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The project

Pont3 is a coordinated project between three research groups from the Universitat Politècnica de València, the Universitat Politècnica de Catalunya and the Universidad de Vigo aiming to develop a novel holistic and cost-effective approach to anticipate failure propagation of ageing bridges and thus contribute to avoiding collapses.

Key Challenges

Improving the resilience of existing transport infrastructures requires solutions to minimise the impact of the threats affecting them and to ensure the safety of end-users. In this context, Pont3 is built based on the following hypotheses:

  • Early detection of initiating damages or failures, that can propagate is key to prevent catastrophic disproportionate collapses, which are always accompanied by severe costs to society.
  • There is a need for optimized monitoring to detect changes in the structural behaviour due to those local structural damages or failures. Designing suitably optimised and cost-effective SHM systems would facilitate their implementation on many structures.
  • Speeding up the costly computations associated to the diagnosis of in-service structures is crucial for delivering real-time assessment for early warning systems and optimising resources. Thus, with the same resources, many bridges can be effectively assessed in real time.

 

Developing proper channels to not only update the condition state of in-service bridges, but also to optimise the collection and processing of monitoring data for providing real-time decision-support will undoubtedly contribute to preventing sudden bridge collapses.

Source: Faleschini F., Zanini M.A., & Pellegrino C. (2018). Quality control, infrastructure management systems and their implementation in medium-size highway networks. COST TU 1406 Meeting, Barcelona, Spain.
Source: Birajdar, H. S., Maiti, P. R., & Singh, P. K. (2014). Failure of Chauras bridge. Engineering Failure Analysis, 45, 339-346.

Objectives

Pont3 aims to develop a novel holistic and cost-effective approach to anticipate failure propagation of ageing bridges and thus contribute to avoiding collapses. In view of its defined overall aim, Pont3 will have six specific objectives:

  1. To identify and characterise types of significant structural local failures that may propagate in steel truss and masonry arch bridges.
  2. To establish cost-effective and interoperability-driven SHM and inspection strategies able to detect damage that is likely to trigger a chain of failures.
  3. To exploit the capabilities of AI techniques for real-time processing of monitoring data and development of models for both, data-driven feature recognition associated with damage propagation scenarios, and for fast surrogate modelling of complex numerical models of in-service structures.
  4. To develop a framework for the risk assessment of in-service bridges based on cost-effective detection of damage indicators and risk quantification from predicted failure propagation scenarios.
  5. To calibrate the proposed methodology with laboratory tests and its implementation in real structures.
  6. To define a practical methodology for the evaluation of similar bridge typologies.

Methodology

Pont3 has been structured into six Work Packages (WPs) that are directly related to each of its defined objectives.

WP1: Defining failure propagation scenarios. The most innovative component of this WP is the identification, for steel truss and masonry arch bridges, of failure propagation scenarios that are likely to result in catastrophic consequences and relevant for risk mitigation. As a result, a set of variables associated to the initiation of failure will be identified.

WP2: Interoperability-driven data collection strategies. Optimised approaches for condition survey, including both inspection and monitoring, are key for this WP. It is necessary to define cost-effective strategies for the deployment of sensors able to detect failures defined in WP1 (e.g., identification of vulnerable zones, optimisation of the number of sensors, number and duration of set-ups, etc).

WP3: AI-based data analysis methods for real-time diagnosis. Efficient and effective data analysis methods are required to produce real-time diagnosis of the monitored structures. Deep Learning techniques will be explored in order to: i) detect relevant features that correlate with the failure propagation modes defined in WP1; and ii) explore the capabilities of deep neural networks to emulate (surrogate) the performance of advanced computational modelling, thus accelerating all the structural simulations needed for WP1 and WP4.

WP4: Risk-based assessment. Pont3 is expected to produce a diagnostic of the actual state of in-service bridges to inform decision-making. Thus, a risk-based assessment approach will be developed considering the nature of WP3 outputs and the eventual consequences of the damage scenarios considered in WP1.

WP5: Laboratory test and implementation in real bridges. Due to the innovative nature of the proposed framework, some representative bridges need to be calibrated through experimental tests in the laboratory. In a second phase, the calibrated methodologies need to be implemented in real in-service bridges to validate the set of tools proposed in Pont3.

WP6: Definition of analysis protocols for similar bridge typologies. Once validation on real bridges is performed, analyses will be carried out to extend the methodologies so that they may be applied to other bridges of similar typology. This will allow us to produce guidelines presenting the protocols to follow for the different WPs of the project.

Case studies selected for the implementation of the proposed system: Benissa (Alicante), Manresa (Barcelona) and Ourense (Ourense).

Impact

Pont3 is expected to have a considerable impact in scientific, technological and social fields:

  • Scientifically, the project will generate knowledge applicable for increasing the safety of bridges, with a clear impact on the resilience of transport infrastructures. A unique and novel system will be created to detect and predict failure propagation, thus allowing effective risk-based assessment of the structure.
  • Technologically, the project will contribute to fostering digitalization in the construction sector, by launching novel tools and solutions at the cutting-edge of the field. New scientific and technical knowledge applicable to the management of existing infrastructures will be generated thanks to the new standards of interoperability generated by the project.
  • Societally, the project will lead to safer bridges, resulting in an increase on the safety of users. It will also have a direct impact on having more sustainable infrastructures. In addition, given that any innovation in the construction and transport fields has a large influence on the economy, the outcomes of Pont3 can ultimately produce considerable economic gain.
Source: Salem, H. M., & Helmy, H. M. (2014). Numerical investigation of collapse of the Minnesota I-35W bridge. Engineering Structures, 59, 635-645.