Modal Strain Energy-based Index Research Articles

Damage identification of trusses using limited modal features and ensemble learning

A damage diagnosis method for trusses based on incomplete free vibration properties utilizing ensemble learning, e.g. Extreme gradient boosting (XGBoost), is presented in this work. Owing to the lack of measurement sensors, modal features are only measured at master degrees of freedom (DOFs) of a few first models instead of all DOFs of a structural system. Accordingly, a modal strain energy-based index (MSEBI) is employed to determine the most potentially damaged candidates. Then, an XGBoost-driven ensemble learning model is constructed from a finite element method (FEM)-simulated dataset. In which, inputs are eigenvectors corresponding to master DOFs, whilst outputs are damage ratios of suspected members. The accuracy of such a model is continuously enhanced by removing low-risk members via a damage threshold. As a consequence, the present paradigm can reliably detect damage to trusses. All test examples are programmed in Python to illustrate the reliability and efficiency of the proposed methodology.

A Two-Stage Approach for Damage Diagnosis of Structures Based on a Fully Distributed Strain Mode under Multigain Feedback Control

The application of distributed fiber sensing technology in civil engineering has been developed to obtain more accurate and reliable information for structural health monitoring (SHM). With this sensing technique, high-density strain data are provided to benefit the stability and robustness in a closed-loop damage detection method which has not yet been investigated. To address this concern, a two-stage approach for structural damage detection combining a modal strain energy-based index (MSEBI) method with a hybrid artificial neural network (ANN) and particle swarm optimization (PSO) algorithm is proposed. In this study, the fully distributed strain measurement is taken advantage of, and a strain-based, closed-loop system with multiple gains aggregated for damage sensitivity enhancement is established, by which high-precision damage location and quantification can be realized through the proposed two-stage method. For the first step, the closed-loop strain mode shapes are used to construct the MSEBI for damage localization. For the second step, we adopt the PSO algorithm to train the parameters (weights and biases) of the neural network in order to reduce the difference between the real and expected outputs and then use the trained network for quantifying the damage extent. Furthermore, validation is completed by contemplating a two-span, bridge-like structure.

A Multistage Damage Detection Approach Using Graph Theory and Water Strider Algorithm

Structures usually suffer from natural or artificial damages that might result in shortening their service life and even triggering their collapse. Therefore, vibrational damage detection methods, as cost-effective and nondestructive techniques, are widely investigated by researchers. In the present paper, the modal-strain energy-based two-stage method is enhanced, and a graph-theoretic hierarchical (GHM) method is proposed, which takes advantage of the modal strain energy-based index (MSEBI) and overcomes its potential drawbacks by identifying all damaged elements. This method assigns orders and allocates the computational resources to elements according to the MSEBI as well as the connections represented by a graph model. The water strider algorithm (WSA) is also utilized as an efficient new optimizer, and its results are compared with those of some other well-known algorithms. Several examples are used to substantiate the results and performance of the new GHM framework, showing that the new method outperforms the general classic method, especially when it is combined with the WSA optimizer.

Damage Detection of Structures Using Modal Strain Energy with Guyan Reduction Method

The subject of structural health monitoring and damage identification of structures at the earliest possible stage has been a noteworthy topic for researchers in the last years. Modal strain energy (MSE) based index is one of the efficient methods which are commonly used for detecting damage in structures. It is also more effective and economical to employ some methods for reducing the degrees of freedom in large-scale structures having a large number of degrees of freedom. The purpose of this study is to identify structural damage via an index based on MSE and reconstructed mode shapes. The Guyan reduction method (GRM) is utilized here to reconstruct the mode shapes. Therefore, in the first step by employing GRM, mode shapes in slave degrees of freedom are estimated by those of master degrees of freedom. In the second step, the modal strain energy based index (MSEBI) is used to find the location of damaged elements. In order to assess the efficiency of the method, two standard examples are considered. Damage is identified with considering complete mode shapes and reconstructed mode shapes, and the results are compared together. The outcomes show that the combination of MSE and GRM can be useful for the structural damage detection, when considering the noise.

Bearing Damage Detection of a Reinforced Concrete Plate Based on Sensitivity Analysis and Chaotic Moth-Flame-Invasive Weed Optimization.

