Severity Of Structural Damage Research Articles

Hippocampal iron overload and spatial reference memory impairment: Insights from a rat model

BackgroundBrain iron overload may induce neuronal death and lead to cognitive impairment. The hippocampus is a critical limbic structure involved in memory. This study aimed to investigate iron overload and its role in hippocampal damage and memory impairment using a rat model. MethodsYoung rats (2 weeks old) received intraperitoneal injections of high-dose iron solution (Group H, n = 10), low-dose iron solution (Group L, n = 10) and normal saline as control (Group D, n = 5). The Morris water maze (MWM) test was performed on all rats to evaluate their spatial reference memory by assessing their escape latency time and number of platform crossing. The iron content and neuronal damage in hippocampal tissue sections of the rats were assessed semi-quantitatively using diaminobenzidine (DAB)-enhanced Perl’s Prussian blue (PPB) staining, and their correlation with spatial reference memory performance was evaluated. ResultsThe escape latency in Group H was significantly longer compared to Groups L and D (P < 0.05). The number of platform crossings was significantly lower in Group H than in Group L or D (P < 0.001). The neuronal cells in Group H had more brown iron deposits than those of Groups L and D. There were significant correlations between the severity of structural damage in the hippocampal tissue and the number of platform crossings (P1 = 0.001 for Group H; P2 = 0.043 for Group L). ConclusionThis study showed an association between hippocampal iron-induced structural damage and spatial reference memory impairment in a rat model. This work should advance our understanding of hippocampal iron overload on cognitive functioning.

Damage detection for structural health monitoring using reinforcement and imitation learning

Structural damages are responsible for expenses associated with maintaining the safety and serviceability of infrastructures. Detecting damages is difficult because they often develop over years affecting structural responses in orders of magnitudes smaller than external effects, such as temperature. When damage occurs, structural responses depart from a normal condition to an abnormal one, which is referred to as an anomaly. Existing anomaly detection methodologies lack a mechanism to quantify the probability of rightfully detecting anomalies as a function of the anomaly’s characteristics, e.g. duration and magnitude, and associate them with the severity of structural damages. This paper proposes a framework addressing these challenges by relying on Bayesian dynamic linear models as well as reinforcement and imitation learning approaches. The former allows separating the changes in the structural responses from the ones caused by external effects, while the latter two enable incorporating information obtained from the changes in the structural responses for detecting anomalies. The proposed methodologies are validated using measurements collected on three instrumented bridge spans in Canada. The results show a good performance of the methods proposed in detecting structural damages with different severity levels and lay the foundation for further applications for other civil infrastructures.

Desintoxicação de poluentes de águas subterrâneas da planície de Al Jifara (Líbia) usando quitosana sintetizada naturalmente: status histológico e antioxidante em ratos Wistar

ABSTRACT To evaluate the efficiency of naturally derived chitosan as a bio-adsorbent to improve water quality in Jefara plain and health condition, male Wistar rats divided into 7 groups: group I (drinking unpolluted water), group II (drinking untreated water from aquifer 1), group III (drinking water from aquifer 1 treated with 0.5gm/L chitosan), group IV (drinking water from aquifer 1 treated with 1 gm/L chitosan), group V (drinking with untreated water from aquifer 2), group VI (drinking water from aquifer 2 treated with 0.5gm/L chitosan, group VII (drinking water from aquifer 2 treated with 1 gm/L chitosan) for 30 days. Adsorptive ability of chitosan was confirmed by X-ray diffraction, scanning electron microscopy with energy dispersive X-ray after the exposure processes. The recorded antioxidant biomarkers showed marked elevations superoxide dismutase, glutathione reduced and thiobarbituric acid reactive substances levels in groups II and V. The application of chitosan, significantly (P&lt; 0.05) reduced the TBARS level compared to untreated groups indicating the improvement of antioxidant status. Severity of structural damages of all recorded alterations in renal and hepatic tissues was more pronounced in the rats groups that were exposed to untreated water. While, chitosan intervention is significantly reduced the above recorded alterations.

