Structural Damage Parameters Research Articles

Substructure damage identification based on sensitivity of Power Spectral Density

In this paper, a new method for substructure damage identification under random excitation is proposed, which is based on the sensitivity of the power spectral density (PSD) analysis. The finite element model is divided into different substructures and combined with the improved reduced system (IRS) method to achieve model condensation. When the system is subjected to excitations, the sensitivity of dynamic response of power spectral density function to structural damage parameters is obtained. The substructure is regarded as an independent structure, and the structural damage identification is realized by using the method of model updating. By increasing the number of frequency points used for damage identification to reduce the selection of measuring points. The identified results are obtained by updating the damaged parameters using sensitivity of PSD with few measurement points. The assembled finite element model is obtained by condensation and the number of elements is greatly reduced. The substructure damage identification method based on the PSD sensitivity is proposed in this paper, it not only reduces the size of analytical model involved in model updating but also decreases the uncertain parameters that need updating, thus resulting in a rapid convergence of the iterative process. The numerical and experimental model of a six-story steel frame structure with different damage conditions demonstrates the satisfactory identification results from the proposed method.

The role of imaging in the diagnosis and management of axial spondyloarthritis.

MRI of the sacroiliac joints is increasingly acknowledged as being indispensable in the early diagnosis of axial spondyloarthritis (axSpA) and as having a prominent role in the prognosis and classification of axSpA. Technological advances include improvements in the resolution of structural lesions and in methodologies for the quantification of lesions. Limited access and expertise in interpretation of MRI have led to a resurgence of interest in CT, especially the development of low radiation protocols for assessing the sacroiliac joints. Trials of TNF inhibitors in patients with non-radiographic axSpA have led to greater understanding of the role of MRI in selecting which patients might respond well to this therapy. The role of MRI features as target end points in treat-to-target strategies remains unclear because the effect of such targeting on structural damage parameters has only recently been explored. The relative importance of active and structural lesions for prognostic risk assessment and selection of appropriate treatment is also an area of current research. Given the increased capacity to visualize a broad array of lesions in both the sacroiliac joints and the spine using MRI and CT, these modalities will probably be increasingly employed for assessment of the disease-modifying activity of new therapies.

Effect of random structural damage on vehicle–track–bridge coupled response

With the rapid development of high-speed railway construction around the world, the structural damage problem of tracks and bridges has attracted more and more attention. In this paper, considering the random structural damages of tracks and bridges, an efficient computational procedure is developed based on the probability density evolution method to systematically investigate the random characteristics of the dynamic responses varying with the damages in vehicle–track–bridge time-dependent systems. Firstly, a vehicle–track–bridge dynamic model with random structural damage parameters is established, and the random motion equations are derived based on the generalized energy principle. Then, the probability density evolution equation for each random response is established and the random feature is investigated by the proposed computational procedure. Finally, the representative numerical examples are applied and some conclusions for the effects of the random structural damages on the dynamic responses of the vehicle–track–bridge systems are presented.

Damage identification in bridge structures subject to moving vehicle based on extended Kalman filter with l1-norm regularization

ABSTRACTAn innovative damage detection method for bridge structures under moving vehicular load is proposed on the basis of extended Kalman filter (EKF) and l1-norm regularization. An augmented state vector includes structural damage parameters and motion state variables of bridge and vehicle. Through a recursive process of the EKF, the structural damage parameters and state variables of a bridge are updated continually to obtain an optimal estimate using bridge responses due to a moving vehicle. The distribution of element stiffness reduction of a structure with local damages is sparse. Thus, l1-norm regularization is introduced into the updating process of the EKF using pseudo-measurement (PM) technology to improve the ill-posedness of the inverse problem. Numerical studies on a simple-supported and continuous beam bridge deck, with a smooth road surface that is subject to a moving vehicle, are performed to test the proposed approach. Furthermore, using the robustness of the EKF, the proposed algorithm is applied as a simplified method to the case where a bridge deck with road roughness is considered. Results show that the proposed identification algorithm is robust and effective for different vehicle speeds and measurement noises under smooth and good road conditions.

Structural damage identification based on power spectral density sensitivity analysis of dynamic responses

A new method is proposed to identify locations and severities of structural damages based on the power spectral density sensitivity analysis. Firstly, the structural responses and power spectral density under stationary and random excitations are calculated using pseudo excitation method. Then, the sensitivities of power spectral density with respect to the structural damage parameters are obtained. Finally, the finite element model updating method is adopted to identify the structural damages from the calculated and the simulated measured power spectral density. Two numerical examples demonstrate the satisfactory identification results obtained from the present method.

Structural damage evolution assessment using the regularised time step integration method

This paper presents an approach to identify both the location and severity evolution of damage in engineering structures directly from measured dynamic response data. A relationship between the change in structural parameters such as stiffness caused by structural damage development and the measured dynamic response data such as accelerations is proposed, on the basis of the governing equations of motion for the original and damaged structural systems. Structural damage parameters associated with time are properly chosen to reflect both the location and severity development over time of damage in a structure. Basic equations are provided to solve the chosen time-dependent damage parameters, which are constructed by using the Newmark time step integration method without requiring a modal analysis procedure. The Tikhonov regularisation method incorporating the L-curve criterion for determining the regularisation parameter is then employed to reduce the influence of measurement errors in dynamic response data and then to produce stable solutions for structural damage parameters. Results for two numerical examples with various simulated damage scenarios show that the proposed method can accurately identify the locations of structural damage and correctly assess the evolution of damage severity from information on vibration measurements with uncertainties.

