• Journal of Navigation and Port Research
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• v.32 no.3
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• pp.215-221
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• 2008

Dynamic characteristics of a structure, i.e., natural frequency and mode shape, have been widely using as an input data in the area of structural integrity or health monitoring which combined with the damage evaluation and structural system identification techniques. It is very difficult, however, to get those information by the conventional modal analysis method from large structures, such as the offshore structure or the long-span bridge, since the source of vibration is not available. In this paper, a method to obtain the frequencies and the mode shapes of a large span truss structure using only acceleration responses is studied. The calculation procedures to obtain acceleration responses and frequency response functions are provided utilizing a numerical model of the truss, and the process to extract natural frequencies and mode shapes from the modal analysis is cleary explained. The extracted mode shapes by proposed method are compared with those from eigenvalue analysis for the estimation of accuracy. The validity of the mode shapes is also demonstrated using an existing damage detection technique for the truss structure by simulated damage cases.

• Development and Reproduction
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• v.15 no.1
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• pp.31-39
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• 2011

Change in intracellular $Ca^{2+}$-concentration ($[Ca^{2+}]_i$) is an essential event for egg activation and further development. $Ca^{2+}$ ion is originated from intracellular $Ca^{2+}$-store via inositol 1,4,5-triphosphate receptor and/or $Ca^{2+}$ influx via $Ca^{2+}$ channel. This study was performed to investigate whether changes in $Ca^{2+}$/calmodulin dependent protein kinase II (CaM KII) activity affect $Ca^{2+}$ influx during artificial egg activation with ethanol using $Ca^{2+}$ monitoring system and whole-cell patch clamp technique. Under $Ca^{2+}$ ion-omitted condition, $Ca^{2+}$-oscillation was stopped within 30 min post microinjection of porcine sperm factor, and ethanol-induced $Ca^{2+}$ increase was reduced. To investigate the role of CaM KII known as an integrator of $Ca^{2+}$- oscillation during mammalian egg fertilization, CaM KII activity was tested with a specific inhibitor KN-93. In the eggs treated with KN-93, ethanol failed to induce egg activation. In addition, KN-93 inhibited inward $Ca^{2+}$ current ($I_{Ca}$) in a time-dependent manner in whole-cell configuration. Immunostaining data showed that the voltage-dependent $Ca^{2+}$ channels were distributed along the plasma membrane of mouse egg and 2-cell embryo. From these results, we suggest that $Ca^{2+}$ influx during fertilization might be controlled by CaM KII activity.

• International Journal of High-Rise Buildings
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• v.3 no.4
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• pp.255-271
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• 2014

A method of smart system identification of super high-rise buildings is proposed in which super high-rise buildings are modeled by a shear-bending system. The method is aimed at finding the story shear and bending stiffnesses of a specific story only from the horizontal floor accelerations. The proposed method uses a set of closed-form expressions for the story shear and bending stiffnesses in terms of the limited floor accelerations and utilizes a reduced shear-bending system with the same number of elements as the observation points. A difficulty of prediction of an unstable specific function in a low frequency range can be overcome by introducing an ARX model and discussing its relation with the Taylor series expansion coefficients of a transfer function. It is demonstrated that the shear-bending system can simulate the vibration records with a reasonable accuracy. It is also shown that the vibration records at two super high-rise buildings during the 2011 Tohoku (Japan) earthquake can be simulated with the proposed method including a technique of inserting degrees of freedom between the vibration recording points. Finally it is discussed further that the time-varying identification of fundamental natural period and stiffnesses can be conducted by setting an appropriate duration of evaluation in the batch least-squares method.

• Smart Structures and Systems
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• v.22 no.4
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• pp.481-493
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• 2018

Ultrasonic guided waves have attracted increasing attention for non-destructive testing (NDT) and structural health monitoring (SHM) of bridge cables. They offer advantages like single measurement, wide coverage of acoustical field, and long-range propagation capability. To design defect detection systems, it is essential to understand how guided waves propagate in cables and how to select the optimal excitation frequency and mode. However, certain cable characteristics such as multiple wires, anchorage, and polyethylene (PE) sheath increase the complexity in analyzing the guided wave propagation. In this study, guided wave modes for multi-wire bridge cables are identified by using a semi-analytical finite element (SAFE) technique to obtain relevant dispersion curves. Numerical results indicated that the number of guided wave modes increases, the length of the flat region with a low frequency of L(0,1) mode becomes shorter, and the cutoff frequency for high order longitudinal wave modes becomes lower, as the number of steel wires in a cable increases. These findings were used in design of transducers for defect detection and selection of the optimal wave mode and frequency for subsequent experiments. A magnetostrictive transducer system was used to excite and detect the guided waves. The applicability of the proposed approach for detecting and locating wire breakages was demonstrated for a cable with 37 wires. The present ultrasonic guided wave method has been found to be very responsive to the number of brokenwires and is thus capable of detecting defects with varying sizes.

• Land and Housing Review
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• v.2 no.4
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• pp.407-413
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• 2011

This paper reports a novel structural health monitoring (SHM) technique for detecting de-bonding between a concrete beam and CFRP (Carbon Fiber Reinforced Polymer) sheet that is attached to the concrete surface. To achieve this, a multi-scale actuated sensing system with a self-sensing circuit using piezoelectric active sensors is applied to the CFRP laminated concrete beam structure. In this self-sensing based multi-scale actuated sensing, one scale provides a wide frequency-band structural response from the self-sensed impedance measurements and the other scale provides a specific frequency-induced structural wavelet response from the self-sensed guided wave measurement. To quantify the de-bonding levels, the supervised learning-based statistical pattern recognition was implemented by composing a two-dimensional (2D) plane using the damage indices extracted from the impedance and guided wave features.