Structural damage severity classification from time-frequency acceleration data using convolutional neural networks

Structural damage identification involves interpretation of vibration measurements to identify patterns that are indicative of changes in structural characteristics. Identification of such patterns using machine learning (ML) algorithms is a primary task in data-driven structural health monitoring. However, providing ML algorithms with features that are sensitive to damage has always been a challenge for accurate damage detection. In recent years, deep Convolutional Neural Networks (CNNs) proved to be versatile and powerful tools to identify the underlying patterns directly from low-level raw data. This study explores the applicability of CNNs to damage severity classification. A new format of input data for CNN damage identifiers is proposed based on time–frequency representations of acceleration data from multiple sensors. First, Continuous Wavelet Transformation (CWT) of acceleration response is employed to derive the time–frequency data, then, the results of transformations from multiple accelerometers are stored in separate channels creating a three-dimensional block of data. Such input data provide a CNN with structural information that expands into time, frequency, and spatial domains. To evaluate the accuracy of such a CNN-based classifier, a dataset comprised of free vibration response of a concrete beam under impact hammer tests is utilized. It is shown that a CNN trained with CWT data can successfully classify various severity of the damage, from minor to severe, with 100% accuracy. Also, this study examines the sensitivity of the CNN classifier to the number of training samples. It is demonstrated that the accuracy of such a network has a relatively lower sensitivity to the number of training samples and/or the number of measurement points to the degree that it maintains an F1-score above 0.813 when trained with data from one impact point and one sensor. On the other hand, for the case of a CNN classifier trained directly with time-series acceleration data the value of the F1-score drops to 0.590. Moreover, when compared with a multilayer perceptron trained with the extracted modal properties, it is illustrated that CNN classifiers have a better capability in capturing key features from the measurement, and thus produce more accurate predictions. Therefore, the combination of CNN classifiers with time–frequency data from multiple sensors results in damage identifiers that are less sensitive to sample size and more robust to changes in excitations and measurement points.

Seismic performance of post-tensioned self-centering concrete frames under near-fault pulse-like ground motions

Post-tensioned self-centering (PTSC) frames have been identified as a class of high-performance structures with capabilities of mitigating damage and minimizing residual deformations. However, the seismic behavior of PTSC frames subjected to near-fault pulse-like ground motions (NPGMs) is not fully understood yet. This study provides a comprehensive assessment on the seismic performance of PTSC frames under NPGMs. 4-story and 12-story PTSC frames with different energy dissipation capacities are numerically modeled in the OpenSees platform. Various degrees of structural damage of the PTSC frames are properly described by particular limit states. Incremental dynamic analysis (IDA) is performed using a set of 91 near-fault ground motions with a wide range of pulse periods. Fragility surfaces for different damage states are developed for the exemplar PTSC frames with respect to both seismic intensity and pulse period. The numerical results indicate that the impacts of pulse period on the PTSC frames of different stories are quite different. Besides, the most unfavorable pulse period changes with the severity of structural damage. The effects of energy dissipation ratio and pulse period on the exceedance probabilities of different damage states are compared, and the limitation of the commonly adopted friction dampers is discussed. Based on the results and observations of structural performance with respect to the pulse characteristics, guides and recommendations for the design of PTSC frames are provided.

Numerical Assessment of the Structural Damage of a Composite Lining Water Conveyance Tunnel Subjected to Reverse Fault Conditions

In this paper, the structural responses and failure characteristics of a new type of water conveyance tunnel lining structure subjected to reverse fault conditions were numerically investigated by considering multiple loads and interaction separation modes between different structural layers. This study proposes a new evaluation standard for the safety level of the damage state of the composite lining water conveyance tunnel. It also discusses the influences of fault dislocation displacement (Δf), dip angle (β), and the mechanical properties of the surrounding rock in the fault fracture zone on the water conveyance tunnel response and damage. The results indicate that the buckling failure of the steel tube under axial compression is the dominant failure mode of the composite lining structure. With increasing fault dislocation displacement, the axial compressive strain and circumferential shear strain of the composite lining are most severely damaged on the sliding plane. With decreasing fault dip angle, the axial compressive strain of the composite lining weakens, while the bending and shear strains increase. The increase in rock stiffness in the fault fracture zone reduces the damage scope but increases the composite lining structural damage severity. Overall, the numerical results of this study provide a better understanding of the failure mode and damage process of composite lining water conveyance tunnels under reverse fault conditions; therefore, this study can serve as a reference for composite lining structure disaster assessments.

