Framework For Damage Identification Research Articles

An ultrafast and robust structural damage identification framework enabled by an optimized extreme learning machine

Artificial intelligence (AI) has recently been implemented in structural health monitoring (SHM) systems for damage detection and identification. However, existing AI methods often involve a high number of parameters, resulting in the laborious workload during model training and implementation. In this study, we develop a lightweight damage identification framework embedded with an optimized extreme learning machine (ELM) using a significantly reduced number of parameters from frequency features of the structural vibrational signals. Coupled with the ensemble empirical mode decomposition (EEMD), the frequency features were abstracted from the structural vibrational signals in the data pre-processing step. Then, a metaheuristic algorithm (chaos game optimization, CGO) was chosen to optimize the model weight of the ELM to guarantee damage identification accuracy. Once the model is trained, the structural damages can be precisely identified despite the noise-contaminated data. We validated the efficiency and accuracy of the proposed framework with a public database and compare its performance with several common metaheuristic algorithms. With the proven capability, the proposed framework shows a promising future as a handy and reliable AI-assisted digital tool for robust development in next-generation structural monitoring systems.

Condition monitoring framework for damage identification in CFRP rotating shafts using Model-Driven Machine learning techniques

Real-time condition monitoring (CM) through vibration measurements is instrumental in detecting faults and enabling predictive maintenance for mechanical systems. The accuracy and robustness of a CM application depends among others on the availability of data for different health states which typically requires complete experimental measurements. In this work, a novel CM framework for damage detection and localization is presented and applied to a lab-scale CFRP transmission shaft, using Support Vector Machine models (SVM) trained by data generated through numerical simulations of an Optimal Multibody Dynamics (MBD) model of the physical system under different rotating speeds. First, the MBD model corresponding to the real shaft structure, including support bearings, is developed and optimized using a small number of initial healthy state measurements throughout the operating speed range. The goal is to perform damage identification on different health states by exploiting simulated instead of experimental responses for supervised health state classification. The study specifically addresses damage scenarios like cracks and delamination at specific locations on the shaft. Simulated damaged states, represented by a local reduction of stiffness on the optimal MBD model, enable the generation of labeled training datasets for SVMs. The trained SVMs’ robustness and accuracy are then validated by accurately classifying faults on the physical system, proving the proposed damage detection method’s generalization capabilities and highlighting its potential.

Damage identification of offshore jacket platforms in a digital twin framework considering optimal sensor placement

A new digital twin (DT) framework with optimal sensor placement (OSP) is proposed to accurately calculate the modal responses and identify the damage ratios of the offshore jacket platforms. The proposed damage identification framework consists of two models (namely one OSP model and one damage identification model). The OSP model adopts the multi-objective Lichtenberg algorithm (MOLA) to perform the sensor number/location optimization to make a good balance between the sensor cost and the modal calculation accuracy. In the damage identification model, the Markov Chain Monte Carlo (MCMC)-Bayesian method is developed to calculate the structural damage ratios based on the modal information obtained from the sensory measurements, where the uncertainties of the structural parameters are quantified. The proposed method is validated using an offshore jacket platform, and the analysis results demonstrate efficient identification of the structural damage location and severity.

Seismic damage identification of high arch dams based on an unsupervised deep learning approach

In actual engineering scenarios of arch dams, the incompleteness and nonstationarity of dynamic monitoring signals limit the accurate cognition of the health state. The effectiveness and robustness of damage characteristics in complex environments restrict the practical application of damage diagnosis theory. In this study, guided by the direct extraction of damage sensitivity features from the acceleration response signals of the arch dam, a seismic damage identification approach of high arch dams based on unsupervised learning is developed. Aiming at the problems of low measurement accuracy and poor identification robustness in the existing artificially designed damage-sensitive features, a denoising contractual sparse deep auto-encoder (DCS-DAE) model is proposed by exploring the mapping relationship between monitoring data and the structural state. This model integrates the advantages of denoising auto-encoder, compressive auto-encoder, and sparse auto-encoder. On this basis, based on the principle of reconstruction error and small probability, combined with box-plot and WKNN algorithm, a damage identification framework based on DCS-DAE is constructed. The effectiveness and noise resistance of the proposed method are verified by an extremely high arch dam. The results demonstrate that the damage identification framework based on multi-objective DCS-DAE constructed in this paper only requires the vibration information of the structure in the intact scenario, which provides a solution with higher stability and robustness for the seismic damage identification of high arch dams under strong noise pollution.

