Present and future resilience research driven by science and technology

Community resilience against major disasters is a multidisciplinary research field that garners an ever-increasing interest worldwide. This paper provides summaries of the discussions held on the subject matter and the research outcomes presented during the Second Resilience Workshop in Nanjing and Shanghai. It, thus, offers a community view of present work and future research directions identified by the workshop participants who hail from Asia – including China, Japan and Korea; Europe and the Americas

Seismic Resilience-based Design and Optimization: A Deep Learning and Cyber-Physical Approach

With the growing acceptance and better understanding of the importance of uncertainties in seismic design, traditional design approaches with deterministic analysis are being replaced with more reliable approaches within a risk-based context. Recently, resilience has been increasingly studied as a comprehensive metric to assess the ability of a system to withstand and recover from disturbances with large uncertainties. For civil infrastructure systems susceptible to natural hazards, especially earthquakes as considered herein, seismic resilience could provide a measurement integrating both earthquake and post-earthquake performance. For structural engineers, improving infrastructure disaster resilience starts with the design of more resilient structures. This requires a quantitative approach to explicitly guild the design towards better resilience. However, when attempting to quantify the seismic resilience of a structure, large uncertainties lead to large computational costs associated with risk-based approaches. Additionally, the accuracy of numerical simulations under wide range of design scenarios is unknown. To address these challenges, this dissertation investigates the role of seismic resilience in structural design. This dissertation starts with a novel seismic protective device to improve structural resilience and follows with the development of a quantitative and efficient design, evaluation, and optimization framework for seismic resilience. This framework proposes metamodeling through deep neural networks for improved efficiency and cyber-physical systems for improved accuracy. Feedforward neural networks are adopted for fragility metamodeling, while online learning long-short term memory neural networks are developed for structural component metamodeling. Real-time hybrid simulation is used for the construction of cyber-physical systems. The proposed framework is demonstrated to have both improved accuracy and significantly reduced computational/experimental cost when compared to existing approaches. The applicability of the framework is illustrated through the optimization of structural systems for improved seismic resilience

Structural performance evaluation and optimization through cyber-physical systems using substructure real-time hybrid simulation

Natural hazards continue to demonstrate the vulnerability of civil infrastructure worldwide. Engineers are dedicated to improving structural performance against natural hazards with improved design codes and computational tools. These improvements are often driven by experiments. Experimental testing not only enables the prediction of structural responses under those dynamic loads but also provide a reliable way to investigate new solutions for hazard mitigation. Common experimental techniques in structural engineering include quasi-static testing, shake table testing, and hybrid simulation. In recent years, real-time hybrid simulation (RTHS) has emerged as a powerful alternative to drive improvements in civil infrastructure as the entire structure’s dynamic performance is captured with reduced experimental requirements. In addition, RTHS provides an attractive opportunity to investigate the optimal performance of complex structures or components against multi-hazards by embedding it in an optimization framework. RTHS stands to accelerate advancements in civil engineering, in particular for designing new structural systems or devices in a performance-based design environment. This dissertation focuses on the use of cyber-physical systems (CPS) to evaluate structural performance and achieve optimal designs for seismic protection. This dissertation presents systematic studies on the development and validation of the dynamic substructuring RTHS technique using shake tables, novel techniques in increasing RTHS stability by introducing artificial damping to an under-actuated physical specimen, and the optimal design of the structure or supplemental control devices for seismic protection through a cyber-physical substructure optimization (CPSO) framework using substructure RTHS

Distributed real-time hybrid simulation: Modeling, development and experimental validation

Real-time hybrid simulation (RTHS) has become a recognized methodology for isolating and evaluating performance of critical structural components under potentially catastrophic events such as earthquakes. Although RTHS is efficient in its utilization of equipment and space compared to traditional testing methods such as shake table testing, laboratory resources may not always be available in one location to conduct appropriate large-scale experiments. Consequently, distributed systems, capable of connecting multiple RTHS setups located at numerous geographically distributed facilities through information exchange, become essential. This dissertation focuses on the development, evaluation and validation of a new distributed RTHS (dRTHS) platform enabling integration of physical and numerical components of RTHS in geographically distributed locations over the Internet.^ One significant challenge for conducting successful dRTHS over the Internet is sustaining real-time communication between test sites. The network is not consistent and variations in the Quality of Service (QoS) are expected. Since dRTHS is delay-sensitive by nature, a fixed transmission rate with minimum jitter and latency in the network traffic should be maintained during an experiment. A Smith predictor can compensate network delays, but requires use of a known dead time for optimal operation. The platform proposed herein is developed to mitigate the aforementioned challenge. An easily programmable environment is provided based on MATLAB/xPC. In this method, (i) a buffer is added to the simulation loop to minimize network jitter and stabilize the transmission rate, and (ii) a routine is implemented to estimate the network time delay on-the-fly for the optimal operation of the Smith predictor.^ The performance of the proposed platform is investigated through a series of numerical and experimental studies. An illustrative demonstration is conducted using a three story structure equipped with an MR damper. The structure is tested on the shake table and its global responses are compared to RTHS and dRTHS configurations where the physical MR damper and numerical structural model are tested in local and geographically distributed laboratories.^ The main contributions of this research are twofold: (1) dRTHS is validated as a feasible testing methodology, alternative to traditional and modern testing techniques such as shake table testing and RTHS, and (ii) the proposed platform serves as a viable environment for researchers to develop, evaluate and validate their own tools, investigate new methods to conduct dRTHS and advance the research in this area to the limits

