One of the major causes of damage to column-supported concrete structures, such as bridges and highways, are collisions from moving vehicles, such as cars and ships. It is essential to quantify the collision damage of the column so that appropriate actions can be taken to prevent catastrophic events. A widely used method to assess structural damage is through the root-mean-square deviation (RMSD) damage index established by the collected data; however, the RMSD index does not truly provide quantitative information about the structure. Conversely, the damage volume ratio that can only be obtained via simulation provides better detail about the level of damage in a structure. Furthermore, as simulation can also provide the RMSD index relating to that particular damage volume ratio, the empirically obtained RMSD index can thus be related to the structural damage degree through comparison of the empirically obtained RMSD index to numerically-obtained RMSD. Thus, this paper presents a novel method in which the impact-induced damage to a structure is simulated in order to obtain the relationship between the damage volume ratio to the RMSD index, and the relationship can be used to predict the true damage degree by comparison to the empirical RMSD index. In this paper, the collision damage of a bridge column by moving vehicles was simulated by using a concrete beam model subjected to continuous impact loadings by a freefalling steel ball. The variation in admittance signals measured by the surface attached lead zirconate titanate (PZT) patches was used to establish the RMSD index. The results demonstrate that the RMSD index and the damage ratio of concrete have a linear relationship for the particular simulation model.
In this paper, a new embeddable spherical smart aggregate (SSA) was utilized to monitor concrete curing in very early age. Overcoming the limitation of the existing PZT-patch-based transducers, the SSA provides vital changing information in all directions of host structure. To verify the advantage of SSA in structural health monitoring (SHM), the sensitivities of SSA and smart aggregate (SA) in monitoring concrete cube deformation and stiffness variation were analyzed and compared by numerical simulation. The feasibility of SSA in monitoring the concrete hydration process was studied by experiments utilizing electromechanical impedance (EMI) technique. At last, four SSAs were embedded in a concrete column to study the practicality of SSA in monitoring the concrete curing process in very early age. The EMI signatures and the root mean square deviation (RMSD) values of the collected information from SSAs were analyzed. The results illustrate that the SSA is more sensitive than SA in monitoring the concrete deformation and stiffness variation. The data measured by SSA in monitoring the concrete hydration process fluctuates more obviously than the data recorded by SA. The new spherical transducer can effectively and reliably monitor the concrete hydration process.
In this study, the concrete damage induced by compression is evaluated quantitatively using spherical smart aggregate sensor based on electro-mechanical impedance method. The sensitivity of the spherical smart aggregate sensor embedded in concrete cubes is investigated by comparing the electrical signals recorded during the compressive process with those of the smart aggregate sensor embedded in concrete cubes. Furthermore, the finite element model of concrete cube with an embedded spherical smart aggregate sensor is developed to simulate the concrete compressive tests. The concrete damaged plasticity constitutive model is utilized to simulate the concrete damage process. The numerical model is verified with the experimentally measured compressive test results. Finally, the damage volume ratio is presented to quantify the damage level of concrete based on the numerical model. The relationship between the root mean square deviation index of the conductance signatures obtained from experiments and the damage volume ratio computed by numerical simulation is established to quantify the concrete damage level. The results show that the spherical smart aggregate sensor is more sensitive than the smart aggregate sensor in monitoring the three-dimensional concrete structures. The proposed empirical fitting curve can effectively evaluate the concrete damage level quantitatively.
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