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In order to ensure the safety and preserve the value of historical buildings, inclination is an essential parameter during the continuous structural health monitoring process. However, the wire and price of a traditional sensor limit application. This paper proposes a low-cost inclination sensor based on a patch antenna with a reconfigurable water load. Only the water directly on the antenna is considered effective. The different volume of the effective water load, which is determined by the inclination of the attached surface, will affect the effective permittivity of the dielectric plate of the patch antenna, further causing a variation in the resonant frequency. Therefore, the proposed antenna sensor can monitor the inclination of the attached surface by interrogating the resonant frequency. The working mechanism is first clarified by theoretically investigating the relationship between the dielectric properties and the inclination of the covering medium. The antenna sensor is then simulated using High-Frequency Structure Simulator ver.15 (HFSS 15), which helps to determine geometric parameters and confirm accuracy and sensitivity. An experiment has been conducted based on the design verified in the simulation. The inclination detection shows a correlation coefficient of 0.9771 with a sensitivity of 7.92 MHz/°, indicating a potential for real application.

Abstract Background Esophageal cancer (ESCA) is one of the most aggressive and lethal human malignant cancers. MicroRNA‐1301‐3p (miR‐1301‐3p) plays vital roles in a majority of malignancies. The aim of this study was to investigate the role of miR‐1301‐3p/NBL1 axis on ESCA cell invasion, migration, epithelial‐mesenchymal transition (EMT) process, as well as its association with prognosis of ESCA patients. Methods The expression levels of miR‐1301‐3p and NBL1 were predicted by bioinformatics and further verified by RT‐qPCR assays. Kaplan–Meier (K–M) plotter analysis and univariate and multivariate Cox analyses were used to evaluate the relationship between miR‐1301‐3p and clinicopathological variables and prognosis. The role of miR‐1301‐3p on cell invasion, migration was detected by transwell invasion, and wound healing assays, respectively. The EMT‐related proteins were detected by western blot. The target genes and the target binding sites were predicted by bioinformatics and further determined by RT‐qPCR assay. Results MiR‐1301‐3p was remarkably upregulated in ESCA tissues and cells, and its high expression was associated with poor prognosis of ESCA. Overexpression of miR‐1301‐3p promoted ESCA cell invasion, migration and mediated EMT process in vitro, whereas knockdown of miR‐1301‐3p showed the opposite effects. Moreover, NBL1 was predicted as a target gene of miR‐1301‐3p. NBL1 was lowly expressed in ESCA cells and significantly decreased after upregulation of miR‐1301‐3p. Meanwhile, we found that low expression of NBL1 was significantly associated with poor prognosis of ESCA patients. Conclusion MiR‐1301‐3p is a potential biomarker for predicting the prognosis of ESCA patients. It may promote ESCA invasion, migration and EMT progression by regulating NBL1 expression.

At present, the midstory isolation (MSI) technology has great potential for application in historical buildings’ retrofitting and multifunction buildings. The coupling effect due to the variability of the location of the isolation layer may amplify the structural seismic response and is required for in-depth analysis. This paper aims to evaluate the magnitude of the coupling effect and delimitate the region of the coupling effect to be considered. Based on the complex mode superposition method, the explicit formulas for calculating the random response of the simplified model are deduced. The root-mean-square (RMS) ratio of the shear force coefficient of the upper isolation system is adopted as the performance indicator to evaluate the coupling amplification effect of the MSI system. Parameter analysis indicates that the coupling region is closely related to the mass ratio and frequency ratio of the upper and lower structures to the isolation layer. In general, the region of the coupling effect to be considered can be divided into two parts according to parameters of frequency ratios, depending on the thresholds of the performance indicator. As the mass ratio of the upper isolation system to the entire system increases, one of the coupling regions shrinks and eventually disappears, indicating that the coupling amplification effect in this region can be neglected under certain conditions. Finally, the time-domain analysis of three representative numerical cases of MSI buildings was performed to verify the reliability of the results obtained from the frequency-domain analysis. The research results can provide technical guidance for the preliminary design of the MSI buildings.

A grout sleeve connection is a typical kind of joint in prefabricated structures. However, for construction and manufacturing reasons, defects in this kind of joint are usually inevitable. The joint quality of a prefabricated structure has a significant influence on its overall performance and can lead to structural failure. Due to the complexity of various types of materials used in grout sleeve connections, traditional non-destructive testing methods, such as Acoustic Emission (AE), Ultrasonic Testing (UT), Guided Wave Testing (GW), are facing great challenges. The recent development of deep learning technology provides a new opportunity to solve this problem. Deep learning can learn the inherent rules and abstract hierarchies of sample data, and it has a powerful ability to extract the intrinsic features of training data in complex classification tasks. This paper illustrates a deep learning framework for the identification of joint defects in prefabricated structures. In this method, defect features are extracted from the acceleration time history response of a prefabricated structure using a convolutional neural network. The proposed method is validated by vibration experiments on a half-scaled, two-floor prefabricated frame structure with column rebars spliced by different defective grout sleeves.