Cyber-Physical Codesign of Wireless Structural Control System

Structural control systems play a critical role in protecting civil infrastructure from natural hazards such as earthquakes and extreme winds. Utilizing wireless sensors for sensing, communication and control, wireless structural control systems provide an attractive alternative for structural vibration mitigation. Although wireless control systems have advantages of flexible installation, rapid deployment and low maintenance cost, there are unique challenges associated with them, such as wireless network induced time delay and potential data loss. These challenges need to be considered jointly from both the network (cyber) and control (physical) perspectives. This research aims to develop a framework facilitating cyber-physical codesign of wireless control system. The challenges of wireless structural control are addressed through: (1) a numerical simulation tool to realistically model the complexities of wireless structural control systems, (2) a codesign approach for designing wireless control system, (3) a sensor platform to experimentally evaluate wireless control performance, (4) an estimation method to compensate for the data loss and sensor failure, and (5) a framework for fault tolerance study of wireless control system withreal-time hybrid simulation. The results of this work not only provide codesign tools to evaluate and validate wireless control design, but also the codesign strategies to implement on real-world structures for wireless structural control

Artificial neural networks for wireless structural control

We live in an age when people desire taller buildings and longer bridges. These increasing demands of more flexible structures challenge civil engineers to ensure structural safety in the state where they are more prone to extreme dynamic loading, such as earthquakes. Extensive wiring required in traditional structural control applications may be expensive and inconvenient, especially for large scale structures. To improve the scalability, wireless sensors offer a promising alternative. However, the presence of time delay and data loss in a wireless sensor network can potentially reduce the performance of the control system. Here an artificial neural network is proposed to improve the performance of a wireless sensor network based control system. The proposed technique is named as Neural Network Wireless Correction Function (NNWCF). By applying this strategy, a wireless structural control can be utilized without experiencing major performance degradation due to the wireless characteristics

Limited Bandwidth Wireless Communication Strategies for Structural Control of Seismically Excited Shear Structures

Structural control is used to mitigate unwanted vibrations in structures when large excitations occur, such as high winds and earthquakes. To increase reliability and controllability in structural control applications, engineers are making use of semi-active control devices. Semi-active control gives engineers greater control authority over structural response versus passive controllers, but are less expensive and more reliable than active devices. However, the large numbers of actuators required for semi-active structural control networks introduce more cabling within control systems leading to increased cost. Researchers are exploring the use of wireless technology for structural control to cut down on the installation cost associated with cabling. However wireless communication latency (time delays in data transmissions) can be a barrier to full acceptance of wireless technology for structural control. As the number of sensors in a control network grows, it becomes increasingly difficult to transmit all sensor data during a single control step over the fixed wireless bandwidth. Because control force calculations rely on accurate state measurements or estimates, the use of strategic bandwidth allocation becomes more necessary to provide good control performance. The traditional method for speeding up the control step in larger wireless networks is to spatially decentralize the network into multiple subnetworks, sacrificing communication for speed. This dissertation seeks to provide an additional approach to address the issue of communication latency that may be an alternative, or even a supplement, to spatial decentralization of the control network. The proposed approach is to use temporal decentralization, or the decentralization of the control network over time, as opposed to space/location. Temporal decentralization is first presented with a means of selecting and evaluating different communication group sizes and wireless unit combinations for staggered temporal group communication that still provide highly accurate state estimates. It is found that, in staggered communication schemes, state estimation and control performance are affected by the network topology used at each time step with some sensor combinations providing more useful information than others. Sensor placement theory is used to form sensor groups that provide consistently high-quality output information to the network during each time step, but still utilize all sensors. If the demand for sensors to communicate data outweighs the available bandwidth, traditional temporal and spatial approaches are no longer feasible. This dissertation examines and validates a dynamic approach for bandwidth allocation relying on an extended, autonomous and controller-aware, carrier sense multiple access with collision detection (CSMA/CD) protocol. Stochastic parameters are derived to strategically alter back-off times in the CSMA/CD algorithm based on nodal observability and output estimation error. Inspired by data fusion approaches, this second study presents two different methods for neighborhood state estimation using a dynamic form of measurement-only fusion. To validate these wireless structural control approaches, a small-scale experimental semi-active structural control testbed is developed that captures the important attributes of a full-scale structure