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.

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.

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.

In this research, new toggled actuator forces were proposed. For this purpose, numerical and experimental investigation of the installation of the actuator in a toggle configuration for the decreasing of active control forces in engineering structures has been carried out. In the first part, numerical studies were investigated. In addition to numerical research on the effects of the toggle configuration on actuator forces, an experimental investigation has been carried out by building a table model of the mentioned system. The algorithm of the system is LQR, and ATmega328 has been used as a control platform. Comparing results through the experimental and numerical processes express high matching that relies on mitigating control forces in the toggled active model. Based on the results, a significant reduction in actuator forces through using the proposed toggle configuration.

All structures are exposed to damage during their lifetime. Timely detection of the damages can prevent the reduction of stiffness/resistance of structures. The large number of elements in structures in comparison to the number of measurable data can limit the performance of the closed-form methods. This study presents a new damage detection method determining damage severity and location in the elements and connections via Improved Genetic Algorithm (IGA) based on limited number of mode shapes. This study describes how damage can be accurately identified based on the least number of modes. In this approach, healthy elements are identified by the IGA algorithm and removed from the search space. In this way, the damaged elements are examined more carefully and the severity of the damage is estimated more accurately. In this study, to evaluate the performance of the proposed method, two numerical examples are used. The numerical study includes a 2D truss structure under 4 damage scenarios and a 3D structure with a much larger number of elements under 6 different damage scenarios. Moreover, the performance of this algorithm in presence of noise in modal information is also examined. The results show that the proposed method can accurately detect damage to elements and connections, even in the presence of noise, by using only one mode in the 2D truss and two modes in the 3D structure. In order to evaluate the efficiency of this method in determining the damage of connections, a cantilever beam has been modeled and experimentally tested. The connection stiffness of the beam has been computed using both IGA and load-deformation measurement methods. In the IGA method only the first mode shape of the beam is employed to determine the connection stiffness. To derive the mode shapes in rotational degrees of freedoms which contain valuable information on connection stiffness, a novel, straightforward, and practical approach has been proposed. The results also indicate the high performance of this method to accurately estimate the connection stiffness.

This study presents a feature-based image stitching method with multi-level constraint criterion for panorama construction and visual inspection of building structures. The comparison of global view and local resolution over building exterior is discussed regarding practical implementation. An inspection-oriented methodology framework with optimized inlier distribution is designed for generating a feasible and reliable building panorama by using ordinary optic images. Two illustrative examples, including an earthquake-damaged masonry wall and a high-rise building with stone curtain walls, are experimentally investigated. The severely developed structural crack is fully mapped with stitched image and extracted in preparation for further quality evaluation. The curtain wall of the high-rise building is successfully constructed by using UAV-based images. The panorama quality is further compared with commercial stitching software and several improvements are illustrated in the particular case. In addition, the reliability of the proposed feature-based stitching approach is parametrically studied with different setups of input images.

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 three-story 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.

In Lamb wave based structural health monitoring (SHM) systems, damage scatter signals are usually used for damage identification. Such scatters are obtained by subtracting the current signal from a baseline. However, changes in the environmental condition, particularly temperature, make false scatters in the damage scatter signal. This affects the overall efficiently of the system. To overcome this obstacle, this study proposes a baseline-free damage identification technique. A dual-PZT actuation scheme is applied to generate a comparatively pure A₀ mode. A wave velocity determination procedure is then developed to actively determine the velocity of the generated A₀ mode in the presence of unmeasured temperature changes. Using a damage scatter separation process, the damage scatter wave is separated from other waves appear in the current signal. Finally, a damage diagnostic image is constructed to illustrate the most probable location of damage. The experimental validation of the developed technique is conducted on two aluminum plates one carrying an L shape crack and the other carrying a hole, subjected to temperatures changes. The experimental results demonstrate the effectiveness of the developed technique for damage identification in the presence of unmeasured temperature. During the proposed procedure no baseline data is used. This bright advantage, qualify the presented technique for practical SHM systems.

This paper presents a numerical and analytical study in the time-frequency domain to control the bifurcation and instability in a forced Duffing oscillator by a linear state feedback control. The proposed method evolves minimizing computational expenses of analytical approaches by an approximate method to suppress the responses of the dynamical system based on pole placement theory. The instability frequency range of Duffing oscillator is identified by approximate analytical methods. Bifurcation and jump points of Duffing oscillator are identified in the frequency domain by perturbation and harmonic balance methods for average and strong nonlinearity of the system, respectively. The Caughey method is used to linearize Duffing oscillator to solve system in the state space form. A linear state feedback controller with pole placement is applied to system in the time domain. The observed controlling force is added to approximate solution equation in frequency domain which vanished bifurcation length. The results reveal that the proposed method can be beneficial in reducing dynamic responses and eliminating jump points of system with high accuracy.

In this research, we synthesized an artificial neural network (ANN) with three metaheuristic algorithms, namely particle swarm optimization (PSO) algorithm, imperialist competition algorithm (ICA), and genetic algorithm (GA) to achieve a more accurate prediction of 28-day compressive strength of concrete. Seven input parameters (including cement, water, slag, fly ash, superplasticizer (SP), coarse aggregate (CA), and fine aggregate (FA)) were considered for this work. 80% of data (82 samples) were used to feed ANN, PSO-ANN, ICA-ANN, and GA-ANN models, and their performance was evaluated using the remaining 20% (21 samples). Referring to the executed sensitivity analysis, the best complexities for the PSO and GA were indicated by the population size = 450 and for the ICA by the population size = 400. Also, to assess the accuracy of the used predictors, the accuracy criteria of root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE) were defined. Based on the results, applying the PSO, ICA, and GA algorithms led to increasing R² in the training and testing phase. Also, the MAE and RMSE of the conventional MLP experienced significant decrease after the hybridization process. Overall, the efficiency of metaheuristic science for the mentioned objective was deduced in this research. However, the combination of ANN and ICA enjoys the highest accuracy and could be a robust alternative to destructive and time-consuming tests.