This paper uses higher-order shear deformation theory and modified couple stress theory (MCST) to the electroelastic results of FG micro-shell integrated with piezoelectric thin sheets subjected to electrical and mechanical loads rested on Pasternak's foundation. Third-order shear deformation theory (TSDT) is used for the description of the displacement field. Effect of micro-size is applied using MCST with the introduction of one micro-length scale parameter. Governing equations are derived based on the principle of virtual work. Micro-shell is composed of a FG micro core and two piezoelectric hollow shells. The numerical results are obtained for the simply-supported boundary conditions. Longitudinal and radial displacements are presented in terms of important parameters such as applied electric potentials, micro length scale parameter, dimensionless geometric parameters and two parameters of Pasternak's foundation.

The enclosed civil structures pose a challenging environment for wireless communication between sensor nodes. Wireless electromagnetic (EM) signal attenuates significantly when transmitting through reinforced concrete structures. This paper simulates the signal attenuation for plain concrete, pure steel rebar lattice and reinforced concrete using finite element method (FEM) in Ansoft High Frequency Structure Simulator (HFSS). Jonscher model is found to be a better concrete dielectric model than Debye model from the attenuation test results. FEM simulation for signal attenuation of reinforced concrete (RC) slab is validated by finite difference time domain (FDTD) simulation and test results from literature. Optimal frequency to minimize the signal attenuation through RC structure is in the range of 0.35 GHz ~ 0.5 GHz. Resonance occurs at t / (λ_c/4) = 2n and t / (λ_c/4) = 2n + 1, n = 1, 2, 3, 4, ⋯ for low concrete volumetric water content (VWC). Signal attenuation is highly linear with slab thickness t for high concrete VWC. 433 MHz is suggested for real application of wireless sensor network considering the antenna size and optimization results. FEM simulation is validated by the experiment using intact wireless sensor nodes.

Structural health monitoring (SHM) is essential for composite unmanned aerial vehicles (UAVs). Additionally, because UAVs are extremely sensitive to weight and volume, the minimal addition of weight and volume by the SHM system is crucial. Therefore, we proposed a compact and lightweight wireless SHM sensor node and an embedded carbon nanotube (CNT) fiber sensor for in-flight SHM of UAVs. The wireless SHM sensor node was composed of an analog sensing circuit, wireless microcontroller unit, and analog low pass filter. The small diameter CNT fiber sensor was developed to be easily embedded inside composite structures and to enhance their structural properties while performing as an SHM sensor. Glass composite skin with embedded CNT fiber sensors composed of ultra-high-molecular-weight polyethylene, polyurethane, CNT, and carbon black were installed in the aircraft. For comparison, a strain gauge attached at the center of a long CNT fiber sensor was also used during in-flight measurement. In-flight strain measurements from both the CNT fiber sensor and the strain gauge were continuously transmitted to the ground station and were compared with the flight data. Furthermore, an impact tester was installed inside the wing to simulate impact during flight, and in-flight impact measurements by the CNT fiber sensor were demonstrated.

In this paper a higher-order shear deformation plate theory is presented to investigate the stress distribution and static deflections of functionally graded sandwich plates with porosity effects. The displacement field of the present theory is chosen based on nonlinear variations in the in-plane displacements through the thickness of the plate. By dividing the transverse displacement into the bending and shear parts and making further assumptions, the number of unknowns and equations of motion of the present theory is reduced a and hence makes them simple to use. The functionally graded materials (FGM) used in plates contain probably a porosity volume fraction which needs taking into account this aspect of imperfection in the mechanical bahavior of such structures. The present work aims to study the effect of the distribution forms of porosity on the bending of simply supported FG sandwich plate. The governing equations of the problem are derived by using the principle of virtual work. In the solution of the governing equations, the Navier procedure is used for the simply supported plate. In the porosity effect, four different porosity types are used for functionally graded sandwich plates. In the numerical results, the effects of the porosity parameters, porosity types and aspect ratio of plates on the normal stress, shear stress and static deflections of the functionally graded sandwich plates are presented and discussed. Also, some comparison studies are performed in order to validate the present formulations.

A passive wireless strain sensing method using antenna sensors has significantly advanced structural health monitoring systems. Since the dimensions of antenna sensors are sensitive to their strain sensing performance and operating frequency, an iterative tuning process is required to achieve a final optimized design. Although multi-physics finite element simulation enables accurate estimation of antenna performance for each turning iteration, the simulation process requires high computational resources. Therefore, antenna tuning processes are recognized as obstacles to delay the final design process. In this study, we explore the potential of multi-physics informed models as an alternative approach for analyzing antenna sensors. Through deep neural networks, as a branch of the machine-learning algorithms, we formulate multi-physics informed models with six input parameters (antenna dimensions) and two outputs (resonance frequency and strain sensitivity). Twenty-two hundred high fidelity data sets are prepared by simulating multi-physics models: 1,600, 400, and 200 data sets are applied to deep neural network regression (DNNR) training, validating, and testing, respectively. From extensive data investigation, an optimized DNNR architecture is obtained to be two layers, with 16 neurons in each layer. Its training, validating, and testing values of mean square errors are 13.01, 44.22, 37.27, respectively. Finally, the proposed multi-physics informed model predicts the resonance frequency and strain sensitivity with errors of 0.1% and 0.07%, respectively. In addition, since the average computation speed for each tuning process is 0.007 seconds, the practical usefulness of the proposed method is also proven.

