Multiple Damage Location Assurance Criterion Research Articles

A damage-informed neural network framework for structural damage identification

In this work, an effective Damage-Informed Neural Network (DINN) is first developed to pinpoint the position and extent of structural damage. Instead of resolving the damage identification problem by conventional numerical methods, a Deep Neural Network (DNN) is employed to minimize the loss function which is designed by combining multiple damage location assurance criterion and flexibility matrices to guide the training process. In our computational framework, the parameters of the network, which include both weights and biases, are treated as new design variables instead of damage ratios. Therein, the training data consists only of a set of spatial coordinates of elements, whilst corresponding the damaged ratios of elements unknown to the network are factored into the output. To achieve the goal, the loss value is calculated relying on the predicted damage ratios with supporting Finite Element Analysis (FEA). Additionally, Bayesian Optimization (BO) algorithm is used to automatically tune hyperparameters of the network for enhancing reliability in damage identification. Several numerical examples for damage localization of truss and frame structures are investigated to evaluate the effectiveness and reliability of the suggested methodology. The obtained results point out that our model not only correctly locates the actual damage sites but also requires the least number of structural analyses and faster convergence rate compared with other algorithms.

A Multi-Objective DIRECT Algorithm Toward Structural Damage Identification With Limited Dynamic Response Information

A major challenge in structural health monitoring (SHM) is to accurately identify both the location and severity of damage using the dynamic response information acquired. While in theory the vibration-based and impedance-based methods may facilitate damage identification with the assistance of a credible baseline finite element model, the response information is generally limited, and the measurements may be heterogeneous, making an inverse analysis using sensitivity matrix difficult. Aiming at fundamental advancement, in this research we cast the damage identification problem into an optimization problem where possible changes of finite element properties due to damage occurrence are treated as unknowns. We employ the multiple damage location assurance criterion (MDLAC), which characterizes the relation between measurements and predictions (under sampled elemental property changes), as the vector-form objective function. We then develop an enhanced, multi-objective version of the dividing rectangles (DIRECT) approach to solve the optimization problem. The underlying idea of the multi-objective DIRECT approach is to branch and bound the unknown parametric space to converge to a set of optimal solutions. A new sampling scheme is established, which significantly increases the efficiency in minimizing the error between measurements and predictions. The enhanced DIRECT algorithm is particularly suited to solving for unknowns that are sparse, as in practical situations structural damage affects only a small region. A number of test cases using vibration response information are executed to demonstrate the effectiveness of the new approach.

Efficiency of Jaya algorithm for solving the optimization-based structural damage identification problem based on a hybrid objective function

ABSTRACTA Jaya algorithm was recently proposed for solving effectively both constrained and unconstrained optimization problems. In this article, the Jaya algorithm is further extended for solving the optimization-based damage identification problem. In the current optimization problem, the vector of design variables represents the damage extent of elements discretized by the finite element model, and a hybrid objective function is proposed by combining two different objective functions to determine the sites and extent of damage. The first one is based on the multiple damage location assurance criterion and the second one is based on modal flexibility change. The robustness and efficiency of the proposed damage detection method are verified through three specific structures. The obtained results indicate that even under relatively high noise level, the proposed method not only successfully detects and quantifies damage in engineering structures, but also shows better efficiency in terms of computational cost.

Multi-stage method to identify structural damage using multiple damage location assurance criterion and an improved differential evolution algorithm

For the damage identification technique of civil structures, the reduction of the computational cost for methods based on the optimization algorithms is the most crucial step. In this study, a fast multi-stage method is developed that uses the multiple damage location assurance criterion and an improved differential evolution algorithm. In the new method, the suitable damage range is selected in different stages to reduce the computational cost of structural analysis. Five mutation operators are analysized not only in the basic differential evolution algorithm but also in the improved one. A new adaptive scaling factor with a segmented function is proposed which can operate the decay rate to avoid the premature phenomenon. The results of the study show that the precise locations and extents of structural damage are successfully realized. It is also shown that the multi-stage method using the improved differential evolution algorithm is substantially faster as compared to the basic one and can be used as an efficient and powerful measure of structural damage identification.

Damage detection of plate-like structures using intelligent surrogate model

Cracks in plate-like structures are some of the main reasons for destruction of the entire structure. In this study, a novel two-stage methodology is proposed for damage detection of flexural plates using an optimized artificial neural network. In the first stage, location of damages in plates is investigated using curvature-moment and curvature-moment derivative concepts. After detecting the damaged areas, the equations for damage severity detection are solved via Bat Algorithm (BA). In the second stage, in order to efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, multiple damage location assurance criterion index based on the frequency change vector of structures are evaluated using properly trained cascade feed-forward neural network (CFNN) as a surrogate model. In order to achieve the most generalized neural network as a surrogate model, its structure is optimized using binary version of BA. To validate this proposed solution method, two examples are presented. The results indicate that after determining the damage location based on curvature-moment derivative concept, the proposed solution method for damage severity detection leads to significant reduction of computational time compared with direct finite element method. Furthermore, integrating BA with the efficient approximation mechanism of finite element model, maintains the acceptable accuracy of damage severity detection.

Damage Identification of Beam Structures Based on Genetic Algorithm and Sensitivity Analysis

Based on the damage scalar model and the sensitivity analysis of frequency and mode shape, the Combined Multiple Damage Location Assurance Criterion (CMDLAC) based on the change of frequencies and mode shapes was proposed. Then, the damage identification problem is transformed to the constrained optimum problem and the genetic algorithm (GA) is adopted for the damage localization of beam structures. At last, the numerical simulation results of a 3-span continuum beam structure verified the proposed method.