This article proposes a novel damage detection method based on the sensitivity analysis and chaotic moth-flame-invasive weed optimization (CMF-IWO), which is utilized to simultaneously identify the damage of structural elements and bearings. First, the sensitivity coefficients of eigenvalues to the damage factors of structural elements and bearings are deduced, the regularization technology is used to solve the problem of equation undetermined, meanwhile, the modal strain energy-based index is utilized to detect the damage locations, and the regularization objective function is constructed to quantify the damage severity. Then, for the subsequent procedure of damage detection, CMF-IWO is proposed based on moth-flame optimization and invasive weed optimization as well as chaos theory, reverse learning, and evolutional strategy. The optimization effectiveness of the hybrid algorithm is verified by five benchmark functions and a damage identification numerical example of a simply supported beam; the results demonstrate it is of great global search ability and higher convergence efficiency. After that, a numerical example of an 8-span continuous beam and an experimental reinforced concrete plate are both adopted to evaluate the proposed damage identification method. The results of the numerical example indicate that the proposed method can locate and quantify the damage of structural elements and bearings with high accuracy. Furthermore, the outcomes of the experimental example show that despite the existence of some errors and uncertain factors, the method still obtains an acceptable result. Generally speaking, the proposed method is proved that it is of good feasibility.

A three-stage damage detection method for large-scale space structures using forward substructuring approach and enhanced bat optimization algorithm

In this study, an efficient three-stage method is proposed for damage detection of large-scale space structures by employing forward substructuring approach, modal strain energy and enhanced bat algorithm (EBA) optimization. EBA is a modified version of BA that is proposed in this paper and used a passive congregation operator to improve the performance of standard BA. In the first stage, the global structure is divided into manageable substructures. The stiffness matrices of independent substructures are obtained based on Kron’s substructuring method. Then a modal strain energy-based index is employed to precisely locate the eventual damages of the structure. In the third stage, damage severities are estimated via EBA using the second-stage results. To demonstrate the ability of the proposed method for detection of multiple structural damages, large-scale space structures with different types of damage scenarios are considered. The results show that the proposed method can detect the exact locations and severity of damages highly accurate in space structures.

Comparative studies of metamodeling and AI-Based techniques in damage detection of structures

Despite advances in computer capacity, the enormous computational cost of running complex engineering simulations makes it impractical to rely exclusively on simulation for the purpose of structural health monitoring. To cut down the cost, surrogate models, also known as metamodels, are constructed and then used in place of the actual simulation models. In this study, structural damage detection is performed using two approaches. In both cases ten popular metamodeling techniques including Back-Propagation Neural Networks (BPNN), Least Square Support Vector Machines (LS-SVMs), Adaptive Neural-Fuzzy Inference System (ANFIS), Radial Basis Function Neural network (RBFN), Large Margin Nearest Neighbors (LMNN), Extreme Learning Machine (ELM), Gaussian Process (GP), Multivariate Adaptive Regression Spline (MARS), Random Forests and Kriging are used and the comparative results are presented. In the first approach, by considering dynamic behavior of a structure as input variables, ten metamodels are constructed, trained and tested to detect the location and severity of damage in civil structures. The variation of running time, mean square error (MSE), number of training and testing data, and other indices for measuring the accuracy in the prediction are defined and calculated in order to inspect advantages as well as the shortcomings of each algorithm. The results indicate that Kriging and LS-SVM models have better performance in predicting the location/severity of damage compared with other methods. In the second approach, after locating precisely the eventual damage of a structure using modal strain energy based index (MSEBI), to efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, the MSEBI of structural elements is evaluated using a properly trained surrogate model. The results indicate that after determining the damage location, the proposed solution method for damage severity detection leads to significant reduction of computational time compared to finite element method. Furthermore, engaging colliding bodies optimization algorithm (CBO) by efficient surrogate model of finite element (FE) model, maintains the acceptable accuracy of damage severity detection.

An efficient indicator for structural damage localization using the change of strain energy based on static noisy data

An efficient method is proposed to find multiple damage locations in structural systems. The change of static strain energy (SSE) due to damage is used to establish an indicator for determining the damage location. The SSE is determined using the static analysis information extracted from a finite element modeling. In order to assess the performance of the proposed method for structural damage detection, some benchmark structures having a number of damage scenarios are considered. Numerical results demonstrate that the method can accurately locate the structural damage when considering the measurement noise. The efficiency of the proposed indicator for finding the damage site is also compared with a modal strain energy based index (MSEBI) provided in the literature.

A two stage method for structural damage detection using a modal strain energy based index and particle swarm optimization

A two-stage method is proposed here to properly identify the site and extent of multiple damage cases in structural systems. In the first stage, a modal strain energy based index ( MSEBI) is presented to precisely locate the eventual damage of a structure. The modal strain energy is calculated using the modal analysis information extracted from a finite element modeling. In the second stage, the extent of actual damage is determined via a particle swarm optimization (PSO) using the first stage results. Two illustrative test examples are considered to assess the performance of the proposed method. Numerical results indicate that the combination of MSEBI and PSO can provide a reliable tool to accurately identify the multiple structural damage.