The use of engineered cementitious composite (ECC) has attracted extensive attention in recent years because of the highly enhanced ductility owing to its unique strain-hardening behavior. In this paper, an electromechanical impedance-based technique is used to monitor the structural damage of RC beams strengthened with an ECC layer at the tensile zone. To achieve this purpose, three specimens are tested under bending loads to evaluate the proposed damage detection methodology. Five externally bonded PZT transducers are uniformly distributed at the surface of the ECC layer of the beams to measure the output conductance signatures in a healthy state and in different damage scenarios induced by different load levels. Test results showed that discrepancies exist between the signals measured in the intact state and each damage state, which can be used to evaluate the structural integrity changes. To assess the damage of ECC-concrete composite beams quantitatively, the statistical scalar index-root mean square deviation (RMSD) is used as the index, which can be calculated from the variations of conductance measurements of PZT sensors. The damage index values of the uniformly distributed PZT sensors provided cogent evidence of damage and revealed the evolution of structural damage. The crack patterns of beams at different damage levels are compared with the damage index values, and it shows the damage location can be derived from the measured conductance signatures of an array of PZT transducers.

This paper presents a numerical procedure for predicting the mechanical behavior of reinforced concrete (RC) beams exposed to fire. Thermal behavior is captured using a two‐dimensional finite element (FE) model in an environment of elevated temperatures, while a one‐dimensional spectral numerical model is formulated to simulate the mechanical response of the beam under increasing loads. Both models are integrated. Previous thermal results obtained from the FE model are provided for the proposed numerical model to calculate the responses of the RC beam for different levels of fire exposure. Few elements are deployed in this study due to the simplicity and efficiency of the proposed numerical model. Consequently, the computational burden of the numerical simulation for predicting the structural performances of RC beams exposed to fire is released comparing with conventional FE model. The numerical results are consistent with the test data, which demonstrate the model’s capability of efficiently simulating the thermomechanical behaviors of RC beams due to its simplicity and accuracy.

This paper is proposed for modelling concrete beams reinforced with fiber reinforced polymer (FRP) bars in a simplified way. In order to appropriately model the FRP-reinforced concrete beams the stiffness matrix is developed in the frequency domain using fast Fourier Transform. Numerical results with the proposed spectral model for the load-displacement response and the shear stress distribution between FRP reinforcement and surrounding concrete are obtained for beams statically tested. Tens of elements are deployed in this work due to the simplicity of the proposed model. Using the same spectral model the natural frequency and mode shapes are evaluated since the frequency-dependent stiffness matrix enables it to apply for dynamic study, e.g. modal analysis. The feasibility of the proposed numerical approach for performing dynamic analysis especially for high frequency excitations in an efficient way makes it a promising tool for use in the field of structural health monitoring according to the changes in dynamic characteristics.

A model updating approach based on a spectral element model and solved with a particle swarm optimization (PSO) method is proposed to identify the vibration-damping properties of composite materials. In comparison with conventional finite element model updating, a composite beam is modeled in a unified way by using a spectral approach whose computational cost is significantly reduced due to its simplicity. In this way, the dynamic response can be captured accurately by using a very limited number of elements. To identify the material properties, experimental tests are carried out to get the initial parameters that are introduced to initialize the spectral model; then, a model updating process solved with a PSO algorithm is implemented to obtain the real material parameters. It has been demonstrated that the proposed spectral model is a potential tool for model updating and parameter identification.