In this study, structural health monitoring (SHM) methods of carbon fiber reinforced plastics (CFRPs) were investigated using electrical resistance. The developed sensing technique monitored electrical resistance in accordance with the impact damage of a CFRP. The changes in electrical resistances with multiple electrode sets enabled SHM without extra sensors so that this technique can be called self-sensing. Moreover, this study proposed electrodes only at peripheral side of a structure to minimize the number of electrodes compared to those in an array which has square number of sensors as the sensing area increases. For the intensive investigation, electromechanical sensitivity in terms of electrode distance was analyzed and optimized under drop weight impact testing. Then, SHM methods with electrodes in an array and electrodes in peripheral edges were comparatively investigated. The developed methods successfully localized impact damages into 2D coordinates. Furthermore, damage severity can be shown with a damage map by calculating electrical resistance change ratio. Therefore, structural health self-sensing system using electrical resistance was successfully developed with the minimum number of electrodes.

A new method is proposed to improve the accuracy of structural instantaneous frequency (IF) extraction. The proposed method combines a new form of improved generalized S-transform (IGST) and a multi-synchrosqueezing operation. The parameters selection of the window function in IGST is derived through the concentration measure (CM) principle. Then, the multi-synchrosqueezing algorithm is employed to improve energy aggregation of time-frequency analysis (TFA). To verify the effectiveness and accuracy of the proposed improved multi-synchrosqueezing generalized S-transform (IMSSGST), a frequency-modulated multi-component signal is investigated. For structural IF extraction, a two-story shear frame and a threestory steel frame structure are introduced. Furthermore, the IF identification of a seven-story RC shear wall structure is conducted to verified the practicability in actual engineering. Numerical simulation and experimental results show that the proposed method can effectively improve the energy aggregation of TFA and effectively improve the accuracy of IF identification.

Pier column, as the most critical load-bearing member of bridge, can bear multiple loads including axial forces,shear forces, bending moments, etc. The varied cross section at the column interface and bearing platform or drilled shaft leads to harmful stress concentration that can potentially compromise the structural integrity. In order to improve the ductility of bridge structure, a pier column is often designed with a variable cross-section region to dissipate energy through plastic deformation. For better understanding the health condition of pier column in its service life, it is of great significance to obtain the damage severity information in the variable cross-section region. This study utilizes an active sensing method enabled by distributed Lead Zirconate Titanate (PZT)-based Smart Aggregate (SA) sensors to monitor the damage initiation and development near the bottom of a pier column. Crack damage in variable cross-section region functions as a stress relief that attenuates propagating stress wave energy between SA pairs. Both the numerical and experimental results show that the reduction ratio of the stress wave energy is consistent with the crack development, thus validating the reliability of the investigated approach. SA-based technology can be used as a potential tool to provide early warning of damage in variable cross-section region of bridge structures.

A new hysteresis model based on curve fitting method is presented in this work to portray the greatly nonlinear and hysteretic relationships between shear force and displacement responses of the magnetorheological (MR) elastomer base isolator. Compared with classical hysteresis models such as Bouc-Wen or LuGre friction model, the proposed model combines the hyperbolic sine function and Gaussian function to model the hysteretic loops of the device responses, contributing to a great decline of model parameters. Then, an improved fruit fly optimization algorithm (FOA) is proposed to optimize the model parameters, in which a self-adaptive step is employed rather than the fixed step to balance the global and local optimum search abilities of algorithm. Finally, the experimental results of the device under both harmonic and random excitations are used to verify the performance of the proposed hybrid model and parameter identification algorithm with the satisfactory results.

Placing controllers and sensors properly is important in structural health monitoring and control. Many optimization methods require much computation efforts. This paper used Hankel norms to develop the placement rules, because they involve the input and output gains and thus could be shaped by the locations. In modal form, their computations are relatively simple. The location and mode influences on norms were arranged in rows and columns, respectively, to form a matrix, and was normalized by the column (mode) root mean square. The optimization goal is to choose locations with higher index values and lower correlations to ensure higher controllability and observability, and with less effort to be compensated for by gains. Hankel norm is compatible with the LQR control objectives in that they are both 2-norm, so the methodology is appropriate to be applied to the base isolation benchmark building for structural control, which is an eight-story irregular building with ninety-two candidate locations for controllers and thirty-six locations for sensors. Following the method, ten controller locations and eighteen sensor locations were determined. Earthquake time history analysis using LQG technique validated the effectiveness of thus determined subset of locations by comparing with other subset of locations.