Resilience-Based Design for Infrastructure and Systems, Community Resilience, and Demonstration Flagship Projects (Living Labs)

A need for resilience-based design, planning, and optimization is arising w'ith reference to infrastructure systems. Indeed, innovative approaches to decision making methods for the design of new' infrastructures in times of climate change, multi-hazard conditions, and increasing interdependencies are expected. The result directly downstream of this innovation process is the creation of new guidelines that are directly available and can lead to a general development in the direction of the creation of resilient communities. These are new standards that need to include new design clauses, technologies, and specifications for new' and existing structures.

Besides, community preparedness is also a crucial aspect toward resilient communities and can be deployed on many levels, from the higher-education level (e.g., engineers) to the training of technicians and citizens. If the first one is mostly developed at the academic level, citizens may be prepared to withstand and cope w'ith disasters through civil protection and emergency agencies.

Demonstration projects can also be considered at this stage, e.g.. Following the devastating 2004 tsunami, the development of the Indian Ocean Tsunami Warning and Mitigation System [6] was initiated at the World Conference for Disaster Reduction in 2005 under the lead of the United Nations Education Scientific and Cultural Organization’s Intergovernmental Oceanographic Commission. From that point, many organizations have been engaged in the task of developing tsunami early warning systems and community-based disaster risk management in coastal regions.

Emerging Technologies for Innovation of Resilient Infrastructure and Systems in Design and Planning

Rapidly increasing urbanization is associated with many challenges that need to be addressed. Emerging technologies are expected to help with improving community resilience and infrastructures supporting urban disaster risk management. Climate change and urbanization are increasing the risk and impact of disasters and rapid urban development has been driving up urban risk. Mega-disasters are happening more frequently, and so-called everyday crises and other stresses are heightening vulnerability and undermining coping capacities. This, coupled with the growing urban populations, makes it critical for organizations to better support community resilience so that people living in urban areas can help themselves, as frequent shocks and stresses become a common part of everyday life.

This leads to the concept of developing smart cities with strengthened infrastructures and improved quality of life.

The use of smart materials, nanoscience, and nanotechnology obviously arises as the natural choice for attaining such objectives. On this basis, emphasis must be made on the requirement of active engagement of scientific research and engineering applications in the area of smart materials and nanotechnologies for future cities; e.g., the addition of nanocomposite materials into cement has shown notable potential in improving its performance and compressive strength.

In this light, traditional disciplines, such as SHM and control, are required to make a new effort to consider and understand the new and evolving conditions. In addition, it is also necessary to push in the direction of new, multidisciplinary solutions, such as civionics, which, like avionics and mechatronics, still need to be fully understood and developed, see Figure 1.2. The obvious intrinsic difficulties for the development of civionics are to take advantage of scale factors and forces as are

FIGURE 1.2 Expected development from classical system health monitoring to advanced approaches.

typical in civil engineering, e.g., to provide infrastructure operators with cheap and safe monitoring devices.

Implementation of existing sensors and the development of new sensors for civil engineering needs (e.g., fatigue sensor, fire sensor) toward real-time monitoring of civil structures and infrastructures is one of the primary actions that is expected. The challenge is to launch existing technologies at the research level toward applications in the real-world.

Data acquisition systems (DAQ or DAS - sensors, to convert physical parameters to electrical signals; signal conditioning circuitry, to convert sensor signals into a form that can be converted into digital values; analog-to-digital converters, to convert conditioned sensor signals into digital values) play a major role in SHM, and they may also be critical components in developing resilient communities and infrastructures.

Developing equipment that is waterproof, robust, and reliable, able to survive in harsh construction processes, is also a foremost requirement toward new and effective solutions. Efforts toward simplification and reliable implementation include the development of repeatable (regarding ad hoc built-up) wireless systems.

Also, the provision of cheap localization of non-rescue persons and objects indoors remains challenging. Such a capability would open up many possibilities, e.g., much better priority-driven rescue coordination.

As a result of an increasingly monitored world, an exponential increase in the data collected should be expected. Therefore, data mining, management, processing, and interpretation will require major research and development efforts at the university and industrial level. Connected with this is the development of web platform bases and cloud monitoring solutions, as part of an emerging process to evaluate, monitor, and manage cloud-based services, applications, and infrastructures.