Structural damage identification based on modal frequency strain energy assurance criterion and flexibility using enhanced Moth-Flame optimization

The development of structural damage identification based on dynamic characteristics has been suffering from certain issues, such as low computational efficiency and lack of high-sensitivity damage indicators. In addition, the application of damage-sensitive objective function and efficient optimization technique control the success of damage identification as two key factors. To this regard, a damage identification framework based on modal frequency strain energy assurance criterion (MFSEAC), modal flexibility and enhanced moth-flame optimization is presented in this paper. To evaluate the performance of the proposed method, three benchmark functions are firstly used to validate the optimization performance of enhanced moth-flame optimization. The results indicate that enhanced moth-flame optimization can achieve better optimization results compared to moth-flame optimization, particle swarm optimization and cuckoo search. Three numerical examples, a 3-span concrete continuous beam considering gradient temperature variations, a 40-story shear frame under random noise and a 31-bar truss structure under the double influences of random noise and temperatures, are then practiced as comparisons by frequency change ratio (FCR) and modal assurance criterion (MAC), modal strain energy (MSE) and modal flexibility, in order to evaluate the damage identification accurateness of the proposed objective function. Finally, two laboratory examples, a simply supported steel beam and a 3-story shear steel frame, are applied to further verify the proposed method. According to the damage identification results, the proposed method features good environmental noise robustness, which boosts the efficiency of symmetric location damage identification.

A Data-Driven Damage Identification Framework Based on Transmissibility Function Datasets and One-Dimensional Convolutional Neural Networks: Verification on a Structural Health Monitoring Benchmark Structure.

Vibration-based data-driven structural damage identification methods have gained large popularity because of their independence of high-fidelity models of target systems. However, the effectiveness of existing methods is constrained by critical shortcomings. For example, the measured vibration responses may contain insufficient damage-sensitive features and suffer from high instability under the interference of random excitations. Moreover, the capability of conventional intelligent algorithms in damage feature extraction and noise influence suppression is limited. To address the above issues, a novel damage identification framework was established in this study by integrating massive datasets constructed by structural transmissibility functions (TFs) and a deep learning strategy based on one-dimensional convolutional neural networks (1D CNNs). The effectiveness and efficiency of the TF-1D CNN framework were verified using an American Society of Civil Engineers (ASCE) structural health monitoring benchmark structure, from which dynamic responses were captured, subject to white noise random excitations and a number of different damage scenarios. The damage identification accuracy of the framework was examined and compared with others by using different dataset types and intelligent algorithms. Specifically, compared with time series (TS) and fast Fourier transform (FFT)-based frequency-domain signals, the TF signals exhibited more significant damage-sensitive features and stronger stability under excitation interference. The utilization of 1D CNN, on the other hand, exhibited some unique advantages over other machine learning algorithms (e.g., traditional artificial neural networks (ANNs)), particularly in aspects of computation efficiency, generalization ability, and noise immunity when treating massive, high-dimensional datasets. The developed TF-1D CNN damage identification framework was demonstrated to have practical value in future applications.

Damage Detection of Steel-Truss Railway Bridges Using Operational Vibration Data

In this paper, a damage identification framework for steel-truss railroad bridges, based on acceleration responses to operational train loading, is presented. The method is based on vertical and longitudinal sensor clustering–based time-series analysis of the operational acceleration response of bridges to the passage of trains. The results are presented in terms of damage features extracted from each sensor, which were obtained by comparing actual acceleration responses from the sensors to the predicted responses from the time-series model. Bridge damage was detected by observing changes in the damage features of the bridges as structural changes occurred in the bridges. The relative severity of damage was quantitatively assessed by observing the magnitude of the changes in the damage features. A finite-element model of a steel-truss railroad bridge was utilized to verify the method. Continuous condition assessment of railway bridges in this manner is deemed very valuable for the early detection of damage and, therefore, for increasing the safety and operational reliability of railway networks.

