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A multi-mode approach for multi-directional damage detection in frame structures
Abstract
Due to their three-dimensional nature, damages have different effects on each vibration mode of a frame structure, depending on the direction of deflection of its structural elements. In this study, a Multi-mode Multi-directional Damage Index (MMDI) method is proposed to benefit from the recognition of the multi-directional effects of damages in order to reduce damage detection errors. The novelty of the method resides in the use of specifically developed modal combination factors for the identification of the vibration modes more relevant to the damage detection and for the assessment of the directionality of the damage effects. The detection capability of the MMDI method is experimentally investigated on a bridge testbed consisting of a two-column moment resisting frame. Individual and combined damages are simulated into columns and beams of the testbed through steel parts removal. The dynamic characteristics of the system in its undamaged and damaged configurations are identified upon ambient-like accelerations from a low dense sensor network. The effectiveness of the MMDI method is assessed for its ability of identifying damages without any pre-selection of vibration modes. The accuracy of the damage assessment is measured through four indicators targeting identification, false detection, localization, and severity estimation of the damages.
... Ghrib et al. [17] presented a method for damage identification of Euler-Bernoulli beams using static-deflection measurements. Bonessio et al. [18] presented a multimode approach for multidirectional damage identification in frame structures; the method reduces damage identification errors by analyzing the multidirectional effects of damages. Vaez and Fallah [19] identified the site and extent of multiple damage cases of frame structure using a two-stage damage-identification approach. ...
This paper presents a method to identify the damages in frame structures with slender beams. This method adjusts the parameters of the structure to match the analytical and the measured displacements. The effect of transverse shear deformation on the nodal analytical displacement is analyzed, and the parameter identification of frame structures with slender beams is performed. The results demonstrate that parameter-identification accuracy can be considerably improved by considering the transverse shear deformation in the frame structure with slender beams. The proposed method can accurately identify the damages in frame structures with slender beams using displacement measurements.
... Shadan and Khoshnoudian [18] utilized the sensitivity of the frequency response function to effectively identify the location and degree of damage. Bonessio et al. [19] proposed a multi-mode multi-directional damage index method for multi-directional damage detection in frame structures. Entezami et al. [20] proposed a new iterative regularization method and an improved sensitivity function for the identification of structural damage. ...
In this study, a particle swarm optimization with a sigmoid increasing inertia weight (SIPSO) algorithm is proposed for structural damage identification based on the optimization of structural vibration response constraints. In view of the existing problems for particle swarm optimization algorithms used for structural damage identification, such as low accuracy of damage identification and easy misjudgment of damage location, the sigmoid increasing inertia weight is introduced to improve the global and local search ability of the algorithm. Simulation results show that the parameters of the sigmoid increasing inertia weight have a significant effect on the performance of the SIPSO algorithm for structural damage identification. Compared with similar improved particle swarm optimization algorithms, the SIPSO algorithm has some advantages of fast convergence speed, high identification accuracy, and strong robustness ability in structural damage identification.
... For damage detection, the responses of a structure including static and dynamic responses perform a vital role. Several studies related to using dynamic responses such as the natural frequencies and mode shapes of a structure can be found in the literature [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Also, damage detection methods based on employing static data have attracted much attention. ...
A crack localization method for beam-column structures is proposed considering axial load effects. Application of the method on a simply supported beam and a cantilever with simply supported beam-column demonstrated that the locations of single and multiple damage scenarios can be well determined in low axial loads. However, in the vicinity of the critical load, healthy and damaged static data are uncommon. Moreover, with increase in the axial load, the locations of single and multiple damage scenarios cannot be precisely specified. A more sensitive damage indicator is subsequently proposed to demonstrate the effects of axial forces comparable to critical loads.