The strain sensor based on microwave patch antenna is proposed to monitor structural strain in structural health monitoring. When patch antenna experiences deformation, the resonant frequency of patch antenna will shift. With these characteristics, the patch antenna can operate as both the strain-sensing element and communication component. This article chooses an RT-5880 rectangular patch antenna for strain measurement, focusing on its sensing performance. For distinguishing the influence of deformation in the antenna’s length direction and width direction, the numerical simulation is implemented, and then two kinds of laboratory experiments are conducted. The first approach is to paste antennas in longitudinal and transverse ways and solve the equation set. The other approach is to design another patch antenna with narrow width and compare the test results with the wide one. All results show that the influence of deformation in wide direction on sensitivity can be neglected, and the resonant frequency shift has a good linear relationship with the strain of antenna in length direction.

The damping estimation of an eight-story steel building equipped with oil dampers is examined, carried out by adopting a proposed framework, which consists of an enhanced strain-energy method and an improved direct method for model updating. The building is located at Tohoku Institute of Technology and is equipped with a structural monitoring system that measures its seismic response, including floor acceleration and displacement and force of oil dampers. The enhanced strain-energy method is first developed and employed to assess the supplemental damping and stiffness provided by oil dampers, herein quantified in the form of equivalent damping ratios and natural frequencies. Then, modal characteristics extracted from the earthquake measurements are modified accordingly and utilized for the building model updating, in which mass and stiffness matrices are corrected by the improved direct method. The updated model accurately reproduces the target modal data, especially measured mode participation factors, and is further used for the building response predictions. Through prediction validations, the precision of the modified modal parameters is verified. Finally, a large earthquake event is chosen to demonstrate the effectiveness of the proposed framework for the damping estimation of the investigated building.

This paper presents a capacitive displacement sensor based on a capacitively fed inverted-F antenna (CFIFA) for displacement detection. The sensor is composed of a grounded L-shape patch and a rectangular upper patch, forming a capacitor between them. The asymmetric dipole model is adopted to explain the frequency shift and current distribution of the proposed antenna sensor at its first-order resonance. The numerical simulation of the CFIFA using the Ansoft high-frequency structure simulator (HFSS) software is carried out to optimize the dimensional parameters, allowing the antenna to perform better. Two sets of CFIFAs are fabricated and tested for verification. Results show that the CFIFA has a good linear relationship between its first resonant frequency and the relative displacement, and is capable of a long range of displacement measuring.

Inerter-based vibration control systems have been developed rapidly in recent years. However, previous studies mainly focus on the development of new devices and parameter optimization strategies, while ignoring the improvements in the utilization efficiency of the inerter system that the bracing system and novel installation methods may bring. In this paper, a cross-layer installed cable-bracing inerter system (CICBIS) is proposed to improve the utilization efficiency of the inerter system, which can cross more layers and is suitable for shear-type multi-degree-of-freedom (MDOF) structures. A demand-based cable-bracing inerter system (CBIS) design method is developed. The mass enhancement and utilization efficiency improvement of the inerter system caused by the cross-layer installation are quantified through calculating the effective inerter-mass ratio of the CBIS-equipped MDOF structure. A 10-story benchmark structure is used to verify the control performance of the CICBIS and the design method. The analysis results show that the proposed design method can exert the cable-bracing system’s adjustability and the damping enhancement of the inerter system. The CICBIS can reduce the total apparent mass and damping coefficient requirements of the inerter systems without increasing the control force. It means that the proposed design method is effective, and the CICBIS has a high efficiency.

This paper presents a passive wireless long-range displacement sensor that is based on the circular patch antenna, and the detecting range of the sensor can be customized. The sensor consists of a chipped circular antenna with two opened rectangular windows, a substrate, and a ground plate with a sloping channel. No bonding between the antenna and the ground plate allows for the chipped antenna to slide along the sloping channel. The channel will drive the current flow on the plate once the chip is activated, increasing the effective electrical length and, consequently, decreasing the resonant frequency of the circular antenna. The sensing mechanism equates the measuring displacement to the relative movement of the antenna with respect to the ground that achieves the measurement of long-range displacement and, thus, the proposed sensor can avoid stress damage to the antenna due to excessive deformation. Three different range sensors were simulated in the the Ansoft high frequency structure simulator (HFSS). The results show that the resonance frequency of the antenna has a linear relationship with the varying chute depth beneath the chip. Three sensors were fabricated, and the experimental results also validated that the sensitivity of the sensor can be adjusted.