Structural Damage Localization Using D-S Evidence Theory

Damage localization is a key issue in the Structural Health Monitoring (SHM). In order to enhance the accuracy of damage localization, an approach based on D-S evidence theory is studied in this paper. First, damage results obtained from the modal strain energy change ratio (MSECR) method and the multiple damage location assurance criterion (MDLAC) method are regarded as two independent information sources, respectively. And then, the D-S evidence theory-based information fusion technique is employed to integrate these two results and make a final decision. To compare the damage identification results under different methods, a practical damage evaluation indicator is presented. Numerical study on a cantilever beam is carried out to verify the proposed method. Results indicate that the damage evaluation indicators calculated by D-S evidence theory are superior to the single method. That is to say, structural damage identification method based on D-S evidence theory performs very well in determining the damage locations.

Structural health monitoring for flexible bridge structures using correlation and sensitivity of modal data

This study investigates the use of correlation-based damage detection methods for long-span, cable-stayed bridges. The proposed approach is based on the multiple damage location assurance criterion (MDLAC), which combines a correlation-based technique with a forward-type estimation of damage-sensitive structural parameters. Observing the level of correlation between the variations in the measured and analytically synthesized natural frequencies enables damage localization. The sensitivity matrix, developed from the finite element model, further accommodates multiple damage detection. The locations of damage are determined by iteratively searching for the combination of structural parameters that maximizes the correlation coefficient through the application of genetic algorithms. It is demonstrated that correlation-based modal analysis is successful for damage detection and localization using a numerical model of a full-scale cable-stayed bridge.

A weighted balance evidence theory for structural multiple damage localization

In order to identify structural multiple damage locations, an improved evidence theory is proposed in this paper. First, the changes of frequencies and mode shapes are regarded as two different information sources, and local decisions can be obtained by using the multiple damage location assurance criterion (MDLAC) method and the frequency change damage detection method (FCDDM), respectively. Then, the local decisions are sent to a fusion center. In the fusion center, the evidence fusion technique is applied to integrate those local decisions and acquire a global decision. It is considered that the multiple evidences from different sources of different importance or reliability are not equally important when they are combined according to evidence theory, which is seldom considered in the existent combination methods. A new approach, i.e. weighted balance evidence theory (WBET), is presented to solve this problem. In order to compare the identification results under different weight coefficients, a multi-damage identification index is proposed. The simulation results demonstrate that the excellent performance of the proposed method to identify multiple damage sites as compared with the MDLAC method and the FCDDM method. In addition, analysis indicates that basic evidence combination is a particular case of the WBET method under certain condition.

Structural damage detection using information fusion technique

In order to precisely identify the multiple damage locations of a structure, an information fusion technique is proposed in this paper. First, the frequency data and the mode shape data are regarded as two different information sources, and local decisions can be obtained using the multiple damage location assurance criterion (MDLAC) method and the frequency change damage detection method (FCDDM), respectively. Then, the local decisions are sent to a fusion centre. In the fusion centre, three main fusion approaches are applied to integrate all the local decisions. Finally, a global decision is acquired. The measurement errors are also taken into account in the fusion process. The numerical example and analysis demonstrate that the identification results of the three information fusion methods are better than those of the MDLAC method and the frequency change damage detection method.

MULTIPLE DAMAGE LOCATION WITH FLEXIBILITY CURVATURE AND RELATIVE FREQUENCY CHANGE FOR BEAM STRUCTURES

The purpose of this paper is to seek efficient methods for multiple damage location in beam structures. Two methods are studied in terms of finite element model (FEM) simulations on beam structure. Firstly, a finite element model of reinforced concrete beam is established to compute changes in flexibility and flexibility curvature of the beam with various damage patterns, and sensitivities of flexibility and flexibility curvature for closely distributed damage patterns of various extents are also compared. Due to its high sensitivity to closely distributed damages, flexibility curvature is recommended for multiple damage location. Secondly, the full correlation between the relative frequency changes and the analytical frequency changes are confirmed with FEM analyses: therefore, the relative frequency changes, rather than the frequency changes themselves, are used in the multiple damage location assurance criterion (MDLAC) to increase the location efficiency. Numerous examples are presented to verify the two methods.

Damage Localization by Directly Using Incomplete Mode Shapes

A sensitivity- and statistical-based method to localize structural damage by direct use of incomplete mode shapes is presented. The method is an extension of the multiple damage location assurance criterion (MDLAC), developed by Messina et al. by using incomplete mode shape instead of modal frequency. This approach makes use of the good sensitivity of mode shape to local damage. The damage detection strategy is to localize the damage sites first by using incomplete measured mode shapes, and then to detect the damage site and extent again by using measured natural frequencies, which have a better accuracy than mode shapes. A plane truss structure is analyzed as a numerical example to compare the performance of the proposed method with the multiple damage location assurance criterion. Results indicate that the new method is more accurate and robust in damage localization with or without noise effect.

STRUCTURAL DAMAGE DETECTION BY A SENSITIVITY AND STATISTICAL-BASED METHOD

During the last 20 years, the development of experimental modal analysis techniques has facilitated the accurate measurement of modal parameters in many types of structure. Alongside this work, several methods have been developed to detect structural damage by using location-dependent changes in the modal parameters. The paper extends the authors' work on a correlation coefficient termed the Multiple Damage Location Assurance Criterion (MDLAC) by introducing two methods of estimating the size of defects in a structure. Their effectiveness is illustrated using numerical data for two truss structures and both location and sizing algorithms are validated experimentally using a three-beam test structure. The paper also introduces a means of improving the computational efficiency of the damage location algorithm. The MDLAC approach offers the practical attraction of only requiring measurements of the changes in a few of the structure's natural frequencies between the undamaged states and is shown to provide good predictions of both the location and absolute size of damage at one or more sites.