Structural Damage Identification Problem Research Articles

Piezoelectric impedance-based high-accuracy damage identification using sparsity conscious multi-objective optimization inverse analysis

Two elements are essential in structural health monitoring utilizing dynamic responses: response measurement with high-frequency contents, i.e., small characteristic wavelengths, that can adequately reflect damage features, and effective inverse identification analysis that is however oftentimes under-determined. The advancement of smart structure integration has led to active interrogation through frequency-sweeping piezoelectric impedance measurement at high frequency range. In this research we develop a multi-objective optimization formulation for the identification of damage location and severity utilizing piezoelectric impedance. While one optimization objective is to match the response measurement with finite element model prediction in the damage parametric space, the other is the number of locations of damage, i.e., the sparsity of damage index as the solution vector, since damage usually occurs within a small number of locations. This multi-objective formulation fits well the under-determined nature of damage identification, as it naturally provides multiple solutions as basis for further elucidation. The challenge remaining is how to find a small solution set that can include the actual damage scenario. Here we develop a novel inverse identification framework utilizing the intelligent swarm optimizer which possesses flexibility for enhancement. We first embed a sparsity enforcement process into the population generation of the optimizer, which yields a solution repository intrinsically possessing sparsity. We then apply reinforcement learning so the agents can adaptively opt for local strategies with the aim of enriching the searching patterns to diversify the solutions. Through the incorporation of a Q-table, searching toward more promising directions will be rewarded. Our case analyses employing experimental data indicate that this sparsity-conscious multi-objective particle swarm optimization technique can lead to a small solution set which generally encompasses the true damage scenario. This effectively solves the structural damage identification problem with piezoelectric impedance measurement.

An integrated surrogate model-driven and improved termite life cycle optimizer for damage identification in dams

Structural Damage Identification (SDI) is a crucial branch in the field of structural health monitoring, providing an essential support for the safe operation of structures. In this paper, a novel structural damage identification method based on a surrogate-assisted evolutionary optimization algorithm is proposed. This method incorporates cost-effective surrogate modelling techniques and swarm intelligence optimization algorithms with powerful global optimization capabilities. Three popular surrogate models are fused based on a weighted average strategy to construct an integrated surrogate model with stronger generalization ability and higher accuracy. In addition, the self-designed movement strategy is more suitable for the characteristics of Termite Life Cycle Optimizer (TLCO), allowing the improved TLCO to have stronger robustness and the capability to escape from local optimum. The effectiveness of the proposed method for solving the SDI problem is confirmed in a damaged dam model with different complexities. Some important findings are as follows: (i) Compared with the conventional SDI methods, which directly combines optimization algorithms and finite element models, the proposed method maintains reliable accuracy, while improving computational efficiency by a factor of more than 100. (ii) There is no direct relationship between the accuracy of the surrogate models and the effectiveness of the SDI method based on their combination with ITLCO. The proposed method can serve as a promising tool for damage identification in large-scale structures.

A new convolutional neural network-based framework and data construction method for structural damage identification considering sensor placement

In the application of data driven structural damage identification (SDI) based on supervised deep learning technology, valid data demarcation is the foundation; a convolutional neural network model with learning ability and capability of processing rich signal information is the core. Based on this understanding, this work makes three contributions: Firstly, the structural damage location and severity are jointly demarcated, and the SDI problem is transformed into a multi-classification task. Secondly, a 3D signal processing convolutional neural networks (3DS-CNN) is designed with an attempt to identify the complex and slight damages using the most basic network structure. Thirdly, a ‘major and subsidiary’ data construction (MSDC) method integrating the key intrinsic mode function is proposed to construct 3D data. Then the proposed schemes are verified by two different structures. The results show that the 3DS-CNN has excellent damage identification ability for small-size data with noise pollution. MSDC method can enrich the feature information of the damage signals and help the network with deep feature excavation, even if the vibration signals are heavily polluted. Going one step further, the impact of sensor placement is discussed, and it is found that when external excitation is obvious, better SDI accuracy can be achieved even using a single sensor signal with slight noise. When the noise interference is obvious, the generalization ability and noise robustness of the network can be enhanced by optimizing sensor placement. In this case, the sensor placement criteria and the sensitive nodes of the structure should be comprehensively and carefully considered to avoid mutual ‘coupling’ interference of data between sensors.

