Damage Locating Vector Method Research Articles

Damage detection in space truss structures using a third-level approach

Monitoring civil engineering infrastructure is crucial to ensuring that the structures can continue to function properly. An important aspect of this assessment is detecting structural damage. This study proposed a method that combined static loading techniques and modal parameters to establish a third level of damage detection. Specifically, the damage locating vector (DLV) was used as the static technique, and mode shapes, along with Modal Assurance Criteria (MAC), were integrated with the Firefly Algorithm (FA) to define the damage percentage. The objective of this method is to determine the location of the damaged members in space truss structures and accurately estimate the reduction in stiffness of the damaged members. DLV was used to show the location of damaged members, while the Modal Assurance Criteria – Firefly Algorithm (MAC-FA) was used to predict the stiffness loss in structural elements. This combined approach improved the DLV method, which could only identify potentially damaged parts. The fundamental concept behind MAC-FA was the relationship between the predicted mode shape and the mode shape of the structure, represented by the MAC value. This method was used to investigate damage detection in two space truss structures. The results demonstrated that the proposed method could identify damaged elements and quantify the loss of stiffness in space truss structures.

Vibration‐based damage localization with load vectors under temperature changes

Damage detection and localization in civil or mechanical structures is a subject of active development and research. A few vibration‐based methods have been developed so far, requiring, for example, modal parameter estimates in the reference and damaged states of the investigated structure, and for localization in addition a finite element model. For structures in operation, temperature has been shown to be a major nuisance to the efficiency of such methods because the modal parameters are varying not only with damage but also due to temperature variations. For detection, a few rejection approaches have been developed. Besides the increased complexity, environmental variation is hardly taken into account in localization approaches. In this paper, we propose a sensitivity‐based correction of the identified modal parameters in the damaged state with respect to the temperature field in the reference state, on the basis of a sensitivity analysis with respect to temperature dependent Parameters of the finite element model in the reference state. The approach is then applied to the stochastic dynamic damage locating vector method, where its improved performance under nonuniform temperature variations is shown in a numerical application on a beam.

Multi-damage identification of large-scale truss structures using a two-step approach

In this paper, a two-step damage detection approach is presented to identify the location and severity of multiple damage cases in large-scale truss structures. In the first step, a damage locating vector (DLV) method based on EDS (exponential decreased stress) considering modal flexibility differences is employed to localize the damaged elements of a structure. Next, a crow search algorithm (CSA) is applied to estimate the severity of reported damaged elements using the results of the first step. Numerical examples including three large-scale trusses are considered to evaluate the efficiency of the proposed approach. In order to assess the effect of noise on the process of damage detection, a statistical study on each example is investigated, and selection of an appropriate threshold is studied in this article. The results indicate that the combination of the DLV method using EDS and CSA can reliably determine the location and severity of multiple structural damage cases even though there are a high number of elements and a low number of considered modes.

Statistical damage localization with stochastic load vectors using multiple mode sets

The stochastic dynamic damage locating vector method is an output-only damage localization method based on both a finite element model of the structure and modal parameters estimated from output-only measurements in the damage and reference states of the system. A vector is obtained in the null space of the changes in the transfer matrix from both states and then applied as a load vector to the model. The damage location is related to this stress where it is close to zero. In previous works, an important theoretical limitation was that the number of modes used in the computation related to the transfer function could not be higher than the number of sensors located on the structure. It would be nonetheless desirable not to discard information from the identification procedure. In this article, the stochastic dynamic damage locating vector method has been extended with a joint statistical approach for multiple mode sets, overcoming this restriction on the number of modes. Another problem is that the performance of the method can change considerably depending on the Laplace variable where the transfer function is evaluated. Particular attention is given to this choice and how to optimize it. The new approach is validated in numerical simulations and on experimental data, where the outcomes for multiple mode sets are compared with only using a single mode set. From these results, it can be seen that the success rate of finding the correct damage localization is increased when using multiple mode sets instead of a single mode set.