Data driven structural damage assessment using phase space embedding and Koopman operator under stochastic excitations

To address the issue faced by phase space trajectory (PST) based methods for identifying structural damage using high dimensional dynamic responses of structures under stochastic excitations, a novel data driven approach for structural damage assessment based on phase space embedding in conjunction with stochastic Koopman operator, is proposed in this study. Vibration acceleration responses are used to reconstruct the phase space representation of system dynamic attractor using embedding strategy, followed by subspace dynamic mode decomposition (subspace DMD) to estimate the unbiased eigenvalues of the corresponding stochastic Koopman operator. Under the hypothesis that the Koopman operator eigenpairs will vary with structural condition change, the Mahalanobis distance of Koopman operator eigenvalues approximated from heathy state and current testing state is introduced as a damage sensitive feature (DSF) to detect structural damage. A planar truss model is adopted in numerical studies to demonstrate the feasibility and applicability of the proposed approach. The robustness of the proposed approach under operational and environmental variations is tested by considering 10% white noise in vibration measurement and six different ambient loading scenarios. Numerical results show that the proposed approach is sensitive to the occurrence and severity of structural damage, and is insensitive to the measurement noise and the variation of stochastic excitation amplitudes. Furthermore, the proposed method is applied to identify damage introduced as the artificially applied settlements of pier in the Z24 benchmark bridge. Results demonstrate that the bridge condition under the reference state and the damage scenarios with different levels of pier settlement are well identified by using the proposed approach with in-field measurement data including different test environment. The defined DSF value can be used to reflect the damage severity in these damage scenarios.

Integrating remote sensing and social sensing for flood mapping

Flood events cause substantial damage to infrastructure and disrupt livelihoods. Timely monitoring of flood extent helps authorities identify severe impacts and plan relief operations. Remote sensing through satellite imagery is an effective method to identify flooded areas. However, critical contextual information about the severity of structural damage or urgent needs of affected population cannot be obtained from remote sensing alone. On the other hand, social sensing through microblogging sites can potentially provide useful information directly from eyewitnesses and affected people. Therefore, this paper explores the integration of remote sensing and social sensing data to derive informed flood extent maps. For this purpose, we employ state-of-the-art deep learning methods to process heterogeneous data obtained from four case-study areas, including two urban regions from Somalia and India and two coastal regions from Italy and The Bahamas. On the remote sensing side, we observe that deep learning models perform generally better than Otsu in flood water prediction. For example, for highly urban areas from Somalia and India, U-Net achieves better F1-scores (0.471 and 0.310, respectively) than Otsu (0.297 and 0.251, respectively). Similarly, for coastal areas, FCN yields a better F1-score for Italy (0.128) than Otsu (0.083) while FCN and Otsu perform on par for The Bahamas (0.102 and 0.105, respectively). Then, on the social sensing side, we add two data layers representing relevant tweet text and images posted from the case-study regions to highlight different ways these heterogeneous data sources complement each other. Our extensive analyses reveal several valuable insights. In particular, we identify three types of signals: (i) confirmatory signals from both sources, which puts greater confidence that a specific region is flooded, (ii) complementary signals that provide different contextual information including needs and requests, disaster impact or damage reports and situational information, and (iii) novel signals when both data sources do not overlap and provide unique information.

