Innovations in earthquake risk reduction for resilience: Recent advances and challenges

The Sendai Framework for Disaster Risk Reduction 2015-2030 (SFDRR) highlights the importance of scientific research, supporting the ‘availability and application of science and technology to decision making’ in disaster risk reduction (DRR). Science and technology can play a crucial role in the world’s ability to reduce casualties, physical damage, and interruption to critical infrastructure due to natural hazards and their complex interactions. The SFDRR encourages better access to technological innovations combined with increased DRR investments in developing cost-effective approaches and tackling global challenges. To this aim, it is essential to link multi- and interdisciplinary research and technological innovations with policy and engineering/DRR practice. To share knowledge and promote discussion on recent advances, challenges, and future directions on ‘Innovations in Earthquake Risk Reduction for Resilience’, a group of experts from academia and industry met in London, UK, in July 2019. The workshop focused on both cutting-edge ‘soft’ (e.g., novel modelling methods/frameworks, early warning systems, disaster financing and parametric insurance) and ‘hard’ (e.g., novel structural systems/devices for new structures and retrofitting of existing structures, sensors) risk-reduction strategies for the enhancement of structural and infrastructural earthquake safety and resilience. The workshop highlighted emerging trends and lessons from recent earthquake events and pinpointed critical issues for future research and policy interventions. This paper summarises some of the key aspects identified and discussed during the workshop to inform other researchers worldwide and extend the conversation to a broader audience, with the ultimate aim of driving change in how seismic risk is quantified and mitigated

INNOVATIONS in earthquake risk reduction for resilience: RECENT advances and challenges

The Sendai Framework for Disaster Risk Reduction 2015-2030 (SFDRR) highlights the importance of scientific research, supporting the ‘availability and application of science and technology to decision making’ in disaster risk reduction (DRR). Science and technology can play a crucial role in the world’s ability to reduce casualties, physical damage, and interruption to critical infrastructure due to natural hazards and their complex interactions. The SFDRR encourages better access to technological innovations combined with increased DRR investments in developing cost-effective approaches and tackling global challenges. To this aim, it is essential to link multi- and interdisciplinary research and technological innovations with policy and engineering/DRR practice. To share knowledge and promote discussion on recent advances, challenges, and future directions on ‘Innovations in Earthquake Risk Reduction for Resilience’, a group of experts from academia and industry met in London, UK, in July 2019. The workshop focused on both cutting-edge ‘soft’ (e.g., novel modelling methods/frameworks, early warning systems, disaster financing and parametric insurance) and ‘hard’ (e.g., novel structural systems/devices for new structures and retrofitting of existing structures, sensors) risk-reduction strategies for the enhancement of structural and infrastructural earthquake safety and resilience. The workshop highlighted emerging trends and lessons from recent earthquake events and pinpointed critical issues for future research and policy interventions. This paper summarises some of the key aspects identified and discussed during the workshop to inform other researchers worldwide and extend the conversation to a broader audience, with the ultimate aim of driving change in how seismic risk is quantified and mitigated

Seismic Soil-Structure Interaction and Foundation Rocking in Unsaturated Ground

Strong earthquake motions often cause severe damage to buildings and foundation systems, during which the interaction between the soil, foundation, and structure may dominate the seismic response. Most shallow foundations are located on, or embedded in, unsaturated and partially saturated soil deposits. Unsaturated soil layers are particularly common in zones above the water table where water can rise through different mechanisms like capillary action. Additionally, the degree of saturation throughout a soil deposit can vary both seasonally and yearly due to groundwater table fluctuation related to infiltration and evaporation. Properties of soil layers below foundations impact the seismic response of structural systems. Since soil moisture impacts soil properties, it is expected that changes in groundwater table depth would impact the seismic response of foundations and structures. However, the understanding of the mechanisms by which the degree of saturation and water table depth influences the foundation and structural response needs improvement. This dissertation aimed to evaluate the effect of the depth of the groundwater table on the seismic response of soil-foundation-structure systems and to extend current seismic design guidelines leading to the implementation of rocking foundations in practice.Three sets of dynamic centrifuge experiments were conducted on four physical models representing three prototype structures. The prototype structures included elastic and inelastic single-degree-of-freedom structures as well as single- and two-span bridge systems. The elastic single-degree-of-freedom structure and bridge systems were designed to incorporate rocking foundations, while the inelastic single-degree-of-freedom structure incorporated structural fuses designed to guide plastic deformations to above-ground structural locations. Physical models were slightly embedded in sandy silt layers with various groundwater table depths and subjected to a series of seismic motions. The experimental findings highlight the influence of the groundwater table depth on changes to the foundation and structural deformations and rotations, foundation-level overturning moments, period lengthening, and damping ratios. Furthermore, design procedures to predict several seismic response properties of a structure resting on unsaturated soil layers are derived in this research based on the fundamentals of unsaturated soil mechanics. These properties include the overturning moment capacity of the foundation, the initial rotational stiffness of the foundation, and the period lengthening and foundation damping ratio. Properties derived from these design guidelines are compared to the experimental results to judge the viability of implementation in practice or signify the need for further improvement

Parameter Identification of Large-Scale Magnetorheological Dampers in a Benchmark Building Platform

Magnetorheological (MR) dampers are devices that can be used for vibration reduction in structures. However, to use these devices in an effective way, a precise modeling is required. In this sense, in this paper we consider a modified parameter identification method of large scale magnetorheological dampers which are represented using the normalized Bouc-Wen model. The main benefit of the proposed identification model is the accuracy of the parameter estimation. The validation of the parameter identification method has been carried out using a black-box model of an MR damper in a smart base-isolated benchmark building. Magnetorheological (MR) dampers are used in this numerical platform both as isolation bearings as well as semiactive control devices

Neural Networks: Training and Application to Nonlinear System Identification and Control

This dissertation investigates training neural networks for system identification and classification. The research contains two main contributions as follow:1. Reducing number of hidden layer nodes using a feedforward componentThis research reduces the number of hidden layer nodes and training time of neural networks to make them more suited to online identification and control applications by adding a parallel feedforward component. Implementing the feedforward component with a wavelet neural network and an echo state network provides good models for nonlinear systems.The wavelet neural network with feedforward component along with model predictive controller can reliably identify and control a seismically isolated structure during earthquake. The network model provides the predictions for model predictive control. Simulations of a 5-story seismically isolated structure with conventional lead-rubber bearings showed significant reductions of all response amplitudes for both near-field (pulse) and far-field ground motions, including reduced deformations along with corresponding reduction in acceleration response. The controller effectively regulated the apparent stiffness at the isolation level. The approach is also applied to the online identification and control of an unmanned vehicle. Lyapunov theory is used to prove the stability of the wavelet neural network and the model predictive controller. 2. Training neural networks using trajectory based optimization approachesTraining neural networks is a nonlinear non-convex optimization problem to determine the weights of the neural network. Traditional training algorithms can be inefficient and can get trapped in local minima. Two global optimization approaches are adapted to train neural networks and avoid the local minima problem. Lyapunov theory is used to prove the stability of the proposed methodology and its convergence in the presence of measurement errors. The first approach transforms the constraint satisfaction problem into unconstrained optimization. The constraints define a quotient gradient system (QGS) whose stable equilibrium points are local minima of the unconstrained optimization. The QGS is integrated to determine local minima and the local minimum with the best generalization performance is chosen as the optimal solution. The second approach uses the QGS together with a projected gradient system (PGS). The PGS is a nonlinear dynamical system, defined based on the optimization problem that searches the components of the feasible region for solutions. Lyapunov theory is used to prove the stability of PGS and QGS and their stability under presence of measurement noise