Seismic Risk of Inter-urban Transportation Networks

AbstractThe paper presents a holistic approach for assessing and managing the seismic risk and potential loss in inter-urban highway networks in earthquake-prone areas. The vulnerability of all elements of the intercity transportation system (i.e., roads, bridges, abutments, retaining walls, and tunnels) is assessed considering the interdependency among the structural, transportational and geotechnical components of the network under different seismic scenarios. Both the direct earthquake-induced damage, as well as the indirect socio-economic loss attributed to reduced network functionality are taken into account in an explicit and transparent formulation that is then displayed in space through an ad-hoc developed GIS-based software. The methodology and the decision-making tools developed are adequately modular, for them to be utilized after appropriate adaptation by local authorities in identifying, prior to a major earthquake event, those vulnerable components of their network whose failure may have a disproportional socio-economic impact. In this way, a rational and effective emergency plan can be deployed to minimize potential human, social and financial loss after a future earthquake. The outline of a foreseen application is also presented for the case of the road network of the Region of Western Macedonia in Greece. Through this pilot application, the methodology is to be optimized in real conditions before being cast in the form of a fully parameterised seismic risk tool, to be used in other earthquake prone areas as well

Site Effects and Damage Patterns

A set of observations on site effects and damage patterns from the M_w 8.8 Maule, Chile, earthquake is presented, focusing on identification of structural damage variability associated with nonuniform soil conditions and subsurface geology. Observations are reported from: (1) the City of Santiago de Chile (Américo Vespucio Norte Ring Highway, Ciudad Empresarial business park), (2) the Municipality of Viña del Mar, and (3) the City of Concepción, extending over 600 km along the Chilean coast. Reconnaissance information and ground motion recordings from the megathrust event are combined with site investigation data in the regions of interest. Comparisons against macroseismic observations related to uneven damage distribution from the M_w 8.0 1985 Valparaíso earthquake are discussed. Complexities associated with identifying the mechanics and underlying physical processes responsible for the manifestation of these effects are elucidated

Feature extraction and identification techniques for the alignment of perturbation simulations with power plant measurements

In this work, a methodology is proposed for the comparison of the measured and simulated neutron noise signals in nuclear power plants, with the simulation sets having been generated by the CORE SIM+ diffusion-based reactor noise simulator. More specifically, the method relies on the computation of the Cross-Power Spectral Density of the detector signals and the subsequent comparison with their simulated counterparts, which involves specific frequency values corresponding to the signals’ high energy content. The different simulated perturbations considered are (i) axially traveling perturbations, (ii) fuel assembly vibrations, (iii) core barrel vibrations, and finally (iv) generic “absorber of variable strength” types. The reactor core used for the current study is a German 4-loop pre-Konvoi Pressurized Water Reactor

Detection and localisation of multiple in-core perturbations with neutron noise-based self-supervised domain adaptation

The use of non-intrusive techniques for monitoring nuclear reactors is becoming more vital as western fleets age. As a consequence, the necessity to detect more frequently occurring operational anomalies is of upmost interest. Here, noise diagnostics — the analysis of small stationary deviations of local neutron flux around its time-averaged value — is employed aiming to unfold from detector readings the nature and location of driving perturbations. Given that in-core instrumentation of western-type light-water reactors are scarce in number of detectors, rendering formal inversion of the reactor transfer function impossible, we propose to utilise advancements in Machine Learning and Deep Learning for the task of unfolding. This work presents an approach to such a task doing so in the presence of multiple and simultaneously occurring perturbations or anomalies. A voxel-wise semantic segmentation network is proposed to determine the nature and sourcelocation of multiple and simultaneously occurring perturbations in the frequency domain. A diffusion-based core simulation tool has been employed to provide simulated training data for two reactors. Additionally, we work towards the application of the aforementioned approach to real measurements, introducing a self-supervised domain adaptation procedure to align the representation distributions of simulated and real plant measurements