Recent artificial intelligence (AI) advancements have significantly impacted the field of structural engineering by offering new solutions to enhance safety, efficiency, and cost-effectiveness in the design, construction, and maintenance of infrastructure and urban areas. This study has investigated a machine learning-based approach to estimate the seismic vulnerability of reinforced concrete (RC) building frames. The study utilized a probabilistic technique to assess the simulation of a 4-story RC building frame in the face of epistemic uncertainty. Data was collected using the Monte-Carlo approach, which created a machine learning (ML) model for predicting structural damage. The problem was formulated as both a regression-based and a classification-based model. An artificial neural network (ANN) feed-forward network architecture model was used to solve the classification problems. The optimal number of neurons in the hidden layer was determined to produce the best estimation model. Three different models were combined for the regression model, including LASSO regression, random forest, and gradient boosting, by implementing the stacking generalization. The investigation results indicate that the stacked predicted model exhibits less variance than other ML models. The classification and regression-based algorithms can forecast damage states with a high degree of accuracy, ranging from 87-95 percent. In conclusion, the study has demonstrated the effectiveness of machine learning techniques for predicting the seismic vulnerability of RC building frames. With the continued advancement of AI and big data, these methods will likely play a crucial role in the structural engineering field.
The rapid expansion of cities in India due to soaring population growth has led to inadequate land utilization and increased risk of seismic disasters. Urban areas with high population density, numerous buildings, and critical infrastructures are particularly vulnerable to such catastrophes. Building collapses are responsible for almost 90% of earthquake-related casualties, making seismic safety a crucial issue in Surat-city. This study aims to develop and apply advanced seismic vulnerability analysis methods to address this issue. The research has been structured into three parts, each with its own focus and objectives. In the first part, the seismic performance of RC frame buildings designed as per the IS 1893 older and
revised provision was evaluated using the R factor as per the ATC-19 guidelines. Capacity spectrums curve were obtained through non-linear static pushover analysis matched with the demand parameter in the acceleration-displacement response spectra (ADRS) format. The uncertainty factor was calculated based on the mean least square of the approximate normal to binomial distribution. Seismic fragility curves were derived based on the capacity spectrum-based approach, while a deterministic approach was employed for modeling of the RC frame
building. The study also presented the likelihood of occurrence for pre-defined damage states, and fragility curves derived using the Nonlinear static pushover analysis and Incremental dynamic analysis performance were compared based on the statistical curve fitting parameters. In the second part of the study focused on the practical application of these methods in the Western zone of Surat-city. The objective was to provide a comprehensive understanding of the seismic vulnerability of residential buildings in the area and to identify effective measures for mitigating the risk of earthquakes. A theoretical framework for probabilistic seismic vulnerability studies was established in the third part of the study. This section delved into mathematical models, advanced developments, and machine-learning approaches for evaluating the physical damage to RC frame buildings. The study found that the R factor improved by up to 16%, and the probability of complete damage decreased by up to 15% for the revised buildings. The coefficient of variation was compared between pushover curves derived from nonlinear static pushover analysis, and it was determined that the curves were reliable. The study found that the seismic vulnerability of the The western zone can be severely damaged during a high-intensity earthquake, with a mean damage index of around 2.39 for RC frame buildings. In particular, the study found that seismic vulnerability was higher for older mid-rise RC frame buildings in Surat city. Overall, this study highlights the importance of implementing and exploring seismic vulnerability assessments at both local and regional levels. Further, Seismic fragility curves are derived using deterministic and probabilistic analysis approaches. A probabilistic analysis was also conducted, revealing an increase of up to 10% in the probability of complete damage compared to a deterministic analysis. The study also proposed a machine learning-based approach to estimate the mean damage state and damage index of the RC frame building, and the ANN model provided more accurate results than the stacking approach. The study develops and applies advanced seismic vulnerability analysis methods to address the critical issue of seismic safety in Surat-city. In the western zone, it is vital to retrofit severely damaged mid-rise RC frame buildings and strongly discourage the design of soft-story structures.
