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The purpose of this paper is to provide an effective and simple technique to structural damage identification, particularly to identify a crack in a structure. Artificial neural networks approach is an alternative to identify the extent and location of the damage over the classical methods. Radial basis function (RBF) networks are good at function mapping and generalization ability among the various neural network approaches. RBF neural networks are chosen for the present study of crack identification.

Analyzing the vibration response of a structure is an effective way to monitor its health and even to detect the damage. A novel two‐stage improved radial basis function (IRBF) neural network methodology with conventional RBF in the first stage and a reduced search space moving technique in the second stage is proposed to identify the crack in a cantilever beam structure in the frequency domain. Latin hypercube sampling (LHS) technique is used in both stages to sample the frequency modal patterns to train the proposed network. Study is also conducted with and without addition of 5% white noise to the input patterns to simulate the experimental errors.

The results show a significant improvement in identifying the location and magnitude of a crack by the proposed IRBF method, in comparison with conventional RBF method and other classical methods. In case of crack location in a beam, the average identification error over 12 test cases was 0.69 per cent by IRBF network compared to 4.88 per cent by conventional RBF. Similar improvements are reported when compared to hybrid CPN BPN networks. It also requires much less computational effort as compared to other hybrid neural network approaches and classical methods.

The proposed novel IRBF crack identification technique is unique in originality and not reported elsewhere. It can identify the crack location and crack depth with very good accuracy, less computational effort and ease of implementation.

The purpose of this paper is to provide an effective and simple technique for structural parameter identification, particularly to identify multiple cracks in a structure using simultaneous measurement of acceleration responses and voltage signals from PZT patches which is a multidisciplinary approach. A hybrid element constituted of one-dimensional beam element and a PZT sensor is used with reduced material properties which is very convenient for beams and is a novel application for inverse problems.

Multi-objective formulation is used whereby structural parameters are identified by minimizing the deviation between the predicted and measured values from the PZT patch and acceleration responses, when subjected to excitation. In the proposed method, a patch is attached to either end of the fixed beam. Using particle swarm optimization algorithm, normalized fitness functions are defined for both voltage and acceleration components with weighted aggregation multi-objective optimization technique. The signals are polluted with 5 percent Gaussian noise to simulate experimental noise. The effects of various weighting factors for the combined objective function are studied. The scheme is also experimentally validated by identification of cracks in a fixed-fixed beam.

The numerical and experimental results shows that significant improvement in accuracy of damage detection is achieved by the combined multidisciplinary method, when compared with only voltage or only acceleration-matching method as well as with other methods.

The proposed multidisciplinary crack identification approach, which is based on one-dimensional PZT patch model as well as conventional acceleration method, is not reported in the literature.

This study aims to develop a numerical identification and characterization of crack propagation through the use of a new optimization metaheuristics called Lichtenberg optimization.

The damage-identification problem is treated as an inverse problem, which combines finite element methods with intelligent computational methods to obtain the best possible response. To optimize the objectives, the Lichtenberg algorithm is applied, which includes concepts of random cluster growth in nature.

The simulations show that it is possible to determine the Lichtenberg spectrum algorithm a part of the structure to be removed and replaced in this case to stop the propagation.

The results show a very good crack identification in plates-like structures using the Lichtenberg algorithm (LA) based only in strain fields. Although many studies have reported on damage-identification-based optimization methods, very few have focused on the crack tip modeling and LA as the main solver.

To investigate the feasibility of using single/multi variable optimisation techniques with vibration measurements in solving the inverse crack identification problem.

The finite element method is used to solve the forward crack problem with a special nodal crack force approach. The multi‐variable optimisation approach is reduced to a much more efficient single‐variable one by decoupling the physical variables in the problem.

It is shown that, for the crack identification problem, global optimisation algorithms perform much better than other algorithms relying heavily on objective function gradients. Simultaneous identification of crack size and location proved to be difficult. Decoupling of the physical variable is introduced and proved to provide efficient results with single‐variable optimisation algorithms.

Need for improving the reliability and accuracy of the procedure for smaller crack sizes. Need for developing and investigation more rigorous and robust multi‐variable optimisation algorithm.

Any information about approximate crack size and location provides significant aid in the maintenance and online monitoring of rotating equipment.

The paper offers practical approach and procedure for online monitoring and crack identification of slow rotating equipment.

The flexible eddy current array sensor has the characteristics of lightweight and flexibility, which has a great application prospect in the field of fatigue crack monitoring. But the exciting layout and feature signal extraction have a great influence on the crack monitoring characteristics of the sensor. This paper aims to propose a method using crack disturbed voltage as sensitivity to characterize crack propagation.

Flexible eddy current array sensors with reverse and codirectional exciting layout are proposed, and the advantages and disadvantages of three characterization methods based on the change of trans-impedance amplitude, the change of the trans-impedance’s real and imaginary part and the crack disturbed voltage are compared and analyzed by finite element simulation. Finally, the fatigue crack monitoring experiment is carried out.

The crack disturbed voltage and the change of trans-impedance’s imaginary part can effectively characterize the crack propagation for sensors with different exciting layouts. The codirectional exciting layout sensor has better crack identification sensitivity than the reverse exciting layout sensor, especially the induction coil 2. When the distance between the exciting coil and the induction coil is 0.1, 0.2 and 0.3 mm, it is increased by 372.09%, 295.24% and 231.43%, respectively.

Crack disturbed voltage can effectively characterize the crack propagation for sensors with two different exciting layouts.

The early detection of cracks, corrosion and structural failure in aging structures is one of the major challenges in the civil, mechanical and aircraft industries. Common inspection techniques are time consuming and hence can have strong economic implications due to downtime. The paper aims to discuss these issues.

