This paper presents new parameters for damping improvement in F²MC tubes performance as tunable vibration absorbers. They offer very good performance with environments having susceptibility to high frequency vibration noise. This study highlights the behavior of changing some parameters of F²MC tubes which never have been studied before. These parameters include thickness ratio between each two respective layers and fluid type that the tubes are filled with. In this paper the beam governing equations with the tube's stress analyses equations are solved for finding the combined system's response by MATLAB^® software function solvers. To ensure accuracy of modifications, validations have been proposed by performing illustrative examples and comparing the results with the existing data available in literature. The results showed improvements of F²MC tubes performance 20% over previous studies achievements by studying the thickness ratio, and another 12.82% can be added by using glycerin instead of water under the same conditions. Finally, the reduction of 34.34 dB in first mode amplitude of vibration was achieved in the beam's frequency response function plot.

Shear connectors play a major role in the development of composite steel concrete systems. The behavior of shear connectors is usually calculated by push-out measurements. These experiments are expensive and take a lot of time. Soft Computation (SC) may be applied as an additional solution to remove the need for push-out testing. The objective of the research is to explore the implementation, as sub-branches of the SC approaches, of artificial intelligence (AI) techniques for the prediction of advanced C-shaped shear connectors. To this end, multiple push-out tests on these connectors will be carried out and the requisite data is obtained for the AI models. The Grey Wolf Optimizer algorithm (GWO) is built to define the parameters that influence the shear strength of angle connectors. Two regression metrics as determination coefficient (R²) and root mean square (RMSE) were used to measure the results of model. Furthermore, only four parameters in the predictive models are sufficient to provide an extremely precise prediction. It was found that GWO is a faster method and is able to achieve marginally higher output indices than in experiments.

There is a growing need to develop sensors which can be embedded into the structures during the construction stage itself for developing smart structures. It is preferred to develop these kinds of sensors with the material same as that of material used in construction for the sake of compatibility and better capturing the actual state of distress in the structure. Towards this, in this study cement based piezo-resistive sensors are developed with the help of conductive nano-fillers (Carbon Nanotubes (CNTs)). Since the sensors are cement based, and porous in nature, the characteristics of the sensor will vary due to water penetration into the sensor. As the structures with such embedded sensors have to perform for years, understanding the variations in the characteristics of the sensor due to pore structure is very important. In this regard, the conductivity of the sensor is assessed where the effect of dosage of CNTs, functionalization of CNTs, type of electrical conductivity measurement (both DC and AC) and pore water are the parameters. The strain sensitivity of the sensors under cyclic stress is also investigated and reported in the present study. The findings of this study will help in developing continuous health monitoring strategies using highly sensitive embeddable cement-based nanocomposites.

Free vibration analysis of a sandwich beam with honeycomb core and piezoelectric face sheets, which is rested on the viscoelastic foundation is investigated. The thermal environment and the electric field are applied to this structure. Also, it is affected by the proportional-derivative (PD) controller. The amount of gain in this controller can affects the vibration frequency. The displacement components are expressed by improved high-order sandwich panel theory (IHSAPT) that considers continuity conditions for transverse shear stress at the interfaces and the zero transverse shear stresses conditions on the upper and lower surfaces of the beam and core flexibility. The motion equations are derived and solved by Hamilton's principle and Navier's method, respectively. This paper examines the effects of various parameters, such as the internal aspect ratio and the cell angle/thickness of the honeycomb core, temperature variations, viscoelastic environment, electric load, and control gain on its natural frequencies. The results show when the honeycomb core's to face sheet's thickness ratio increases, the beam dimensionless frequency increases, too. Also, by increasing the internal aspect ratio of honeycomb core, the frequency of sandwich beam decreases. The results of this study can be used to vibration control in aerospace engineering and constructions.

Friction pendulum bearing (FPB) in bridges with different pier heights has various degrees of effectiveness of seismic isolation. To determine the applicability of FPB under different bridge pier height conditions, this paper focuses on the simply supported girder railway bridges that have three types of piers: solid piers with uniform cross-section, solid piers with non-uniform cross-section, and hollow piers with non-uniform cross-section. All of these bridges are first installed with FPB (isolation bearing) and later with non-isolation bearing, modeled by using OpenSEES finite element software. A shake table test is used to verify the related models. Based on nonlinear dynamic time history analysis, the seismic responses of isolated and non-isolated bridges are compared, and their corresponding seismic isolation ratios are calculated. Further, this paper introduces a fuzzy comprehensive evaluation method to determine the seismic isolation effect of FPB on bridges with different pier heights, by weighing and balancing the isolation ratios of different seismic responses of bridges. The results show that the transverse seismic isolation ratios of FPB are generally larger than the longitudinal seismic isolation ratios. In addition, FPB has poorer seismic isolation effect on tall piers compared with short piers.