The modal parameters identified under operation conditions are normally employed for bridge damage detection. However, the moving vehicles are usually deemed as part of the operation conditions without considering their mass property. Thus, the identified modal parameters belong to the vehicle-bridge system rather than the bridge itself, which would affect the effectiveness of subsequent damage detection. In this paper, the effects of moving vehicles on the identified frequencies and mode shapes under operation conditions are investigated via finite element model. The necessary of considering the moving vehicle effects is demonstrated by comparing the modal parameters variations induced by the moving vehicle and bridge damage. Then the empirical formulas to eliminate the moving vehicle effects considering the vehicle mass, velocity, bridge span and relative position are established by using the orthogonal test and least square method. Finally, examples are conducted to verify of the effectiveness of the proposed empirical formulas.

Structural health monitoring (SHM) is currently applied to control regularly the health of high-rise buildings which have deteriorated after being subjected to a sudden loading. Damage detection at element levels of a structure consisting of an enormous number of elements becomes the main objective. In this study, the complicated problem is simplified by a two-step solution. Damaged storeys are preliminarily detected before a full damage scenario at an element level is achieved. In Step 1, to overcome the issues related to the huge number of degrees of freedom (DOFs), the full building is simplified to a beam-like system using the Guyan condensation technique. As the natural characteristics of the two lowest modes at the intact and a damaged stage are obtained, the eigenvalue problem based inverse solution is applied to approximately detect damaged storeys. Furthermore, an updating procedure that is proposed in this study effectively enhances the first prediction. In Step 2, an artificial neural network (ANN) model is designed to indicate damaged members on detected storeys using only the first three modal modes. Compared to other approaches applied to detect damages on high-rise buildings, the robustness of the proposed method is that the required number of lowest modal modes is two and three in Step 1 and Step 2 respectively. Furthermore, regardless of the extension of the building in the horizontal direction, only one lateral displacement of each storey is measured to detect damaged storeys in Step 1 and generally detect damaged elements in Step 2. For light and asymmetrical damage scenarios, two more vertical displacements should be considered to obtain accurate element-level detection. However, for all cases, the required number of DOFs is significantly lower than the full system.

Wind-induced dynamic response of the nonlinear structure is critical for the structural safety and reliability. The traditional approaches for this response including observation or simulation focus on the structural health monitoring, the experiment, or finite element model development. However, all these approaches require high cost or computational investment. This paper proposes to predict the wind-induced dynamic response of the nonlinear structure with a novel deep learning approach, LSTM, and applies this in a structural lifeline system, the transmission tower-line system. By constructing the optimized LSTM architectures, the proposed method applies to both the linear structure, the single transmission tower and the nonlinear structure, the transmission tower-line system, with promising results for the dynamic and extreme response prediction. It can conclude that the layers and the hidden units have a strong impact on the LSTM prediction performance, and with proper training data set, the computational time can significantly decrease. A comparison surrogate model developed by CNN is also utilized to demonstrate the robustness of the LSTM-based surrogate model with limited data scale.

Anomaly and damage detection is an important research topic in the field of structural health monitoring (SHM). It is in general difficult to establish a precise computational model and measure multiple dynamic loads for complex structures. Model-free identification methods using only response measurements are therefore highly desired. Based on second-order statistics blind separation (SOSBS), this study explores response-only blind excitation separation and structural feature extraction when the structure is subject to multiple periodic excitations. The proposed method proceeds with two steps: (i) a transformation to convert the measurement space to eigenspace with identity covariance matrix and compact the measurement dimension to independent source dimension; and (ii) joint diagonalization of covariances with various time shifts to determine the mixture features. Neither structural model nor measurement of excitations is required in this method, and the extracted mixture matrix representative of structural dynamic characteristics can be used for structural anomaly detection and damage diagnosis. Both numerical simulation of a 3-degree-of-freedom vibration system and experimental study of a 5-story physical structure are conducted to verify the proposed method.

While recognized as most rigorous procedure leading to 'exact' structural seismic responses, nonlinear time history analysis is usually time consuming and computational demanding, especially when numerous structures remain to be analyzed. This paper proposes a framework to improve the time efficiency in evaluating the structural seismic demands, using ensemble machine learning models based on 'classification-regression' philosophy. Typical tall pier bridges widely located in southwest China are employed as illustrative examples to validate the efficiency and performance of this proposed framework. The results and discussion show that with properly selected input variables, the proposed ensemble model (ORF-ANN herein) performs better in predicting seismic demands than other single learning algorithms (i.e., ANN and ORF), while the time efficiency is improved over 90%. This proposed model could drastically improve the efficiency for determining structural parameters in preliminary design process, and thus reduce the iterations of trail analysis. Additionally, the model constructed from proposed framework is believed especially favored for evaluating the post-earthquake states/resilience of a region and/or highway network, where thousands of structures might be contained, and conducting nonlinear time history analysis for each one would be prohibitively time consuming and delay the rescue operations.