Artificial intelligence (AI) is proving to be an efficient alternative approach to classical modeling techniques. AI refers to the branch of computer science that develops machines and software with human-like intelligence. Compared to traditional methods, AI offers advantages to deal with problems associated with uncertainties and is an effective aid to solve such complex problems. In addition, AI-based solutions are good alternatives to determine engineering design parameters when testing is not possible, thus resulting in significant savings in terms of human time and effort spent in experiments. AI is also able to make the process of decision making faster, decrease error rates, and increase computational efficiency. Among the different AI techniques, machine learning (ML), pattern recognition (PR), and deep learning (DL) have recently acquired considerable attention and are establishing themselves as a new class of intelligent methods for use in structural engineering. The objective of this review paper is to summarize techniques concerning applications of the noted AI methods in structural engineering developed over the last decade. First, a general introduction to AI is presented and the importance of AI in structural engineering is described. Thereafter, a review of recent applications of ML, PR, and DL in the field is provided, and the capability of such methods to address the restrictions of conventional models are discussed. Further, the advantages of employing such algorithmic methods are discussed in detail. Finally, potential research avenues and emerging trends for employing ML, PR, and DL are presented, and their limitations are discussed.
In this paper, a systematic methodology for identifying damages in bridges is presented, which includes the baseline calibration through field testing data, damage simulation and identification, and an in-lab experimental verification of the model. Using the test data from an incompletely documented bridge 09-125-16 in Cass County North Dakota, a Grillage numerical model has been first created to simulate the bridge responses due to traffic loads, with the purpose to find an accurate baseline data. Through adjustment of boundary stiffness, a validated bridge model, matching the field test data, has been developed. Based on the chosen validated numerical model, numerical simulation of the corresponding bridge under different damage scenarios and traffic loads has been performed. Using the difference of the displacement mode shape data, a modified curvature method is suggested for identifying damages in bridges, which has been proven successful through the modeling results of bridges with fictitious damages. Following the numerical model development, an in-lab experiment of a steel plate with and without damages under impact forces has been adopted to produce vibration data through an accelerometer. The modified curvature has been then computed using the experimental mode shape data and its change has been found to correlate very well with the embedded damage as anticipated by the suggested theory.
This paper briefly outlines the rationale for structural health monitoring as an integral component of bridge management systems. Two different approaches, system identification and statistical pattern recognition, are summarised and applied in turn to vibration data collected from three scale model-reinforced concrete bridges. The results show that the system identification paradigm can successfully locate and quantify the damage to the decks when they are loaded to incipient collapse, especially when experience is used to determine the parameters to use in the finite element updating procedure. However, the study also demonstrated that this approach requires a large amount of high quality data, requirements that cannot always be met readily in the field. In contrast, although the statistical pattern recognition approach was not able to quantify or locate the damage, it was able to clearly indicate that damage had occurred from relatively few measurements. A comparison of the strengths and weaknesses of the two approaches suggests that they should be used in a complementary manner. The statistical pattern recognition approach can be employed as a simple, cost efficient way to indicate that damage has occurred. It can then trigger a more detailed investigation using system identification.
This paper reviews stochastic system identification methods that have been used to estimate the modal parameters of vibrating structures in operational conditions. It is found that many classical input-output methods have an output-only counterpart. For instance, the Complex Mode Indication Function (CMIF) can be applied both to Frequency Response Functions and output power and cross spectra. The Polyreference Time Domain (PTD) method applied to impulse responses is similar to the Instrumental Variable (IV) method applied to output covariances. The Eigensystem Realization Algorithm (ERA) is equivalent to stochastic subspace identification.
A comprehensive review on modal parameter-based damage identification methods for beam- or plate-type structures is presented, and the damage identification algorithms in terms of signal processing are particularly emphasized. Based on the vibration features, the damage identification methods are classified into four major categories: natural frequency-based methods, mode shape-based methods, curvature mode shape-based methods, and methods using both mode shapes and frequencies, and their merits and drawbacks are discussed. It is observed that most mode shape-based and curvature mode shape-based methods only focus on damage localization. In order to precisely locate the damage, the mode shape-based methods have to rely on optimization algorithms or signal processing techniques; while the curvature mode shape-based methods are in general a very effective type of damage localization algorithms. As an implementation, a comparative study of five extensively-used damage detection algorithms for beam-type structures is conducted to evaluate and demonstrate the validity and effectiveness of the signal processing algorithms. This brief review aims to help the readers in identifying starting points for research in vibration-based damage identification and structural health monitoring and guides researchers and practitioners in better implementing available damage identification algorithms and signal processing methods for beam- or plate-type structures.
