Aumento de la Resiliencia de las Carreteras Mediante el Uso Combinado de Tecnología Multisensor y Modelos Climáticos


Le projet PANOPTIS, financé par la Commission européenne dans le cadre du programme H2020, vise à accroître la résilience (capacité d’adaptation) des routes aux conditions météorologiques défavorables, telles que les phénomènes météorologiques extrêmes ou les inondations, et à d’autres événements à risque comme les tremblements de terre ou les glissements de terrain. L’objectif principal du projet est de combiner des scénarios de changement climatique régionalisés (appliqués aux infrastructures), avec des outils de simulation structurels et géotechniques et des données réelles tirées directement des infrastructures routières (ponts, pentes, routes) par un réseau multi-capteurs comprenant des capteurs au sol, des drones et des satellites, afin de fournir aux gestionnaires d’infrastructures de transport un outil de contrôle intégré, capable d’améliorer la gestion des infrastructures dans les phases de planification, de maintenance et d’exploitation. Le projet PANOPTIS a débuté en juin 2018, et pendant la première phase du projet, qui couvre approximativement les deux premières années, toutes les technologies innovantes qui composent l’outil PANOPTIS sont en cours de mise au point. Au cours de la deuxième phase du projet, qui débutera à l’été 2020, ACCIONA Engineering mettra en oeuvre toutes les technologies et méthodologies développées dans la section 2 de l’autoroute A-2, qui est longue de 77,5 km et qui traverse la province de Guadalajara. Il s’agit d’une section de concession d’autoroute de première génération gérée par ACCIONA Concessions et entretenue par ACCIONA Maintenance.

Pixel-Level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Contour Segmentation


Corrosion detection on metal constructions is a major challenge in civil engineering for quick, safe and effective inspection. Existing image analysis approaches tend to place bounding boxes around the defected region which is not adequate both for structural analysis and pre-fabrication, an innovative construction concept which reduces maintenance cost, time and improves safety. In this paper, we apply three semantic segmentation-oriented deep learning models (FCN, U-Net and Mask R-CNN) for corrosion detection, which perform better in terms of accuracy and time and require a smaller number of annotated samples compared to other deep models, eg CNN. However, the final images derived are still not sufficiently accurate for structural analysis and pre-fabrication. Thus, we adopt a novel data projection scheme that fuses the results of color segmentation, yielding accurate but over-segmented contours of a region, with a processed area of the deep masks, resulting in high-confidence corroded pixels.

Multi-Label Deep Learning Models for Continuous Monitoring of Road Infrastructures


A multi-class, multi-label deep learning model for the monitoring of road infrastructures is presented in this paper. The employed detection methodology can identify animals, debris, road defects, fire, fog, flooded areas and humans. All these categories are strongly related to the efficient movement of vehicles through a transportation network. Possible detections indicate roadway disruptions of various types. Therefore, they should be detected as fast as possible. Experimental results indicate that the proposed scheme presents high detection results and, thus, can be used in any motorway monitoring process.

Improving Resilience of Transport Instrastructure to Climate Change and other natural and Manmande events based on the combined use of Terrestrial and Airbone Sensors and Advanced Modelling Tools


