Samir Mustapha

Debonding Detection in Composite Sandwich Structures Based on Guided Waves

timeorfrequencydomainswerecalculatedtodefinethedamageindex forindividual sensingpaths,whichwereused to develop an imaging algorithm to identify the presence of damage in the monitoring area enclosed by the active sensor network. The results confirm that incident wave signals and their reconstructed time-reversed counterparts can be used to accurately detect damage in sandwich composite structures.

Robust dimensionality reduction and damage detection approaches in structural health monitoring

Structural health monitoring has been increasingly used due to the advances in sensing technology and data analysis, facilitating the shift from time-based to condition-based maintenance. This work is part of the efforts which have applied structural health monitoring to the Sydney Harbour Bridge – one of Australia’s iconic structures. It combines dimensionality reduction and pattern recognition techniques to accurately and efficiently distinguish faulty components from well-functioning ones. Specifically, random projection is used for dimensionality reduction on the vibration feature data. Then, healthy and damaged patterns of bridge components are learned in the lower dimensional projected space using supervised and unsupervised machine learning methods, namely, support vector machine and one-class support vector machine. The experimental results using data from a laboratory-based building structure and the Sydney Harbour Bridge showed high feasibility of applying machine learning techniques to dimensionality reduction and damage detection in structural health monitoring. Random projection combined with support vector machine significantly reduces the computational time while maintaining the detection accuracy. The proposed method also outperformed popular dimensionality reduction techniques. The computational time of the method using random projection can be more than 200 times faster than that without using dimensionality reduction while still achieving similar detection accuracy.

Concise analysis of wave propagation using the spectral element method and identification of delamination in CF/EP composite beams

This paper presents a two-dimensional spectral element method for characterizing wave propagation in composite beam structures for the purpose of damage detection. The interaction of Lamb waves with delamination in an 8-ply carbon fiber/epoxy (CF/EP) laminate is investigated, and some unique mechanisms of interaction between Lamb wave modes and delamination are revealed in detail. It is demonstrated that the reflection at the far end of the delamination is much stronger in magnitude than that from the near end, and when the delamination length is comparable to the wavelength of the wave mode, the reflections from both ends of the delamination merge into one. The fundamental antisymmetric (A0) mode is more suitable for identification of delamination in multilayered composite structures than the fundamental symmetric (S0) mode, especially when the delamination is in the symmetric plane. The curves of the reflection coefficient and transmission coefficient are analyzed, showing an undulating shape, and the values of reflection or transmission coefficient are dependent on the ratio of the delamination length to the wavelength of the Lamb wave. A quantitative identification method for delamination in composite beam structures is proposed and validated using an experimental study.

Damage detection in rebar-reinforced concrete beams based on time reversal of guided waves

The propagation properties of ultrasonic waves in rebar-reinforced concrete beams were investigated and their ability for damage identification was demonstrated. Rectangular piezoelectric ceramics were attached at the exposed ends of the rebar to monitor the wave transmission along the rebar with and without simulated corrosion, which was introduced in the form of partial removal of material from the rebar. Experimental testing demonstrated that the presence of concrete had a significant influence on the propagation characteristics of guided waves along the rebar. In consideration of the inevitable discrepancies in different concrete beams due to individual specimen preparation and sensor installation, the time-reversal process was applied to identify the damage. A damage index was defined based on the correlation coefficient between the actuated and the reconstructed wave signals. Wavelet transform was applied to overcome the wave conversion difficulty and to reduce the noise in the captured wave signals. Damage of different sizes was introduced and then was correlated with the damage index. Enlarging the damage size resulted in an increase in the level of distortion in the reconstructed wave signals, and consequently, a higher damage index was obtained. The results demonstrate the efficiency of the time-reversal process in identifying damage in rebar-reinforced concrete structures.

Spectral-based damage identification in structures under ambient vibration

AbstractThe motivation behind this paper is to develop a spectral-based damage detection and damage localization scheme using in-service ambient vibration in the context of non–model-based damage characterization. In this regard, a response parameter known as spectral moment is implemented for structural damage identification. The damage identification procedure starts with developing response power spectral density (PSD). The principal structural response features are then extracted from the frequency distribution of the spectrum using spectral moments. It is demonstrated that, although, spectral moment is a nonmodal characteristic of a process, it is related to modal parameters of a response signal since the spectral moment at each location is proportional to its corresponding modal vector. Hence, it is expected that due to damage occurrence spectral moment undergoes a variation. On this point, a damage sensitive feature is defined by comparing the spectral moments of two successive states of the struct...