In this paper, a newly derived algorithm to predict locations and severities of damage in structures using changes in modal characteristics is presented. First, two existing algorithms of damage detection are reviewed and the new algorithm is formulated in order to improve the accuracy of damage localization and severity estimation by eliminating erratic assumptions and limits in the existing algorithms. Next, the damage prediction accuracy is numerically assessed for each algorithm when applied to a two-span continuous beam for which pre- and post-damage modal parameters are available for only a few modes of vibration. Compared to the existing damage detection algorithms, the new algorithm improved the accuracy of damage localization and severity estimation results in the test beam.
This paper addresses the issue of system identi#cation for linear structural systems using earthquake induced time-histories of the structural response. The proposed methodology focuses on the Eigensystem Realization Algorithm #ERA# and on the Observer Kalman #lter IDenti#cation algorithm #OKID#, to perform system identi#- cation using general input-output data via Markov parameters. The e#ciency of the proposed technique is shown bynumerical examples for the case of a three dimensional eight story building subjected to earthquake excitation. The e#ects of noise in the measurements and of inadequate instrumentation are investigated. It is shown that the identi#ed models show excellent agreement with the real system in predicting the structural response time-histories when subjected to di#erent ground excitations. Introduction The state space representation of linear dynamical systems has been widely used for systems' analysis and design, especially with the implementation of controlle...
In conjunction with a fatigue test of a full-scale in situ three-span highway bridge, an investigation was undertaken to evaluate the use of changes in dynamic properties of the bridge as a possible means of detecting structural deterioration due to fatigue cracks in the girders. Cyclic-loading tests (transient and steady-state) were conducted to determine the changes in dynamic properties. The loading was imposed by a moving-mass, closed-loop electro-hydraulic actuator system. Several different dynamic tests were employed in the investigation to determine the modal viscous damping ratios, stiffness, and mechanical impedance of the bridge at selected intervals during the fatigue loading.
Vibration-based damage detection in plates has been investigated by various methods relying on mode shapes, among which the 2D curvature mode shape is a damage feature attracting much attention of researchers. Unlike the sound understanding of the use of the 1D curvature mode shape for detecting damage in beams, however, use of the 2D curvature mode shape to detect damage in plates is not yet well elucidated, major unresolved issues including lack of clarity about the mechanism of characterizing damage, susceptibility to noise, and insensitivity to sight damage. These deficiencies severely hamper use of the 2D curvature mode shape to portray damage in plates. To deal with these deficiencies, the mechanism of using 2D curvature mode shape to depict damage is analytically clarified in light of thin plate theory. On the basis of this clarification, a synergy between wavelet transform and a Teager energy operator is proposed to tackle the other deficiencies of susceptibility to noise and insensitivity to sight damage, leading an enhanced 2D curvature mode shape. The efficacy of the enhanced 2D curvature mode shape is numerically demonstrated using finite element simulations and experimentally validated through noncontact measurement by a scanning laser vibrometer, whereby its advances of clear mechanism of characterizing damage, robustness against noise, and sensitivity to slight damage are sufficiently corroborated.
This paper deals with a procedure for the identification of the damage in bridge structures equipped with isolators and/or energy dissipating devices. The procedure is based on the availability of accelerometric records from any simple sensor network installed on existing bridges. The proposed algorithm provides an assessment of the performance degradation of conventional structural components as well as installed isolators and energy dissipators, obtained from changes in modal characteristic of the structural response.