Based on the classical Timoshenko beam theory, the rotary inertia caused by shear deformation is further considered and then the equation of motion of the Timoshenko beam theory is modified. The dynamic characteristics of this new model, named the modified Timoshenko beam, have been discussed, and the distortion of natural frequencies of Timoshenko beam is improved, especially at high-frequency bands. The effects of different cross-sectional types on natural frequencies of the modified Timoshenko beam are studied, and corresponding simulations have been conducted. The results demonstrate that the modified Timoshenko beam can successfully be applied to all beams of three given cross sections, i.e., rectangular, rectangular hollow, and circular cross sections, subjected to different boundary conditions. The consequence verifies the validity and necessity of the modification.

Due to the increased service life, environmental corrosion, unreasonable construction, and other issues, local defects inevitably exist in civil structures, which affect the structural performance and can lead to structural failure. However, research on grout defect identification of precast reinforced concrete frame structures with rebars spliced by sleeves faces great challenges owing to the complexity of the problem. This study presents a multiple-variable spatiotemporal regression model algorithm to identify local defects based on structural vibration responses collected using a sensor network. First, numerical simulations were carried out on precast beam–column connection models by comparing the identification results based on a single-variable regression model, two-variable spatial regression model, and two-variable spatiotemporal regression model; furthermore, a multiple-variable spatiotemporal regression model was proposed and robustness analysis of the damage indicator was carried out. Then, to explore the validity of the proposed method, a nondestructive vibration experiment was considered on a half-scaled, two-floor, precast concrete frame structure with column rebars spliced by defective grout sleeves. The results show that local defects were successfully identified based on a multiple-variable spatiotemporal regression model.

Due to the uncertainties originating from the underlying physical model, material properties and the measurement data in fatigue crack growth (FCG) processing, the prediction of fatigue crack growth lifetime is still challenging. The objective of this paper was to investigate a methodology for uncertainty quantification in FCG analysis and probabilistic remaining useful life prediction. A small-timescale growth model for the fracture mechanics-based analysis and predicting crack-growth lifetime is studied. A stochastic collocation method is used to alleviate the computational difficulties in the uncertainty quantification in the small-timescale model-based FCG analysis, which is derived from tensor products based on the solution of deterministic FCG problems on sparse grids of collocation point sets in random space. The proposed method is applied to the prediction of fatigue crack growth lifetime of Al7075-T6 alloy plates and verified by fatigue crack-growth experiments. The results show that the proposed method has the advantage of computational efficiency in uncertainty quantification of remaining life prediction of FCG.

Structural parameters are the most important factors reflecting structural performance and conditions. As a result, their identification becomes the most essential aspect of the structural assessment and damage identification for the structural health monitoring. In this article, a structural parameter identification method based on Monte Carlo method and likelihood estimate is proposed. With which, parameters such as stiffness and damping are identified and studied. Identification effects subjected to three different conditions with no noise, with Gaussian noise, and with non-Gaussian noise are studied and compared. Considering the existence of damage, damage identification is also realized by the identification of the structural parameters. Both simulations and experiments are conducted to verify the proposed method. Results show that structural parameters, as well as the damages, can be well identified. Moreover, the proposed method is much robust to the noises. The proposed method may be prospective for the application of real structural health monitoring.

Information about the positions of the sensors in sensor networks is very important, and the deployment of more and more sensors is increasing the need for automatic sensor localization. This article therefore describes a novel two-phase ranging algorithm that first obtains rough estimate of the distance to a sensor’s position using time difference of arrival or time of arrival methods and then obtains a high-resolution estimate based on the rough one using a phase-based ranging scheme. This algorithm can easily resolve the otherwise intractable integer ambiguity that often appears in localization systems, and experimental results show that it can greatly decrease the ranging error in a decentralized distance-based localization system having transmitter beacons and receivers in the nodes. Related problems such as signal filtering and multipath effect are also discussed. This algorithm can make the deployment of large numbers of sensors very simple and the determination of their positions so accurate that it would be feasible to use dense networks of sensors to monitor the structural integrity of large structures.

