Structural health monitoring of high voltage electrical switch ceramic insulators in seismic areas

High voltage electrical switches are crucial components to restart rapidly the electrical network right after an earthquake. But there currently exists no automatic procedure to check if these ceramic insulators have suffered after an earthquake, and there exists no method to recertify a given switch. To deploy a vibration-based structural health monitoring method on ceramic insulators a large shake table able to generate accelerations up to 3 g was used. The idea underlying the SHM procedure proposed here is to monitor the apparition of cracks in the ceramic insulators at their early stage through the change of the resonant frequency of the first mode of the structure and the non-linearity that they generate in its dynamic response. The Exponential Sine Sweep Method is used to estimate a nonlinear model of the structure under test from only one dynamic measurement. A classic linear damage index (DI) based on the variation of the frequency of the first mode is compared to an original nonlinear one using the ratio of the amplitudes of the third harmonic and the fundamental frequency. Results show that both DIs increase monotonically with the number of solicitations, thus validating the use of the nonlinear DI. It is also shown that the nonlinear DI presented here seems more sensitive than the linear one

Holistic Design Platform for Sustainable and Resilient Building Design

In this paper we introduce the Societal Holistic Design Platform (HDP) under uncertainty for sustainable and resilient building design. The integration of classical Risk Analysis, Stochastic Dynamics, Structural Health Monitoring, multicriteria Decision Making, Artificial Intelligence and IoT, gives rise to an innovative Cyber-Physical System under uncertainty centered around humans. The potential of the platform is presented through developed applications. Although the HDP is here applied to a building, it can be easily extended to any system of civil engineering. The proposed platform aims to lead the paradigm shift from the existing notion of Smart City to Resilient Engaged Community, targetting the sustainable development of the urban communitiesThis research was funded by the Republic of Singapores National Research Foundation through a grant to the Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) program. BEARS has been established by the University of California, Berkeley, as a center for intellectual excellence in research and education in Singapore. K.M. Mosalam is a core principal investigator of Tsinghua-Berkeley Shenzhen Institute (TBSI). The authors acknowledge the funding support from Sin-BerBEST and the partial support from TBSI

Distribution with Independent Components for Uncertainty Quantification and Structural Reliability Analysis

This paper presents a novel method based on the Information Theory, Machine Learning and Independent Component analysis for Uncertainty Quantification and Structural Reliability Analysis. At first, it is shown that the optimal probabilistic model may be determined through minimum relative entropy and the theory of statistical learningit is also discussed that methods based on the maximum entropy may perform well for the evaluation of the marginal distributions, including the tails. To determine the joint distribution of the basic random variables it is introduced the multivariate probabilistic model of Distributions with Independent Components (DIC). It has same computational simplicity of Nataf, but it is more accurate, since it does not pursue any assumption about the tail dependency. The proposed framework is applied to determine the joint distribution of wave height and period of wave data. Its extension for high dimensional reliability analysis of complex structural systems is straightforward.This research was funded by the Republic of Singapores National Research Foundation through a grant to the Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) program. BEARS has been established by the University of California, Berkeley, as a center for intellectual excellence in research and education in Singapore. K.M. Mosalam is a core principal investigator of Tsinghua-Berkeley Shenzhen Institute (TBSI). The authors acknowledge the funding support from Sin-BerBEST and the partial support from TBSI

Risk-Adaptive Learning of Seismic Response using Multi-Fidelity Analysis

Performance-based earthquake engineering often requires a large number of sophisticated nonlinear time-history analyses and is therefore demanding both with regard to computing resources and technical expertise. We develop a risk-adaptive statistical learning method based on multi-fidelity analysis that enables engineers to conservatively predict structural response using only low-fidelity analyses such as Pushover analyses. Using a structural model of a 35-story building in California and a training data set consisting of nonlinear time-history and pushover analyses for 160 ground motions, we accurately and conservatively predict maximum story drift ratio, top-story drift ratio, and normalized base shear under the effect of 40 ground motions not seen during the training

Hybrid Simulation Theory for Continuous Beams

Hybrid simulation is an experimental technique involving the integration of a physical system and a computational system with the use of actuators and sensors. This method has a long history in the experimental community and has been used for nearly 40 years. However, there is a distinct lack of theoretical research on the performance of this method. Hybrid simulation experiments are performed with the implicit assumption of an accurate result as long as sensor and actuator errors are minimized. However, no theoretical results confirm this intuition nor is it understood how minimal the error should be and what the essential controlling factors are. To address this deficit in knowledge, this study considers the problem as one of tracking the trajectory of a dynamical system in a suitably defined configuration space. To make progress, the study strictly considers a theoretical hybrid system. This allows for precise definitions of errors during hybrid simulation. As a model system, the study looks at an elastic beam as well as a viscoelastic beam. In both cases, systems with a continuous distribution of mass are considered as occur in real physical systems. Errors in the system are then tracked during harmonic excitation using space-time L2-norms defined over the system's configuration space. A parametric study is then presented of how magnitude and phase errors in the control system relate to the performance of hybrid simulation. It is seen that there are sharp sensitivities to control system errors. Further, the existence of unacceptably high errors whenever the excitations exceed the system's fundamental frequency is shown to be present in hybrid simulation

The 3rd Global Summit of Research Institutes for Disaster Risk Reduction: Expanding the Platform for Bridging Science and Policy Making

The Global Alliance of Disaster Research Institutes held its 3rd Global Summit of Research Institutes for Disaster Risk Reduction at the Disaster Prevention Research Institute, Kyoto University, Japan, 19–21 March, 2017. The Global Alliance seeks to contribute to enhancing disaster risk reduction (DRR) and disaster resilience through the collaboration of research organizations around the world. The summit aim was to expand the platform for bridging science and policy making by evaluating the evidence base needed to meet the expected outcomes and actions of the Sendai Framework for Disaster Risk Reduction 2015–2030 and its Science and Technology Roadmap. The summit reflected the international nature of collaborative research and action. A pre-conference questionnaire filled out by Global Alliance members identified 323 research projects that are indicative of current research. These were categorized to support seven parallel discussion sessions related to the Sendai Framework priorities for action. Four discussion sessions focused on research that aims to deepen the understanding of disaster risks. Three cross-cutting sessions focused on research that is aimed at the priorities for action on governance, resilience, and recovery. Discussion summaries were presented in plenary sessions in support of outcomes for widely enhancing the science and policy of DRR