Guided Waves Benchmark Dataset and Classifier Comparison

In this paper, a new damage detection and quantification method has been presented to perform detection and quantification of structural damage under ambient vibration loadings. To extract modal properties of the structural system under ambient excitation, natural excitation technique (NExT) and eigensystem realization algorithm (ERA) are employed. Sensitivity matrices of the dynamic residual force vector have been derived and used in the parameter subset selection method to identify multiple damaged locations. In the sequel, the steady state genetic algorithm (SSGA) is used to determine quantified levels of the identified damage by minimizing errors in the modal flexibility matrix. In this study, performance of the proposed damage detection and quantification methodology is evaluated using a finite element model of a truss structure with considerations of possible experimental errors and noises. A series of numerical examples with five different damage scenarios including a challengingly small damage level demonstrates that the proposed methodology can efficaciously detect and quantify damage under noisy ambient vibrations.

Need for developing efficient non-destructive damage detection procedures for civil engineering structures is growing rapidly. This paper presents a methodology for detection and quantification of structural damage using modal information obtained from transfer matrix technique. Vibration characteristics of beam-like structure have been determined using the computer program developed based on the formulations presented in the paper. It has been noted from reported literature that detection and quantification of damage using mode shape information is difficult and further, extraction of mode shape information has practical difficulties and limitations. Hence, a methodology for detection and quantification of damage in structure using tranfer matrix technique based on the changes in the natural frequencies has been developed. With an assumption of damage at a particular segment of the beam-like structure, an iterative procedure has been formulated to converge the calculated and measured frequencies by adjusting flexural rigidity of elements and then, the intersections are used for detection and quantification of damage. Eventhough the developed methodology is iterative, computational effort is reduced considerably by using transfer matrix technique. It is observed that the methodology is capable of predicting the location and magnitude of damage quite accurately.

Structural damage detection of a cantilever beam under varying temperature using a collection of time series

Advances in optic fiber sensing technology provide easy and reliable way for the vibration-based strain measurement of engineering structures. As a typical optic fiber sensing techniques with high accuracy and resolution, long-gauge Fiber Bragg Grating (FBG) sensors have been widely employed in health monitoring of civil engineering structures. Therefore, the development of macro strain-based identification methods is crucial for damage detection and structural condition evaluation. In the previous study by the authors, a damage detection algorithm for a beam structure with the direct use of vibration-based macro-strain measurement time history with neural networks had been proposed and validated with experimental measurements. In this paper, a damage locating and quantifying method was proposed using modal macrostrain vectors (MMSVs) which can be extracted from vibration induced macro-strain response measurement time series from long-gage FBG sensors. The performance of the proposed methodology for damage detection of a beam with different damage scenario was studied with numerical simulation firstly. Then, dynamic tests on a simply-supported steel beam with different damage scenarios were carried out and macro-strain measurements were employed to detect the damage severity. Results show that the proposed MMSV based structural identification and damage detection methodology can locate and identify the structural damage severity with acceptable accuracy.

Structural ageing and material deterioration require infrastructure managers to continuously seek for improved solutions for bridge condition management. In the last two decades, vehicle-assisted bridge monitoring has emerged among researchers and engineers as a promising tool to support visual inspections, being a cost-efficient alternative to direct Structural Health Monitoring systems. In this work, the authors present a sparse-autoencoder-based damage detection methodology which exploits the vertical acceleration of train’s leading bogie to assess bridge health condition. The bridge under analysis in this work is a Warren truss bridge, whose FE model was designed based on the technical drawings of an actual structure, which belongs to the Italian railway line, and then validated through dynamic measurements. Raw bogie vertical accelerations are preprocessed through Continuous Wavelet Transform, allowing for the extraction of a specific frequency region of interest, governed by the modular configuration of the bridge as well as the forward speed of the convoy. Starting from the average curve of the computed wavelet coefficients, bridge health status is assessed through the use of a sparse autoencoder exploiting multiple train transits and two different damage indices. First of all the Hotelling’s statistic computed at the latent space level, and, secondly, the batch mean absolute reconstruction error. Different damage scenarios and intensities are tested in this work, considering the partial failure of stringers or cross-girder members due to corrosion, modelled as material mass loss and stiffness reduction. A large set of simulations allowed for testing the robustness of the methodology against operational variables, such as travelling speed, geometrical track irregularity evolution, and different weights of the convoy. Globally, promising damage detection performances were obtained when considering batches of 40 trains, even in presence of measurement noise and speed estimation inaccuracies.

