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.

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

Parallel Jacobi Transformation Algorithm for Generalized Eigen-Solution With Improved Damage Detection of Truss/Bridge-Type Structures

Serial Jacobi transformation algorithm for the solution of “standard eigen-problems” is re-visited to facilitate the explanation of the proposed parallel transformation algorithm, for which computational efficiency can be realized in this study through “pattern recognition” for the development and explanation of “explicit formulas” to avoid costly matrix time matrix operations. The proposed parallel Jacobi transformation for the solution of “generalized eigen-problems” has also been incorporated into the “improved damage detection” algorithm. Computational efficiency and robust behaviors for the entire proposed procedures (eigen-solution, damage detection and damage quantification) can be validated through several academic and real-life numerical examples. Numerical results obtained from this study have indicated that our proposed generalized Jacobi transformation is more robust/reliable as compared to MATLAB eigen-solver. Furthermore, our proposed simple rule of thumb for damage detection of aging bridge structures also give better results than existing algorithms