A chaos game Optimization-based model updating technique for structural damage identification under incomplete noisy measurements and temperature variations

Dealing with structural damage identification (SDI) considering limited noise-contaminated measurements and varying environmental conditions is still a challenge due to the difficulties regarding the localization and quantification of structural damage. If the challenging issue is not considered in SDI process, the SDI techniques may become unreliable and have limitations in their implementation in in-service structures. In this regard, the article proposes an optimization-based model updating technique for SDI taking into consideration both incomplete noisy measurements and temperature variations. First, it is assumed in the finite element model of the monitored structure that the relation between temperature conditions and structural materials is temperature-dependent to synthetically generate damage-induced vibration data under varying environmental temperatures. Then, the inverse SDI problem is recast into a constrained optimization problem and a newly developed algorithm called Chaos Game Optimization (CGO) is adopted for solving this problem with high efficiency. The efficiency of the CGO algorithm is assessed by comparison with other four parameter-less optimization algorithms. Finally, three engineering numerical examples are conducted to demonstrate the performance of the proposed technique. The results of the numerical investigations indicated that for the monitored structures made of metallic material (i.e., steel and aluminum), the proposed technique can accurately localize and quantify single and multi-damage even in incomplete and noisy modal data and environmental temperature changes. Meanwhile, for the monitored structures made of concrete material, the ambient temperature variations significantly affect the damage detectability and localizability of the proposed technique.

Output-only structural damage identification using hybrid Jaya and differential evolution algorithm with reference-free correlation functions

To solve the optimization-based structural damage identification problem, a novel hybrid algorithm based on Jaya and differential evolution algorithm (HJDEA) is proposed to detect, locate and quantify structural damages by effectively incorporating the powerful local exploitation capacity of Jaya algorithm and global exploration capability of differential evolution. Meanwhile, Hammersley sequence initialization and Lévy flight search mechanism are introduced into HJDEA to further improve convergence rate and refining the quality of the best solution. Four different algorithms, genetic algorithm, particle swarm optimization, Jaya and the proposed HJDEA are employed for comparative study. In addition, the objective function is established by adjacent acceleration correlation function so as to avoid false identification caused by defining improper reference point. The performance of the proposed damage identification strategy based on HJDEA and adjacent acceleration correlation function is investigated with numerical examples involving an 8-DOF lumped mass model and a cantilever beam, as well as an experimental study of the ASCE benchmark structure under white noise excitation. Results show that the proposed hybrid identification method is accurate, efficient and robust in the identification of the damage existence, location and severity of stiffness and mass parameters even with partial output-only responses and 20% noise-polluted measurements.

Efficiency of Plasma Generation Optimization for Structural Damage Identification of Skeletal Structures Based on a Hybrid Cost Function

In this paper, a newly developed population-based metaheuristic algorithm, namely plasma generation optimization (PGO), is applied to solving the optimization-based damage detection problem. PGO is a physically based optimizer inspired by the process of plasma generation. Excitation, de-excitation, and ionization are the three physical processes occurring in a repeated manner for providing plasma. In the current optimization problem, an efficient hybrid damage-sensitive cost function is developed based on combing two cost functions, modal strain energy and generalized flexibility matrix changes. The structural damage identification problem is formulated as an inverse optimization problem in which the vector of design variables represents damage severity of the structural elements. To verify the efficiency and robustness of the proposed method in detecting the locations and severity of damage precisely, five skeletal structures are investigated numerically. The results of the paper revealed that the proposed method can identify and quantify the damaged elements accurately using only the first few vibrating modes, even for noise-contaminated data.

A numerical study on multi‐site damage identification: A data‐driven method via constrained independent component analysis

This paper presents a solution to the multi-site structural damage identification problem using a data-driven method and constrained independent component analysis (cICA). While existing studies in this field presented encouraging results for single-site damage identification, limited research effort has been devoted to identifying multi-site damage due its complexity. Efficient features for single-site damage identification may lose their effectiveness when multi-site damage occurs. This paper extracts damage-sensitive features from the ICA outcome of the structural responses under certain excitations. The information on structural damage contained in the response is compacted into the mixing matrix by enforcing identical independent components to that of intact structures. Hence, the cICA can significantly reduce the feature dimension and preserve all the valuable information of damage. A case study indicates that the mixing matrix elements, when used as damage features, can distinguish multi-site damage cases from single-site damage cases and locate the single-site damage. Furthermore, the mixing matrix columns of multi-site damage cases exhibit distinct correlation with that of the corresponding single-site damage cases. As a result, the proposed method can progressively locate the structural damage. Moreover, the present study has the potential to identify multi-site damage identification in data-driven structural health monitoring without requiring multi-site damage data as a reference. This relieves the burden from data incompleteness when using data-based damage identification methods and pattern recognition.