Detecting structural damage in operational conditions still encounters some difficulties, especially in early-stage, as environmental varieties impose challenges in real engineering applications and may require large computational efforts in the structural health monitoring and potential maintenance. Unlike conventional strategies employing frequency response function or response data, a damage detection methodology is addressed in this study by employing transmissibility functions that retains a strong interrelation with structural damage or deterioration, in order to avoid the measurement of excitation, together with principal component analysis that leads to reduction in computational costs. In this procedure, transmissibility is extracted from the structural responses and main features are selected by principal component analysis for less computational costs. Then, via distance measures damage indicators are constructed for both intact and damaged states, and finally a numerical simulation with a clamped-clamped beam and a four-story benchmark are adopted to verify the applicability of the proposed procedure. The results demonstrate a good performance in structural damage detection.

Deep learning for seismic structural monitoring by accounting for mechanics-based model uncertainty

This paper presents a numerical investigation of the Mode Shape Area Difference (MSAD) methodology for structural damage detection in steel frame systems. The MSAD approach quantifies the variation between undamaged and damaged mode shapes by computing the area difference of their displacement curves and spatial derivatives. Three damage indexes – MDAD, MRAD, and MCAD– are formulated based on modal displacement, rotation, and curvature, respectively. The methodology is applied to an industrial steel portal frame modeled using Bernoulli beam elements, with damage simulated as localized reductions in flexural stiffness. A total of 154 damage scenarios are evaluated, encompassing various locations and severity levels. The results show that the MCAD index is the most effective in detecting and localizing damage, demonstrating robustness even for low stiffness reductions (sr = 1%). However, a loss of sensitivity with respect to damage severity is observed in some scenarios near modal nodes or structural supports. These findings suggest that MSAD, especially in its curvature-based form, is a promising non-model-dependent strategy for condition assessment in frame structures. Future developments are discussed.

The assessment of structural health is an important research topic in many engineering fields and, for that reason, damage detection methodologies are being developed. The goal of this dissertation is to verify the applicability of genetic algorithms (GAs) for detecting damage using dynamic parameters changes between undamaged and damaged condition of the structure. Three different GAs are implemented in order to compare the performance of the algorithms. Undamaged and damaged dynamic parameters are computed using the finite element model of the structure. Incomplete and noisy measurements are considered with the objective of simulating the real condition of the information in a real dynamic test. GAs are applied in some different structures: beam, truss and frame. The results indicate the good performance of the GAs for detecting damage in a structure.

The propagation of Lamb waves in elastic plates is analyzed both numerically and experimentally. A Scanning Laser Doppler Vibrometer (SLDV) is here used to detect and visualize transient waveforms propagating in an elastic plate at low ultrasonic frequencies. The waves are excited by a piezoelectric crystal glued to the plate surface and actuated by sinusoidal pulses of varying frequency. The pulse sequence is triggered by the SLDV internal controller so that phase and delay information are preserved. Such information allows visualization of the waveform pattern as it propagates over the plate surface. The experiment produces animated displacement maps where the interaction with discontinuities in the plate such as defects becomes apparent. This capability suggests application of the SLDV technique as part of an overall damage detection methodology which combines the recognized sensitivity of ultrasonic waves with the localization of damage via wavefield visualization. The interpretation of the experimental results is aided by numerical simulations of ultrasonic waves in plate structures. The simulations are performed using a Local Interaction Simulation Approach (LISA), which represents a simple and effective tool for simulating and visualizing waveforms in isotropic or orthotropic plate‐like structures.

