800 publications from this institution
With the ever-increasing demand in urban mobility and modern logistics sector, the vehicle population has been steadily growing over the past several decades. One natural consequence of the vehicle population growth is the increase in traffic congestion. Almost all (metropolitan) cities including the major ones, like Los Angeles, Beijing, New York, are suffering from heavy traffic congestion. Statistics show that, in 2015, 43 cities in China are suffering a prolonged travel time of more than 1.5 h every day during rush hours. In the meanwhile, traffic accidents are plaguing the economic development as well.
A specklegram in a multimode fiber (MMF) has successfully been used as a sensor for detecting external disturbance. Our experiments showed that the sensitivity in the sensor with a multiple longitudinal-mode laser as its source was much higher than that with a single longitudinal-mode laser. In addition, the near-field pattern observations indicated that the coupling between different transverse modes in the MMF is quite weak. Based on the experimental results, a theoretical model for the speckle formation is proposed, taking a bend-caused phase factor into consideration. It is shown in the theoretical analysis that the interferences between different longitudinal modes make a larger contribution to the specklegram signals.
The prevention and control of hospital-acquired infections remain a significant challenge worldwide, as textiles used in hospital wards are highly involved in transmission processes. This paper reports a new antibacterial medical fabric used to prepare hospital pillowcases, bottom sheets and quilt covers for controlling and reducing hospital-acquired infections.
Triboelectric nanogenerator (TENG) based on Maxwell's displacement current as the driving force has broad applications in energy harvesting and self-powered sensors. However, the effect of doped ferromagnetic materials with bearing an external magnetic field on TENG's displacement current has not been clearly revealed. Herein, a ferromagnetic-assisted Maxwell's displacement current is proposed for improving TENG output based on polymer/iron composite film (PICF). Based on Maxwell's equations, the magnetizing current of the ferromagnetic medium coupled with the displacement current is investigated theoretically for boosting TENG output and verified simulationally and experimentally. Comparing TENG output with the magnetization method of PICF, the initial magnetization significantly determines the coupling mechanism between magnetization current and displacement current. Moreover, the strength and direction of an external magnetic field further reveal the mechanism impacting the initial magnetization on TENG's displacement current. A short-circuit current density of 27 mA/m2 and instantaneous power density of 2 W/m2 are achieved by the champion TENG, which are 800% and 8200% higher than those of pristine polymer. Finally, a distributed TENG array is developed for efficiently harvesting distributed energy to demonstrate the application potential of the improved TENG in self-powered systems. This work elaborates the coupling mechanism of the magnetization current with TENG's displacement current, which provides general guidance for investigating efficient energy harvester toward self-powered sensors, wearable electronics and flexible electromagnetic shielding devices.
This paper presents a finite element (FE) analysis of the mechanical responses for stitched CFRP laminates under different mechanical loads. Firstly, the through-thickness stitch was simplified to z-pin like reinforcement with a uniform displacement constraint on the upper and lower surfaces of the laminate. Then, a mesoscale 3D representative volume element (RVE) of the stitched composite was proposed and modeled in the FE code ABAQUS, where the reinforcing stitch, composite layers and interfaces were built. A 3D Hashin damage model and built-in cohesive elements were respectively used to predict the mechanical failure of the stitch and the damageable behavior of cohesive interfaces. Simulation results reveal the progressive damage and rupture processes of the RVE under tensile and shear mechanical loads, and macroscopic nonlinear load-displacement responses of the mesoscale model are also captured.
Precast segmental beams with prestressed external tendons have become increasingly common in bridge constructions owing to the merits of high-quality control and fast onsite construction with minimum environmental impact. Due to the nonlinear characteristics of the dynamic behavior of these segmental beams associated to the joint opening, the traditional condition monitoring and damage assessment methods based on vibration parameters either in time or frequency domain using the linear theory are unsuitable. To overcome this challenge, this paper proposes a damage assessment method through analyzing nonlinear structural dynamic responses for monitoring the conditions of segmental beams with different joint types and prestressed external tendons. Two damage indices are proposed to identify the occurrence of damage and to qualitatively indicate the damage severity. In the proposed approach, the measured dynamic responses of the segmental concrete beams under hammer impact are first adaptively decomposed into a finite number of mono-components by using variational mode decomposition (VMD) technique. Then, the instantaneous frequencies of the decomposed mono-components from the dynamic responses are obtained by conducting the Hilbert transform. Two damage indicators are defined for the damage assessment and for the identification of the damage location, respectively. Three four-span segmental beams with different types of joints (i.e. dry joints or epoxy joints) and prestressing tendon types (i.e. steel and carbon fiber reinforced polymer tendons) were constructed and tested under five loading levels with different damage severities. The feasibility and accuracy of the proposed approach are verified by comparing the identification results with the secant and initial stiffness extracted from the load–deflection curves at different loading levels. The effectiveness of these two damage indicators is also validated with the structural conditions observed in the tests. The results show that the proposed approach can successfully identify damage in segmental concrete beams.
In this paper, damage detection/identification for a seven-storey steel structure is investigated via using the vibration signals and deep learning techniques. Vibration characteristics, such as natural frequencies and mode shapes are captured and utilized as input for a deep learning network while the output vector represents the structural damage associated with locations. The deep auto-encoder with sparsity constraint is used for effective feature extraction for different types of signals and another deep auto-encoder is used to learn the relationship of different signals for final regression. The existing SAF model in a recent research study for the same problem processed all signals in one serial auto-encoder model. That kind of models have the following difficulties: (1) the natural frequencies and mode shapes are in different magnitude scales and it is not logical to normalize them in the same scale in building the models with training samples; (2) some frequencies and mode shapes may not be related to each other and it is not fair to use them for dimension reduction together. To tackle the above-mentioned problems for the multi-scale dataset in SHM, a novel parallel auto-encoder framework (Para-AF) is proposed in this paper. It processes the frequency signals and mode shapes separately for feature selection via dimension reduction and then combine these features together in relationship learning for regression. Furthermore, we introduce sparsity constraint in model reduction stage for performance improvement. Two experiments are conducted on performance evaluation and our results show the significant advantages of the proposed model in comparison with the existing approaches.
In order to improving load efficiency of the driving machine,we analyzed the loading machine's power,star wheel's rotation rate,scraper conveyor and loading machine's structure relative location.
This paper proposes a data-driven method using subspace projection residual of the responses to identify the damage locations in bridges subjected to moving loads. In this method, a moving window with a certain length determined by the sampling frequency and the fundamental frequency of the measured responses is used to cut out the acceleration responses of the bridge subjected to a moving vehicle. The characteristic subspaces of the windowed signals are subsequently extracted to calculate the local damage index using the subspace projection residual. When the window moves to the damage location, the orthogonality between the active subspace of the damaged state and the null subspace of the healthy state is invalid, which leads to a relatively large projection residual that can be used to localize the damage. To improve the reliability of the proposed approach, a one-side upper confidence limit is introduced. A simply supported beam bridge subjected to a moving mass is simulated to verify the effectiveness of the proposed method. Numerical results indicate that the proposed approach can accurately localize the single and multiple damages, even when the responses are smeared with a significant noise. Experimental tests conducted on a steel beam bridge model also demonstrate the performance and accuracy of the proposed approach. The results demonstrate that the proposed method can localize the damage even with a small number of sensors, indicating the method has a good and promising performance for practical engineering applications.