A combination of damage locating vector method (DLV) and differential evolution algorithm (DE) for structural damage assessment

In this study, a two-stage method is presented for identifying multiple damage scenarios. In the first stage, the damage locating vector (DLV) method using normalized cumulative energy (nce) is employed for damage localization in structures. In the second stage, the differential evolution algorithm (DE) is used for damage severity of the structures. In addition, in the second stage, a modification of an available objective function is made for handing the issue of symmetric structures. To verify the effectiveness of the present technique, numerical examples of a 72-bar space truss and a one-span steel portal frame are considered. In addition, the effect of noise on the performance of the identification results is also investigated. The numerical results show that the proposed combination gives good assessment of damage location and extent for multiple structural damage cases.

A two-stage assessment method using damage locating vector method and differential evolution algorithm for damage identification of cross-ply laminated composite beams

This article proposes a two-stage damage identification method to identify the location and extent of multiple damages at individual layers in laminated composite beams. In the first stage, the dam...

Comparative Study of Mode Shapes Curvature and Damage Locating Vector Methods for Damage Detection of Structures

A number of damage detection techniques have been proposed to be applied for structural health monitoring systems. In this paper a comparative study of mode shape curvature and damage locating vector methods were considered for predicting damage of structures. The mode shape curvature method is based on the difference of mode shapes between damage and undamaged structures, while damage locating vector is based on difference of flexibility matrix of damage and undamaged structures. Three type of structures were considered in this paper. The first test structure is a shear building, the second is a beam-type, and the third is a plane truss system. For each type of structures a numerical model is carried out for simulations. Several damage scenarios were considered to evaluate both methods. From the numerical experiment, it can be shown that both methods in general have demonstrated a good sensitivity for damage detections. For shear building and beam structures both methods have the ability to predict the damage locations. Due to the nature of the methods, however, for plane truss structures, while the damage locating vector method can predict the damaged members, the mode shape curvature method can predict only the nodes in the vicinity of the damaged member of plane truss structures.

A new energy indicator in damage locating vector method (DLV) for detecting multiple damaged positions in beam and truss structures

The paper presents a new indicator called normalized energy index (nei) in damage locating vector method (DLV) for detecting multiple damaged positions in beam and truss structures. In the DLV method, a set of load vectors, which is extracted from the change in flexibility matrix between an undamaged structure and a damaged one, is applied as static loads to the undamaged structure which are evaluated via the finite element modeling. Then, the nei values are computed for each element by using the displacements. In order to verify the accuracy and efficiency of a proposed indicator, a cantilevered beam and a 14-bay planar truss are considered

Rank-revealing QR decomposition applied to damage localization in truss structures

The purpose of this work is the development of an efficient and high-sensitive damage localization technique for truss structures, based on the rank-revealing QR decomposition (RRQR) of the difference-of-flexibility matrix. The method is an enhancement of the existing techniques of damage detection, which rely on the set of so-called damage locating vector (DLV). The advantages of the RRQR decomposition-based DLV (RRQR-DLV) method are its less computational effort and high sensitivity to damage. Compared with the frequently used stochastic DLV (SDLV) method, RRQR-DLV offers higher sensitivity to damage, which has been validated based on the presented numerical simulation. The effectiveness of the proposed RRQR-DLV method is also illustrated with the experimental validation based on a laboratory-scale Bailey truss bridge model. The proposed method works under ambient excitation such as traffic excitation and wind excitation; therefore, it is promising for real-time damage monitoring of truss structures. Copyright © 2016 John Wiley & Sons, Ltd.

Damage assessment of laminated composite beam structures using damage locating vector (DLV) method

In this paper, the damage locating vector (DLV) method using normalized cumulative energy (nce) is employed to locate multiple damage sites in laminated composite beam structures. Numerical simulations of two laminated composite beams are employed to investigate several damage scenarios in which the degradation of elements is modeled by the reduction in the longitudinal Young’s modulus and transverse Young’s modulus of beam layers. The results show that the DLV method gives good performance for this kind of structure.

