Structural Health Monitoring (SHM) is expected to play a predominant role in the management of the transport infrastructure. Yet, SHM techniques continue to rely on point-based, as opposed to spatial, sensing requiring a dense network of these point-sensors increasing considerably the monitoring cost. Additionally, commercially available, strain sensors cannot measure strains beyond 1% to 2% and, thus, are not able to provide an alarm for an imminent catastrophe. SENSKIN aims to: (a) develop a dielectric-elastomer and micro-electronics-based skin-like sensing solution for the structural monitoring of the transport infrastructure that will offer spatial sensing of reversible (repeated) strains in the range of 0.012% to more than 10%, that requires little power to operate, is easy to install on an irregular surface, is low cost compared to existing sensors, allows simple signal processing and includes the ability of self-monitoring and self-reporting. (b) use the new and emerging technology of Delay Tolerant Network to secure that strain measurements acquired through the 'sensing skin' will reach the base station even under extreme environmental conditions and natural disaster events such as, high winds or an earthquake, where some communication networks could become inoperable. (c) develop a Decision-Support-System for proactive condition-based structural intervention under operating loads and intervention after extreme events. It will be based on an accurate structural assessment based on input from the strain sensors in (a) above and will examine the life-cycle economic, social and environmental implications of the feasible rehabilitation options and the resilience of the infrastructure to future changes in traffic demand that these options offer. (d) implement the above in the case of bridges and test, refine, evaluate and benchmark the monitoring system (integrated a and b) and package (integrated a, b and c) on actual bridges.

RISKADAPT will provide, in close cooperation with the end-users/other stakeholders, a novel, integrated, modular, interoperable, public and free, customizable user-friendly platform (PRISKADAPT), to support systemic, risk-informed decisions regarding adaptation to CC induced compound events at the asset level, focusing on the structural system. PRISKADAPT will explicitly model dependencies between infrastructures, which, inter alia, will provide a better understanding of the nexus between climate hazards and social vulnerabilities and resilience. Moreover, this project will identify gaps in data and propose ways to overcome them and advance the state of the art of asset level modelling through advanced climate science to predict CC forcing on the structure of interest, structural analyses, customized to the specific structure of interest, that consider all major CC induced load effects in tandem with material deterioration, novel probabilistic environmental life cycle assessment (LCA) and life cycle cost (LCC) of structural adaptation measures and a new model to assess climate risk that will combine technical risk assessment with assessment of social risks. PRISKADAPT will provide values to a set of indicators for each asset of interest, quantifying primary parameters and impacts, in the form of a Model Information System (MIS) that will provide all required information for adaptation decisions. PRISKADAPT will be implemented in the case studies in the pilots that involve specific assets, however, it will permit customization with local values of parameters and data, so it can be applicable throughout Europe for CC adaptation decisions involving assets of similar function, exposed to multiple climate hazards.

The latest developments in low flying unmanned robots with arms and the associated fields of intelligent control, computer vision and sensors open the floor for robotic solutions, exploitable in the near term, in the field of inspection of difficult-toaccess areas of the civil infrastructure in general and bridges in particular. The latter infrastructure is ageing requiring inspection and assessment. Presently, bridge inspection is primarily done through visual observations by inspectors. It relies upon the inspector having access to bridge components via access equipment (ladders, rigging and scaffolds) and vehicular lifts (manlifts, bucket trucks and under-bridge inspection vehicles). This is uncomfortable and potentially dangerous for the inspectors, while it interferes with the traffic contributing to bottlenecks and congestion. The results of the inspection are used to structurally assess the bridge in a following step. AEROBI, driven by the bridge inspection industry, adapts and integrates recent research results in low flying unmanned robots with arms, intelligent control in robotics, computer vision and sensing, in an innovative, integrated, low flying, robotic system with a specialised multi-joint arm that will scan concrete beams and piers in a bridge for potential cracks on the surface or concrete swelling or spalling. In case the width of the above cracks exceeds given limits, it will measure distance between parallel cracks, while it will contact the bridge to non-destructively measure the depth of cracks and deformation. In case of concrete swelling or spalling it will also contact the bridge to non-destructively measure delamination and the diameter of the reinforcing steel bars. The above will provide input for a structural bridge assessment that will be automatically performed by the proposed robotic system. The latter system, which is expected to be exploitable in the short term, will be field evaluated and demonstrated at two actual bridges.