Recent advances in nonlinear passive vibration isolators

The dynamic behavior of rigid-block structures resting on a rigid foundation subjected to horizontal harmonic excitation is examined. For slender structures, the nonlinear equation of motion is approximated by a piecewise linear equation. Using this approximation for an initially quiescent structure, safe or no-toppling and unsafe regions are identified in an excitation amplitude versus excitation frequency plane. Furthermore, several possible modes of steady-state response are detected, and analytical procedures are developed for determining the amplitudes of the predominant modes and for performing stability analyses. It is shown that the produced stability diagrams can be beneficial to assessing the toppling potential of a rigid-block structure under a given amplitude-frequency combination of harmonic excitation; in this manner the integration of the equation of motion is circumvented.

Closure of "Rocking of Rigid Blocks Due to Harmonic Shaking"

Machine learning tools represent key enablers for empowering material scientists and engineers to accelerate the development of novel materials, processes and techniques. One of the aims of using such approaches in the field of materials science is to achieve high-throughput identification and quantification of essential features along the process-structure-property-performance chain. In this contribution, machine learning and statistical learning approaches are reviewed in terms of their successful application to specific problems in the field of continuum materials mechanics. They are categorized with respect to their type of task designated to be either descriptive, predictive or prescriptive; thus to ultimately achieve identification, prediction or even optimization of essential characteristics. The respective choice of the most appropriate machine learning approach highly depends on the specific use-case, type of material, kind of data involved, spatial and temporal scales, formats, and desired knowledge gain as well as affordable computational costs. Different examples are reviewed involving case-by-case dependent application of different types of artificial neural networks and other data-driven approaches such as support vector machines, decision trees and random forests as well as Bayesian learning, and model order reduction procedures such as principal component analysis, among others. These techniques are applied to accelerate the identification of material parameters or salient features for materials characterization, to support rapid design and optimization of novel materials or manufacturing methods, to improve and correct complex measurement devices, or to better understand and predict fatigue behavior, among other examples. Besides experimentally obtained datasets, numerous studies draw required information from simulation-based data mining. Altogether, it is shown that experiment- and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner. Potentials of further utilizing applied machine learning in materials science and empowering significant acceleration of knowledge output are pointed out.

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A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

Structural weakening and addition of damping is an approach previously proposed for the reduction of seismic forces and drifts in the retrofit of structures. It is also used in the design of new buildings with damping systems. While this approach is efficient, it does not significantly reduce and may even amplify inelastic excursions and permanent deformations of the structural system during a seismic event. This paper describes a negative stiffness device (NSD) that can emulate weakening of the structural system without inelastic excursions and permanent deformations. The NSD simulates yielding by engaging at a prescribed displacement and by applying a force at its installation level that opposes the structural restoring force. The NSD consists of (a) a self-contained highly compressed spring in a double negative stiffness magnification mechanism; and (b) a gap spring assembly (GSA) mechanism which delays the engagement of negative stiffness until the structural system undergoes a prescribed disp...

Negative Stiffness Device for Seismic Protection of Structures

Negative Stiffness Device for Seismic Protection of Structures 13-0005.pdf

This work presents a soft-sensor approach for estimating critical mechanical properties of sandcrete materials. Feed-forward (FF) artificial neural network (ANN) models are employed for building soft-sensors able to predict the 28-day compressive strength and the modulus of elasticity of sandcrete materials. To this end, a new normalization technique for the pre-processing of data is proposed. The comparison of the derived results with the available experimental data demonstrates the capability of FF ANNs to predict with pinpoint accuracy the mechanical properties of sandcrete materials. Furthermore, the proposed normalization technique has been proven effective and robust compared to other normalization techniques available in the literature.

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Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials.

Dynamic analysis of stacked rigid blocks

This paper extends the scope of seismic isolation by introducing an innovative uplift-restraining Friction Pendulum system. Termed the XY-FP isolator, the new isolation device consists of two orthogonal opposing concave beams interconnected through a sliding mechanism that permits tension to develop in the bearing, thereby preventing uplift. Owing to its distinct configuration, the XY-FP isolator possesses unique properties for a seismic isolator, including uplift restraint, decoupling of the bi-directional motion along two orthogonal directions, and capability of providing independent stiffness and energy dissipation along the principal horizontal directions of the bearing. The study concentrates on introducing the concept and establishing the underlying principles of operation of the new XY-FP isolator, formulating the mathematical model for the XY-FP isolator, and presenting its mechanical behaviour through a displacement-control testing program on a single XY-FP isolator. Copyright © 2005 John Wiley & Sons, Ltd.

Uplift‐restraining Friction Pendulum seismic isolation system

Revealing the nature of metakaolin-based concrete materials using artificial intelligence techniques

The Bolu viaduct is a 2.3-km-long seismically isolated structure that was nearly complete when it was struck by the 1999 Duzce earthquake in Turkey. It suffered complete failure of the seismic isolation system and narrowly avoided total collapse due to excessive superstructure movement. This paper presents an evaluation of the design of the viaduct's seismic isolation system and an assessment of its performance in the Duzce earthquake. Evaluation of the seismic isolation system's design has revealed that it did not meet the requirements of the AASHTO Guide Specifications for Seismic Isolation Design. Analysis of the viaduct with motions scaled in accordance with the AASHTO Guide Specifications resulted in a displacement demand of 820 mm, which is far more than the 210 mm displacement capacity of the existing isolation system. Analysis of the viaduct for a simulated near-fault motion with characteristics consistent with the site conditions resulted in an isolation system displacement demand of 1,400 mm. This indicates that, even if the isolation system had been designed in compliance with the AASHTO, it would have still suffered damage in the earthquake. CE Database subject headings: Seismic isolation; Bridges, viaduct; Earthquake damage; Turkey.

Panayiotis C. Roussis

Papers

Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials.

Dynamic analysis of stacked rigid blocks

Uplift‐restraining Friction Pendulum seismic isolation system

Revealing the nature of metakaolin-based concrete materials using artificial intelligence techniques

Assessment of Performance of Seismic Isolation System of Bolu Viaduct