The project PANOPTIS, funded by the European Commission under the H2020 Programme, aims at increasing the resilience of the transport infrastructures (focusing on roads) and ensuring reliable network availability under unfavourable conditions, such as extreme weather, landslides, and earthquakes. The main target is to combine downscaled climate change scenarios (applied to road infrastructures) with structural and geotechnical simulation tools and with actual data from a multi-sensor network (terrestrial and airborne-based), so as to provide the operators with an integrated tool able to support more effective management of their infrastructures at planning, maintenance and operation level. During the first stage of the project, the consortium will develop advanced technologies to monitor and control transport infrastructures, such as a Geotechnical and Structural Simulation Tool (SGSA) to predict structural and geotechnical risks in road infrastructures; drone-technologies applied to road upkeep and incident management; improved computer vision and machine learning techniques for damage diagnosis of infrastructure, and early warning systems to help operators identify and communicate emerging systemic risks. At the same time, experts in climate modelling, will analyse the possible short and long term effects of climate change on transport infrastructure (e.g. flooding, heavier snows). All the information from the different sensors, models and applications will be integrated and processed through a unique Resilience Assessment Platform that will support operators in the introduction of adaptation and mitigation strategies based on multi-risk scenarios. During the second stage of the project, ACCIONA Engineering will implement the developed technologies and methodologies in a section of the Spanish A-2 motorway, in the province of Guadalajara. PANOPTIS integrated Platform will help optimize the management and maintenance of the Ministry of Public Works’ concession for a 77.5-km section, all in collaboration with ACCIONA Infrastructure Maintenance (AMISA) and ACCIONA Concessions. In parallel, PANOPTIS platform will also be implemented in a section of 62 Km of a Greek motorway, renowned for its seismic activity. The trials in Greece hosted by the operator Egnatia Odos will integrate the motorway that serves the Airport of Thessaloniki. So the scenario will integrate a modal transfer segment.

Funky Structure Behavior Factors

Behavior (q-)factors are funky. They are fun and they are magic. Just look at how the symbol breaks the boring symmetry of a circle with a random squiggly or straight hanging tail. It perfectly embodies the spirit of the q-factor that on the surface, above the straight line of writing, seems profoundly round and deterministically predictable, while in reality it is all about the tail of unknown shape and magnitude that is hanging underneath. One may choose to ignore this, sweeping all uncertainty under a carpet of expert opinion, or attempt to directly measure it, using the best that the current state-of-art has to offer. The first option may be attractive for typical buildings, where considerable experience has been amassed, but will probably fail in misery for newer systems, interesting structures or unfamiliar situations. To capture this funky nature of the q-factor, let us try to provide a mathematically tractable definition and discuss ways of quantifying it, for a building, an ensemble of similar buildings, or a class of dissimilar buildings of the same structural system, spread over one or more sites.

Downtime Assessment of Base-isolated Liquid Storage Tanks


Seismic base isolation is examined as a design alternative for supporting industrial facility liquid storage tanks against earthquake loading. A 160,000 m3 liquid storage tank is adopted as a case study, for which two designs are assessed, one with and one without base isolation. Using a nonlinear surrogate model and a set of ground motion records selected using the conditional spectrum approach for the average spectral acceleration intensity measure, Incremental Dynamic Analysis is employed to derive seismic fragility curves. Consequences of damage are evaluated in terms of downtime, considering the characteristics of petrochemical storage tanks, whereby any repair requires a lengthy list of actions dictated by health and safety requirements. The results reveal considerable benefits when base-isolation is employed, by drastically reducing downtime when sufficient displacement capacity is provided in the isolators.

Mixed Probabilistic Seismic Demand Models for Fragility Assessment

A mixture model is presented for combining the results of different models or analysis approaches into a single probabilistic seismic demand model that is suitable for fragility assessment. A structure can be represented using different model types or even levels of resolution for the same type, while it may also be analysed via methods of different complexity, most notably static versus dynamic nonlinear approaches. Combining the results from different models or analysis methods can be beneficial as it allows updating the results of a simpler approach or combining the strengths of two different models. For example, different model types may offer accuracy advantages in complementary response regions. This is the case of distributed-plasticity fiber models that offer higher fidelity for reinforced concrete frames at low (pre-capping) deformations, while lumped-plasticity models are more reliable for larger (postcapping) deformations closer to collapse. Through the combination of the results of both models we can potentially better capture the performance of the frame at all levels of seismic intensity. By employing a minimal 5 parameter power-law-based model we offer viable options for forming mixed probabilistic seismic demand models that can combine both different models and different analysis methods into a single output suitable for fragility assessment.

