Shirley J. Dyke

Bio: Shirley J. Dyke is an academic researcher from Purdue University. The author has contributed to research in topics: Damper & Magnetorheological fluid. The author has an hindex of 42, co-authored 246 publications receiving 11431 citations. Previous affiliations of Shirley J. Dyke include University of Valle & Washington University in St. Louis.

Phenomenological model for magnetorheological dampers

Abstract: Semiactive control devices have received significant attention in recent years because they offer the adaptability of active control devices without requiring the associated large power sources. Magnetorheological (MR) dampers are semiactive control devices that use MR fluids to produce controllable dampers. They potentially offer highly reliable operation and can be viewed as fail-safe in that they become passive dampers should the control hardware malfunction. To develop control algorithms that take full advantage of the unique features of the MR damper, models must be developed that can adequately characterize the damper's intrinsic nonlinear behavior. Following a review of several idealized mechanical models for controllable fluid dampers, a new model is proposed that can effectively portray the behavior of a typical MR damper. Comparison with experimental results for a prototype damper indicates that the model is accurate over a wide range of operating conditions and is adequate for control design an...

Modeling and Control of Magnetorheological Dampers for Seismic Response Reduction

Abstract: Control of civil engineering structures for earthquake hazard mitigation represents a relatively new area of research that is growing rapidly. Control systems for these structures have unique requirements and constraints. For example, during a severe seismic event, the external power to a structure may be severed, rendering control schemes relying on large external power supplies ineffective. Magnetorheological (MR) dampers are a new class of devices that mesh well with the requirements and constraints of seismic applications, including having very low power requirements. This paper proposes a clipped-optimal control strategy based on acceleration feedback for controlling MR dampers to reduce structural responses due to seismic loads. A numerical example, employing a newly developed model that accurately portrays the salient characteristics of the MR dampers, is presented to illustrate the effectiveness of the approach.

Phenomenological Model of a Magnetorheological Damper

Abstract: Semi-active control devices have received significant attention in recent years because they offer the adaptability of active control devices without requiring the associated large power sources. Magnetorheological (MR) dampers are semi-active control devices that use MR fluids to produce controllable dampers. They potentially offer highly reliable operation and can be viewed as fail-safe in that they become passive dampers should the control hardware malfunction. To develop control algorithms that take maximum advantage of the unique features of the MR damper, models must be developed that can adequately characterize the damper’s intrinsic nonlinear behavior. Following a review of several idealized mechanical models for controllable fluid dampers, a new model is proposed that can effectively portray the behavior of a typical magnetorheological damper. Comparison with experimental results for a prototype damper indicates that the model is accurate over a wide range of operating conditions and is adequate for control design and analysis.

Semiactive Control Strategies for MR Dampers: Comparative Study

Abstract: This paper presents the results of a study to evaluate the performance of a number of recently proposed semiactive control algorithms for use with multiple magnetorheological (MR) dampers. Various control algorithms used in recent semiactive control studies are considered including the Lyapunov controller, decentralized bang-bang controller, modulated homogeneous friction algorithm, and a clipped optimal controller. Each algorithm is formulated for use with the MR damper. Additionally, each algorithm uses measurements of the absolute acceleration and device displacements for determining the control action to ensure that the algorithms could be implemented on a physical structure. The performance of the algorithms is compared through a numerical example, and the advantages of each algorithm are discussed. The numerical example considers a six-story structure controlled with MR dampers on the lower two floors. In simulation, an El Centro earthquake is used to excite the system, and the reduction in the drif...

Benchmark Control Problems for Seismically Excited Nonlinear Buildings

Abstract: This paper presents the problem definition and guidelines of a set of benchmark control problems for seismically excited nonlinear buildings. Focusing on three typical steel structures, 3-, 9-, and 20-story buildings designed for the SAC project for the Los Angeles, California region, the goal of this study is to provide a clear basis to evaluate the efficacy of various structural control strategies. A nonlinear evaluation model has been developed that portrays the salient features of the structural system. Evaluation criteria and control constraints are presented for the design problems. The task of each participant in this benchmark study is to define (including sensors and control algorithms), evaluate, and report on their proposed control strategies. These strategies may be either passive, active, semiactive, or a combination thereof. The benchmark control problems will then facilitate direct comparison of the relative merits of the various control strategies. To illustrate some of the design challenges, a sample control strategy employing active control with a linear quadratic Gaussian control algorithm is applied to the 20-story building.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks

Abstract: A number of image processing techniques IPTs have been implemented for detecting civil infrastructure defects to partially replace human-conducted onsite inspections. These IPTs are primarily used to manipulate images to extract defect features, such as cracks in concrete and steel surfaces. However, the extensively varying real-world situations e.g., lighting and shadow changes can lead to challenges to the wide adoption of IPTs. To overcome these challenges, this article proposes a vision-based method using a deep architecture of convolutional neural networks CNNs for detecting concrete cracks without calculating the defect features. As CNNs are capable of learning image features automatically, the proposed method works without the conjugation of IPTs for extracting features. The designed CNN is trained on 40 K images of 256 × 256 pixel resolutions and, consequently, records with about 98% accuracy. The trained CNN is combined with a sliding window technique to scan any image size larger than 256 × 256 pixel resolutions. The robustness and adaptability of the proposed approach are tested on 55 images of 5,888 × 3,584 pixel resolutions taken from a different structure which is not used for training and validation processes under various conditions e.g., strong light spot, shadows, and very thin cracks. Comparative studies are conducted to examine the performance of the proposed CNN using traditional Canny and Sobel edge detection methods. The results show that the proposed method shows quite better performances and can indeed find concrete cracks in realistic situations.

Phenomenological model for magnetorheological dampers

Abstract: Semiactive control devices have received significant attention in recent years because they offer the adaptability of active control devices without requiring the associated large power sources. Magnetorheological (MR) dampers are semiactive control devices that use MR fluids to produce controllable dampers. They potentially offer highly reliable operation and can be viewed as fail-safe in that they become passive dampers should the control hardware malfunction. To develop control algorithms that take full advantage of the unique features of the MR damper, models must be developed that can adequately characterize the damper's intrinsic nonlinear behavior. Following a review of several idealized mechanical models for controllable fluid dampers, a new model is proposed that can effectively portray the behavior of a typical MR damper. Comparison with experimental results for a prototype damper indicates that the model is accurate over a wide range of operating conditions and is adequate for control design an...

Structural control: past, present, and future

Abstract: This tutorial/survey paper: (1) provides a concise point of departure for researchers and practitioners alike wishing to assess the current state of the art in the control and monitoring of civil engineering structures; and (2) provides a link between structural control and other fields of control theory, pointing out both differences and similarities, and points out where future research and application efforts are likely to prove fruitful. The paper consists of the following sections: section 1 is an introduction; section 2 deals with passive energy dissipation; section 3 deals with active control; section 4 deals with hybrid and semiactive control systems; section 5 discusses sensors for structural control; section 6 deals with smart material systems; section 7 deals with health monitoring and damage detection; and section 8 deals with research needs. An extensive list of references is provided in the references section.