Numerical Studies on the Damage Locating Vector Approach for Damage Detection Using Static Load Data

Abstract Structural Health Monitoring (SHM) is an emerging field in civil engineering, offering the potential for continuous and periodic assessment of the integrity of civil infrastructures. Numerous damage detection methods to identify possible damage locations have been proposed. Most of these methods depend on dynamic characteristics of structure. Damage Locating Vector (DLV) method as suggested by Bernal in 2002 [1] has received significant attention because this method was verified by a series of proof-of-concept tests measuring accelerations. This paper presents the details of the numerical studies carried out on the application of DLV method for damage localization using deflection data from static analysis. Several structures includingtrusses and beams were modeled and analysed for various damage scenarios. From the studies it was found that the method was able to identify the locations of damage in most of the cases.

Damage detection in frame structures using damage locating vectors

Among the numerous vibration based damage detection methods, some are using dynamically measured flexibility matrix to detect and locate damage in the structure. One of the methods in this category is damage locating vector (DLV). It is based on extracting a set of load vectors from flexibility shifts that cause the same deformation in damaged and undamaged structures. As a result, the damaged member can be located by studying the characterizing stress of all members. In this study, the application of DLV method on damage detection of 3D frame structure was conducted. Some factors like the number of measured DOFs and the accuracy of modal data were assumed to be ideal so that other parameters could be examined. The characterizing stress for a 3D structure and the accuracy of damage index in different damage scenarios are discussed.

Structural health monitoring and damage assessment Part II: Application of the damage locating vector (DLV) method to the ASCE benchmark structure experimental data

This paper builds on the review of structural health monitoring (SHM) and damage assessment approaches provided in a companion paper by presenting an application of the damage locating vector (DLV) approach to the experimental (Phase II) data obtained from the experimental benchmark structure of the IASC-ASCE task group on SHM, which is a laboratory (scaled) size steel frame. Different damage conditions were simulated in the frame for braced and unbraced configurations, and the DLV technique was used to detect and localize damage. The damage identification results were presented and the successes and limitations of the DLV method in detecting and locating the simulated damages were discussed. Key words: Structural health monitoring (SHM), damage locating vector (DLV), damages, braced, unbraced.

Damage Localization of Output-Only Frame Systems Using Stochastic Subspace Identification

Assurance of integrity of structures is the main task of structural health monitoring. The condition (health) of a structure may be revealed from its dynamic characteristics in response to natural or man-made loads. As the measurement of the natural or operating forces on actual real-life structures is generally formidable, dynamic characteristics of the structures have to be extracted from the available output signals only. The stochastic subspace identification (SSI) technique is adopted in this study to identify the equivalent system parameters of the discrete-time state equation using covariance functions of the measured output signals. With the system parameters realized, the method of damage locating vector (DLV) is then considered for further assessment. Members with nearly zero stress under the loadings of DLVs are considered potentially damaged, whereas the DLVs are derived from singular value decomposition of the change in flexibility matrix of the structure before and after the damage state. In this study, the feasibility of DLV method for damage detection of a planar structure is explored based on its seismic responses. To comply with the desired output-only scenario, the information of ground motion is discarded in the stage of SSI analysis. Despite the non-stationary nature of earthquakes, the proposed scheme has been proved sufficient for damage localization of structures from global responses (floor accelerations). The damaged locations can be identified when the structure is fully observed, regardless of single or multiple damages. In the case of partial but co-located observation (damaged floors observed), the damaged locations can still be identified with acceptable accuracy and reliability.

Experimental and numerical studies on model updating method of damage severity identification utilizing four cost functions

SUMMARY As the final stage of damage identification, damage severity identification has great significance to structural safety assessment and decision-making in maintenance. Take the damage detection of truss structures for instance; the stochastic damage locating vector method has great advantages. However, the method is a localization technique designed to provide information in damage location only. Many present damage severity identification methods suffer from great error due to high noise. Therefore, it is imperative to develop a new identification method for truss structural health monitoring. To solve this problem, this paper presents the model updating method of damage severity identification based on four cost functions: (i) correlation coefficient of free vibration accelerations; (ii) correlation coefficient of local mode shapes; (iii) free vibration accelerations assurance criterion; and (iv) local modal assurance criterion. In these functions, correlation coefficient and correlation degree of free vibration accelerations of measured nodes are first proposed to identify damage severity. Moreover, a simple supported bailey steel-truss bridge Benchmark Model has been designed and constructed. The span is 8 m with the scaled ratio 1:25. Based on the model, both experimental and numerical simulation results using these procedures under pulse excitation indicate that they are feasible and effective. In addition, the proposed techniques exhibit high-noise insusceptibility. Copyright © 2011 John Wiley & Sons, Ltd.

