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As an efficient self-healing intelligent material, the encapsulation-based self-healing resin mineral composite (SHC) has a broad application prospect.

Aiming at the cracking performance of SHC, the dynamic load condition is employed to replace the traditional static load condition, the initial damage of the material is considered and the triggered cracking process and influencing factors of SHC are analyzed based on the extended finite element method (XFEM). In addition, the mechanism of matrix cracking and microcapsule triggered cracking process is explained from the microscopic point of view, and the cracking performance conditions of SHC are studied. On this basis, the response surface regression analysis method is used to obtain a second-order polynomial model of the microcapsule crack initiation stress, the interface bonding strength and the matching relationship between elastic modulus. Therefore, the model could be used to predict the cracking performance parameters of the microcapsule.

The interfacial bonding strength has an essential effect on the triggered cracking of the microcapsule. In order to ensure that the microcapsule can be triggered cracking normally, the design strength should meet the following relationship, that is crack initiation stress of microcapsule wall < crack initiation stress of matrix < interface bonding strength. Moreover, the matching relationship between elastic modulus has a significant influence on the triggered cracking of the microcapsule.

The results provide a theoretical basis for further oriented designing of the cracking performance of microcapsules.

This paper gives a bibliographical review of the finite element methods (FEMs) applied to the analysis of ceramics and glass materials. The bibliography at the end of the paper contains references to papers, conference proceedings and theses/dissertations on the subject that were published between 1977‐1998. The following topics are included: ceramics – material and mechanical properties in general, ceramic coatings and joining problems, ceramic composites, ferrites, piezoceramics, ceramic tools and machining, material processing simulations, fracture mechanics and damage, applications of ceramic/composites in engineering; glass – material and mechanical properties in general, glass fiber composites, material processing simulations, fracture mechanics and damage, and applications of glasses in engineering.

This paper aims to focus on the stochastic analysis of composite rotor blades with matrix cracking in forward flight condition.

The effect of matrix cracking and uncertainties are introduced to the aeroelastic analysis through the cross-sectional stiffness properties obtained using thin-walled beam formulation, which is based on a mixed force and a displacement method. Forward flight analysis is carried out using an aeroelastic analysis methodology developed for composite rotor blades based on the finite element method in space and time. The effects of matrix cracking are introduced through the changes in the extension, extension-bending and bending matrices of composites, whereas the effect of uncertainties are introduced through the stochastic properties obtained from previous experimental and analytical studies.

The stochastic behavior of helicopter hub loads, blade root forces and blade tip responses are obtained for different crack densities. Further, assuming the behavior of progressive damage in same beam is measurable as compared to its undamaged state, the stochastic behaviors of delta values of various measurements are studied. From the stochastic analysis of forward flight behavior of composite rotor blades at various matrix cracking levels, it is observed that the histograms of these behaviors get mixed due to uncertainties. This analysis brings out the parameters which can be used for effective prediction of matrix cracking level under various uncertainties.

The behavior is useful for the development of a realistic online matrix crack prediction system.

Instead of introducing the white noise in the simulated data for testing the robustness of damage prediction algorithm, a systematic approach is developed to model uncertainties along with damage in forward flight simulation.

To develop a new method for estimation of damage configuration in composite laminate structure using acoustic wave propagation signal and a reduction‐prediction neural network to deal with high dimensional spectral data.

A reduction‐prediction network, which is a combination of an independent component analysis (ICA) and a multi‐layer perceptron (MLP) neural network, is proposed to quantify the damage state related to transverse matrix cracking in composite laminates using acoustic wave propagation model. Given the Fourier spectral response of the damaged structure under frequency band‐selective excitation, the problem is posed as a parameter estimation problem. The parameters are the stiffness degradation factors, location and approximate size of the stiffness‐degraded zone. A micro‐mechanics model based on damage evolution criteria is incorporated in a spectral finite element model (SFEM) for beam type structure to study the effect of transverse matrix crack density on the acoustic wave response. Spectral data generated by using this model is used in training and testing the network. The ICA network called as the reduction network, reduces the dimensionality of the broad‐band spectral data for training and testing and sends its output as input to the MLP network. The MLP network, in turn, predicts the damage parameters.

Numerical demonstration shows that the developed network can efficiently handle high dimensional spectral data and estimate the damage state, damage location and size accurately.

Only numerical validation based on a damage model is reported in absence of experimental data. Uncertainties during actual online health monitoring may produce errors in the network output. Fault‐tolerance issues are not attempted. The method needs to be tested using measured spectral data using multiple sensors and wide variety of damages.

The developed network and estimation methodology can be employed in practical structural monitoring system, such as for monitoring critical composite structure components in aircrafts, spacecrafts and marine vehicles.

A new method is reported in the paper, which employs the previous works of the authors on SFEM and neural network. The paper addresses the important problem of high data dimensionality, which is of significant importance from practical engineering application viewpoint.