A methodological approach towards evaluating structural damage severity using 1D CNNs

Evaluating the severity of structural damage is a critical component of Structural Health Monitoring (SHM). Convolutional Neural Networks (CNNs) have been used before to detect structural damage and evaluate its severity by utilising only raw vibration data. However, these vibration-based CNN applications were limited to discrete user-defined levels of damage. To provide a more accurate representation of structural damage, this paper aims to design and validate a framework for evaluating structural damage severity within a continuous range of damage levels, using 1D CNNs and distributed raw acceleration data. To this purpose, a simple Finite Element (FE) cantilever model with non-rigid rotational spring support was adopted. Damage was simulated at the support as reduction of the rotational spring stiffness. The performance of the proposed framework was assessed under different excitation scenarios and data pre-processing techniques. The results demonstrate the ability of 1D CNNs to evaluate damage severity with high accuracy. By estimating the reduced value of the rotational spring stiffness, the proposed framework can also be used towards FE model updating in parallel with damage severity evaluation.

A Review Paper on Importance of SHM System in India

Abstract: India being the home of rich historical background inherits varied amount of historical structures like Taj Mahal (one of the seven wonders), Red fort, Temples etc. Due to their historical importance, it becomes very important to assess health condition of these structures and take the required measures to improve their life. Buildings like stadiums, sports arenas, malls, lifeline structures like hospitals, public buildings which could cause harm to large number of people at a time and are something to be taken care on a regular basis. India also has various ambitious, complex, and critical structure, like nuclear plants, Dams, bridges and tunnels should be mandated with monitoring as their failure cause more losses than any other. Structural health monitoring will help us to achieve increased durability and health of structure, reduced maintenance and improved safety. Health monitoring of civil infrastructures consists of determining by measured parameter, the location and severity of damage in structures and inform us before it became to late. The main goal of SHM is to detect the damage based on the measurement by various types of sensors, locate the geometric position of the damage and quantify the damage so that we can take the necessary action timely and improve the life of the structure. Keywords: Bridge, Structural Health Monitoring (SHM), sensors, data management system, instrumentation scheme, Performance parameters

A Novel Optimization Algorithm Based on Modal Force Information for Structural Damage Identification

This paper proposes a novel optimization algorithm called modal force information-based optimization (MFIBO) to identify the location and severity of damage in structures. The main idea behind the MFIBO is to take advantage of information captured from the modal force of structural elements to seek the optimum damage variables. The modal element force, defined as the internal element force caused by the action of mode shapes, allows the MFIBO to recognize promising directions in the search space and assists in accelerating the optimization process. Indeed, unlike meta-heuristic optimization algorithms, which disregard explicit information about the problem and rely only upon time-consuming stochastic search computations, the MFIBO employs an informed search strategy to perform optimization in a rational and directed manner. In order to assess the effectiveness and applicability of the proposed MFIBO algorithm, four benchmark damage identification examples of truss and frame structures are conducted under both noise-free and noisy conditions. In each example, the results of the MFIBO are also compared with those attained by two well-known meta-heuristic algorithms, namely the differential evolution and the teaching–learning-based optimization. The obtained results reveal that the MFIBO is able to accurately and reliably identify structural damage with a significantly lower computational burden compared to the meta-heuristic algorithms.

Research on Damage Identification of Nonuniform Microcrack in Beam Structures

Nonuniform microcrack identification is of great significance in mechanical, aerospace, and civil engineering. In this study, the nonuniform crack is simplified as a semielliptical crack, and simplified calculation methods are proposed for damage severity and damage identification of semielliptical cracks. The proposed methods are based on the calculation method for uniform cracks. The wavelet transform and the intelligent algorithm (IA) are used to identify the damage location and the damage severity of the structure, respectively. The singularity of the wavelet coefficient can be used to identify the signal singularity quickly and accurately, and IA efficiently and accurately calculates the structural damage severity. The particle swarm optimization (PSO) algorithm and the genetic algorithm (GA), widely used, are applied to identify the damage severity of the beam. Numerical simulations and experimental analyses of beams with transfixion and semielliptical cracks are carried out to evaluate the accuracy of the semielliptical crack calculation method and the method of wavelet analysis combined with PSO and GA for nonuniform crack identification. The results show that the wavelet-particle swarm optimization (WPSO) and the wavelet-genetic algorithm (WGA) can accurately and efficiently identify the structural semielliptical damage location and severity and that these methods are not easily influenced by noise. The damage severity calculation method for semielliptical cracks can accurately calculate the semielliptical size and can be used to identify damage in beams with semielliptical cracks.