A new index localization and severity index is introduced to be used for ordinary structural elements and anti-seismic devices. For the validation of the procedure, the algorithm has been applied to a continuous beam as well as to a bridge structure equipped with Friction Pendulum devices. The proposed procedure shows a high level of accuracy in the damage localization and severity assessment also in a complex scenario of damage. The severity index is also interpreted in terms of physical quantities (e.g. friction coefficient) representative of the specific device performance. For this reason the procedure appears feasible for implementation on real structures with the advantage of providing direct indicators of the early stages of degradation of performance parameters. This information can be used to design inspection and maintenance plans. Copyright
Vibration based condition monitoring refers to the use of in situ non-destructive sensing and analysis of system characteristics –in the time, frequency or modal domains –for the purpose of detecting changes, which may indicate damage or degradation. In the field of civil engineering, monitoring systems have the potential to facilitate the more economical management and maintenance of modern infrastructure. This paper reviews the state of the art in vibration based condition monitoring with particular emphasis on structural engineering applications.
Modal testing by impact is a practical tool for structural identification, integrity monitoring, and diagnostics of bridges. Typically, nonlinearities in bridge response as well as sensitivity to ambient conditions are observed during modal tests. Therefore, impact tests should be planned and executed so that the effects of nonlinearities are incorporated in estimating modal parameters of a test structure. Furthermore, indexes more reliable than thc modal parameters are needed to diagnose local and/or obscure damage. These and other shortcomings in current modal test-based diagnostic studies are reviewed. A multireference impact testing of a bridge, in which frequency-response functions (FRF) are measured and a large number of modal parameters are reliably identified, is presented. The mode shape coefficients obtained through processing measured FRF are directly transformed into flexibility of the test bridge without assuming mass. Analytical studies of a calibrated analytical model are presented to demonstrate that flexibility coefficients are more sensitive to local damage than either frequencies or mode shapes.
Laboratory model tests were conducted to examine the feasibility of detecting structural deterioration in highway bridges by vibrational signature analysis. The model is a two-span aluminum plate-girder bridge that permits vibrations to be induced using vehicular excitation. The ambient vibration method was used to obtain vibrational signature elements. Data was processed both by curve fitting and by using a more automatable analytical approach. Using low-mass vehicular excitation, ambient vibration results compare well with conventional modal analyses for resonant frequencies and mode shapes, but damping is overestimated. Roadway roughness and vehicle velocity do not influence resonant frequencies or mode shapes, although variable mass can have a significant impact on resonant frequencies. Vehicular mass influences on mode shapes appear to be minimal. Major structural degradation can cause significant changes to both resonant frequencies and mode shapes. Degradation is detectable using a readily automatable analytical approach. Preliminary full-scale tests suggest that vibrational signatures are obtainable in the field using the same methodology employed in the laboratory.
A vibration measurement was carried out on an existing prestressed concrete bridge during its failure test. The vertical and horizontal vibrations were measured based on the ambient vibration method. The change of vibrational characteristics due to deterioration of the bridge during the failure test was obtained. Moreover, a measured mechanism on the change of vibrational characteristics is discussed compared with a numerical analysis.
A newly developed damage severity estimation method, termed as cross-modal strain energy (CMSE) method, which is capable of accurately estimating the damage magnitude of multiple damaged members, is presented. While all existing damage severity estimation methods that utilize modal strain energy are either employing an iterative solution procedure or involving significant approximations, the CMSE method is an exact, noniterative solution method. Furthermore, the development of the CMSE method is under the assumption that the mass distributions of the baseline and damaged structures are unknown, but identical. Implementing this method requires only the information of a few modes measured from the damaged structure. Numerical studies are demonstrated for a three-dimensional five-story frame structure based on synthetic data generated from finite element models.
This paper addresses the first generation benchmark problem on structural health monitoring developed by the ASCE Task Group on Structural Health Monitoring. The focus of the problem is a four-story model of an existing physical model at the University of British Columbia where simulated data are used for the system identification. Modal parameters were extracted using the frequency domain decomposition method. Rather than relying on data from the undamaged structure, a new proposed methodology based on ratios between stiffness and mass values from the eigenvalue problem is presented to identify the undamaged state of the structure. Once the structural identification is complete, the damage index method is used to detect the location and severity of damage. By not relying on undamaged structure information, this approach may be applicable to existing structures that may already incorporate some amount of damage.
