Condition assessment of wall ties in masonry cavity wall using vibration-based methods

A comprehensive review on modal parameter-based damage identification methods for beam- or plate-type structures is presented, and the damage identification algorithms in terms of signal processing are particularly emphasized. Based on the vibration features, the damage identification methods are classified into four major categories: natural frequency-based methods, mode shape-based methods, curvature mode shape-based methods, and methods using both mode shapes and frequencies, and their merits and drawbacks are discussed. It is observed that most mode shape-based and curvature mode shape-based methods only focus on damage localization. In order to precisely locate the damage, the mode shape-based methods have to rely on optimization algorithms or signal processing techniques; while the curvature mode shape-based methods are in general a very effective type of damage localization algorithms. As an implementation, a comparative study of five extensively-used damage detection algorithms for beam-type structures is conducted to evaluate and demonstrate the validity and effectiveness of the signal processing algorithms. This brief review aims to help the readers in identifying starting points for research in vibration-based damage identification and structural health monitoring and guides researchers and practitioners in better implementing available damage identification algorithms and signal processing methods for beam- or plate-type structures.

Experimental modal analysis has proven effective in damage identification of civil structures but has not been extensively applied to multi-leaf masonry structures, particularly in the context of wall tie inspection. This paper investigates the applicability of non-destructive, vibration-based damage identification methods to a one-storey masonry veneer wall to detect wall tie deterioration based on changes in modal parameters. An impact hammer was used to collect vibration data from eight different wall tie deterioration test cases by disconnecting the wall ties at various locations. The downshift of natural frequencies was recorded for all deterioration test cases, and a reduction of up to 38% was observed when the top row of wall ties was disconnected, highlighting the importance of wall ties to the overall stiffness of the masonry veneer wall system. In terms of damage localisation accuracy, the parameter-based method performed the best by successfully identifying seven out of eight damaged scenarios without additional noise. The findings show that the detection of wall tie deterioration using non-destructive, vibration-based damage identification methods is viable, providing an alternative wall tie inspection method with significant benefits to infrastructure management, thereby enhancing safety, efficiency, and sustainability in maintaining and preserving masonry veneer walls.

This paper outlines various vibration-based methods for detecting and locating damage in mechanical and civil structural systems. The basic premises of these methods are that any changes in physical characteristics (mass and stiffness) of the structure will induce detectable changes in the modal properties of the structure (natural frequency, mode shape and damping). A comparative study of eight extensively used damage identification methods is done on a simulated simply supported beam to demonstrate the validity and effectiveness. The outcomes of various methods are compared in terms of damage detection and localization under single and multiple damage scenarios.

Vibration-based methods for local damage identification of breathing cracks in truss-like structures

A comparative assessment of different frequency based damage detection in unidirectional composite plates using MFC sensors

In the arena of structural health monitoring (SHM), ultrasonic guided wave-based and vibration-based damage detection and identification methods constitute two seemingly distinct approaches, oftentimes treated separately. Guided-waves-based methods are typically used for ``local'' damage diagnosis due to their increased sensitivity while vibration-based methods are based on the premise that damage has an impact on the global structural dynamic response, therefore are typically used for tackling ``global'' damage diagnosis. In this work, we present the comparison and critical assessment of the two state-of-the-art time-series-based methods, a local method based on guided waves and a global method based on structural vibrations, via a series of progressive damage analysis (PDA) numerical simulations and experiments on a composite unmanned aerial vehicle (UAV) wing structure. At first, a finite element model is established for progressive damage analysis on a composite plate fitted with piezoelectric sensors/actuators. Then, ultrasonic guided wave signals are generated and collected in a pitch-catch configuration as damage progression occurs while the low-frequency vibratory response and modal analysis results are also generated to allow the comparison of the local and global diagnostic methods. Next, both methods are implemented and assessed on a composite UAV wing structure, outfitted with accelerometers and piezoelectric sensors, to collect active-sensing pitch-catch ultrasonic and vibration response signals under different damage locations and magnitudes. The diagnostic methods presented are based on: (i) stochastic functional series time-varying autoregressive (FS-TAR) models to represent the ultrasonic guided wave propagation and enable the subsequent FS-TAR model-based local damage detection and identification tasks, and (ii) functionally pooled autoregressive models with exogenous excitation (FP-ARX) models to enable the vibration-based global damage diagnosis. It is shown that by incorporating a local guided wave-based damage diagnosis scheme within the global vibration-based damage detection and identification framework, both the sensitivity and robustness for detecting and identifying (localizing and quantifying) minor damage can be significantly increased.