Modal analysis has developed into a major technology for the study of structural dynamics in the past several decades. Through it, complex structural dynamics phenomena can be represented in terms of structural invariants, i.e., the modal parameters: natural frequencies, damping ratios and mode shapes. Operational Modal Analysis (OMA) deals with the estimation of modal parameters on vibration data measured for operational conditions, when the excitation on the structure is not measured. In this work, OMA is performed on a wind turbine blade undergoing wind tunnel testing. These tests included different wind speeds, pitch angles and also different health conditions of the blade, where masses of different magnitude were fixed to the blade on different locations to emulate damage conditions. In order to monitor the modal parameters across multiple days of varied tests in the wind tunnel, the Polymax modal parameter estimator was implemented, coupled with an Automated Modal Analysis methodology. This methodology included an automatic modal parameter selection technique, using a Machine Learning (ML) clustering algorithm, coupled with a modal tracking procedure which applied statistical thresholds on the modal parameters’ values. The tracking procedure searches for modes similar to the ones calculated for healthy conditions. The results show how the modal parameters of the wind turbine blade vary with the different measured conditions in the wind tunnel. Moreover, a damage detection methodology is implemented to differentiate between the healthy and damaged conditions on the blade, by leveraging an anomaly detection algorithm using the Multivariate Gaussian Distribution (MGD). This algorithm takes as input the modal parameters calculated by the previous Automated Modal Analysis methodology and detects statistical deviations among them which could indicate the presence of damage. All steps of this work contribute to developing an automatic framework able to detect damages on a wind turbine blade, and therefore perform Structural Health Monitoring (SHM) for different operational conditions.

Towards the generation of controlled one-inch impact damage in thick CFRP composites for SHM and NDE validation

The aim of this work was the implementation of an identification and evaluation methodology for external potato damage detection. A system for automatic image recognition was used and a methodology validation, by comparison with human visual selection, was performed. The potato surface image was acquired with a monochromatic video camera that operated in the visible spectrum and in the near infrared. This device was connected to a frame grabber card, interfaced to a PC, for image acquisition and elaboration. The software for the image elaboration has enabled the definition of algorithms for the automatic recognition and measurement of the damaged area. The obtained data were compared with the human visual evaluation demonstrating an adequate level of reliability of the applied methodology.

Assessment of the Number of Mode Shapes Required in the Modal Strain Energy Methodology for Structural Damage Detection

This paper presents an enhanced version and the validation of a recently proposed methodology for earthquake-induced damage detection and localization in masonry towers by using long-term vibration monitoring data. The proposed enhanced method is based on continuous operational modal analysis through stochastic subspace identification, dynamic multiple linear regressive analysis to remove the effects of changing environmental conditions and finite element (FE) model updating. Any anomalous frequency deviation from normal conditions resulting from an earthquake triggers the damage localization process. This task is performed by solving an inverse FE model calibration problem, where equivalent elastic properties of macrostructural elements are identified by minimizing an objective function considering experimentally identified and numerically predicted damage-induced decays in natural frequencies and changes in eigenvector components. To minimize the computational effort of this calibration procedure, a quadratic surrogate model is constructed using a tuned numerical FE model of the structure. The methodology is validated through application to the “Sciri Tower”, an historic civic masonry tower located in Perugia, Italy, that has been continuously monitored by the authors for more than 1 year. The validation is carried out by using simulated damage scenarios and a set of real far-field earthquake data and is based on a FE model of the tower including surrounding buildings, calibrated on the basis of the measured data from ambient vibration tests. The results demonstrate that the proposed procedure is capable of correctly detecting and localizing earthquake-induced damages.