This article presents a newly developed modal strain energy decomposition method for damage localization that is capable of identifying damage to individual members of three-dimensional (3D) frame structures. This method is based on decomposing the modal strain energy of each structural member (or element) into two parts, one associated with the element's axial coordinates and the other with its transverse coordinates. In turn, two damage indicators are calculated for each member to perform the damage localization analysis. Implementing this method requires only a small number of mode shapes identified from both the damaged and baseline structures. Numerical studies are conducted of a 3D five-story frame structure and also a complicated offshore template platform, based on synthetic data generated from finite-element models. In addition to providing theoretical insights to illustrate the advantages of using this newly developed method, this article also demonstrates numerically that the new method is capable of localizing various kinds of damaged elements (a vertical pile, horizontal beam, or slanted brace) at a template offshore structure.
This paper addresses the issue of system identification for linear structural systems using earthquake induced time histories of the structural response. The proposed methodology is based on the Eigensystem Realization Algorithm (ERA) and on the Observer/Kalman filter IDentification (OKID) approach to perform identification of structural systems using general input–output data via Markov parameters. The efficiency of the proposed technique is shown by numerical examples for the case of eight-storey building finite element models subjected to earthquake excitation and by the analysis of the data from the dynamic response of the Vincent-Thomas cable suspension bridge (Long Beach, CA) recorded during the Whittier and the Northridge earthquakes. The effects of noise in the measurements and of inadequate instrumentation are investigated. It is shown that the identified models show excellent agreement with the real systems in predicting the structural response time histories when subjected to earthquake-induced ground motion. Copyright © 1999 John Wiley & Sons, Ltd.
This paper extends the study of damage identification algorithms summarized in the accompanying paper `Comparative study of damage identification algorithms: I. Experiment' to numerical examples. A finite element model of a continuous three-span portion of the I-40 bridges, which once crossed the Rio Grande in Albuquerque, NM, was constructed. Dynamic properties (resonant frequencies and mode shapes) of the undamaged and damaged bridge that were predicted by the numerical models were then correlated with experimental modal analysis results. Once correlated with the experimental results, eight new damage scenarios were introduced into the numerical model including a multiple damage case. Also, results from two undamaged cases were used to study the possibility that the damage identification methods would produce false-positive readings. In all cases analytical modal parameters were extracted from time-history analyses using signal processing techniques similar to those used in the experimental investigation. This study provides further comparisons of the relative accuracy of these different damage identification methods when they are applied to a set of standard numerical problems.
Strong motion data acquired from instrumented bridges during seismic events provides an excellent opportunity to gain insight into the behaviour of bridges and performance of their components. Using system identification, modal parameters of bridges can be estimated and the performance during various level of earthquake can be studied. In this study dynamic behaviour of Yokohama-Bay Bridge is investigated using seismic response recorded from six earthquakes. Modal parameters of the structure are estimated using system realization of state–space model. The realization method used here is based on the system realization using information matrix (SRIM), which makes use of the correlations between earthquake input and output data to identify the coefficient matrices of state–space model. Identification results from six earthquakes show that the system identification can be used to capture global behaviour of the bridge by estimating modal parameters and also to explain local behaviour of its component such as performance of link-bearing connections during earthquake. Copyright © 2005 John Wiley & Sons, Ltd.