Damage Identification Based on Power Spectral Density Sensitivity Analysis of Structural Responses

A new method is proposed to identify structural damages based on the power spectral density sensitivity analysis. The responses of the structure under stationary and random excitations are obtained using pseudo excitation method, and then the sensitivities of power spectral density with respect to the structural damage parameters are obtained similarly. Finite element model updating method is adopted to identify the structural damages. A numerical example of a shearing structure demonstrates the satisfactory results obtained from the present method.

Suppressing damage identification errors from selected natural frequencies and mode shape points

This article presents a damage identification method based on structural natural frequencies and mode shape points. According to the well-posedness analysis of this method, an algorithm is proposed to find the optimal combination of natural frequencies and mode shape points. Then, an integrated scheme that consists of the optimal point selection, the damage identification and the measurement noise updating is presented. In such a way, the damage identification errors due to measurement noises can be greatly suppressed. Numerical examples demonstrate that although structural damage parameters are very sensitive to the measurement noises, especially the noises in natural frequencies, they can be reliably identified if the natural frequencies and mode shape points are properly selected using the proposed method.

An evaluation of effective design parameters on earthquake performance of RC buildings using neural networks

The extent of building damage is related to the features of the structural system, which have many parameters. In particular, it is difficult to determine the extent to which structural parameters affect structural performance to identify the main parameters that may cause damage. In the present study, changes in the quality of a load-bearing system and reinforced concrete (RC) structure materials during an earthquake were determined. The related structural parameters were determined by considering the structural damage parameters observed in earthquakes: concrete compressive strength, yield and ultimate strength of steel, transverse reinforcement, infill wall ratio, short column, strong column–weak beam, and shear wall ratio. A total of 256 RC buildings with between 4 and 7 floors were modeled, and pushover analysis was applied to each to obtain the building capacity curves. A performance assessment was performed predicated on the basic criteria of the Turkish Earthquake Code (TEC-2007), which was revised in parallel with FEMA-356. In addition, the influence of the structural parameters was determined using a set of Artificial Neural Network (ANN) algorithms, and a parametric study was performed accordingly. The load-bearing system and material were discussed by matching the findings obtained from the study with the documented damage from previous earthquakes. The effect of each parameter tested in this study had various affecting ratios on the earthquake performance of the structure. It was found that shear wall ratio and short column formation are the most significant structural components that affect performance. The compressive strength of concrete and transverse reinforcement were determined to be the least significant parameters. In addition, the ANN determined the structural performance with quite satisfactory rate. The earthquake performance estimation percentages of the selected ANN algorithms varied between 91.68% and 98.47% depending on the algorithm type and other parameters of the ANN modeling.

Inter-individual differences in anti-proteinuric response to ACEi in established adriamycin nephrotic rats are predicted by pretreatment renal damage.

ACE inhibition (ACEi) reduces proteinuria and provides reno-protection, but not all subjects benefit from ACEi. Individual differences in the reduction in proteinuria at the onset of treatment and in residual proteinuria during therapy predict differences in renal outcome. The present study investigated whether individual differences in the anti-proteinuric efficacy of ACEi are explained by differences in the severity of pretreatment renal structural damage and whether differences in the level of residual proteinuria during therapy are explained by the severity of renal structural damage at that time, in adriamycin nephrosis in the rat. Pretreatment renal structural damage was assessed in biopsies 6 weeks after exposure to adriamycin (2 mg/kg iv). Then ACEi (75 mg/l lisinopril, n = 23) or vehicle (n = 10) was administered; renal biopsies were repeated after stabilization of the anti-proteinuric response (week 8). Early renal damage (interstitial alpha-smooth muscle actin expression and macrophage accumulation) and established lesions [focal glomerulosclerosis (FGS) and interstitial fibrosis] were scored. During ACEi, proteinuria fell from 834 (487-851) mg/24 h pretreatment to 153 (66-265) mg/24 h at week 8 (p < 0.05); FGS stabilized from 27 (4-70) arbitrar units (AU) pretreatment to 26 (4-84) at week 12, whereas the vehicle did not affect proteinuria, resulting in progressive FGS: 18 (10-26) AU pretreatment versus 88 (46-130) at week 12 (p < 0.05). All parameters of pretreatment damage significantly predicted the anti-proteinuric response. Residual proteinuria during ACEi correlated significantly with renal structural damage parameters at that time. Pretreatment renal damage also predicted renal outcome during extended treatment. Thus, in this experimental setting, in rats with the same renal disorder and the same duration of disease, individual differences in pretreatment renal damage, albeit relatively modest, explain individual differences in renal responsiveness to ACEi. This implies that the limits of the efficacy of ACEi are set by prevalent renal damage. Further studies into the mechanisms of individual resistance to the anti-proteinuric action of ACEi are needed to develop additive intervention strategies.