1,720,986 research outputs found
Robust design of a Single Tuned Mass Damper for controlling torsional response of asymmetric-plan systems
No full textA new robust design methodology to control the seismic performance of asymmetric structures
equipped with a Single Tuned Mass Damper (STMD) is presented in this paper. This design
approach aims to control the seismic response of such systems by reducing both flexible and stiff
edge maximum displacement. The dynamic problem has been investigated in the state space
representation showing that the TMD works as a closed-loop feedback control action. A synthetic
index to estimate the seismic performance of the main system has been defined by using H„V norm.
Wide-ranging parametric numerical experimentation has been carried out to obtain design formulae
for the STMD in order to minimize such a performance index. These formulae allow for a simple
design of STMD position and stiffness to optimally control both translational and rotational motion
components, whereas two mass devices are generally considered to improve the seismic
performance of asymmetric structural systems The effectiveness and efficiency of the obtained
design formulae have been tested by investigating the dynamic behavior of the asymmetric structure
after being subjected to different recorded seismic inputs
ROBUST DESIGN OF TUNED MASS DAMPER TO CONTROL SEISMIC RESPONSE OF ASYMMETRIC-PLAN SYSTEMS
Full text linkMachine learning modelling of structural response for different seismic signal characteristics: A parametric analysis
Full text linkThe present study investigates the best seismic parameters for modeling the dynamic response of various non-linear structural systems by comparing different Machine Learning (ML) algorithms. A total of 400 synthetic excitations were generated and analyzed against 23 seismic parameters. These signals were used in a step-by-step numerical analysis to calculate the dynamic responses of 1000 single-degree-of-freedom (SDOF) systems with varying mechanical properties. The data obtained from these responses were processed using 20 ML algorithms, including linear regression, tree, support vector machine (SVM), boosted and bagged trees, and artificial neural network (ANN). Each ML algorithm used a single seismic parameter as input to determine the most predictive parameters for modeling structural responses, defining the high predictive seismic parameters (HPSP) set. To validate the obtained results, the most effective model predictions have been compared with the results of the parametric step-by-step analyses performed for a new group of natural ground motions. The findings demonstrate that with a properly calibrated training phase, considering the specific site hazard and selecting seismic parameters from the HPSP set, the ML model can accurately estimate seismic responses whit a significantly reduced computational effort. This study underscores the potential of integrating ML techniques into the performance-based seismic design approach
Neural Networks to Optimize Design Parameters of Bridges Isolated with Double Concave Friction Pendulum
No full textThe use of machine learning techniques in the field of numerical optimization represents a promising strategy to approach dynamic problems characterized by strong non-linearities and a high number of parameters. Seismic vibration control is undoubtedly one of the fields that can benefit most from this approach, especially when aiming at studying the effectiveness of innovative techniques on a large scale and the definition of new design guidelines. In this context, this study aims to model the seismic behavior of Double Concave Friction Pendulum (DCFP) isolated deck bridges through a trained artificial neural network (ANN). Specifically, the investigation employs several data representing the seismic response to a significant number of synthetic seismic excitations related to different soil conditions and PGV/PGA ratio values. A comprehensive analysis was conducted by using the ANN model, leading to empirical considerations regarding the optimal design of the DCFP device on varying the dynamic characteristics of both the structure and the input excitation
State-of-the-Art of Resilience Using Bibliometric Analysis
Full text linkThe interest in the concept of Resilience in the scientific community has been growing consistently over the past few years to study the functionality and behavior of systems against natural and man-made hazards. This is reflected by the number of journal articles that can be accessed in the Web of Science database. In this paper, a bibliometric and visualization method is applied to explore the status of resilience research in civil engineering applications by analyzing the journal papers published from 1996 to 2020. The bibliometric analysis aims at consolidating the state of the art by identifying influential journals, most cited articles, the geographic distribution of resilience publications including the research institutions by country, the author keywords distribution, and the co-authorship status. The concept of resilience is investigated through eight subject categories identified by the authors in the literature: Recovery time strategies and Downtime, Critical infrastructures, Probabilistic approaches, Fuzzy logic approaches, Structural health monitoring, Health Care facilities, Emergency management and Decision-making, Community and Urban Resilience. Results show that resilience research has increased rapidly since its introduction, most notably in the last seven years. In terms of the geographical region of the studies, most of them have been carried out in the USA, the United Kingdom, China, and Italy. Finally, based on the author keywords analysis, it is possible to observe that recovery strategies, critical infrastructures, vulnerability, and community resilience have attracted prominent attention during the past decade
Information theory-guided machine learning to estimate seismic response of non-linear SDOF structures
Full text linkThis study presents a parametric numerical analysis for the selection of the best seismic parameters characterising seismic records to model the dynamic response of non-linear single-degree-of-freedom (SDOF) structural systems by using Machine Learning (ML) techniques. This analysis is carried out using appropriate indices within Information Theory (IT), which allow for estimation of the amount of usable information from input data. Specifically, 400 artificial seismic excitations were generated, and, for each one, 23 seismic parameters were evaluated. Subsequently, step-by-step numerical analyses were conducted to study the seismic responses of 1000 equivalent elastic perfectly-plastic SDOF systems with different mechanical properties. The “conditional information” index was thus evaluated for both peak relative displacement and hysteretic energy response, given the input values of specific seismic parameters. The same data were treated using supervised learning techniques with 20 ML algorithms: linear regression, decision trees, support vector machine (SVM), boosted trees, bagged trees and artificial neural networks (ANN). Each analysis considered the identical set of seismic parameters, used for the conditional information index, to verify whether a higher theoretical amount of information, obtainable from the input parameters, can lead to a more efficient ML modelling. Finally, the most effective model estimation, derived from a single ML algorithm with the best combination of the input parameters, have been compared with the results of the parametric step-by-step analyses performed for some natural ground motions. The results validate the proposals and show that a higher amount of information, gained from the input parameters, generally corresponds to a better performance estimation of the ML models. This allows for the identification of which and how many seismic parameters should be considered as the best-performing combination of the input parameters for the modelling algorithm. Furthermore, when the training phase is suitably calibrated, considering the specific site hazard and the best seismic parameters, the ML model can effectively estimate the seismic performance. This highlights considerable potentials of integrating ML techniques within the performance-based seismic design approach
An Energy Framework to Control Viscoelastic Semi‐Active Devices in Plan‐Wise One‐Way Asymmetric Systems
Full text linkThis study proposes new strategies for the semi-active control of the dynamic response of a plan-wise asymmetrical structural system using viscoelastic devices. Different from some literature proposals, these innovative strategies are designed to be immediately interpretable, aiming to optimize the different terms of the energy balance equation through a set of closed-form analytical control algorithms to manage the properties of semi-active devices. Specifically, four algorithms have been developed to maximize the energy dissipated by the system or minimize the elastic energy, kinetic energy, and input energy. These algorithms have been tested through an extensive numerical investigation by modifying the main structural parameters of the asymmetrical system and considering 85 accelerometric input signals with different dynamic characteristics related to both far-field and near-fault records. The effectiveness of the four proposed strategies, aimed to modify the semi-active device properties, was evaluated by comparing the seismic responses of asymmetric systems, in terms of both relative displacement and energy components, with the regular configuration of semi-active devices (i.e., passive control) and other algorithms, such as “Kamagata & Kobori” and “sky hook” finalized, respectively, to manage stiffness and damping extra-structural resources. The results demonstrated the effectiveness of the proposed strategies, especially, in the presence of flexible systems and high-demanding near-fault seismic events
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