850 research outputs found
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
Seismic retrofitting of substandard frame buildings using steel shear walls
The use of steel shear panels represents an effective strategy to enhance the seismic performance of substandard framed buildings not designed to resist earthquakes. The seismic response of framed structures equipped with steel walls can be predicted using finite element models with accurate shell elements for representing the steel panels. However, such a detailed numerical description requires significant computational resources, especially for nonlinear dynamic analysis of large retrofitted buildings with steel infill plates. Besides, the design of steel shear walls for seismic retrofitting has been addressed mainly by trial-and-error methods in previous research and practical applications. Therefore, there is a clear need for more simplified and efficient numerical models for accurate simulations of steel shear walls under earthquake loading and enhanced seismic retrofitting design procedures with automatic selection of the retrofitting components.
In this research, an 8-noded macroelement formulation is first proposed incorporating six nonlinear springs with asymmetric constitutive relationships. To improve the macroelement performance, material parameters are calibrated via genetic algorithms (GAs) based on the numerical results from validated shell element models. Subsequently, simple functions for macroelement material parameters in terms of steel plate geometrical properties are determined using multiple linear regressions. Applications to numerical examples have confirmed the accuracy and computational efficiency of the proposed macroelement with calibrated material properties.
An improved optimal seismic retrofitting design procedure utilising steel shear wall macroelements is developed based on the capacity spectrum method. The proposed approach regards the selection and design of infill plates as a multi-objective optimisation problem with constraints solved by GA procedures. Nonlinear regression for equivalent viscous damping of steel shear walls is also carried out to determine the hysteretic damping ratio as a function of plate dimensions and drift demand. Afterwards, the proposed optimal design strategy is applied to the seismic retrofitting of a deficient 4-storey RC frame building. Seismic assessment is finally conducted for the retrofitted structure, where a significant enhancement of the seismic performance is observed.Open Acces
Intracranial EEG structure-function coupling predicts surgical outcomes in focal epilepsy
Alterations to structural and functional brain networks have been reported
across many neurological conditions. However, the relationship between
structure and function -- their coupling -- is relatively unexplored,
particularly in the context of an intervention. Epilepsy surgery alters the
brain structure and networks to control the functional abnormality of seizures.
Given that surgery is a structural modification aiming to alter the function,
we hypothesized that stronger structure-function coupling preoperatively is
associated with a greater chance of post-operative seizure control. We
constructed structural and functional brain networks in 39 subjects with
medication-resistant focal epilepsy using data from intracranial EEG
(pre-surgery), structural MRI (pre-and post-surgery), and diffusion MRI
(pre-surgery). We investigated pre-operative structure-function coupling at two
spatial scales a) at the global iEEG network level and b) at the resolution of
individual iEEG electrode contacts using virtual surgeries. At global network
level, seizure-free individuals had stronger structure-function coupling
pre-operatively than those that were not seizure-free regardless of the choice
of interictal segment or frequency band. At the resolution of individual iEEG
contacts, the virtual surgery approach provided complementary information to
localize epileptogenic tissues. In predicting seizure outcomes,
structure-function coupling measures were more important than clinical
attributes, and together they predicted seizure outcomes with an accuracy of
85% and sensitivity of 87%. The underlying assumption that the structural
changes induced by surgery translate to the functional level to control
seizures is valid when the structure-functional coupling is strong. Mapping the
regions that contribute to structure-functional coupling using virtual
surgeries may help aid surgical planning
Investigating the cross-disciplinary components of earthquake early warning systems
Earthquake early warning (EEW) systems typically provide early estimates of earthquake magnitude, hypocentre location and/or ground-shaking estimates, as well as alerts ranging from a few seconds to tens of seconds, before the arrival of the damaging ground shaking at a target site. The warnings provided by these systems allow for the implementation of fast protection actions carried out by individuals like ‘drop, cover, and hold-on’, or the evacuation of buildings if the lead time is long enough. Nevertheless, the information and warning time provided by an EEW system could also be used by earthquake engineers as EEW seems to bear a powerful potential for the automatic activation of protection measures for infrastructure and critical systems, aiming at the reduction of risk due to earthquakes. Such automatic actions may include stopping elevators at the nearest floor, opening firehouse doors, slowing rapid-transit vehicles and high-speed trains to avoid accidents, to mention some. Few are the attempts found in literature about engineering applicability of EEW. This scarcity might be related to the fact that the real-time estimation of earthquake source parameters contains considerable uncertainty that may lead to potential economic losses if false or missed alarms are not avoided. However, different state-of-the-art studies regarding decision-making procedures for EEW have suggested more reliable approaches that can potentially reduce the uncertainty in the estimates provided by the system (e.g., earthquake source parameters and ground shaking), reducing the probability of triggering missed/false alarms, and therefore minimising the expected losses. The potential of designing new real-time advanced building protection applications for EEW is the motivation of this thesis. Mainly, two applications are considered: 1) Design of controlled structural systems using the early warning information, particularly, the use of semi-active devices denominated magnetorheological dampers. A control algorithm that governs the behaviour of the dampers is calibrated to obtain the most favourable response of a benchmark structure equipped with one damper. The results reveal that the developed EEW-based control algorithm can effectively reduce the expected loss of the considered case-study structure. 2) Prediction of shaking demands that can be expected in mid-rise to high-rise buildings, using a simplified continuum building model. A series of illustrative examples show how the newly developed prediction models can be efficiently used, in a Bayesian framework, for building-specific EEW applications based on the (acceleration) response in buildings, such as a) early warning of floor-shaking sensed by occupants; and b) control of elevator in buildings. The progress of technology and advances in the scientific understanding of engineering and seismology have promoted the rapid development of EEW systems around the world. However, their effectiveness is often limited as they lack the integration between their technical and social components. This thesis also aims at filling this gap to investigate which measures could be needed to increase the organisational resilience of local community stakeholders and the private sector. This topic is explored by implementing a mixed-method approach on the case study Mexico City (Mexico), that can be considered an area at risk due to the combination of high seismic hazard, structural and social vulnerabilities. This thesis shows the promising applicability of engineered applications of EEW systems and suggests a robust framework for the integration of the technical and societal components of EEW
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
Application of adaptive wavelet networks for vibration control of base isolated structures
Accepted version of an article from the journal: International Journal of Wavelets, Multiresolution & Information Processing. Official version article published as International Journal of Wavelets, Multiresolution & Information Processing, 2010 8(5), 773-791. doi: 10.1142/s0219691310003778 © World Scientific Publishing Company http:// http://www.worldscinet.com/ijwmip/This paper presents an application of wavelet networks (WNs) in identification and control design for a class of structures equipped with a type of semiactive actuators, which are called magnetorheological (MR) dampers. The nonlinear model is identified based on a WN framework. Based on the technique of feedback linearization, supervisory control and H∞ control, an adaptive control strategy is developed to compensate for the nonlinearity in the structure so as to enhance the response of the system to earthquake type inputs. Furthermore, the parameter adaptive laws of the WN are developed. In particular, it is shown that the proposed control strategy offers a reasonably effective approach to semiactive control of structures. The applicability of the proposed method is illustrated on a building structure by computer simulation
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