Adaptive, Intelligent Methods for Real Time Structural Control and Health Monitoring

By framing the structural health monitoring and control problem as being one of enhancing structural system intelligence, novel solutions can be achieved through applications of computational strategies that mimic human learning and attempt to replicate human response to sensory feedback. This thesis proposes several new methods which promote adaptive, intelligent decision making by structural systems relying on sensory feedback and actuator compensation. Four significant contributions can be found in this thesis study. The first method employs an adaptable subclass of Artificial Neural Networks (ANNs), called Radial Basis Function Networks (RBFNs) for robust control in the presence of sensory failure. The second method exploits this computationally efficient network to detect and isolate system faults in real time. The third algorithm utilizes an RBFN to effectively linearize the nonlinear actuator dynamics of a Magnetorheological (MR) damper, thereby improving control of the semiactive device. Lastly, an open loop observer is implemented experimentally to both detect damage and act as a trigger for control of the newly developed Adaptive Length Pendulum-Smart Tuned Mass Damper (ALP-STMD). Some limitation of existing algorithms in the field of real time structural health monitoring and control are that they rely heavily on fixed parameter methods, assume standard linear time invariant assumptions, or mandate accurate modeling of system dynamics. By embedding the proposed reasoning and decision making algorithms into the feedback methodology and design, greater generalization and system adaptivity is possible. Specifically, the proposed methods develop novel solutions for adaptive neural control, fault (sensor failure) tolerant control, real time damage detection, adaptive dynamic inversion, and control applications for STMDs. The neural network adaptive control formulation is successful in rejecting first mode disturbances despite online sensor failure. It is also capable of improving the performance of a baseline Hoc controller in the presence of sensor failure and earthquake ground motion. The proposed fault tolerant controller is validated on a two degree of freedom shear frame subjected to six earthquake records. Furthermore, this application involves the use of piezoelectric patches as sensors and actuators. The RBFN algorithm in combination with an open loop observer is capable of both detecting and isolating stiffness degradation and recovery in multi-degree of freedom systems in real time. The method is validated on experimental data taken from online damage tests using the Semi-Active Independent Variable Stiffness (SAIVS) device. Other validations involve simulations on a two degree of freedom system and a ten degree of freedom system with both independent and coupled damage case scenarios. In all scenarios, the RBFN is capable of identifying the length of time and degree of freedom in which stiffness variation occurred. A neural network formulation is developed to perform dynamic inversion for semiactive control of an MR damper. The MR damper acts as a base isolator in a scaled two story building. Both the building and damper models were based on tests performed at Rice University. The control performance of the adaptive RBFN dynamic inversion method is compared to both passive-off and passive-on methods of semiactive control for MR dampers. The last contribution serves to combine both real time structural health monitoring and control in a proof of concept experimental study. An open loop observer is used to trigger an ALP -STMD device in the presence of base excitation and stiffness damage. The stiffness damage is generated from strategically regulating the current applied to Shape Memory Alloy (SMA) braces in a two degree of freedom shear frame. Once damage exceeds a predefined threshold, the ALP-STMD uses a another SMA to adjust its pendulum length to tune in real time to the dominant pulse present in the base excitation

HISTOLOGICAL STUDIES OF BREWERY SPENT GRAINS IN DIETARY PROTEIN FORMULATION IN DONRYU RATS

The increasing production of large tonnage of products in brewing industries continually generates lots of solid waste which includes spent grains, surplus yeast, malt sprout and cullet. The disposal of spent grains is often a problem and poses major health and environmental challenges, thereby making it imminently necessary to explore alternatives for its management. This paper focuses on investigating the effects of Brewery Spent Grain formulated diet on haematological, biochemical, histological and growth performance of Donryu rats. The rats were allocated into six dietary treatment groups and fed on a short-term study with diet containing graded levels of spent grains from 0, 3, 6, 9, 12 and 100% weight/weight. The outcome demonstrated that formulated diet had a positive effect on the growth performance of the rats up to levels of 6% inclusions, while the haematological and biochemical evaluation revealed that threshold limit should not exceed 9% of the grain. However, the histological study on the liver indicated a limit of 3% inclusion in feed without serious adverse effect. Thus invariably showing that blend between ranges 1-3% is appropriate for the utilization of the waste in human food without adverse effect on the liver organ. The economic advantage accruing from this waste conversion process not only solves problem of waste disposal but also handle issues of malnutrition in feeding ration

Enhancing the collaboration of earthquake engineering research infrastructures

Towards stronger international collaboration of earthquake engineering research infrastructures International collaboration and mobility of researchers is a means for maximising the efficiency of use of research infrastructures. The European infrastructures are committed to widen joint research and access to their facilities. This is relevant to European framework for research and innovation, the single market and the competitiveness of the construction industry.JRC.G.4-European laboratory for structural assessmen

Development and Evaluation of AI-based Parkinson's Disease Related Motor Symptom Detection Algorithms

