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 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.

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.

This study proposes a combination of nondestructive testing methods for detecting soil cavities behind retaining walls, which adversely affect the stability of the sloping ground or retaining structures. An experimental study is conducted using a soil chamber filled with dry sands retained by a concrete wall plate. A hemispherical soil cavity is simulated just behind the wall plate, and elastic wave reflections of impacts on the wall are measured using accelerometers. The measured elastic waves are analyzed using the signal energy in time domain and predominant frequency and mobility spectrum in frequency domain. In addition, spatiotemporal changes in the surface of the wall during heating and cooling sequences are monitored using infrared thermography. The captured thermal image is then used for identifying the cavity. Experimental results show that the cavity leads to increases in the signal energy, predominant frequency, and flexibility in the mobility spectrum. The contrasts in the thermal images effectively reveal the shapes and locations of the soil cavity. This study demonstrates that the hybrid testing method that conducts a careful inspection by elastic waves on areas suspected in the preliminary scanning by the infrared thermography can be competitive and effective for detecting soil cavities.

In recent years, bacteria-based self-healing concrete has been widely exploited to improve the compressive strength of concrete using different bacterial species. However, both the identification of the optimal involved reaction parameters and theoretical framework information are still limited. In the present study, both experimentally and numerical modelling using machine learning (ANN and ANFIS) and response surface methodology (RSM) were implemented to evaluate and optimse the evolution of bacterial concrete strength. Therefore, a total of 58 compressive strength tests of the concrete incorporating new bacterial species were designed using different concentrations of urea, cells concentration, calcium, nutrient and time. Based on the results, the compressive strength of the bacterial concrete improved by 16% due to the decrement of the pore percentage in the concrete skin; specifically, 5 mm from the concrete surface, compared to that of the control concrete. In the same context, both machine the learning and RSM models indicated that the optimal range of urea, calcium, nutrient and bacterial cells were (18-23 g/L), (150-350 mM), (1-3 g/L) and 2×107 cells/mL, respectively. Based on the statistical analysis, RMSE, R2, MPE, RAE and RRSE were (0.793, 0.785), (0.985, 0.986), (1.508, 1.1), (0.11, 0.09) and (0.121, 0.12) from both the ANN and ANFIS models, respectively, while; the following values (0.839, 0.972, 1.678, 0.131 and 0.165) was obtained from RSM model, respectively. As such, it can be concluded that a high correlation and minimum error were obtained, however, machine learning models provided more accurate results compared to that of the RSM model.

This study proposes a new hybrid method that uses both of post-earthquake reconnaissance data and numerical analysis results based on a finite element (FE) model. As the uncertainty of a capacity threshold for a structural damage state needs to be estimated carefully, in the proposed method, the probabilistic distribution parameters of capacity thresholds are evaluated based on post-earthquake reconnaissance data. Subsequently, the hybrid fragility curves were derived for several damage states using the updated distribution parameters of capacity thresholds. To illustrate the detailed process of the proposed hybrid method, it was applied to piloti-type reinforce concrete (RC) buildings which were affected by the 2017 Pohang earthquake, Korea. In the example, analytical fragility curves were derived first, and then hybrid fragility curves were obtained using the distribution parameters of capacity thresholds which were updated based on actual post-earthquake reconnaissance data about the Pohang city. The results showed that the seismic fragility estimates approached to the empirical failure probability at 0.27 g PGA, corresponding to the ground motion intensity of the Pohang earthquake. To verify the proposed method, hybrid fragility curves were derived with the hypothetical reconnaissance data sets created based on assumed distribution parameters with errors of 10% and 1%. As a result, it was identified that the distribution parameters accurately converged to the assumed parameters and the case of 1% error had better convergence than that of 10% error.

In this study, a corrosion threshold-controllable sensing system of long period fiber gratings (LPFG) is developed and validated for life-cycle monitoring of steel bars in corrosive environments. Three Fe-C coated LPFG sensors with two bare LPFG sensors in LP06 and LP07 modes for strain and temperature compensation were multiplexed and deployed inside three miniature, coaxial steel tubes to measure three (long-term in years) critical mass losses through the penetration of tube walls and their corresponding (short-term in hours) corrosion rates in the life span of steel bars. The strain/temperature and mass loss measurements are based on the changes in grating period and refractive index of surrounding medium, respectively. Thermal/mechanical loading and accelerated corrosion tests were conducted to validate the functionality, sensitivity, accuracy, and robustness of the proposed sensing system. Since both the steel tube and Fe-C layer represent the material composition of steel bars in the context of corrosion, the mass loss correlation among any two of the steel tube, Fe-C layer and steel bar is independent of the test conditions such as the current density and sample length, and thus applicable to engineering practices. The outer tube can notably delay and decelerate the corrosion process of its inner steel tube due to the reduced current effect.

The goal of this manuscript is to develop a nonclassical size dependent model to study and analyze the dynamic behaviour of the perforated Reddy nanobeam under moving load including the length scale and microstructure effects, that not considered before. The kinematic assumption of the third order shear deformation beam theory in conjunction with modified continuum constitutive equation of nonlocal strain gradient (NLSG) elasticity are proposed to derive the equation of motion of nanobeam included size scale (nonlocal) and microstructure (strain gradient) effects. Mathematical expressions for the equivalent geometrical parameters due to the perforation process of regular squared pattern are developed. Based on the virtual work principle, the governing equations of motion of perforated Reddy nanobeams are derived. Based on Navier's approach, an analytical solution procedure is developed to obtain free and forced vibration response under moving load. The developed methodology is verified and checked with previous works. Impacts of perforation, moving load velocity, microstructure parameter and nonlocal size scale effects on the dynamic and vibration responses of perforated Reddy nanobeam structures have been investigated in a wide context. The obtained results are supportive for the design of MEMS/NEMS structures such as frequency filters, resonators, relay switches, accelerometers, and mass flow sensors, with perforation.

Excessive vibration may cause premature fatigue failure on structural components if it is not properly controlled. One effective way to attenuate vibration is to attach a tuned vibration absorber to the main structural component. Passive tuned vibration absorbers are mainly effective to attenuate vibration at a specific range of frequencies and thus they become infective under varied environmental conditions which can significantly alter the tuning frequencies. The present study aims at development of a wide-bandwidth and light-weight adaptive tuned vibration absorber (ATVA) featuring a magnetorheological elastomer (MRE) which is tuned to absorb the vibrations of a flexible beam. The accelerance transfer function is derived for both beam with and without ATVA. The effectiveness of the ATVA to control vibration of the flexible beam caused by external excitation under wide range of frequencies is demonstrated. The proposed ATVA consists of C-Shape frame with winding coils, two isometric MRE specimens with 40% volume fraction, and active mass. An empirical model for the MRE has been developed through an experimental identification method in order to predict the MRE's elastic modulus under various levels of excitation frequencies and applied magnetic fields. Using MRE models and magneto-circuit analysis, the frequency bandwidth of the ATVA is analytically obtained. The analytical model is then used to develop a multidisciplinary design optimization formulation to minimize the mass and maximize the frequency bandwidth of an ATVA featuring MRE given several geometrical and physical constraints. Finally, a tuning algorithm has been presented to determine the required applied magnetic flux density to the MRE layers based on the identified phase difference between the absolute acceleration of the host and relative acceleration of the host and ATVA's resonator.