On Damage Identification in Civil Structures Using Tensor Analysis

Structural health monitoring is a condition-based technology to monitor infrastructure using sensing systems. In structural health monitoring, the data are usually highly redundant and correlated. The measured variables are not only correlated with each other at a certain time but also are autocorrelated themselves over time. Matrix-based two-way analysis, which is usually used in structural health monitoring, can not capture all these relationships and correlations together. Tensor analysis allows us to analyse the vibration data in temporal, spatial and feature modes at the same time. In our approach, we use tensor analysis and one-class support vector machine for damage detection, localization and estimation in an unsupervised manner. The method shows promising results using data from lab-based structures and also data collected from the Sydney Harbour Bridge, one of iconic structures in Australia. We can obtain a damage detection accuracy of 0.98 and higher for all the data. Locations of damage were captured correctly and different levels of damage severity were well estimated.

Bonding Piezoelectric Wafers for Application in Structural Health Monitoring–Adhesive Selection

Piezoelectric (PZT) wafers are widely applied in the field of Structural Health Monitoring (SHM), and a common practice is to permanently attach them to the inspected structure using different types of adhesive systems. The effect of five adhesive systems on the excitation of guided waves, in particular on the fundamental antisymmetric A0 and symmetric S0 Lamb wave modes are experimentally assessed. The curing progress of the adhesive systems at room temperature was monitored and the effect on signal magnitude is characterized. In addition, the effect of the adhesive on the waveform and the propagation velocity of the transmitted wave signals are investigated. The bondline thickness and the shear modulus of the adhesive system selected is considered in order to increase the magnitude of the excited wave signals.

Damage Identification and Assessment in Tapered Sandwich Structures Using Guided Waves

Damage detection using guided waves in the inspection of tapered sandwich structures with high density foam core (Dyvinicell HP100) is investigated. Characterisation of the fundamental symmetric and anti-symmetric Lamb wave modes is carried out in terms of their velocity and magnitude variation as they encounter a change in the thickness of a composite sandwich plate, aiming at optimising the mode selection to improve the capability and increase the sensitivity of guided waves in inspection of tapered sandwich structures. In addition, an imaging algorithm based on time reversal is developed to detect multiple debonding and artificial damage in tapered sandwich panels based guided waves from an active sensor network. The correlation coefficients between the original and reconstructed time reversal signals are calculated to define a damage index for individual sensing paths, which are used later in the fusion process, identifying the presence of damage in the monitoring area enclosed by the active sensor network. The results confirm that the incident wave signals and their reconstructed time-reversed counterparts can be used to accurately detect the debonding/damage in tapered sandwich structures.

Adaptive One-Class Support Vector Machine for Damage Detection in Structural Health Monitoring

Machine learning algorithms have been employed extensively in the area of structural health monitoring to compare new measurements with baselines to detect any structural change. One-class support vector machine (OCSVM) with Gaussian kernel function is a promising machine learning method which can learn only from one class data and then classify any new query samples. However, generalization performance of OCSVM is profoundly influenced by its Gaussian model parameter \(\sigma \). This paper proposes a new algorithm named Appropriate Distance to the Enclosing Surface (ADES) for tuning the Gaussian model parameter. The semantic idea of this algorithm is based on inspecting the spatial locations of the edge and interior samples, and their distances to the enclosing surface of OCSVM. The algorithm selects the optimal value of \(\sigma \) which generates a hyperplane that is maximally distant from the interior samples but close to the edge samples. The sets of interior and edge samples are identified using a hard margin linear support vector machine. The algorithm was successfully validated using sensing data collected from the Sydney Harbour Bridge, in addition to five public datasets. The designed ADES algorithm is an appropriate choice to identify the optimal value of \(\sigma \) for OCSVM especially in high dimensional datasets.

Inverse estimation of impact force on a composite panel using a single piezoelectric sensor

Identification of location and magnitude of impact forces on a rectangular carbon fibre–epoxy honeycomb composite panel has been experimentally investigated through an inverse approach. The dynamic signals captured by a single piezoelectric (PZT) sensor installed on the panel remotely from the impact locations are utilized to identify the impact forces generated by an instrumented hammer. A number of potential impact locations on the panel are assumed to be known a priori. An actual impact is then occurred at one or two of these locations. The objective is to simultaneously identify the location and magnitude of the impact forces using the PZT sensor. The problem is solved through minimization of an extended matrix form of the convolution integral incorporating linear superposition of the responses due to impact at different locations. The under-determined problem is ill-posed and is regularized by Tikhonov and generalized cross validation methods. It is revealed that impact forces occurred at any location among four possible locations can be well identified.