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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.

Crack damage detection for aluminum alloy materials using fiber Bragg Grating (FBG) sensor is a kind of structure health monitoring. In this paper, the damage index of full width at half maximum (FWHM) was extracted from the distorted reflection spectra caused by the crack-tip inhomogeneous strain field, so as to explain the crack propagation behaviors.

The FWHM variations were also investigated through combining the theoretical calculations with simulation and experimental analyses. The transfer matrix algorithm was developed to explore the mechanism by which FWHM changed with the linear and quadratic strain. Moreover, the crack-tip inhomogeneous strain field on the specimen surface was computed according to the digital image correlation measurement during the experiments.

The experimental results demonstrated that the saltation points in FWHM curve accorded with the moments of crack propagation to FBG sensors.

The interpretation of reflected spectrum deformation mechanism with crack propagation was analyzed based on both simulations and experiments, and then the performance of potential damage features – FWHM were proposed and evaluated. According to the correlation between the damage characteristic and the crack-tip location, the crack-tip of the specimen could be measured rapidly and accurately with this technique.

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.

In this paper, an approximate analytical approach is developed for the determination of natural longitudinal frequencies of a cantilever-cracked beam based on the Lagrange inversion theorem.

The crack is modeled by an equivalent axial spring with stiffness according to Castigliano's theorem. Thus, an implicit frequency equation corresponding to cantilever-cracked bar is obtained. The resulting equation is solved using the Lagrange inversion theorem.

Effect of different crack depths and crack positions on natural frequencies of the cracked beam is analyzed. It is shown that an increase in the crack depth ratio produces a decrease in the fundamental longitudinal natural frequency of a cracked bar. Furthermore, approximate analytical results are compared with those obtained numerically as well as from experimental tests.

A new approximate analytical expression of a fundamental longitudinal frequency, as a function of crack depth and crack location, is obtained.

This study aims to develop an adaptive finite element method for structural eigenproblems of cracked Euler–Bernoulli beams via the superconvergent patch recovery displacement technique. This research comprises the numerical algorithm and experimental results for free vibration problems (forward eigenproblems) and damage detection problems (inverse eigenproblems).

The weakened properties analogy is used to describe cracks in this model. The adaptive strategy proposed in this paper provides accurate, efficient and reliable eigensolutions of frequency and mode (i.e. eigenpairs as eigenvalue and eigenfunction) for Euler–Bernoulli beams with multiple cracks. Based on the frequency measurement method for damage detection, using the difference between the actual and computed frequencies of cracked beams, the inverse eigenproblems are solved iteratively for identifying the residuals of locations and sizes of the cracks by the Newton–Raphson iteration technique. In the crack detection, the estimated residuals are added to obtain reliable results, which is an iteration process that will be expedited by more accurate frequency solutions based on the proposed method for free vibration problems.

Numerical results are presented for free vibration problems and damage detection problems of representative non-uniform and geometrically stepped Euler–Bernoulli beams with multiple cracks to demonstrate the effectiveness, efficiency, accuracy and reliability of the proposed method.

The proposed combination of methodologies described in the paper leads to a very powerful approach for free vibration and damage detection of beams with cracks, introducing the mesh refinement, that can be extended to deal with the damage detection of frame structures.

The purpose of this paper is to employ analytical and numerical techniques to generate modal displacement data of damaged beams containing very small crack-like surface flaws or slots and to use the data in the development of damage detection methodology. The detection method involves the use of double differentiation of the modal data for identification of the flaw location and magnitude.

The modal displacements of damaged beams are simulated analytically using the Bernoulli-Euler theory and numerically using the finite element method. The principle used in the analytical approach is based on changes in the transverse displacement due to the localized reduction of the flexural rigidity of the beam. Curvature analysis is employed to identify and locate the structural flaws from the modal data. The curvature mode shapes are calculated using a central difference approximation. The effects of random noise on the detectability of the structural flaws are also computed.

The analytical approach is much more robust in simulating modal displacement data for beams with crack-like surface flaws or slots than the finite element analysis (FEA) approach especially for crack-like surface flaws or slots of very small depths. The structural flaws are detectable in the presence of random noise of up to 5 per cent.

Simulating the effects of small crack-like surface flaws is important because it is essential to develop techniques to detect cracks at an early stage of their development. The FEA approach can only simulate the effects of crack-like surface flaws or slots with depth ratio greater than 10 per cent. On the other hand, the analytical approach using the Bernoulli-Euler theory can simulate the effects of crack-like surface flaws or slots with depth ratio as small as 2 per cent.

Wavelets are being increasingly used for damage diagnostics. The purpose of this paper is to present an algorithm that uses the wavelet transform for detecting mixed-mode, also known as combined mode, cracks in large truss structures.

The mixed-mode crack is modeled by superposing two damage modes, and this model is combined with a finite element model of the truss. The natural modes of the truss are processed through the wavelet transform and then used to determine the damage location. The influence of multiple parameters such as truss geometry, crack geometry, number of truss members, orientation of truss members, etc. is investigated as part of the study.

The proposed damage detection algorithm is found to be successful in detecting single mode as well as mixed-mode cracks even in the presence of significant end effects, and even when a relatively coarse sampling of natural modes is used. Results from multiple simulations that involve three commonly used truss structures are presented. A correlation between damage severity and the magnitude of wavelet coefficients is observed.

The proposed algorithm is found to be successful in accurately detecting damage, but direct determination of damage severity is found to be challenging.

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.

In outside constructions (e.g. aircraft frames, bridges, tanks and ships) real‐life noises reduce significantly the capability of location and characterization of crack events. Among the most important types of noise is the rain, producing a signal similar to crack. This paper seeks to present a robust crack detection system with simultaneous raining conditions and additive white‐Gaussian noise at −20 to 20 dB signal‐to‐noise ratio (SNR).

The proposed crack detection system consists of two sequentially, connected modules: the feature extraction module where 15 robust features are derived from the signal and a radial basis function neural network is built up in the pattern classification module to extract the crack events.

The evaluation process is carried out in a database consisting of over 4,000 simulated cracks and drops signals. The analysis showed that the detection accuracy using the most robust 15 features ranges from 77.7 to 93 percent in noise‐free environment. This is a promising method for non‐destructive testing (NDT) by acoustic emission method of aircraft frame structures in extremely noisy conditions.

Continuous monitoring of crack events in the field requires the development of advance noise reduction and signal identification techniques. Robust detection of crack signals in noisy environment, including raining drops, improves significantly the reliability of real‐time monitoring systems in large and complex constructions and in adverse weather conditions.

As far as is known this is the first time that an efficient system is presented and evaluated which deals with the problem of crack detection in adverse environment including both stationary and non‐stationary noise components. Moreover, it provides further information on the engineering and efficiency problems associated with NDT techniques in the aircraft industry.