Bolted spherical joints are widely used to form space steel structures. The stiffness and load capacity of the structures are affected by the looseness of bolted spherical joint connections in the structures. The looseness of the connections, which can be caused by fabrication error, low modeling accuracy, and “false twist” in the installation process, may negatively impact the load capacity of the structure and even lead to severe accidents. Furthermore, it is difficult to detect bolted spherical joint connection looseness from the outside since the bolts connect spheres with rods together from the inside. Active sensing methods are proposed in this paper to monitor the tightness status of the bolted spherical connection using piezoceramic transducers. A triangle-on-triangle offset grid composed of bolted spherical joints and steel tube bars was fabricated as the specimen and was used to validate the active sensing methods. Lead Zirconate Titanate (PZT) patches were used as sensors and actuators to monitor the bolted spherical joint tightness status. One PZT patch mounted on the central bolted sphere at the upper chord was used as an actuator to generate a stress wave. Another PZT patch mounted on the bar was used as a sensor to detect the propagated waves through the bolted spherical connection. The looseness of the connection can impact the energy of the stress wave propagated through the connection. The wavelet packet analysis and time reversal (TR) method were used to quantify the energy of the transmitted signal between the PZT patches by which the tightness status of the connection can be detected. In order to verify the effectiveness, repeatability, and consistency of the proposed methods, the experiments were repeated six times in different bolted spherical connection positions. The experimental results showed that the wavelet packet analysis and TR method are effective in detecting the tightness status of the connections. The proposed active monitoring method using PZT transducers can monitor the tightness levels of bolted spherical joint connections efficiently and shows its potential to guarantee the safety of space steel structures in construction and service.

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.

This paper presents a numerical and experimental investigation of the concrete quantitative damage assessment using the smart aggregate (SA) sensor based on the electro-mechanical impedance method. In the numerical simulation, a concrete cube finite element model with an embedded SA sensor is established using concrete damaged plasticity constitutive model to simulate the damage process under uniaxial compression. The concrete damage degree is evaluated using the conductance signatures of the embedded SA sensor and damage volume ratio (DVR). In the experimental study, three concrete cubes are casted and subjected to compressive loading. The experimental study verifies the numerical simulation model by comparing the root mean square deviation (RMSD) indices of conductance signals. Furthermore, the DVR is proposed as an effective damage evaluation index. The relationship curve between the DVR and the RMSD is fitted. The results illustrate that the RMSD index of the SA sensor and the DVR of the concrete have a linear relationship. The level of concrete damage can be quantitatively evaluated by RMSD index according the relationship between the RMSD and DVR.