A new model for handling non‐orthogonal cracks within the smeared crack concept is described. It is based on a decomposition of the total strain increment into a concrete and into a crack strain increment. This decomposition also permits a proper combination of crack formation with other non‐linear phenomena such as plasticity and creep and with thermal effects and shrinkage. Relations are elaborated with some other crack models that are currently used for the analysis of concrete structures. The model is applied to some problems involving shear failures of reinforced concrete structures such as a moderately deep beam and an axisymmetric slab. The latter example is also of interest in that it confirms statements that ‘reduced integration’ is not reliable for problems involving crack formation and in that it supports the assertion that identifying numerical divergence with structural failure may be highly misleading.

To avoid the structural defect, early crack detection is oneof the important aspects in the recent area of research. The purpose of this paper is to detect the crack before its failure by means of its position and severity.

This paper uses two trees based regressors, namely, decision tree (DT) regressor and random forest (RF) regressor for their capabilities to adopt different types of parameter and generate simple rules by which the method can predict the crack parameters with better accuracy, making it possible to effectively predict the crack parameters such as its location and depth before failure of the beam.

The predicted parameters can be achieved, if the relationship between vibration and crack parameters can be attained. The relationship yields the results of beam natural frequencies, which is used as the input value for the regression techniques. It is observed that the RF regressor predicts the parameters with better accuracy as compared to DT regressor.

The idea is used the developed regression techniques to identify the crack parameters which are more effective as compared to other developed methods because the alternate name of prediction is called regression. The authors have used DT regressor and RF regressor to achieve the target. In this paper care has been given to the generalization of the model, so that the adaptability of the model can be ensured. The robustness of proposed methods has been verified in support of numerical and experimental analysis.

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.

Self-healing concrete is a revolutionary building material that will generally reduce the maintenance cost of concrete constructions. Self-healing of cracks in concrete structure would contribute to a longer service life of the concrete and would make the material more durable and more sustainable. The cementitious mortar with/without incorporating encapsulates at different percentages of slag replacement with the cement mix improves autogenous healing at different ages. Therefore, this study’s aim is to develop a self-healing cementitious matrix for repair and retrofitting of concrete structures.

In the present work, waste straw pipes are used as a capsule, filled with the solution of sodium hydroxide (NaOH), sodium silicate (Na₂SiO₃) and colloidal nano-silica as self-healing activators. An artificial micro-crack on the control and blended mortar specimens at different percentages of slag replacement with cement (with/without encapsulation) is developed by applying a compressive load of 50% of its ultimate load-carrying capacity. The mechanical strength and ultrasonic pulse velocity, water absorption and chloride ion penetration test are conducted on the concrete specimen before and after the healing period. Finally, the self-healing activity of mortar mixes with/without encapsulation is analysed at different ages.

The encapsulated mortar mix with 10% of slag content has better self-healing potential than all other mixes considering mechanical strength and durability. The enhancement of the self-healing potential of such mortar mix is mainly due to hydration of anhydrous slag on the crack surface and transformation of amorphous slag to the crystalline phase in presence of encapsulated fluid.

The self-healing activities of the slag-based cementitious composite are studied for a healing period of 90 days only. The strength and durability performance of the cracked specimen may be increased after a long healing period.

The outcome of the work will help repair the cracks in the concrete structure and enhances the service life.

This study identifies the addition encapsulates with a self-healing activator fluid that can recover its strength after minor damage.

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.

The current study mainly focuses on the effect of varying diameter recycled steel fibers (RSF) on mechanical properties of concrete prepared with 25 and 50% of recycled coarse aggregate (RCA) as well as 100% natural aggregate (NA). Two types of RSF with 0.84 mm and 1.24 mm diameter having 30 mm length were incorporated into normal and recycled aggregate concrete (RAC).

The fresh behavior, compressive, splitting tensile, flexural strengths and modulus of elasticity of all the mixes were investigated to evaluate the mechanical properties of RACs. In addition, specimen crack and testing co-relation were analyzed to evaluate fiber response in the RAC.

According to the experimental results, it was observed that mechanical properties decreased with the increment replacement of NA by RCA. However, the RSF greatly improves the mechanical properties of both normal concrete and RACs. Moreover, mixes containing 1.24 mm diameter RSF had a more significant positive impact on mechanical properties than mixes containing 0.84 mm diameter RSF. The 0.84 mm and 1.24 mm RSF addition improved the mixes' compressive, splitting tensile and flexural strength by 10%–19%, 19%–30% and 3%–11%, respectively when compared to the null fiber mix. Therefore, based on the mechanical properties, the 1.24 mm diameter of RSF with 25% replacement of RCA was obtained as an optimum solution in terms of performance improvement, environmental benefit and economic cost.

The practice of RCA in construction is a long-term strategy for reducing natural resource extraction and the negative ecological impact of waste concrete.

This is the first study on the effects of varying size (0.84 mm and 1.24 mm diameter) RSF on the mechanical properties of RAC. Additionally, varying sizes of RSF and silica fume added a new dimension to the RAC.