MEMBRANE TECHNOLOGY FOR OSMOTIC POWER GENERATION BY PRESSURE RETARDED OSMOSIS (PRO)

Ph.DDOCTOR OF PHILOSOPH

A two-phase ranging algorithm for sensor localization in 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

Definition of Yield Seismic Coefficient Spectrum Considering the Uncertainty of the Earthquake Motion Phase

Earthquake engineers are typically faced with the challenge of safely and economically designing structures in highly uncertain seismic environments. Yield strength demand spectra provide basic information for the seismic design of structures and take nonlinear behavior into account. The designed structures, however, must be checked for seismic performance through dynamic analysis. Design-response spectra compatible earthquake motions (DRSCEM) are commonly used for this purpose. Because DRSCEM are strongly affected by the assigned phase characteristics, in this paper, we simulate realistic earthquake motion phase based on a stochastic process that modifies fractional Brownian motion (fBm). The parameters that control this process were determined via regression equations as functions of the earthquake magnitude and epicenter distance, which were obtained through a regression analysis that was performed on data from a database of recorded ground motions. After validating the efficiency of the modeled phase spectrum, large numbers of DRSCEMs were simulated with which several ductility demand spectra were obtained. By statistically analyzing these results, a rigorous yield seismic coefficient demand spectrum is proposed

Experimental Investigation of the Fatigue Behavior of Basalt Fiber Reinforced Polymer Grid-Concrete Interface

Fatigue behavior is an important factor for mechanical analysis of concrete members reinforced by basalt fiber reinforced polymer (BFRP) grid and polymer cement mortar (PCM) and plays a critical role in ensuring the safety of reinforced concrete bridges and other structures. In this study, on the basis of the static loading test results of concrete specimens reinforced by BFRP grid and PCM, a series of fatigue tests with different loading levels were conducted on interfaces between BFRP grid and concrete to investigate the fatigue behavior of BFRP grid-concrete interfaces. The test results indicate that with high loading level, the fatigue failure mode of interface is interfacial peeling failure while it transforms to the fatigue fracture of the BFRP grid under low loading level. The fatigue life (S-N) curves of BFRP grid-concrete interface are obtained and fitted in stages according to different failure modes, and the critical point of the two failure modes is pointed out. The relative slip evolution of interface during fatigue is further revealed in different stages with two failure modes, and the law of interface strain is studied with the increase of fatigue times. The relation of effective bonding length of interface and fatigue times is also described

The Therapeutic Potential of Exosomes in Soft Tissue Repair and Regeneration

Soft tissue defects are common following trauma and tumor extirpation. These injuries can result in poor functional recovery and lead to a diminished quality of life. The healing of skin and muscle is a complex process that, at present, leads to incomplete recovery and scarring. Regenerative medicine may offer the opportunity to improve the healing process and functional outcomes. Barriers to regenerative strategies have included cost, regulatory hurdles, and the need for cell-based therapies. In recent years, exosomes, or extracellular vesicles, have gained tremendous attention in the field of soft tissue repair and regeneration. These nanosized extracellular particles (30–140 nm) can break the cellular boundaries, as well as facilitate intracellular signal delivery in various regenerative physiologic and pathologic processes. Existing studies have established the potential of exosomes in regenerating tendons, skeletal muscles, and peripheral nerves through different mechanisms, including promoting myogenesis, increasing tenocyte differentiation and enhancing neurite outgrowth, and the proliferation of Schwann cells. These exosomes can be stored for immediate use in the operating room, and can be produced cost efficiently. In this article, we critically review the current advances of exosomes in soft tissue (tendons, skeletal muscles, and peripheral nerves) healing. Additionally, new directions for clinical applications in the future will be discussed

Parameter Identification for Structural Health Monitoring with Extended Kalman Filter Considering Integration and Noise Effect

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

A Deep Learning-Based Approach for the Identification of a Multi-Parameter BWBN Model

A restoring-force model is a versatile mathematical model that can describe the relationship between the restoring force and the deformation obtained from a large number of experiments. Over the past few decades, a large body of work on the development of restoring-force models has been reported in the literature. Under high intensity cyclic loadings or seismic excitations, reinforced concrete (RC) structures undergo a wide range of hysteretic deteriorations such as strength, stiffness and pinching degradations. These characteristic behaviors can be described by the multi-parameter Bouc-Wen-Baber-Noori (BWBN) model, which offers a wide range of applicability. This model has been applied for the response prediction and modeling restoring-force behavior in structural and mechanical engineering systems, by adjusting the distribution range of this model’s parameters. However, a major difficulty in utilizing the multi-parameter BWBN model is the parameters’ identification. In this paper, a deep neural network model is used to estimate the hysteresis parameters of the BWBN model. This model is one of the most versatile and widely used general hysteresis models that can describe the hysteretic behavior of RC columns. The experimental data of the RC columns used in this paper are collected from the database of the Pacific Earthquake Engineering Research Center (PEER). Firstly, the hysteretic loop obtained from a physical experiment is described by the BWBN model, and the parameters of the BWBN model are identified via a genetic optimization algorithm. Then a neural network is established by a backpropagation (BP) algorithm for associating the identified BWBN model parameters with physical parameters of the RC column. Finally, the regression analysis of the identified parameters is carried out to obtain the regression characteristics of the RC columns. The trained neural network model can directly identify the parameters of BWBN model based on the physical parameters of RC columns, and is effective and computationally efficient for multi-parameter BWBN model identification. The proposed approach overcomes the difficult problem of identifying the parameters of BWBN model and provides a promising approach for a wider application of this multi-parameter hysteresis model

A Deep Learning-Based Approach for the Identification of a Multi-Parameter BWBN Model

A restoring-force model is a versatile mathematical model that can describe the relationship between the restoring force and the deformation obtained from a large number of experiments. Over the past few decades, a large body of work on the development of restoring-force models has been reported in the literature. Under high intensity cyclic loadings or seismic excitations, reinforced concrete (RC) structures undergo a wide range of hysteretic deteriorations such as strength, stiffness and pinching degradations. These characteristic behaviors can be described by the multi-parameter Bouc-Wen-Baber-Noori (BWBN) model, which offers a wide range of applicability. This model has been applied for the response prediction and modeling restoring-force behavior in structural and mechanical engineering systems, by adjusting the distribution range of this model’s parameters. However, a major difficulty in utilizing the multi-parameter BWBN model is the parameters’ identification. In this paper, a deep neural network model is used to estimate the hysteresis parameters of the BWBN model. This model is one of the most versatile and widely used general hysteresis models that can describe the hysteretic behavior of RC columns. The experimental data of the RC columns used in this paper are collected from the database of the Pacific Earthquake Engineering Research Center (PEER). Firstly, the hysteretic loop obtained from a physical experiment is described by the BWBN model, and the parameters of the BWBN model are identified via a genetic optimization algorithm. Then a neural network is established by a backpropagation (BP) algorithm for associating the identified BWBN model parameters with physical parameters of the RC column. Finally, the regression analysis of the identified parameters is carried out to obtain the regression characteristics of the RC columns. The trained neural network model can directly identify the parameters of BWBN model based on the physical parameters of RC columns, and is effective and computationally efficient for multi-parameter BWBN model identification. The proposed approach overcomes the difficult problem of identifying the parameters of BWBN model and provides a promising approach for a wider application of this multi-parameter hysteresis model