Inverse structural damage identification problem in CFRP laminated plates using SFO algorithm based on strain fields

Damage detection methods are an important field of engineering and crucial in terms of structural safety. However, in many practical cases, the process of monitoring and identifying damage is extremely difficult or even impractical due to the conditions of access and operation of a given component/structure. In this study, an inverse algorithm based on strain fields for damage identification in composite plate structures is presented. The inverse analyses combine experimental tests and digital image correlation (DIC) with numerical models based on finite element update method with great advantage of being a non-contact method. The proposed technique identifies the location and dimension of damages in a CFRP plate using static strains formulated as an objective function to be minimized. By model updating, the discrepancies between the experimental and the numerical results are minimized. For the success of the model updating, the efficiency of the optimization algorithm is essential. A powerful new metaheuristic sunflower optimization (SFO) is employed to update the unknown model parameters. Experimental results showed the excellent efficiency in the combined use of DIC, numerical modeling and SFO optimization to accurately identify the location of damage in numerical and experimental tests. The obtained results indicate that the proposed method can be used to determine efficiently the location and dimension of structural damages in mechanical structures.

Structural Damage Identification Based on l1Regularization and Bare Bones Particle Swarm Optimization with Double Jump Strategy

Structural damage identification (SDI) plays a major role in structural health monitoring (SHM), which has been demanded by researchers to better face the challenges in the aging civil engineering, such as bridge structure and building structure. Many methods have been developed for the application to the real structures, but there are still some difficulties which result in inaccurate, even false damage identification. As a variant of particle swarm optimization (PSO), bare bones particle swarm optimization (BBPSO) is a simple but very powerful optimization tool. However, it is easy to be trapped in the local optimal state like other PSO algorithms, especially in SDI problems. In order to improve its performance in SDI problems, this paper aims to propose a novel optimization algorithm which is named as bare bones particle swarm optimization with double jump (BBPSODJ) for finding a new solution to the SDI problem in SHM field. To begin with, after the introduction of sparse recovery theory, the mathematical model for SDI is established where an objective function based on l1 regularization is constructed. Secondly, according to the basic theory of the BBPSODJ, a double jump strategy based on the BBPSO is designed to enhance the dynamic of particles, and it is able to make a large change in particle searching scopes, which can improve the search behaviour of BBPSO and prevent the algorithm from being trapped into local minimum state. Thirdly, three optimization test functions and a numerical example are utilized to validate the optimization performance of BBPSO, traditional PSO, and genetic algorithm (GA) comparatively; it is obvious that the proposed BBPSODJ shows great self-adapting property and good performance in the optimization process by introducing the novel double jump strategy. Finally, in the laboratory, an experimental example of steel frame with 4 damage cases is implemented to further assess the damage identification capability of the BBPSODJ with l1 regularization. From the damage identification results, it can be seen that the proposed BBPSODJ algorithm, which is efficient and robust, has great potential in the field of SHM.

Simultaneous identification of structural stiffness and mass parameters based on Bare-bones Gaussian Tree Seeds Algorithm using time-domain data

This paper mainly illustrates the Tree Seeds Algorithm (TSA) to tackle structural damage identification problem. The damage model is simulated by the alterations of both stiffness and mass parameters. The objective function is introduced by minimizing the differences between the measured and calculated acceleration data. To enhance the performance of the standard TSA, two modifications including the bare-bones Gaussian updated mechanism and the withering process are introduced. The modified algorithm is named after the BGTSA. In the numerical simulation part, the BGTSA is firstly used to make comparisons with several state-of-the-art algorithms on the CEC05. Secondly, the BGTSA is utilized to deal with the structural damage identification problem by optimizing the acceleration-based nonlinear objective function. Numerical experiments involving a simply supported beam and a truss are carried out to verify the effectiveness of the proposed algorithm. The final results show that with low amount of acceleration data, the BGTSA can acquire better identification results compared with other evolutionary algorithms. Therefore the proposed algorithm could be viewed as a potential tool to solve the structural damage identification problem.

A Combined Modal Correlation Criterion for Structural Damage Identification with Noisy Modal Data

Structural damage identification is a scientific field that has attracted a lot of interest in the scientific community during the recent years. There have been many studies intending to find a reliable method to identify damage in structural elements both in location and extent. Most damage identification methods are based on the changes of dynamic characteristics and static responses, but the incompleteness of the test data is a great obstacle for both. In this paper, a structural damage identification method based on the finite element model updating is proposed, in order to provide the location and the extent of structural damage using incomplete modal data of a damaged structure. The structural damage identification problem is treated as an unconstrained optimization problem which is solved using the differential evolution search algorithm. The objective function used in the optimization process is based on a combination of two modal correlation criteria, providing a measure of consistency and correlation between estimations of mode shape vectors. The performance and robustness of the proposed approach are evaluated with two numerical examples: a simply supported concrete beam and a concrete frame under several damage scenarios. The obtained results exhibit high efficiency of the proposed approach for accurately identifying the location and extent of structural damage.

