From model-driven to data-driven : a review of hysteresis modeling in structural and mechanical systems

Hysteresis is a natural phenomenon that widely exists in structural and mechanical systems. The characteristics of structural hysteretic behaviors are complicated. Therefore, numerous methods have been developed to describe hysteresis. In this paper, a review of the available hysteretic modeling methods is carried out. Such methods are divided into: a) model-driven and b) datadriven methods. The model-driven method uses parameter identification to determine parameters. Three types of parametric models are introduced including polynomial models, differential based models, and operator based models. Four algorithms as least mean square error algorithm, Kalman filter algorithm, metaheuristic algorithms, and Bayesian estimation are presented to realize parameter identification. The data-driven method utilizes universal mathematical models to describe hysteretic behavior. Regression model, artificial neural network, least square support vector machine, and deep learning are introduced in turn as the classical data-driven methods. Model-data driven hybrid methods are also discussed to make up for the shortcomings of the two methods. Based on a multi-dimensional evaluation, the existing problems and open challenges of different hysteresis modeling methods are discussed. Some possible research directions about hysteresis description are given in the final section

Structural and seismic monitoring of historical and contemporary buildings: general principles and applications

Structural Health Monitoring (SHM) indicates the continuous or periodic assessment of the conditions of a structure or a set of structures using information from sensor systems, integrated or autonomous, and from any further operation that is aimed at preserving structural integrity. SHM is a broad and multidisciplinary field, both for the spectrum of sciences and technologies involved and for the variety of applications. The technological developments that have made the advancement of this discipline possible come from many fields, including physics, chemistry, materials science, biology, but above all aerospace, civil, electronic and mechanical engineering. The first applications, at the turn of the sixties and seventies, concerned the integrity control of remote structural elements, such as foundation piles and submerged parts of off-shore platforms, but nowadays this type of monitoring is practiced on airplanes, vehicles spacecraft, ships, helicopters, automobiles, bridges, buildings, civil infrastructure, power plants, pipelines, electronic systems, manufacturing and processing facilities, and biological systems. This paper carries out an extensive examination of the theoretical and applicative foundations of structural and seismic monitoring, focusing in particular on methods that exploit natural vibrations and their use both in the diagnosis and in the prediction of the seismic response of civil structures, infrastructure networks, and traditional and modern architectural heritage

Smart Finite State Devices: A Modeling Framework for Demand Response Technologies

We introduce and analyze Markov Decision Process (MDP) machines to model individual devices which are expected to participate in future demand-response markets on distribution grids. We differentiate devices into the following four types: (a) optional loads that can be shed, e.g. light dimming; (b) deferrable loads that can be delayed, e.g. dishwashers; (c) controllable loads with inertia, e.g. thermostatically-controlled loads, whose task is to maintain an auxiliary characteristic (temperature) within pre-defined margins; and (d) storage devices that can alternate between charging and generating. Our analysis of the devices seeks to find their optimal price-taking control strategy under a given stochastic model of the distribution market.Comment: 8 pages, 8 figures, submitted IEEE CDC 201

Reinforcement Learning for Active Length Control and Hysteresis Characterization of Shape Memory Alloys

Shape Memory Alloy actuators can be used for morphing, or shape change, by controlling their temperature, which is effectively done by applying a voltage difference across their length. Control of these actuators requires determination of the relationship between voltage and strain so that an input-output map can be developed. In this research, a computer simulation uses a hyperbolic tangent curve to simulate the hysteresis behavior of a virtual Shape Memory Alloy wire in temperature-strain space, and uses a Reinforcement Learning algorithm called Sarsa to learn a near-optimal control policy and map the hysteretic region. The algorithm developed in simulation is then applied to an experimental apparatus where a Shape Memory Alloy wire is characterized in temperature-strain space. This algorithm is then modified so that the learning is done in voltage-strain space. This allows for the learning of a control policy that can provide a direct input-output mapping of voltage to position for a real wire. This research was successful in achieving its objectives. In the simulation phase, the Reinforcement Learning algorithm proved to be capable of controlling a virtual Shape Memory Alloy wire by determining an accurate input-output map of temperature to strain. The virtual model used was also shown to be accurate for characterizing Shape Memory Alloy hysteresis by validating it through comparison to the commonly used modified Preisach model. The validated algorithm was successfully applied to an experimental apparatus, in which both major and minor hysteresis loops were learned in temperature-strain space. Finally, the modified algorithm was able to learn the control policy in voltage-strain space with the capability of achieving all learned goal states within a tolerance of +-0.5% strain, or +-0.65mm. This policy provides the capability of achieving any learned goal when starting from any initial strain state. This research has validated that Reinforcement Learning is capable of determining a control policy for Shape Memory Alloy crystal phase transformations, and will open the door for research into the development of length controllable Shape Memory Alloy actuators

Post-seismic response and repair of earthquake-damaged reinforced concrete bridges

“In bridge structures, column members are typically designed to be the primary source of energy dissipation during an earthquake. Therefore, reinforced concrete (RC) bridges that are damaged in an earthquake tend to have damage to the column members. While many studies have been conducted on seismic strengthening of RC bridge columns, most are focused on retrofit instead of repair. In addition, the limited research on seismic repair of RC bridges has focused on evaluating the response of individual columns (member level), not the bridge structure (system level), due to limitations in modeling and especially testing of full bridge structures. Local modifications (interventions) from the repair of a member can change its performance, and changes in column member performance can influence the bridge structure performance, especially under seismic loading. This study evaluated the impact of RC bridge column seismic repair on the member level, system level, and community level responses. Numerical simulation was used to model the response of repaired RC bridge columns (member level) and study the post-repair response of a prototype bridge with repaired columns (system level). The model was also extended to develop a methodology to minimize the level of pre-earthquake retrofit such that the RC bridge can withstand an earthquake without collapse, suffering minor or moderate damage that can be rapidly repaired later. Finally, a discrete-event-based simulation model was developed to estimate the time needed to bring the situation under control for a given volume of resources under a variety of scenarios after an earthquake occurs in a case-study community (community response), and to study the sensitivity of the restoration times to different variables”--Abstract, page iii

Memristors for the Curious Outsiders

We present both an overview and a perspective of recent experimental advances and proposed new approaches to performing computation using memristors. A memristor is a 2-terminal passive component with a dynamic resistance depending on an internal parameter. We provide an brief historical introduction, as well as an overview over the physical mechanism that lead to memristive behavior. This review is meant to guide nonpractitioners in the field of memristive circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page

Advanced Composites

Engineering practice has revealed that innovative technologies’ structural applications require new design concepts related to developing materials with mechanical properties tailored for construction purposes. This would allow the efficient use of engineering materials. The efficiency can be understood in a simplified and heuristic manner as the optimization of performance and the proper combination of structural components, leading to the consumption of the least amount of natural resources. The solution to the eco-optimization problem, based on the adequate characterization of the materials, will enable implementing environmentally friendly engineering principles when the efficient use of advanced materials guarantees the required structural safety. Identifying fundamental relationships between the structure of advanced composites and their physical properties is the focus of this book. The collected articles explore the development of sustainable composites with valorized manufacturability corresponding to Industrial Revolution 4.0 ideology. The publications, amongst others, reveal that the application of nano-particles improves the mechanical performance of composite materials; heat-resistant aluminium composites ensure the safety of overhead power transmission lines; chemical additives can detect the impact of temperature on concrete structures. This book demonstrates that construction materials’ choice has considerable room for improvement from a scientific viewpoint, following heuristic approaches

Deep Learning Methods for Industry and Healthcare

L'abstract è presente nell'allegato / the abstract is in the attachmen