The uncertainty in parameter estimation arises from structural systems’ input and output measured errors and from structural model errors. An experimental verification of the shuffled complex evolution metropolis algorithm (SCEM-UA) for identifying the optimal parameters of structural systems and estimating their uncertainty is presented. First, the estimation framework is theoretically developed. The SCEM-UA algorithm is employed to search through feasible parameters’ space and to infer the posterior distribution of the parameters automatically. The resulting posterior parameter distribution then provides the most likely estimation of parameter sets that produces the best model performance. The algorithm is subsequently validated through both numerical simulation and shaking table experiment for estimating the parameters of structural systems considering the uncertainty of available information. Finally, the proposed algorithm is extended to identify the uncertain physical parameters of a nonlinear structural system with a particle mass tuned damper (PTMD). The results demonstrate that the proposed algorithm can effectively estimate parameters with uncertainty for nonlinear structural systems, and it has a stronger anti-noise capability. Notably, the SCEM-UA method not only shows better global optimization capability compared with other heuristic optimization methods, but it also has the ability to simultaneously estimate the uncertainties associated with the posterior distributions of the structural parameters within a single optimization run.

In this paper, cables are proposed to connect the inerter and main frame for translation-to-rotation conversion, i.e., the cable-bracing inerter system (CBIS), with a magnified mass and enhanced damping effect. This novel configuration has the benefits of deformation relaxation at the connecting joints, easy installation, and an adaptive layout for nonconsecutive-story deployment. Dynamic motion equations were established for a single degree-of-freedom (SDOF) model equipped with a CBIS. The influence of dimensionless parameters, such as inertance-mass ratio, stiffness ratio and additional damping ratio on vibration mitigation were studied in terms of displacement response and force output. A single objective and multiple objective optimal design method were developed for a CBIS-equipped structure based on a performance-oriented design framework. Finally, the mitigation effect was illustrated and verified by a numerical simulation in a time-domain. The results showed that a CBIS is an effective structural response mitigation device used to mitigate the response of structural systems under earthquake excitation. Using the proposed optimization method, CBIS parameters can be effectively designed to satisfy the target vibration control level.

Monolithic patch antennas for deformation measurements are designed to be stressed. To avoid the issues of incomplete strain transfer ratio and insufficient bonding strength of stressed antennas, this paper presents a passive wireless crack sensor based on an unstressed patch antenna. The rectangular radiation patch of the proposed sensor is partially covered by a radiation sub-patch, and the overlapped length between them will induce the resonate frequency shift representing the crack width. First, the cavity model theory is adopted to show how the resonant frequencies of the crack sensor are related to the overlapped length between the patch antenna and the sub-patch. This phenomenon is further verified by numerical simulation using the Ansoft high-frequency structure simulator (HFSS), and results show a sensitivity of 120.24 MHz/mm on average within an effective measuring range of 1.5 mm. One prototype of proposed sensor was fabricated. The experiments validated that the resonant frequency shifts are linearly proportional to the applied crack width, and the resolution is suitable for crack width measuring.

This paper presents a passive displacement sensor based on a normal mode helical antenna. The sensor consists of an external helical antenna and an inserting dielectric rod. First, the perturbation theory is adopted to demonstrate that both the electric intensity and magnetic intensity have a noticeable gradient change within the in-and-out entrance of the helical antenna, which will cause the sensor to experience a resonant frequency shift. This phenomenon was further verified by numerical simulation using the Ansoft high frequency structure simulator (HFSS), and results show the linear correlation between the retrieved resonant frequency and the displacement. Two sets of proposed sensors were fabricated. The experiments validated that the resonant frequency shifts are linearly proportional to the applied displacement, and the sensing range can be adjusted to accommodate the user’s needs.

Since physical parameters are much more sensitive than modal parameters, structural parameter identification with an extended Kalman filter (EKF) has received extensive attention in structural health monitoring for civil engineering structures. In this paper, EKF-based parameter identification technique is studied with numerical and experimental approaches. A four-degree-of-freedom (4-DOF) system is simulated and analyzed as an example. Different integration methods are examined and their influence to the final identification results of the structural stiffness and damping is also studied. Furthermore, the effect of different kinds of noise is studied as well. Identification results show that the convergence speed and estimation accuracy under Gaussian noises are better than those under non-Gaussian noises. Finally, experiments with a five-story steel frame are conducted to verify the damage identification capacity of the EKF. The results show that stiffness with different damage degrees can be identified effectively, which indicates that the EKF is capable of being applied for damage identification and health monitoring for civil engineering structures.

In recent years, sensors are often deployed to sense the target environment. Besides the sensed environmental information, sensor's position information itself is very important in most cases. With the increasing number of sensors, automatic sensor localization becomes necessary. For accurately localizing sensors, line of sight between nodes and beacons is often required. However, in many cases, it is very difficult to ensure that every beacon has the line of sight to a target sensor node. In this paper, a non-line-of-sight beacon identification approach is proposed. With which, the beacons that have no line of sight to a target sensor can be found out and then ruled out, thus the final localization result can be rectified and the accuracy can be improved. Simulations are conducted, and results show that non-line-of-sight beacons can be identified effectively. The proposed approach will be helpful in the real sensor localization in sensor networks, especially for the systems that beacons are sparsely deployed.