A 2-year longitudinal study of bone health in adolescent patients with axial spondyloarthritis.

Axial spondyloarthritis (axSpA) is a chronic inflammatory disease that primarily affects the axial skeleton and typically has an early onset. Although earlier onset is associated with worse prognosis, there have been few studies of bone mineral density (BMD) in adolescent patients with axSpA. We analysed the clinical characteristics of 43 adolescent patients with axSpA at a baseline assessment and at a follow-up 2 years later. The baseline assessment included age, disease duration, treatment agents, and clinical, radiologic, and laboratory data. BMD of the lumbar spine, femoral neck, and total hip were measured by dual-energy X-ray absorptiometry during both the baseline assessment and the 2-year follow-up. We performed multivariate linear regression analyses to identify factors independently associated with BMD. We analysed the associations between changes in BMD and reductions in inflammatory markers. The average age of participants was 17.9 years and the mean disease duration was 2.2 years. Of the 43 patients, 10 (23%) had low BMD at any site (lumbar spine, femoral neck, and/or total hip). At baseline, multivariate analysis showed that body mass index (BMI), erythrocyte sedimentation rate (ESR), and spinal structural damage were associated with lumbar spine Z-scores. Increases in BMD in the lumbar spine were correlated with reductions in ESR (r = 0.40, P = 0.02) and C-reactive protein (CRP) (r = 0.40, P = 0.02). Increases in BMD in the total hip were correlated with reductions in CRP (r = 0.38, P = 0.03). In adolescent axSpA patients, bone health was associated with systemic inflammation and the severity of structural damage. Reduced systemic inflammation was associated with improvements in bone health.

Structural damage identification based on substructure method and improved whale optimization algorithm

In this study, a method based on the substructure method, element relative modal strain energy, and improved whale optimization algorithm (LWOA) is implemented to identify structural damage. In this method, firstly, the global structure is decomposed into several substructures based on the substructure method, which greatly reduces the size of the model to be analyzed and improves the efficiency of analysis. Secondly, LWOA algorithm is used to calculate the severity of structural damage, Levy-flight is introduced to improve the performance of the whale optimization algorithm and solve the convergence problem of the optimization algorithm. The performance of the improved WOA algorithm is verified by four benchmarks. Then, the objective function is constructed by using the element relative mode strain energy index, which is mainly based on the change of the ratio of the element modal strain energy before and after structural damage to the modal strain energy of the global structure as the damage index. Finally, three examples, a numerical plane frame, an experimental simply supported beam, and an ASCE Benchmark frame, are used to identify the assumed damage under different conditions using the proposed method. It is found that the element relative modal strain energy near the damage location changes greatly after structural damage occurs, while that of the element relatively far away from the damaged element area is less affected. The results show that the method can accurately identify the exact location and severity of damage in different structures, which can effectively improve the efficiency of damage identification.

Integrated electromechanical impedance technique with convolutional neural network for concrete structural damage quantification under varied temperatures