Damage detection of steel girder railway bridges utilizing operational vibration response

In this paper, we develop a damage identification framework based on acceleration responses for railroad bridges. The methodology uses sensor-clustering-based time series analysis of bridge acceleration responses to the motion of the train. The results are expressed in terms of damage features, and damage to the bridge is investigated by observing the magnitude of these damage features. The investigation demonstrates the damage features by comparing the fit ratios of locations of interest so that damage can be identified and located and the relative severity of the damage assessed. The damage cases considered are stiffness loss, moment capacity reduction, and change in boundary conditions. In this study, a finite element analysis of a railway bridge model is used to verify our methodology. Our findings show that the proposed damage detection framework is very promising for continuously assessing the condition of railway bridges and thus will facilitate early detection of potential structural damage. This will be valuable for infrastructure owners seeking to develop more economical and effective maintenance strategies.

Data mining based damage identification using imperialist competitive algorithm and artificial neural network

Abstract Currently, visual inspections for damage identification of structures are broadly used. However, they have two main drawbacks; time limitation and qualified manpower accessibility. Therefore, more precise and quicker technique is required to monitor the condition of structures. To aid the aim, a data mining based damage identification approach can be utilized to solve these drawbacks. In this study, to predict the damage severity of single-point damage scenarios of I-beam structures a data mining based damage identification framework and a hybrid algorithm combining Artificial Neural Network (ANN) and Imperial Competitive Algorithm (ICA), called ICA-ANN method, is proposed. ICA is employed to determine the initial weights of ANN. The efficiency coefficient and mean square error (MSE) are used to evaluate the performance of the ICA-ANN model. Moreover, the proposed model is compared with a pre-developed ANN approach in order to verify the efficiency of the proposed methodology. Based on the obtained results, it is concluded that the ICA-ANN indicates a better performance in detection of damage severity over the ANN method used only.

Fatigue damage identification for composite pipeline systems using electrical capacitance sensors

This research investigates a damage identification framework for carbon fiber reinforced polymer composite pipeline systems using electrical capacitance sensors. First, a finite element pipeline model is established under the structural-thermal-electrostatic coupled field. Based on this model, an accumulative damage model is introduced to model the pipeline damage subjected to fatigue effect, and ten damage cases are considered. Both static and transient external excitations are loaded into the pipeline by installing distributed electrical capacitance sensors. A system transfer function is proposed and applied to the ‘open loop’ pipeline system. Results show these approaches perform great promises when damage evolves covering electrode pairs, the signal will change sensitively and abruptly with an order of magnitude.

Structural damage identification via a combination of blind feature extraction and sparse representation classification

This paper addresses two problems in structural damage identification: locating damage and assessing damage severity, which are incorporated into the classification framework based on the theory of sparse representation (SR) and compressed sensing (CS). The sparsity nature implied in the classification problem itself is exploited, establishing a sparse representation framework for damage identification. Specifically, the proposed method consists of two steps: feature extraction and classification. In the feature extraction step, the modal features of both the test structure and the reference structure model are first blindly extracted by the unsupervised complexity pursuit (CP) algorithm. Then in the classification step, expressing the test modal feature as a linear combination of the bases of the over-complete reference feature dictionary—constructed by concatenating all modal features of all candidate damage classes—builds a highly underdetermined linear system of equations with an underlying sparse representation, which can be correctly recovered by ℓ1-minimization; the non-zero entry in the recovered sparse representation directly assigns the damage class which the test structure (feature) belongs to. The two-step CP–SR damage identification method alleviates the training process required by traditional pattern recognition based methods. In addition, the reference feature dictionary can be of small size by formulating the issues of locating damage and assessing damage extent as a two-stage procedure and by taking advantage of the robustness of the SR framework. Numerical simulations and experimental study are conducted to verify the developed CP–SR method. The problems of identifying multiple damage, using limited sensors and partial features, and the performance under heavy noise and random excitation are investigated, and promising results are obtained.

