Identifying Self-excited Vibrations with Evolutionary Computing

AbstractThis study uses differential evolution to identify the coeffic ients of second-order differentia l equations of self-e xc ited vibrations fro m a time signal. The motivation is found in the ample occurrence of this vibration type in engineering and physics, in particu lar in the real -life proble m of v ibrations of hydraulic structure gates. In the proposed method, an equation structure is assumed at the level of the ordinary differentia l equation and a population of candidate coefficient vectors undergoes evolutionary training. In this way the numerical constants of non-linear terms of various self-e xc ited vibration types were recovered fro m the time signal and the velocity value only at the initial t ime. Co mparisons are given regarding accuracy and computing time. Dependency of the test errors on the algorith m para meters is studied in a sensitivity analysis. The presented evolutionary method shows good promise for future applicat ion in engineering systems, in particular operational early -wa rning systems that recognise oscillations with negative damping before they can cause damage

A classical reactive potential for molecular clusters of sulphuric acid and water

We present a two-state empirical valence bond (EVB) potential describing interactions between sulphuric acid and water molecules and designed to model proton transfer between them within a classical dynamical framework. The potential has been developed in order to study the properties of molecular clusters of these species, which are thought to be relevant to atmospheric aerosol nucleation. The particle swarm optimisation method has been used to fit the parameters of the EVB model to density functional theory (DFT) calculations. Features of the parametrised model and DFT data are compared and found to be in satisfactory agreement. In particular, it is found that a single sulphuric acid molecule will donate a proton when clustered with four water molecules at 300 K and that this threshold is temperature dependent

Quantum fluctuations and life

There have been many claims that quantum mechanics plays a key role in the origin and/or operation of biological organisms, beyond merely providing the basis for the shapes and sizes of biological molecules and their chemical affinities. These range from the suggestion by Schrodinger that quantum fluctuations produce mutations, to the conjecture by Hameroff and Penrose that quantum coherence in microtubules is linked to consciousness. I review some of these claims in this paper, and discuss the serious problem of decoherence. I advance some further conjectures about quantum information processing in bio-systems. Some possible experiments are suggested.Comment: 10 pages, no figures, conference pape

Artificial Intelligence Approach for Seismic Control of Structures

Abstract In the first part of this research, the utilization of tuned mass dampers in the vibration control of tall buildings during earthquake excitations is studied. The main issues such as optimizing the parameters of the dampers and studying the effects of frequency content of the target earthquakes are addressed. Abstract The non-dominated sorting genetic algorithm method is improved by upgrading generic operators, and is utilized to develop a framework for determining the optimum placement and parameters of dampers in tall buildings. A case study is presented in which the optimal placement and properties of dampers are determined for a model of a tall building under different earthquake excitations through computer simulations. Abstract In the second part, a novel framework for the brain learning-based intelligent seismic control of smart structures is developed. In this approach, a deep neural network learns how to improve structural responses during earthquake excitations using feedback control. Abstract Reinforcement learning method is improved and utilized to develop a framework for training the deep neural network as an intelligent controller. The efficiency of the developed framework is examined through two case studies including a single-degree-of-freedom system and a high-rise building under different earthquake excitation records. Abstract The results show that the controller gradually develops an optimum control policy to reduce the vibrations of a structure under an earthquake excitation through a cyclical process of actions and observations. Abstract It is shown that the controller efficiently improves the structural responses under new earthquake excitations for which it was not trained. Moreover, it is shown that the controller has a stable performance under uncertainties

Particle swarm optimization with sequential niche technique for dynamic finite element model updating

Peer reviewedPostprin

Data Mining Technology for Structural Control Systems: Concept, Development, and Comparison

Structural control systems are classified into four categories, that is, passive, active, semi-active, and hybrid systems. These systems must be designed in the best way to control harmonic motions imposed to structures. Therefore, a precise powerful computer-based technology is required to increase the damping characteristics of structures. In this direction, data mining has provided numerous solutions to structural damped system problems as an all-inclusive technology due to its computational ability. This chapter provides a broad, yet in-depth, overview in data mining including knowledge view (i.e., concept, functions, and techniques) as well as application view in damped systems, shock absorbers, and harmonic oscillators. To aid the aim, various data mining techniques are classified in three groups, that is, classification-, prediction-, and optimization-based data mining methods, in order to present the development of this technology. According to this categorization, the applications of statistical, machine learning, and artificial intelligence techniques with respect to vibration control system research area are compared. Then, some related examples are detailed in order to indicate the efficiency of data mining algorithms. Last but not least, capabilities and limitations of the most applicable data mining-based methods in structural control systems are presented. To the best of our knowledge, the current research is the first attempt to illustrate the data mining applications in this domain