Dynamic stiffness as an acoustic specification parameter for wall ties used in masonry cavity walls

The basis of vibration-based damage detection method is that when there are changes in physical properties of a structure, there will also be changes in vibration characteristics such as natural frequencies, mode shapes and damping ratios. Artificial neural network has become one of the most powerful approaches capable of pattern recognition, classification, and nonlinear modelling employing computational intelligence techniques to tackle damage detection as a complex problem. In this paper, ensemble neural networks based damage identification techniques were developed and applied for damage localization and severity identification in I-beam structures using dynamic parameters. Experimental modal analysis and numerical simulations of I-beam with triple-point damage cases were carried out to generate the natural frequencies and mode shapes of structures. In the procedure of damage identification, five different neural networks corresponding to mode 1 to mode 5 were constructed, and then an approach based on a neural network ensemble was proposed to combine the outcomes of the individual neural networks into a single network. The ensemble neural network has the superiorities of all the individual networks from different vibrational modes. The ensemble network produced better damage identification outcomes than the individual networks and the results revealed the ability of the ensemble neural networks to identify damage.

A comparative analysis of damage identification methods based on multi-channel data fusion and convolutional neural network for frame structures

VIBRATION-BASED DAMAGE IDENTIFICATION USING RECONSTRUCTED FRFS IN COMPOSITE STRUCTURES

Experimental and numerical analyses were carried out in order to identify damage in metal-composite bonded joints, which were manufactured from carbon-fiber reinforced polymers and titanium plates joined by an epoxy resin. The monitoring was performed by using vibration-based method through changes in frequency response function (FRF). First, free–free vibration tests were performed on four different specimens (with presence or not of damage and with presence or not piezoelectric sensor). Finite element analyses for the conditions without the transducer were also carried out and compared with the experimental data. FRFs were obtained by using the response of the PZT placed over the titanium plate and accelerometers located at other positions of the joint. The damage was reproduced by replacing 50% of the overlap with a layer of Teflon. Lastly, based on damage identification metric, FRFs for the undamaged and damaged structure were compared, evaluating not only the potentialities and limitations of the applied experimental detection technique, but also the computational model. The experimental and numerical results showed that the vibration-based damage identification methods combined to the metrics can be used in structural health monitoring systems.

1. The process of defect accumulation in composite materials, based on a study of the reduction in natural vibration frequency of specimens under cyclic loading, is considerably different from that of quasi-isotropic materials. 2. During comparative tests under severe conditions it is possible to define “failure” as a specific reduction in the natural frequency (Δf, Hz). This, however, should be confirmed by metallographic studies. 3. The defect accumulation process in composite materials, based on the reduction in natural vibration frequency of specimens, is of a damped and nonuniform nature with periods of acceleration and retardation. Cracks form and develop until they reach the fiber-matrix interface, at which stage they stop progressing further. 4. The average parameters of specimen life for composite materials can be calculated assuming a logarithmic-normal distribution of life. 5. The life of composite material specimens has a multimodal distribution which requires an indepth study with considerable data.