A One Stage Damage Detection Technique Using Spectral Density Analysis and Parallel Genetic Algorithms

This paper describes a new global damage identification framework for the continuous/periodic monitoring of civil structures. In order to localize and estimate the severity of damage regions, a one-stage model-based Bayesian probabilistic damage detection approach is proposed. This method, which is based on the response power spectral density of the structure, enjoys the advantage of broadband frequency information and can be implemented on input-output as well as output-only damage identification studies. A parallel genetic algorithm is subsequently used to evolve the optimal model parameters introduced for different damage conditions. Given the complex search space and the need to perform multiple time-consuming objective function evaluations, a parallel meta-heuristic provides a robust optimization tool in this domain. It is shown that this approach is capable of detecting structural damage in both noisy and noise-free environments.

Bayesian Probabilistic Framework for Damage Identification of Steel Truss Bridges under Joint Uncertainties

The vibration-based structural health monitoring has been traditionally implemented through the deterministic approach that relies on a single model to identify model parameters that represent damages. When such approach is applied for truss bridges, truss joints are usually modeled as either simple hinges or rigid connections. The former could lead to model uncertainties due to the discrepancy between physical configurations and their mathematical models, while the latter could induce model parameter uncertainties due to difficulty in obtaining accurate model parameters of complex joint details. This paper is to present a new perspective for addressing uncertainties associated with truss joint configurations in damage identification based on Bayesian probabilistic model updating and model class selection. A new sampling method of the transitional Markov chain Monte Carlo is incorporated with the structure’s finite element model for implementing the approach to damage identification of truss structures. This method can not only draw samples which approximate the updated probability distributions of uncertain model parameters but also provide model evidence that quantify probabilities of uncertain model classes. The proposed probabilistic framework and its applicability for addressing joint uncertainties are illustrated and examined with an application example. Future research directions in this field are discussed.

Advanced Feature Selection for Simplified Pattern Recognition within the Damage Identification Framework

The paper is concerned with adopting a data-driven approach to damage detection and location on an aerospace structure without recourse to an artificial neural network. Five advanced features are selected, each detecting the removal of only one of five inspection panels on the structure. The features give perfect classification for damage location for single-site damage and 98.1% correct classification for multi-site damage scenarios, using a statistically calculated threshold. However, if the threshold values for two of the five features are altered slightly, 100% correct classification would be possible for single- and multi-site damage.

Advanced Feature Selection for Simplified Pattern Recognition within the Damage Identification Framework

Advanced Feature Selection for Simplified Pattern Recognition within the Damage Identification Framework

Particle Swarm vs. Evolutionary Optimization Techniques in a Multiobjective Framework for Damage Identification

In the context of real-world damage detection problems, the lack of a clear objective function advises to perform simultaneous optimizations of several objectives with the purpose of improving the performance of the procedure. Evolutionary algorithms have been considered to be particularly appropriate to these kinds of problems. However, evolutionary techniques require a relatively long time to obtain a Pareto front of high quality. Particle swarm optimization (PSO) is one of the newest techniques within the family of optimization algorithms. The PSO algorithm relies only on two simple PSO self-updating equations whose purpose is to try to emulate the best global individual found, as well as the best solutions found by each individual particle. Since an individual obtains useful information only from the local and global optimal individuals, it converges to the best solution quickly. PSO has become very popular because of its simplicity and convergence speed. However, there are many associated problems that require further study for extending PSO in solving multi-objective problems. The goal of this paper is to present the first application of PSO to multiobjective damage identification problems and investigate the applicability of several variations of the basic PSO technique. The potential of combining evolutionary computation and PSO concepts for damage identification problems is explored in this work by using a multiobjective evolutionary particle swarm optimization algorithm.