Experimental sensitivity analysis of sensor placement based on virtual springs and damage quantification in CFRP composite

This paper suggests a method for vibration sensor placement in Carbon Fibre Reinforced Polymer (CFRP) composite structures in small structure applications where the measuring instrument weight can affect the vibrational characteristics. Considering the actual weight of the beam and the actual weight of the vibrational sensor and connecting cables. We performed a set of structural vibration experiments in various sensor positions and used the experimental results as a reference through the inverse problems technique. And Finite Element Analysis (FEA) for numerical modelling, in which the sensors are modelled as an additional mass on the beam and the virtual springs are modelled with variable rigidity. We employ the Teaching-Learning-Based Optimization Algorithm (TLBO) to identify the optimal sensor placement location. The results indicate that this application can explain the effect of sensor placement. In a second application, we consider the problem of the cracked beam and the prediction of damage severity and crack depth with the help of a formulation for crack location. Results of this Application show that the proposed approach can serve in solving both problems.

Experimental sensitivity analysis of sensor placement based on virtual springs and damage quantification in CFRP composite

This paper suggests a method for vibration sensor placement in Carbon Fibre Reinforced Polymer (CFRP) composite structures in small structure applications where the measuring instrument weight can affect the vibrational characteristics. Considering the actual weight of the beam and the actual weight of the vibrational sensor and connecting cables. We performed a set of structural vibration experiments in various sensor positions and used the experimental results as a reference through the inverse problems technique. And Finite Element Analysis (FEA) for numerical modelling, in which the sensors are modelled as an additional mass on the beam and the virtual springs are modelled with variable rigidity. We employ the Teaching-Learning-Based Optimization Algorithm (TLBO) to identify the optimal sensor placement location. The results indicate that this application can explain the effect of sensor placement. In a second application, we consider the problem of the cracked beam and the prediction of damage severity and crack depth with the help of a formulation for crack location. Results of this Application show that the proposed approach can serve in solving both problems.

Crack prediction in beam-like structure using ANN based on frequency analysis

The dynamic experimental and numerical analysis of cracked beams has been studied with the aim of quantifying the influence of depth crack on the dynamic response of steel beams. Artificial Neural Method ANN has been used where a numerical simulation was improved in Matlab. A finite element model has also been developed by using the Ansys software, and the obtained results were compared with exact crack length. The study takes into account different hidden layer values in order to determine the sensitivity of the predicted crack depth.  The results show that the response of the beam (frequencies) is strongly related to the crack depth which significantly affects the beam behavior, especially when the crack is very deep where the ANN allows us to identify the crack in lower computational time. Based on the provided results, we can detect that the effect of hidden layer size can affect the results. &nbsp

Numerical-Experimental characterization of honeycomb sandwich panel and numerical modal analysis of implemented delamination

The objective of this paper is to investigate the influence of the delamination on the vibration behavior of honeycomb sandwich panel, firstly, numerical characterization to provide the constant properties of the core only are performed using initial finite element model of Representative Volume Element (RVE) which does not take into account the double thickness wall existing in aluminum core structure. According to these initial parameters, finite element model of sandwich composite plate is constructed to extract its elasto-dynamic parameters. In order to validate the numerical results, Experimental Modal Analysis of sandwich plate specimens was performed. Secondly, the double thickness wall is selected to be introduced in the RVE because of important error between numerical and experimental achievements. Comparative study validates the improved mechanical characteristics. The knowledge of these constants is not sufficient and additional information about the delamination effects on the dynamical parameters of honeycomb composite panel is required. In present investigation this defect was implemented on the validated 3-D finite element model. The frequencies and associated modes shapes are obtained and analyzed

Experimental sensitivity analysis of sensor placement based on virtual springs and damage quantification in CFRP composite

This paper suggests a method for vibration sensor placement in Carbon Fibre Reinforced Polymer (CFRP) composite structures in small structure applications where the measuring instrument weight can affect the vibrational characteristics. Considering the actual weight of the beam and the actual weight of the vibrational sensor and connecting cables. We performed a set of structural vibration experiments in various sensor positions and used the experimental results as a reference through the inverse problems technique. And Finite Element Analysis (FEA) for numerical modelling, in which the sensors are modelled as an additional mass on the beam and the virtual springs are modelled with variable rigidity. We employ the Teaching-Learning-Based Optimization Algorithm (TLBO) to identify the optimal sensor placement location. The results indicate that this application can explain the effect of sensor placement. In a second application, we consider the problem of the cracked beam and the prediction of damage severity and crack depth with the help of a formulation for crack location. Results of this Application show that the proposed approach can serve in solving both problems

Effect of glass powder on the behaviour of high performance concrete at elevated temperatures

International audienceIn recent years, many studies have been done on the performance of concrete containing glass powder (GP). For the purpose of widespread use of GP in concrete mixes, a knowledge of the performance of such a mixture after a fire is essential for the perspective of structural use. This research work was carried out to evaluate the performance of High Performance Concrete (HPC) made with GP after being exposed to elevated temperature. The studied mixtures include partial replacement of cement by GP with up to 30%. The mechanical performance and structural alterations were assessed after high temperature treatment from 200 degrees C to 800 degrees C. The mechanical performance was evaluated by testing the specimens to the compressive and tensile strength. In addition, the mass loss and the porosity were measured to notice the structural alterations. Changes in microstructure due to temperature was also investigated by the X-ray diffraction (XRD) and thermal gravimetric analyses (TGA) as well as porosity adsorption tests. The results of the concrete strength tests showed a slight difference in compressive strength and the same tensile strength performance when replacing a part of the cement by GP. However, after high temperature exposition, concrete with GP showed better performance than the reference concrete for temperature below 600 degrees C. But, after heating at 800 degrees C, the strength of the concrete with GP drop slightly more than reference concrete. This is accompanied by an important increase in mass loss and water porosity. After the microstructure analysis, no important changes happened differently for concrete with GP at high temperature except a new calcium silica form appears after the 800 degrees C heating

Crack prediction in beam-like structure using ANN based on frequency analysis

The dynamic experimental and numerical analysis of cracked beams has been studied with the aim of quantifying the influence of depth crack on the dynamic response of steel beams. Artificial Neural Method ANN has been used where a numerical simulation was improved in Matlab. A finite element model has also been developed by using the Ansys software, and the obtained results were compared with exact crack length. The study takes into account different hidden layer values in order to determine the sensitivity of the predicted crack depth. The results show that the response of the beam (frequencies) is strongly related to the crack depth, which significantly affects the beam behavior, especially when the crack is very deep where the ANN allows us to identify the crack in lower computational time. Based on the provided results, we can detect that the effect of hidden layer size can affect the results

Crack prediction in pipeline using ANN-PSO based on numerical and experimental modal analysis

In this paper, a crack identification using Artificial Neural Network (ANN) is investigated to predict the crack depth in pipeline structure based on modal analysis technique using Finite Element Method (FEM). In various fields, ANN has become one of the most effective instruments using computational intelligence techniques to solve complex problems. This paper uses Particle Swarm Optimization (PSO) to enhance ANN training parameters (bias and weight) by minimizing the difference between actual and desired outputs and then using these parameters to generate the network. The convergence study during the process proves the advantage of using PSO based on two selected parameters. The data are collected from FEM based on different crack depths and locations. The provided technique is validated after collecting the data from experimental modal analysis. To study the effectiveness of ANN-PSO, different hidden layers values are considered to study the sensitivity of the predicted crack depth. The results demonstrate that ANN combined with PSO (ANN-PSO) is accurate and requires a lower computational time in terms of crack identification based on inverse problem