Vibration of a new concrete bridge was monitored and change in the bridge structural stiffness was identified accordingly over a 5-year period. This three-span 111-m long bridge is instrumented with 13 acceleration sensors at both the superstructure and the columns. The sensor data are transmitted to a server computer wirelessly. Modal parameters of the bridge, that is, the frequencies and the modal shapes were identified by processing 1,707 vibration data sets collected under traffic excitations, based on which the bridge structural parameters, stiffness and mass, and the soil spring values were identified by employing the neural network technique. The identified superstructure stiffness at the beginning of the monitoring was 97% of the stiffness value based on the design drawings. In the identified modal frequencies, a variation from −10% to +10% was observed over the monitoring period. In the identified stiffness values of the bridge superstructure, a variation from −3% to +3% was observed over the monitoring period. Based on the statistical analysis of the collected data for each year, 5% decrease in the first modal frequency and 2% decrease in the superstructure stiffness were observed over the 5-year monitoring period. Probability density functions were obtained for stiffness values each year. Stiffness threshold values for the collapse of the bridge under the operational loading can be determined. Then the number of years can be assessed for which the area under the proposed probability density functions is greater than the threshold value. So the information obtained in this study is valuable for studying aging and long-term performance assessment of similar bridges.
On 27 August 2006, the Vincent Thomas Bridge, a 1850-m suspension bridge located in the larger metropolitan Los Angeles region, was struck by a large cargo ship passing under the bridge. Moderate damage to the maintenance scaffolding at the main span of the bridge was observed. This incident left transportation authorities wondering about the structural integrity of the bridge. A real-time continuous monitoring system that had been recently installed on the bridge successfully recorded dynamic response before and after the incident, as well as during the collision. Analysis of these valuable data allows transportation authorities to quantify the effects of the collision on the bridge structural condition, which would otherwise be infeasible with traditional visual bridge inspection approaches. A forensic study was performed to assess the structural condition of the bridge before and after the incident. Both global (multi-sensor) and local (single-sensor) identification methods were applied to detect whether significant changes occurred in the bridge vibration signature. Copyright © 2007 John Wiley & Sons, Ltd.
In this paper the problem of using measured modal parameters to detect and locate damage in plate-like structures is investigated. Many methods exist for locating damage in a structure given the modal properties before and after damage. Unfortunately, many of these methods require a correlated finite element model or mass normalized mode shapes. If the modal properties are obtained using ambient excitation then the mode shapes will not be mass normalized. In this paper a method based on the changes in the strain energy of the structure will be discussed. This method was originally developed for beam-like structures, that is, structures characterized by one-dimensional curvature. In this paper the method will be generalized to plate-like structures that are characterized by two-dimensional curvature. This method only requires the mode shapes of the structure before and after damage. To evaluate the effectiveness of the method it will be applied to both simulated and experimental data.
The Vincent Thomas Bridge in the Los Angeles metropolitan area, is a critical artery for commercial traffic flow in and out of the Los Angeles Harbor, and is at risk in the seismically active Southern California region, particularly because it straddles the Palos Verdes fault zone. A combination of linear and non‐linear system identification techniques is employed to obtain a complete reduced‐order, multi‐input–multi‐output (MIMO) dynamic model of the Vincent Thomas Bridge based on the dynamic response of the structure to the 1987 Whittier and 1994 Northridge earthquakes.
Starting with the available acceleration measurements (which consists of 15 accelerometers on the bridge structure and 10 accelerometers at various locations on its base), an efficient least‐squares‐based time‐domain identification procedure is applied to the data set to develop a reduced‐order, equivalent linear, multi‐degree‐of‐freedom model. Although not the main focus of this study, the linear system identification method is also combined with a non‐parametric identification technique, to generate a reduced‐order non‐linear mathematical model suitable for use in subsequent studies to predict, with good fidelity, the total response of the bridge under arbitrary dynamic environments.
Results of this study yield measurements of the equivalent linear modal properties (frequencies, mode shapes and non‐proportional damping) as well as quantitative measures of the extent and nature of non‐linear interaction forces arising from strong ground shaking. It is shown that, for the particular subset of observations used in the identification procedure, the apparent non‐linearities in the system restoring forces are quite significant, and they contribute substantially to the improved fidelity of the model. Also shown is the potential of the identification technique under discussion to detect slight changes in the structure's influence coefficients, which may be indicators of damage and degradation in the structure being monitored. Difficulties associated with accurately estimating damping for lightly damped long‐span structures from their earthquake response are discussed. The technical issues raised in this paper indicate the need for added spatial resolution in sensor instrumentation to obtain identified mathematical models of structural systems with the broadest range of validity. Copyright © 2003 John Wiley & Sons, Ltd.
