Carbon fiber reinforced polymers (CFRP) have shown considerable potential in the repair and rehabilitation of deficient reinforced concrete (RC) structures. To date, several CFRP strengthening schemes have been studied and employed practically. In particular, strengthening of shear damaged RC members with CFRP materials has received much attention as an effective repair and strengthening approach. Most existing studies on strengthening shear-deficient RC members have used unidirectional CFRP strips. Recent studies on strengthened T-beams demonstrated that a bidirectional CFRP layout was more effective than a unidirectional layout. As such studies are limited, in this study, the feasibility of bidirectional CFRP layouts for the shear strengthening of rectangular RC beams was experimentally evaluated. Bidirectional layout details with CFRP anchors as well as rehabilitation timing were considered and investigated. The test results showed that the members with a bidirectional CFRP layout carried less shear strength capacity than those with unidirectional layouts for the same quantity of CFRP material. Nevertheless, the bidirectional CFRP layout allowed for a uniformly distributed stirrup strain compared to the unidirectional CFRP layout at the same load level, which increased the efficiency of the transverse reinforcement. Additionally, the shear contribution of CFRP material according to the CFRP strengthening timing was verified.

In recent years, integration of electric vehicles (EVs) has increased dramatically due to their lower carbon emissions and reduced fossil fuel dependency. However, charging EVs could have significant impacts on the electrical grid. One promising method for mitigating these impacts is the use of renewable energy systems. Renewable energy systems can also be useful for charging EVs where there is no local grid. This paper proposes a new strategy for designing a renewable energy charging station consisting of wind turbines, a photovoltaic system, and an energy storage system to avoid the use of diesel generators in remote communities. The objective function is considered to be the minimization of the total net present cost, including energy production, components setup, and financial viability. The proposed approach, using stochastic modeling, can also guarantee profitable operation of EVs and reasonable effects on renewable energy sizing, narrowing the gap between real-life daily operation patterns and the design stage. The proposed strategy should enhance the efficiency of conventional EV charging stations. The key point of this study is the efficient use of excess electricity. The infrastructure of the charging station is optimized and modeled.

As the occurrence of earthquakes is increasing in South Korea, the earthquake early warning (EEW) system becomes indispensable for the protection of high-speed railways. Although the importance of EEW system has been increasing, the number of installed seismic accelerometers in South Korea is not sufficient to provide rapid information. This study uses a stochastic signal analysis technique to utilize the smartphone sensors for the rapid EEW system. From the train vibration data from the low fidelity on-board accelerometer, the virtual earthquake detection data in the train by smartphone sensor has been constructed. To analyze the stochastic characteristics of the constructed data, the short time Fourier transform (STFT) approach has been applied. The study’s overall objective is to offer stochastic approaches that provide effective analysis of the low fidelity sensor data, such as smartphone sensor data, for the rapid EEW system.

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Abstract:Wind turbines within an array do not experience free-stream undisturbed flow fields. Rather, the flow fields on internal turbines are influenced by wakes generated by upwind unit and exhibit different dynamic characteristics relative to the free stream. The International Electrotechnical Commission (IEC) standard 61400-1 for the design of wind turbines only considers a deterministic wake model for the design of a wind plant. This study is focused on the development of a stochastic model for waked wind fields. First, high-fidelity physics-based waked wind velocity fields are generated using Large-Eddy Simulation (LES). Stochastic characteristics of these LES waked wind velocity field, including mean and turbulence components, are analyzed. Wake-related mean and turbulence field-related parameters are then estimated for use with a stochastic model, using Multivariate Multiple Linear Regression (MMLR) with the LES data. To validate the simulated wind fields based on the stochastic model, wind turbine tower and blade loads are generated using aeroelastic simulation for utility-scale wind turbine models and compared with those based directly on the LES inflow. The study's overall objective is to offer efficient and validated stochastic approaches that are computationally tractable for assessing the performance and loads of turbines operating in wakes.