Multimetric Sensing for Structural Damage Detection

Vibration-based damage detection methods have been widely studied for structural health monitoring of civil infrastructure. Acceleration measurements are frequently employed to extract the dynamic characteristics of the structure and locate damage because they can be obtained conveniently and possess relatively little noise. However, considering the fact that damage is a local phenomenon, the sole use of acceleration measurements that are intrinsically global structural responses limits damage detection capabilities. This paper investigates the possibility of using both global and local measurements to improve the accuracy and robustness of damage detection methods. A multimetric approach based on the damage locating vector method is proposed. Numerical simulations are conducted to verify the efficacy of the proposed approach.

Structural damage detection using enhanced damage locating vector method with limited wireless sensors

For detection of damage in structures, the damage locating vector ( DLV) method is adapted to account for the different types and variations of internal forces and capacities along the length of each element by using the normalized cumulative energy instead of the normalized cumulative stress. To filter out the actual damaged elements from the identified set of potential damaged elements, an intersection scheme is proposed. A 2-D warehouse structure comprising beam and column elements with constant and varied cross-sectional areas, and truss elements is used to verify the enhancements to the DLV method. With wireless sensors being integrated into damage detection systems, practical issues need to be addressed in conjunction with the detection algorithm employed. For cases where raw signals are transmitted, the intermittent loss of data packets during transmission from the sensor nodes to the base station needs to be addressed. An algorithm to patch the lost data is proposed and when integrated with the DLV damage detection methodology, is experimentally shown to be feasible using a 3-D modular truss structure.

Load Vectors for Damage Location in Systems Identified from Operational Loads

A method to interrogate changes in transfer matrices regarding damage localization is extended to cases where these changes cannot be experimentally obtained because the input is not known deterministically. The extension is realized by noting that the method does not require the transfer matrix change explicitly, but only vectors from its kernel, and that some of these vectors can be estimated— for a fictive collocated input distribution—from constraints between realization matrices and results of an output-only identification. The interrogation approach, denoted as the stochastic dynamic damage locating vector method, is demonstrated in the context of simulations.

Damage Localization from Transmission Zeros of Delta Systems

Conditioning difficulties often thwart the characterization of damage by model updating. Methods that can provide information on damage location without explicit selection of free parameters are important for improving the likelihood of success. A technique that satisfies this objective known as the dynamic damage locating vector method locates damage as regions where a stress field that is computed in a model of the reference state, using loads extracted from changes in the transfer matrix, is identically zero. Central to the present paper is the fact that existence of the noted loads requires that the change in the transfer matrix be rank deficient everywhere in . Specifically, the paper examines the situation where the change in the transfer matrix is full normal rank and shows that although zero stress excitations are not generally feasible, useful surrogates can often be obtained from conditions defined by the invariant zero triplets (i.e., zero, zero direction, and zero input) of a mathematical structure, designated as the delta system, which maps inputs to changes in the output. The paper discusses the formulation of the delta system, the extraction of the invariant zeros, and the computation of the associated stress fields. Selection of the potentially damaged set of elements is based on a decision logic that uses the aggregate information from all (or any subset) of the identified zeros.

Reliability-based evaluation of the performance of the damage locating vector method

Structural health monitoring (SHM) has the potential to be an effective way to maintain and improve the public safety of a nation. In recent years, numerous SHM methods have been developed by various researchers. Among these, the damage locating vector (DLV) method proposed by Bernal has received significant attention because of demonstrated efficacy when implemented on a wireless sensor network. However, the DLV method is a model-based approach requiring a numerical model of the structure. This numerical model cannot perfectly represent the physical structure due to intrinsic model idealization and environmental uncertainty. In this paper, the performance of the DLV method in the presence of model uncertainty is examined using a system reliability analysis by using the first order reliability method (FORM) and compared with Monte Carlo simulation. In addition, the validity of the threshold value, that is critical but empirically determined in previous research, is also examined.