Modal parameter estimation requires a lot of user interaction, especially when parametric system identification methods are
used and the modes are selected in a stabilization diagram. In this paper, a fully automated, three-stage clustering approach
is developed for interpreting such a diagram, that does not require any user-specified parameter or threshold value. The three
stages correspond to the three stages in a manual analysis: setting stabilization thresholds for clearing out the diagram,
detecting columns of stable modes, and selecting a representative mode from each column. A validation study, where nine real-life
noisy operational modal bridge data sets are both automatically and manually analyzed, illustrates the accuracy and robustness
of the proposed automatization strategy.
The development of a methodology for accurate and reliable condition assessment of civil structures has become very important. The finite element (FE) model updating method provides an efficient, non-destructive, global damage identification technique, which is based on the fact that the modal parameters (eigenfrequencies and mode shapes) of the structure are affected by structural damage. In the FE model the damage is represented by a reduction of the stiffness properties of the elements and can be identified by tuning the FE model to the measured modal parameters. This paper describes an iterative sensitivity based FE model updating method in which the discrepancies in both the eigenfrequencies and unscaled mode shape data obtained from ambient tests are minimized. Furthermore, the paper proposes the use of damage functions to approximate the stiffness distribution, as an efficient approach to reduce the number of unknowns. Additionally the optimization process is made more robust by using the trust region strategy in the implementation of the Gauss–Newton method, which is another original contribution of this work. The combination of the damage function approach with the trust region strategy is a practical alternative to the pure mathematical regularization techniques such as Tikhonov approach. Afterwards the updating procedure is validated with a real application to a prestressed concrete bridge. The damage in the highway bridge is identified by updating the Young's and the shear modulus, whose distribution over the FE model are approximated by piecewise linear functions.
Because of the sensitive characteristic to local defects, strain has been widely utilized to identify the presence, location, and severity of damage in structures, and this paper reviews recent publications regarding strain-related techniques on structural damage identification. A brief description on the strain–displacement relationship at defective areas is first presented. Then, damage identification using various strain-related parameters, such as strain (curvature) mode shape, strain energy, strain frequency response function, etc., is explored and the hypersensitivity of corresponding damage indicators will be examined systematically. Finally, some generalized criteria on the selection of strain-based parameters for damage identification are concluded and suggestions for future work are discussed.
The important advances achieved in the modal identification, sensors, and structural monitoring of bridges have motivated the bridge engineering community to develop damage detection methods based on vibration monitoring. Some of these methods have already been demonstrated under certain conditions in bridges with deliberate damage. However, the performance of these methods for damage detection in bridges has not been fully proven so far and more research needs to be done in this direction. In this article, six damage detection methods based on vibration monitoring are evaluated with two case studies. First, the dynamic simulation and modal parameters of a cracked composite bridge are obtained. Here, the damage detection methods are evaluated under different crack depth, extension of the damage, and noise level. Second, damage is identified in a reinforced concrete bridge. This bridge was deliberately damaged in two phases. In this example, damage detection methods, which do not require comparison between different structural conditions, were applied. In the first case study, evaluated damage detection methods could detect damage for all the damage scenarios; however, their performance was notably affected when noise was introduced to the vibration parameters. In the second case study, the evaluated methods could successfully localize the damage induced to the bridge.
Structural health Monitoring of Bridges with Seismic Response Modification Devices
- G Benzoni
- N Bonessio
- G Lomiento
Benzoni G, Bonessio N, Lomiento G. Structural health Monitoring of Bridges
with Seismic Response Modification Devices. Report no. SSRP-13/02
Department of Structural Engineering UCSD, May 2013, San Diego.
Experimental study of bridge monitoring technique
- D F Mazurek
- De Wolf
Mazurek DF, De Wolf JT. Experimental study of bridge monitoring technique". J
Struct Eng 1990;116(9):2532-49.