Parkinson's Disease (PD) is a chronic, progressive, neurodegenerative disorder that is typically characterized by a loss of (motor) function, increased slowness and rigidity. Due to a lack of feasible biomarkers, progression cannot easily be quantified with objective measures. For the same reason, neurologists have to revert to monitoring of (motor) symptoms (i.e. by means of subjective and often inaccurate patient diaries) in order to evaluate a medication's effectiveness. Replacing or supplementing these diaries with an automatic and objective assessment of symptoms and side effects could drastically reduce manual efforts and potentially help in personalizing and improving medication regime. In turn, appearance of symptoms could be reduced and the patient's quality of life increased. The objective of this thesis is two-fold: (1) development and improvement of algorithms for detecting PD related motor symptoms and (2) to develop a software framework for time series analysis

Approaches to Disaster Management

Approaches to Disaster Management regards critical disaster management issues. Ten original research reports by international scholars centered on disaster management are organized into three general areas of hazards and disaster management. The first section includes discussions of perspectives on vulnerability and on evolving approaches to mitigation. The second section highlights approaches to improve data use and information management in several distinct applications intended to promote prediction and communication of hazard. The third section regards the management of crises and post-event recovery in the private sector, in the design of urban space and among the victims of disaster. This volume contributes both conceptual and practical commentary to the disaster management literature

Sliding Mode Control

The main objective of this monograph is to present a broad range of well worked out, recent application studies as well as theoretical contributions in the field of sliding mode control system analysis and design. The contributions presented here include new theoretical developments as well as successful applications of variable structure controllers primarily in the field of power electronics, electric drives and motion steering systems. They enrich the current state of the art, and motivate and encourage new ideas and solutions in the sliding mode control area

Multi-axial Real-time Hybrid Simulation Framework for Testing Nonlinear Structural Systems with Multiple Boundary Interfaces

Hybrid simulation is a widely accepted laboratory testing approach that partitions a proposed structure into numerical and physical substructures, for a space- and cost-effective testing method. Structural elements that are expected to remain in the linear elastic range are usually modeled numerically, while computationally intractable nonlinear elements are tested physically. The loads and conditions at the boundaries between the numerical and physical substructures are imposed by servo-hydraulic actuators, with the responses measured by loadcells and displacement transducers. Traditionally, these actuators impose boundary condition displacements at slow speeds, while damping and inertial components for the physical specimen are numerically calculated. This slow application of the boundary conditions neglects rate-dependent behavior of the physical specimen. Real-time hybrid simulation (RTHS) is an alternative to slow speed hybrid simulation approach, where the responses of numerical substructure are calculated and imposed on the physical substructure at real world natural hazard excitation speeds. Damping, inertia, and rate-dependent material effects are incorporated in the physical substructure as a result of real-time testing. For a general substructure, the boundary interface has six degrees-of-freedom (DOF); therefore, an actuation system that can apply multi-axial loads is required. In these experiments, the boundary conditions at the interface between the physical and numerical substructures are imposed by two or more actuators. Significant dynamic coupling can be present between the actuators in such setups. Kinematic transformations are required for operation of each actuator to achieve desired boundary conditions. Furthermore, each actuator possesses inherent dynamics that needs appropriate compensation to ensure an accurate and stable operation. Most existing RTHS applications to date have involved the substructuring of the reference structures into numerical and physical components at a single interface with a one-DOF boundary condition and force imposed and measured. Multi-DOF boundary conditions have been explored in a few applications, however a general six-DOF stable implementation has never been achieved. A major research gap in the RTHS domain is the development of a multi-axial RTHS framework capable of handling six DOF boundary conditions and forces, as well as presence of multiple physical specimens and numerical-to-physical interfaces. In this dissertation, a multi-axial real-time hybrid simulation (maRTHS) framework is developed for realistic nonlinear dynamic assessment of structures under natural hazard excitation. The framework is comprised of numerical and physical substructures, actuator-dynamics compensation, and kinematic transformations between Cartesian and actuator/transducer coordinates. The numerical substructure is compiled on a real-time embedded system, comprised of a microcontroller setup, with onboard memory and processing, that computes the response of finite element models of the structural system, which are then communicated with the hardware setup via the input-output peripherals. The physical substructure is composed of a multi-actuator boundary condition box, loadcells, displacement transducers, and one or more physical specimens. The proposed compensation is a model-based strategy based on the linearized identified models of individual actuators. The concepts of the model-based compensation approach are first validated in a shake table study, and then applied to single and multi-axis RTHS developments. The capabilities of the proposed maRTHS framework are demonstrated via the multi-axial load and boundary condition boxes (LBCBs) at the University of Illinois Urbana-Champaign, via two illustrative examples. First, the maRTHS algorithm including the decoupled controller, and kinematic transformation processes are validated. In this study, a moment frame structure is partitioned into numerical beam-column finite element model, and a physical column with an LBCB boundary condition. This experiment is comprised of six DOFs and excitation is only applied in the plane of the moment frame. Next, the maRTHS framework is subjected to a more sophisticated testing environment involving a multi-span curved bridge structure. In this second example, two LBCBs are utilized for testing of two physical piers, and excitation is applied bi-directionally. Results from the illustrative examples are verified against numerical simulations. The results demonstrate the accuracy and promising nature of the proposed state-of-the-art framework for maRTHS for nonlinear dynamic testing of structural systems using multiple boundary points.Ope

Proceedings of the Workshop on Applications of Distributed System Theory to the Control of Large Space Structures

Two general themes in the control of large space structures are addressed: control theory for distributed parameter systems and distributed control for systems requiring spatially-distributed multipoint sensing and actuation. Topics include modeling and control, stabilization, and estimation and identification