaSy damper. The test results showed that fracture patterns of all specimens were almost identical except in the crack number and pattern associated with shear loading condition. Among the specimens, HD-3 shoed a well distributed cracks patterns along with good performance with respect to energy dissipation capacity, stiffness deterioration, and strength degradation.

• Journal of the Society of Naval Architects of Korea
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• v.52 no.1
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• pp.88-95
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• 2015

For the frequency-domain spectral fatigue analysis, the probability density function of stress range needs to be estimated based on the stress spectrum only, which is a frequency domain representation of the response. The probability distribution of the stress range of the narrow-band spectrum is known to follow the Rayleigh distribution, however the PDF of wide-band spectrum is difficult to define with clarity due to the complicated fluctuation pattern of spectrum. In this paper, efforts have been made to figure out the links between the probability density function of stress range to the structural response of wide-band Gaussian random process. An artificial neural network scheme, known as one of the most powerful system identification methods, was used to identify the multivariate functional relationship between the idealized wide-band spectrums and resulting probability density functions. To achieve this, the spectrums were idealized as a superposition of two triangles with arbitrary location, height and width, targeting to comprise wide-band spectrum, and the probability density functions were represented by the linear combination of equally spaced Gaussian basis functions. To train the network under supervision, varieties of different wide-band spectrums were assumed and the converged probability density function of the stress range was derived using the rainflow counting method and all these data sets were fed into the three layer perceptron model. This nonlinear least square problem was solved using Levenberg-Marquardt algorithm with regularization term included. It was proven that the network trained using the given data set could reproduce the probability density function of arbitrary wide-band spectrum of two triangles with great success.

• Earthquakes and Structures
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• v.17 no.5
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• pp.511-519
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• 2019

The concrete gravity dam is one of the most important parts of the nation's infrastructure. Besides the benefits, the dam also has some potentially catastrophic disasters related to the life of citizens directly. During the lifetime of service, some degradations in a dam may occur as consequences of operating conditions, environmental aspects and deterioration in materials from natural causes, especially from dynamic loads. Cumulative Absolute Velocity (CAV) plays a key role to assess the operational condition of a structure under seismic hazard. In previous researches, CAV is normally used in Nuclear Power Plant (NPP) fields, but there are no particular criteria or studies that have been made on dam structure. This paper presents a method to calculate the limitation of CAV for the Bohyeonsan Dam in Korea, where the critical Peak Ground Acceleration (PGA) is estimated from twelve sets of selected earthquakes based on High Confidence of Low Probability of Failure (HCLPF). HCLPF point denotes 5% damage probability with 95% confidence level in the fragility curve, and the corresponding PGA expresses the crucial acceleration of this dam. For determining the status of the dam, a 2D finite element model is simulated by ABAQUS. At first, the dam's parameters are optimized by the Minitab tool using the method of Central Composite Design (CCD) for increasing model reliability. Then the Response Surface Methodology (RSM) is used for updating the model and the optimization is implemented from the selected model parameters. Finally, the recorded response of the concrete gravity dam is compared against the results obtained from solving the numerical model for identifying the physical condition of the structure.