The electromechanical impedance/admittance (inverse of impedance, EMI/EMA) technique has demonstrated its high sensitivity to structural incipient damages, however, it is also vulnerable to ambient temperature fluctuation that may adversely cause false alarms in real-life monitoring applications. This paper proposed an innovative approach integrating the EMA technique with Convolutional Neural Network (CNN) to quantify concrete structural damage severity under varied temperatures. In the approach, EMA signals were first split into multiple sub-range responses, and the corresponding statistical indices namely Pearson Correlation Coefficient (PCC), were calculated and utilized as input of one-dimensional CNN. In order to meet high accuracy for training the CNN model, an Orthogonal Matching Pursuit (OMP) algorithm based EMA data reconstruction technique was employed to generate sufficient data for the establishment of CNN data library. Effectiveness of the proposed connectionist approach was verified through crossover experiments of diagnosing crack damages on a standardized concrete cube subjected to varied temperatures. Both temperature recognition and damage quantification under elevated temperatures were accomplished via performing the EMA signal based CNN procedure. Experimental results revealed that the proposed approach was of high accuracy for temperature recognition and damage quantification, which potentially facilitates the in-situ monitoring of engineering structures under varied temperature conditions.

Functional brain network organization measured with magnetoencephalography predicts cognitive decline in multiple sclerosis

Background: Cognitive decline remains difficult to predict as structural brain damage cannot fully explain the extensive heterogeneity found between MS patients. Objective: To investigate whether functional brain network organization measured with magnetoencephalography (MEG) predicts cognitive decline in MS patients after 5 years and to explore its value beyond structural pathology. Methods: Resting-state MEG recordings, structural MRI, and neuropsychological assessments were analyzed of 146 MS patients, and 100 patients had a 5-year follow-up neuropsychological assessment. Network properties of the minimum spanning tree (i.e. backbone of the functional brain network) indicating network integration and overload were related to baseline and longitudinal cognition, correcting for structural damage. Results: A more integrated beta band network (i.e. smaller diameter) and a less integrated delta band network (i.e. lower leaf fraction) predicted cognitive decline after 5 years (), independent of structural damage. Cross-sectional analyses showed that a less integrated network (e.g. lower tree hierarchy) related to worse cognition, independent of frequency band. Conclusions: The level of functional brain network integration was an independent predictive marker of cognitive decline, in addition to the severity of structural damage. This work thereby indicates the promise of MEG-derived network measures in predicting disease progression in MS.

Damage identification in steel girder bridges using modal strain energy-based damage index method and artificial neural network

Health monitoring of infrastructures is of utmost significance, as they play a vital role in transportation. Identifying damages at the onset is essential to ensure the structure’s health state. The vibration-based damage identification methods use the vibration characteristics of structures to detect damages. Also, artificial neural networks (ANN) have been used to estimate damage magnitude with varied success. In this study, a two-stage damage identification technique was proposed to locate and estimate damages in steel girder bridges. The performance and feasibility of the proposed method were evaluated by applying several single and multiple damage scenarios to a validated finite element (FE) model of I-40 Bridge. Initially, the damage location was determined using the modal strain energy-based damage index method. For this purpose, the damage index was calculated separately for the first three bending modes of the bridge. The calculated damage vectors were combined along the length of girders. The peak value of the combined damage index showed the damage location along the length of steel plate girders. The results revealed that all the damage locations were detected with appropriate accuracy even if multiple damage scenarios existed in plate girders. In the second step, an ANN was used for estimation of damage severity. The damage index was used as input parameters to train the ANN. Then, the trained ANN could predict the severity of structural damages. The results indicated appropriate accuracy proposed by ANN for estimating the damage magnitude as well as proper performance of the proposed method.

The role of sorbents and probiotics in prevention of structural and morphological disorders in the small intestine of animals developing in dysbiosis