Enhanced optimization-based structural damage detection method using modal strain energy and modal frequencies

To identify the location and severity of structural damages precisely, a two-stage damage detection method is proposed here. In the first stage, the most probable elements that maybe damaged are determined using the modal strain energy concept of structure. For such purpose, an efficient modal strain energy conversion-based index is proposed. In the second stage, the structural damage identification problem is defined as an optimization problem that to be solved by a modified genetic algorithm. In the optimization problem, the damage extents of structure’s elements which are determined in the previous stage are considered as the variables. An efficient objective function considering the first few modal frequencies of the structure, before and after damage occurrence, is utilized for optimization. The robustness of the proposed method is assessed by several benchmark examples. The numerical results exhibit the effectiveness of the proposed method for precisely determining the location and severity of multiple structural damages.

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.

Identificação de danos estruturais em placas baseada em um modelo de dano contínuo

The present work addresses the structural damage identification problem in plates by using the flexibility matrix. The damage is continuously described by a cohesion parameter defined in the elastic structure domain. The damage identification problem is, then, defined as an optimization one, whose aim is to minimize, with respect to the cohesion field, a functional based on the difference between the experimental flexibility matrix and the corresponding one predicted by a finite element model of the structure. The particle swarm optimization method was considered to solve the optimization problem. The numerical tests of the proposed damage identification method are carried out taking into account a rectangular Kirchhoff plate with one clamped edge. The considered damage scenario is composed by two damaged regions and the mode shapes are corrupted with different additive noise levels. The numerical results show the potentiality of the proposed method in identifying damages from a reduced number of noise corrupted experimental data.

Variations of Ant Colony Optimization for the Solution of the Structural Damage Identification Problem

In this work the inverse problem of identification of structural stiffness coefficients of a damped spring-mass system is tackled. The problem is solved by using different versions of Ant Colony Optimization (ACO) metaheuristic solely or coupled with the Hooke-Jeeves (HJ) local search algorithm. The evaluated versions of ACO are based on a discretization procedure to deal with the continuous domain design variables together with different pheromone evaporation and deposit strategies and also on the frequency of calling the local search algorithm. The damage estimation is evaluated using noiseless and noisy synthetic experimental data assuming a damage configuration throughout the structure. The reported results show the hybrid method as the best choice when both rank-based pheromone deposit and a new heuristic information based on the search history are used.

An Analysis of the Effectiveness of Application of Rotation Rate Sensors in Non Destructive Damage Evaluation

This paper reports the analysis of the effectiveness of the application of rotation rate sensors in structural damage identification problem using harmonic vibrations for plexiglass models of cantilever beams. The two types of sensors are applied: angular rate sensors and conventional translational, piezoelectric accelerometers. The amplitudes of dynamic response under harmonic kinematic excitations were subsequently used in the stiffness reconstruction procedure. Particularly promising results are obtained by using only rotation rate sensors in the reconstruction procedure.

Structural Damage Identification Using Improved Particle Swarm Optimization

In order to solve structural damage identification problem, a damage identification method based on improved Particle Swarm Optimization (IPSO) is presented. First, structural frequency and modal strain energy are utilized to obtain damage information. Then, evidence fusion theory is utilized to integrate the information and preliminarily identify structural damage locations. After the damaged locations are determined, Particle Swarm Optimization (PSO) is used to identify structural damage extent. Some improved strategies are presented to enhance the search efficiency. The simulation results demonstrate that the proposed method can estimate the damage locations and extent with good accuracy.

Structural Damage Identification Using Improved Particle Swarm Optimization

In order to solve structural damage identification problem, a damage identification method based on improved Particle Swarm Optimization (IPSO) is presented. First, structural frequency and modal strain energy are utilized to obtain damage information. Then, evidence fusion theory is utilized to integrate the information and preliminarily identify structural damage locations. After the damaged locations are determined, Particle Swarm Optimization (PSO) is used to identify structural damage extent. Some improved strategies are presented to enhance the search efficiency. The simulation results demonstrate that the proposed method can estimate the damage locations and extent with good accuracy.