• Smart Structures and Systems
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• v.25 no.3
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• pp.369-384
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• 2020

A wavefield sparse reconstruction technique based on compressed sensing is developed in this work to dramatically reduce the number of measurements. Firstly, a severely underdetermined representation of guided wavefield at a snapshot is established in the spatial domain. Secondly, an optimal compressed sensing model of guided wavefield sparse reconstruction is established based on l₁-norm penalty, where a suite of discrete cosine functions is selected as the dictionary to promote the sparsity. The regular, random and jittered undersampling schemes are compared and selected as the undersampling matrix of compressed sensing. Thirdly, a gradient projection method is employed to solve the compressed sensing model of wavefield sparse reconstruction from highly incomplete measurements. Finally, experiments with different excitation frequencies are conducted on an aluminum plate to verify the effectiveness of the proposed sparse reconstruction method, where a scanning laser Doppler vibrometer as the true benchmark is used to measure the original wavefield in a given inspection region. Experiments demonstrate that the missing wavefield data can be accurately reconstructed from less than 12% of the original measurements; The reconstruction accuracy of the jittered undersampling scheme is slightly higher than that of the random undersampling scheme in high probability, but the regular undersampling scheme fails to reconstruct the wavefield image; A quantified mapping relationship between the sparsity ratio and the recovery error over a special interval is established with respect to statistical modeling and analysis.

• Steel and Composite Structures
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• v.33 no.3
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• pp.445-461
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• 2019

The ultrasonic guided wave-based technique has become one of the most promising methods in non-destructive evaluation and structural health monitoring, because of its advantages of large area inspection, evaluating inaccessible areas on the structure and high sensitivity to small damage. To further advance the development of damage detection technologies using ultrasonic guided waves for the inspection of welded components in structures, the transmission characteristics of the ultrasonic guided waves propagating through welded joints with various types of defects or damage in steel plates are studied and presented in this paper. A three-dimensional (3D) finite element (FE) model considering the different material properties of the mild steel, high strength steel and austenitic stainless steel plates and their corresponding welded joints as well as the interaction condition of the steel plate and welded joint, is developed. The FE model is validated against analytical solutions and experimental results reported in the literature and is demonstrated to be capable of providing a reliable prediction on the features of ultrasonic guided wave propagating through steel plates with welded joints and interacting with defects. Mode conversion and scattering analysis of guided waves transmitted through the different types of weld defects in steel plates are performed by using the validated FE model. Parametric studies are undertaken to elucidate the effects of several basic parameters for various types of weld defects on the transmission performance of guided waves. The findings of this research can provide a better understanding of the transmission behaviour of ultrasonic guided waves propagating through welded joints with defects. The method could be used for improving the performance of guided wave damage detection methods.

• Smart Structures and Systems
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• v.30 no.1
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• pp.75-88
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• 2022

Engineering structures in operation essentially belong to time-varying or nonlinear structures and the resultant response signals are usually non-stationary. For such time-varying structures, it is of great importance to extract time-dependent dynamic parameters from non-stationary response signals, which benefits structural health monitoring, safety assessment and vibration control. However, various traditional signal processing methods are unable to extract the embedded meaningful information. As a newly developed technique, variational mode decomposition (VMD) shows its superiority on signal decomposition, however, it still suffers two main problems. The foremost problem is that the number of modal components is required to be defined in advance. Another problem needs to be addressed is that VMD cannot effectively separate non-stationary signals composed of closely spaced or overlapped modes. As such, a new method named generalized adaptive variational modal decomposition (GAVMD) is proposed. In this new method, the number of component signals is adaptively estimated by an index of mean frequency, while the generalized demodulation algorithm is introduced to yield a generalized VMD that can decompose mode overlapped signals successfully. After that, synchrosqueezing wavelet transform (SWT) is applied to extract instantaneous frequencies (IFs) of the decomposed mono-component signals. To verify the validity and accuracy of the proposed method, three numerical examples and a steel cable with time-varying tension force are investigated. The results demonstrate that the proposed GAVMD method can decompose the multi-component signal with overlapped modes well and its combination with SWT enables a successful IF extraction of each individual component.

• Smart Structures and Systems
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• v.29 no.1
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• pp.17-28
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• 2022

The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge.

• Smart Structures and Systems
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• v.31 no.4
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• pp.351-363
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• 2023

Visual structural inspections are an inseparable part of post-earthquake damage assessments. With unmanned aerial vehicles (UAVs) establishing a new frontier in visual inspections, there are major computational challenges in processing the collected massive amounts of high-resolution visual data. We propose twin deep learning models that can provide accurate high-resolution structural components and damage segmentation masks efficiently. The traditional approach to cope with high memory computational demands is to either uniformly downsample the raw images at the price of losing fine local details or cropping smaller parts of the images leading to a loss of global contextual information. Therefore, our twin models comprising Trainable Resizing for high-resolution Segmentation Network (TRS-Net) and DmgFormer approaches the global and local semantics from different perspectives. TRS-Net is a compound, high-resolution segmentation architecture equipped with learnable downsampler and upsampler modules to minimize information loss for optimal performance and efficiency. DmgFormer utilizes a transformer backbone and a convolutional decoder head with skip connections on a grid of crops aiming for high precision learning without downsizing. An augmented inference technique is used to boost performance further and reduce the possible loss of context due to grid cropping. Comprehensive experiments have been performed on the 3D physics-based graphics models (PBGMs) synthetic environments in the QuakeCity dataset. The proposed framework is evaluated using several metrics on three segmentation tasks: component type, component damage state, and global damage (crack, rebar, spalling). The models were developed as part of the 2^nd International Competition for Structural Health Monitoring.