Bayesian-formulated neural network architecture is implemented using a hybrid Monte Carlo method for probabilistic fault identification in a population of ten nominally identical cylindrical shells using vibration data. Each cylinder is divided into three substructures. Holes of 12 mm in diameter are introduced in each of the substructures. Vibration data are measured by impacting the cylinders at selected positions using a modal hammer and measuring the acceleration responses at a fixed position. Modal energies, defined as the integrals of the real and imaginary components of the frequency response function over 12-Hz frequency bandwidths, are extracted and transformed into the coordinate modal energy assurance criterion. This criterion and the identity of faults are used to train the frequency response function (FRF) neural network. Modal analysis is then employed to identify modal properties. Mode shapes are transformed into the coordinate modal assurance criterion. The natural frequencies and the coordinate modal assurance criterion, as well as the identities of faults, are utilized to train the modal-property neural network. The weighted average of the modal-property network and the FRF network form a committee of two networks. The committee approach is observed to give lower mean square errors and standard deviations (thus, a higher probability of giving the correct solution) than the individual methods. This approach gives accurate identities of damage and their respective confidence intervals while requiring affordable computational resources.

Monitoring the changes in geometric or structural properties of systems during their operation to evaluate the presence of damage is a well-established process entitled Structural Health Monitoring (SHM). It is one of the most widely discussed topics in mechanical, aerospace, and civil engineering, and exhibits an increasing importance in many other fields. SHM has a crucial relevance in determining the remaining useful life of a structure, which is essential in avoiding catastrophic failure. However, these analyses can be complicated and sometimes require destructive methods in order to achieve accurate results. Nonetheless, vibration-based methods have shown promising results as non-destructive approaches, relying primarily on variations of the modal parameters. In addition, previous studies demonstrated that natural frequencies are easily determined and provide great accuracy of results. The present work proposes the identification of single and multiple damages in beam-like structures using measures of natural frequencies. First, the beam is modeled using three different methods with increasing complexity. The first model is built in Ansys® APDL module with 100 elements using BEAM188 element type, and damage is introduced as a stiffness reduction in a particular element; the second model is constructed as a beam with solid elements, and damage is presented as a local reduction in stiffness; the third one is also built with solid elements, but with the damage mechanically introduced in the beam. The results for the natural frequencies are obtained via simulation. Then, an experimental setup is assembled in order to acquire results for the beam's natural frequencies and compare them to the values obtained via numerical analysis. These results are further processed by employing a Bayesian data fusion algorithm to predict the location of damage. Three possible damage locations are examined with varying intensities, ranging from a decrease in local stiffness from 10% to 50%. Different types of beam supports are also investigated. The results demonstrate that the algorithm successfully detects single and multiple damage and locates it with great accuracy under various types of supports.

Impact hammer test is a vibration-based monitoring method used to detect cracking or damage in concrete structures by observing the vibration behavior such as the natural frequency, mode shapes and damping ratio. Damage or cracks in the structure can cause structure failure. The ability to quickly perform maintenance, if necessary, depends on the ability to identify damage or cracks. Concrete cracking has traditionally been detected via vibration analysis utilizing the impact hammer test. The main objectives of this research were to determine the natural frequency of the cracked and uncracked reinforced concrete slabs and to determine the depth of the crack using the impact hammer test. Two samples of reinforced concrete slabs were constructed in the laboratory. The reinforced concrete slabs were designed as Grade 25 concrete. After 28 days, the impact hammer test was conducted on the uncracked slabs. After that, the slabs were subjected to a four-point bending test with loads of 3 kN, 6 kN, 12 kN and 15 kN. For each load increase, the impact hammer test and the crack mapping were performed. The natural frequency of the cracked and uncracked slabs was compared after that. The natural frequency of both Slab 1 and Slab 2 decreased after the slab was cracked compared to before cracked. The width of the crack was also determined by analyzing the wave acceleration from the impact hammer test. The outcome demonstrated that the wave's acceleration increased as it propagates through the crack region. The percentage difference in the waves’ acceleration was calculated. The result was also compared with crack depth measurement from PUNDIT. The result showed that the location of the maximum crack width and crack depth was the same. It also justified that when the percentage difference of the acceleration decreased, the crack width increased. However, in previous studies, these findings had not been discussed yet. Therefore, further research needs to be done to understand the propagation of the wave on the cracked slab to validate the findings.