The past decade is characterized by a noticeable increase in the interest of physicians in all areas of activity in the development of new and improvement of existing approaches to the correction of dysbiotic disorders. Among them, the concept of using probiotics occupies a leading position. At the same time, some enterosorbents, the mechanism of action of which is largely due to the sanitation of the intestinal lumen and due to this improvement of conditions for the vital activity of the physiological microbiota, can be attributed to the group of means of improving the normal microflora. In the context of an increase in the level of resistance to antibacterial agents, the inclusion of enterosorbents in the complex therapy of dysbiosis is an important and pathogenetically justified approach. The aim of the work was to clarify the effectiveness of the use of sorbents and probiotics for the prevention of structural and morphological disorders in the small intestine of white mice developing against the background of antibiotic-induced dysbiosis. Electron-microscopic methods showed that in the mucous membrane of the small intestine of mice after using the probiotic “Simbiter” the extinction of manifestations of cytodestructive disorders is observed. In addition, the obtained electron microscopic data, indicating the ability of probiotic drugs with the simultaneous introduction into the body of animals with a complex of antibiotics, to stimulate the body’s immune response. As a result of ultrastructural analysis of the mucous membrane of the small intestine of mice, the formation of dysbiosis in which occurred against the background of the use of enterosorbents, a decrease in the severity of structural damage was found, compared with the group of animals that received only antibiotics. After using “Symbiogel”, activation of plasma cells was registered, which can be an indicator of the inflammatory process and the activity of the immune response in general, as evidenced by the detection of plasma cells with dilated tubules. In general, it should be noted that the use of “Symbiogel” for the prevention of dysbiotic disorders contributes to the formation of a more pronounced immune response, compared with probiotic drugs. So, on the model of antibiotic-induced dysbiosis at the ultrastructural level, the ability of multiprobiotics “Simbiter®” and sorbent “Symbiogel” to reduce cytodestructive changes in the mucous membrane of the small intestine of mice and normalize morphoimunogenesis was proved.

Damage Detection in Initially Nonlinear Structures Based on Variational Mode Decomposition

Nonlinear characteristics in the dynamic behaviors of civil structures degrade the performance of damage detection of the linear theory based traditional time- and frequency-domain methods. To overcome this challenge, this paper proposes a damage detection approach for nonlinear structures based on Variational Mode Decomposition (VMD). In this approach, the measured dynamic responses from nonlinear structures under earthquake excitations are adaptively decomposed into a finite number of monocomponents by using VMD. Each decomposed mono-component represents an amplitude modulated and frequency modulated (AMFM) signal with a limited frequency bandwidth. Hilbert transform is then employed to identify the instantaneous modal parameters of the decomposed monomodes, including instantaneous frequencies and mode shapes. Based on the identified modal parameters from the decomposed structural dynamic responses, two damage indices are defined to identify the location and severity of structural damage, respectively. To validate the effectiveness and accuracy of the proposed approach, a nonlinear seven-storey shear building model with four different damage cases under earthquake excitations is used in the numerical studies. In experimental verifications, data from shake table tests on a 12-storey scaled reinforced concrete frame structure with different earthquake excitations are analyzed with the proposed approach. The results in both numerical studies and experimental validations demonstrate that the proposed approach can be successfully applied for nonlinear structural damage identification.

Locating and Quantifying Damage in Deck Type Arch Bridges Using Frequency Response Functions and Artificial Neural Networks

Vibration-based methods can be used to detect damage in a structure as its vibration characteristics change with physical changes in the structure. Arch bridge is a popular type of bridge with rather complex vibration characteristics which pose a challenge for using existing vibration-based methods to detect damage in the bridge. Further, its particular geometry with a curved arch rib and vertical members (either in compression or tension) to support the horizontal deck makes the process of damage quantification using vibration-based methods harder and challenging. This paper develops and presents a vibration-based method that utilizes damage pattern changes in frequency response functions (FRFs) and artificial neural networks (ANNs) to locate and quantify damage in the rib of deck-type arch bridge, which is the most important load bearing component in the bridge. Principal component analysis, which is performed to reduce the dimension of original FRF data series and to obtain limited principal component analysis (PCA)-compressed FRF data is used in the development of the proposed method. FRF change, which is the difference in the FRF data between the intact and the damaged structure, is compressed to a few principal components and fed to ANNs to predict the location and severity of structural damage. The process and the hierarchy of developed ANN systems are presented, including the “fusion network” concept, which individually analyses FRF-based damage indicators separated by sensor locations. Finally, results obtained for many tested damage cases (inverse problems) are presented, which demonstrate the applicability of the proposed method for locating and quantifying damage in the rib of deck type arch bridge.