As a result, during the past decade a number of methodologies have been proposed for detecting crack in structure based on variations in the structure's dynamic characteristics. This work showcases the efficacy of particle swarm optimization (PSO) and genetic algorithm (GA) in damage assessment of structures.

Efficiency of these tools has been tested on structures like beam, plane and space truss. The results show the effectiveness of PSO in crack identification and the possibility of implementing it in a real-time structural health monitoring system for aircraft and civil structures.

The methodology presented establishes the PSO as robust and competent tool over GA for crack identification using changes in natural frequencies.

This paper presents a numerical study of inverse parameter identification problems in fracture mechanics. Inverse methodology is applied to the detection of subsurface cracks and to the study of propagating cracks. The procedure for detecting subsurface cracks combines the finite element method with a sequential quadratic programming algorithm to solve for the unknown geometric parameters associated with the internal flaw. The procedure utilizes finite element substructuring capabilities in order to minimize the processing and solution time for practical problems. The finite element method and non‐linear optimization are also used in determining the direction a crack will propagate in a heterogeneous planar domain. This procedure involves determining the direction that produces the maximum strain energy release for a given increment of crack growth. The procedure is applied to several numerical examples. The results of these numerical studies coincide with theoretical predictions and experimentally observed crack behavior.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

In the case of machines, structures and assemblies, the crack generation and propagation is becoming a great concern, especially in airplane wings, turbine blades and such other applications. This is because these parts are very large in size and the crack size is very small, i.e. in microns. Hence, there is an important need to locate the crack and to find its severity before it starts to propagate and also to detect these parameters by on-site non-destructive testing methods. This paper aims to develop and test the methodology to locate an unknown single open crack in steel cantilever beam along with its severity.

This study covers analytical, numerical and experimental analysis for healthy and cracked beams. Vibration-based approach and finite element analysis (FEA) approach is used for analytical and numerical study respectively. Own designed and dedicated experimental set-up is used for testing purpose along with fast fourier transform analyzer. An anti-resonance technique is used to locate and to find the severity of unknown crack. The statistical approach helps to validate the results.

The comparison of the natural frequency of healthy and cracked steel cantilever beam shows that the crack in the beam reduces its natural frequency. The accuracy of results is achieved by finding actual density and Young's modulus of steel specimen under consideration. It is helpful to verify the health of the non-cracked beam by applying dye testing. The study of natural frequency and anti-resonance gives the location of crack and its depth also. The FEA approach proved to be an important tool for numerical analysis of cracked beam.

The research is limited to steel material and surface cracks only.

Practically, this study highlights how to locate a surface crack in steel beam along with its depth, i.e. severity with great accuracy. Identification of the factors such as location and depth of a crack provide the severity of damage in airplane wings, turbine blades, bridges and many more, and thereby, it helps in safety at working vicinity.

The identification and solutions of current research helps to predict the operational life of machine elements such as airplane wings, turbine blades, bridges and many more, and thereby, it helps in the safety of people in working vicinity of such structures.

The work presented, is based on original research and experimentation. This work is valued contribution in the field of methodologies applied for fault detection in structures and also determining its correctness by numerical and experimental work.

This study aimed to solve the engineering problem of free vibration disturbance and local mesh refinement induced by microcrack damage in circularly curved beams. The accurate identification of the crack damage depth, number and location depends on high-precision frequency and vibration mode solutions; therefore, it is critical to obtain these reliable solutions. The high-precision finite element method for the free vibration of cracked beams needs to be developed to grasp and control error information in the conventional solutions and the non-uniform mesh generation near the cracks. Moreover, the influence of multi-crack damage on the natural frequency and vibration mode of a circularly curved beam needs to be detected.

A scheme for cross-sectional damage defects in a circularly curved beam was established to simulate the depth, location and the number of multiple cracks by implementing cross-section reduction induced by microcrack damage. In addition, the h-version finite element mesh adaptive analysis method of the Timoshenko beam was developed. The superconvergent solution of the vibration mode of the cracked curved beam was obtained using the superconvergent patch recovery displacement method to determine the finite element solution. The superconvergent solution of the frequency was obtained by computing the Rayleigh quotient. The superconvergent solution of the eigenfunction was used to estimate the error of the finite element solution in the energy norm. The mesh was then subdivided to generate an improved mesh based on the error. Accordingly, the final optimised meshes and high-precision solution of natural frequency and mode shape satisfying the preset error tolerance can be obtained. Lastly, the disturbance behaviour of multi-crack damage on the vibration mode of a circularly curved beam was also studied.

Numerical results of the free vibration and damage disturbance of cracked curved beams with cracks were obtained. The influences of crack damage depth, crack damage number and crack damage distribution on the natural frequency and mode of vibration of a circularly curved beam were quantitatively analysed. Numerical examples indicate that the vibration mode and frequency of the beam would be disturbed in the region close to the crack damage, and a greater crack depth translates to a larger frequency change. For multi-crack beams, the number and distribution of cracks also affect the vibration mode and natural frequency. The adaptive method can use a relatively dense mesh near the crack to adapt to the change in the vibration mode near the crack, thus verifying the efficacy, accuracy and reliability of the method.

The proposed combination of methodologies provides an extremely robust approach for free vibration of beams with cracks. The non-uniform mesh refinement in the adaptive method can adapt to changes in the vibration mode caused by crack damage. Moreover, the proposed method can adaptively divide a relatively fine mesh at the crack, which is applied to investigating free vibration under various curved beam angles and crack damage distribution conditions. The proposed method can be extended to crack damage detection of 2D plate and shell structures and three-dimensional structures with cracks.