Multiple variable frequency pendulum isolator (MVFPI) has been recently developed as a superior alternative to the traditional friction pendulum bearing (FPB) especially for the seismic isolation in near-fault regions. The MVFPI is characterized by its variable frequency and self-adaptability, which are achieved by piecewise function of sliding surface and shape memory alloy (SMA). The objective of this study is to propose the design algorithm of the MVFPIs in highway bridge as an extension of the direct displacement-based design (DDBD) framework. The nonlinearities of the structural components are taken into account in the design procedure, and the corresponding damage states satisfy the two-stage design philosophy. The accuracy and robustness of the design procedure are verified by an isolated four-span highway bridge through nonlinear time history (NLTH) analyses. The analytical results indicate that the proposed design procedure can predict the profile of deck displacement and amplitude, as well as the damage states of the piers. From statistic aspect, the fragility analyses illustrate that the bridge isolated by MVFPIs exhibits better seismic performance than that of the bridge isolated by FPBs.

Because of the limited land resources and preference of centralized services, more structures are often built close to each other, correspondingly yielding a demand that mitigates the dynamic responses of adjacent structures. Utilizing the intrinsic potential of the inerter to improve structural energy performances, an inerter-system chain is proposed for the adjacent single-degree-of-freedom structures, which forms a novel configuration featuring the reduction in input energy transmitted to the adjacent structures. The inerter-system chain is realized by two end-placed inerter-dashpot dampers and inter-placed spring-inerter-dashpot elements arranged in parallel. Stochastic energy balance analysis is conducted to derive a closed-form energy equation that reveals the energy basis of the inerter-system chain. An energy-based and bi-objective optimization strategy is developed with simultaneous consideration of displacement and energy performances, particularly easy-to-use design formulae being derived. The findings of this study show that a complete inerter-system chain exhibits a significant multi-reduction in the structural displacement, shear force, and dissipation energy burden. Particularly, the effectiveness of reducing the input power and vibrational energy transmitted into the entire structures counts on the series inerter-chain, which differentiates the proposed chain from alternative layouts. The proposed energy-based design framework is capable of minimizing the energy dissipation cost, with target displacement control demand satisfied.

Substructure pseudo-dynamic hybrid simulation (SPDHS) is an advanced structural seismic testing method which combines physical experiment and numerical simulation. Generally, the key components which display nonlinearity first are taken as experimental substructures for actual test, and the remaining parts are modeled in simulation. Model updating techniques can be effectively applied to enhance the model precision of nonlinear numerical elements. Specifically, the constitutive model of the experimental substructure is identified online by the instantaneously-measured data, and the corresponding numerical elements with similar hysteretic behaviors are updated synchronously. Artificial neural network (ANN) can recognize the system which cannot be represented by definite numerical model, and thus avoids the structural response distortion caused by the inherent numerical model defects. In this study, a framework for online model updating in SPDHS with ANN method is expanded to implement actual test validation. Moreover, the effectiveness of ANN method is demonstrated by practical tests of a two-story frame model with bending dampers. Additionally, the unscented Kalman filter technique and offline ANN identification approach are both examined in the test validation. The experimental results show that, under the identical loading history, the online ANN method can significantly reduce the model errors and improve the accuracy of SPDHS.

In this paper, a novel real-time structural health monitoring (SHM) system based on streaming data is proposed. In contrast to a traditional SHM system, the proposed system implements a series of optimizations for data transmission and processing to reduce the latency and better satisfy the real-time requirement. The concept of the watermark in the streaming system is adopted to address the problem of when to trigger the time window calculation under the real-time requirement. Moreover, a well-designed parallel mechanism is used to satisfy the multistage computation requirement in the parallel data stream. A case study in which the proposed system is applied to the Shanghai Tower is presented. The peak picking method is used as an example in the test environment to track the latency of each main operation under different parallelism schemes. The results show that computing in parallel effectively reduces the latency and provides a reference for integrating the random decrement technique (RDT), stochastic subspace identification (SSI), or other more complex but effective algorithms in parallel into the system in the future. The total latency under the test environment from data generation to data transmission to the web server is approximately only 200-400 ms, which indicates the excellent real-time performance of the system.

An indispensable process of geotechnical modeling with transparent soils involves analyzing images and soil property simulations. This study proposes an objective framework for quantitative analysis of the influential mechanism of three key factors, namely, different aggregate proportions (DAP), solvent ratio (SR), and solute solution ratio (SSR) on transparent soils' transparency and shear strength. 125 groups of transparent soil samples considering these three factors were prepared to investigate their impact on transparency and shear strength through Elastic Net regression. Spearman correlation analysis was performed for transparency and shear strength. Furthermore, by comparing the performance of XGBoost, GBDT, Random Forest, and SVR after hyperparameter tuning in predicting transparency and shear strength, XGBoost proved to be the optimal machine learning model with the lowest MSE of 0.0048 and 0.0306 and was innovatively adopted to analyze how various factors affect the transparency and shear strength, thus enhancing the interpretability of machine learning. A ranking system, according to the importance scores of XGBoost, shows that SSR was the most important factor affecting both shear strength and transparency of transparent soils, with importance scores being 0.45 and 0.57, respectively. Our study may shed light on the preparation and performance study of transparent soils.