Decision Support for Road Infrastructure Resilience: The PANOPTIS Perspective


The PANOPTIS consortium aims to leverage existing tools and services as well as remote sensing technologies to deliver an integrated platform that can address road infrastructure (RI) multi-hazard resilience. The scope of the project incorporates RI structural components (bridges, overpasses, interchanges, tunnels, slopes, retaining walls, pavements, and surface water drains), non-structural components (tunnel ventilation systems, traffic cameras and signposts), as well as interconnected non-RI components, such as power transmission lines and telecommunication towers. Both detailed and surrogate structural models will be developed for RI and non-RI components, quantifying and incorporating the epistemic uncertainty due to the detailed models’ reduction to surrogacy to allow a rapid high-resolution assessment of vulnerability, whereby loss, functionality and downtime become directly tied to rehabilitation/emergency action planning. The focus is on the development of a rapid-response decision-support tool that will employ measured data immediately after any seismic event to issue inspection prioritization protocols, facilitate the rapid assessment of the state of the RI, and help increase its resilience to catastrophic events.

Prescriptive Approaches in Performance-based Design? A Case-study on Base Isolation

The collapse performance of code-designed base-isolated structures has recently received considerable criticism, having been found to be deficient vis-à-vis conventional buildings in several situations. As a remedy, prescriptive minima with a tenuous probabilistic justification have been recommended in the literature for the bearing deformation capacity. These are independent of structure or site characteristics, yet they are already finding use in design. We put this concept to the test by means of a case study of a seismically isolated steel structure that rests on the roof of two adjacent high-rise reinforced concrete towers. To seismically isolate the steel structure, Friction Pendulum Bearings (FPBs) are used, and their displacement capacity is determined to comply with a performance objective of 1% probability of collapse in 50 years. The case study possesses two salient features that distinguish it from pertinent past investigations. The first is that the isolated steel structure rests on top of two others and consequently it is subjected to narrow-band roof acceleration time histories, shaped by the filtering of the ground motion excitation through the supporting buildings. The second is that the two supporting towers have different modal characteristics, thus displacement demands imposed to the FPBs are mainly affected by their in-phase or out-of-phase movement. Overall, a case-specific true performance-based design is shown to achieve the desired safety while requiring 1.5 times lower displacement capacities for the bearings, when compared to prescriptive “performance-based” approaches.

Issues in Harmonization of Seismic Performance via Risk Targeted Spectra


Current seismic design code provisions are mainly based on checking structural performance at a single seismic intensity associated with a pre-defined return period. For instance, in EN1998, a ground motion with 10% probability of exceedance in 50 years is used for design. This design procedure, with the inclusion of partial safety factors, is assumed to provide sufficient safety margin against earthquakes for newly designed buildings. Nevertheless, it does not specifically determine the expected seismic risk related to any performance level or limit state. Therefore, it may result in non-uniform risk for buildings located in different sites within a region (or country), even for places with identical design intensities. Instead, ASCE 7-10 incorporates Risk Targeted design maps that suggest the application of suitable spectra adjustment factors, in order to ensure a reasonably low uniform collapse risk. Making use of simplified single degree of freedom structures defined in several configurations of period and ductility, our aim is to test the effectiveness of the adjustment factors computed under different assumptions. It is shown that, although matching is not practically possible, harmonization remains a viable target, offering insights for possible future adoption of Risk Targeted Spectra in forthcoming seismic codes.

Combined Convolutional Neural Networks and Fuzzy Spectral Clustering for Real Time Crack Detection in Tunnels


A computer vision module is proposed for crack detection in tunnels, a challenging process due to the low visibility, the curvature from, and the structures of the cracks which, though being very narrow in width, they are very deep. Our system is embedded on a robot which surveys tunnels in real-time as it is moving in the infrastructure. Initially, a Convolutional Neural Network is employed to detect the cracks which, however, yields only approximate regions due to the great complexity of the scene. Then, a combined fuzzy spectral clustering is then introduced to refine the detected crack regions exploiting spatial and orientation coherency. The algorithms have been tested in real-life tunnels in Egnatia Highway. Our scheme yields high detection accuracy than existing methods and the capacity of the robot to touch the crack to allow in-situ measurements within a precision of 2-3cm in a tunnel of 7m height.

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This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No  769129