Technologies like augmented reality in construction are emerging to digitalize the construction industry, making it significantly more effective. Furthermore, the digital twin - a concept of having a real-time digital representation of a physical object - is also an emerging technology toward resilient communities and infrastructures. Digital data is formed from sensors that continuously monitor changes in the environment and report back the updated state in the form of measurements and pictures.

Big data and data mining play an important role in an increasingly complex world, as well as machine learning, deep learning, and bio-inspired algorithms. Indeed, these last-mentioned technologies have changed the old paradigm “input-algorithm- output” toward a new scientific creativity in a wide range of fields and applications.

Roadmaps and Strategies Proposed for Future Implementation

The following goals can be aligned into an ambitious timeline, with appropriate measures along all phases (short term, medium term, long term):

• • Refinement of infrastructure taxonomies, ontologies, and definitions along with key functionalities and interdependencies.
• • Provision of fast reference models for individual infrastructure systems, including interfacing models.

• • Provision of fast algorithms over various levels of rigor and modeling refinement for such reference models, e.g., abstract, engineering-parameterizing, system-analytic to engineering-physical-simulative.
• • Development of interaction and uncertainty assessment models and simulations and well-founded fundamental theories.
• • Including uncertainty modeling at all levels.
• • Advancement of single key approaches and technologies, including:
• • Better understanding of tipping-point damage modeling, e.g., collapse, cascade initiation, emergent unintended behavior
• • Technologies for the safety of individuals, e.g., localization, orientation, personal protection
• • Large-scale natural hazard countering, e.g., urban fires, forest fires, countering draft events with natural approaches supported by technology
• • Advanced material with resilience built-in and green properties
• • Overall green life-cycle resilience
• • Avionics for advanced in-situ and efficient and safe air-based monitoring
• • Leveraging the “sensor dust” idea into reality
• • Taking advantage of the ever better sensors in smartphones, smart- watches, and further mobile devices
• • AI and machine-learning-driven active protection systems with no material or physical redundancy, i.e., with nothing but intelligent system immediate response resilience back-up.
• • Massive actor systems that can project or allocate almost any resources in a short time are needed to protect, respond, rebuild, or adopt successfully, rather than generating massive local redundancies.

It is expected that future implementations of technological and scientific advances toward resilience improvements will be made through research results and also by the development of new guidelines and standards. Indeed, although it is vital to continue scientific research and develop new technologies, and therefore advance the various fields that can support new infrastructures and resilient communities, it is essential to create a strong link between research and scientific application. This link, obviously adapted to real-life conditions and local social, economic, and political issues, is represented by guidelines and standards. In other words, the key rules are established by governments themselves in order to unify and improve the conditions of their countries. Here, supporting scientists and country authorities should take up existing approaches and collaborate to further pool their expertise.

Summary

Based on a detailed definition of the resilience of critical infrastructure, infrastructures at risk, and the assessment of expected interdependencies, the present report delivers key working areas (see Sections 1.3.5 to 10 for future resilience as well as (additional) appropriate approaches (see Section 1.3.11)). Many of the proposed future innovations are envisioned to be ready for operational use on mid-term time scales or even shorter, especially in the area of automated air-born inspection and rescue forces support. Further envisioned research activities are recommended to cover such areas as leveraging computational parallel graphical processor approaches for large-scale infrastructure models and simulation and taking advantage of dedicated sensor and open-source data. Real-time capabilities need to be developed to better support operators, responders, and infrastructure users.

Thus working group 2 analyzed how to improve critical infrastructure systems by using the emerging technologies. The main critical infrastructures and their interdependencies were identified. Modeling and simulation issues were deepened with the emerging technologies related to monitoring and early warning systems. These two are the two sides of the same coin, the virtual simulation environment and the systems that may collect the data in real-time from the real-world. Both can be implemented separately or integrated into a hybrid framework. The current existing application and the emerging technologies are also discussed as lessons learned and future visions for resilience infrastructures. Finally, the need for standard and guideline developments for a comprehensive implementation of resilience was also identified as a strategic roadmap for the future.