Identification of dynamic models of Metsovo (Greece) Bridge using ambient vibration measurements

Available methods for structural model updating are employed to develop high fidelity models of the Metsovo bridge using ambient vibration measurements. The Metsovo bridge, the highest bridge of the Egnatia Odos Motorway, is a two-branch balanced cantilever ravine bridge. It has a total length of 357m, a very long central span of 235m, and a height of 110m for the taller pier. Ambient vibration measurements are available during different construction phases of the bridge for both bridge branches, as well as after the completed construction phases of the bridge. Operational modal analysis software is used to obtain the modal characteristics of the bridge for the various sets of vibration measurements. The modal characteristics are then used to update an increasingly complex set of finite element models of the bridge. These models are based on beam and solid elements. A multi-objective structural identification method is used for estimating the parameters of the finite element structural models based on minimising the modal residuals. The method results in multiple Pareto optimal structural models that are consistent with the measured modal data and the modal residuals used to measure the discrepancies between the measured modal values and the modal values predicted by the model. Single objective structural identification methods are also evaluated as special cases of the proposed multi-objective identification method. The effectiveness of the updated models and their predictive capabilities are assessed. In particular, the variability of the Pareto optimal models and their associated response prediction variability are explored. It is demonstrated that the Pareto optimal structural models may vary, depending on the fidelity of the model class employed and the size of measurement errors. The developed high fidelity finite element models are used for checking design assumptions and for carrying out more accurate predictions of structural response

Bridge monitoring system based on vibration measurements

This work outlines the main algorithms involved in a proposed bridge monitoring system based on ambient and earthquake vibration measurements. The monitoring system can be used to predict the existence, location and size of structural modifications in the bridge by monitoring the changes in the modal characteristics and updating the finite element model of the bridge based on the modal characteristics. Sophisticated system identification methods, combining information from a sensor network with the theoretical information built into a fi-nite element model for simulating structural behaviour, are incorporated into the monitoring system in order to track structural changes and identify the location, type and extent of these changes. Emphasis in this work is given on presenting theoretical and computational issues relating to structural modal identification and structural model updating methods. Specifical-ly, the proposed work outlines the algorithms and software that has been developed for com-puting the modal properties using ambient and earthquake data, as well as recent methodologies and software for finite element model updating using the modal characteristics. Various issues encountered in the optimization problems involved in model updating are demonstrated, including the existence of multiple local optima and the effects of weight values in conventional weighted modal residual methods for selecting the optimal finite element model. Selected features are demonstrated using vibration measurements from a four-span bridge of the Egnatia Odos motorway in Greece

Bayesian Model-Updating Using Features of Modal Data: Application to the Metsovo Bridge

A Bayesian framework is presented for finite element model-updating using experimental modal data. A novel likelihood formulation is proposed regarding the inclusion of the mode shapes, based on a probabilistic treatment of the MAC value between the model predicted and experimental mode shapes. The framework is demonstrated by performing model-updating for the Metsovo bridge using a reduced high-fidelity finite element model. Experimental modal identification methods are used in order to extract the modal characteristics of the bridge from ambient acceleration time histories obtained from field measurements exploiting a network of reference and roving sensors. The Transitional Markov Chain Monte Carlo algorithm is used to perform the model updating by drawing samples from the posterior distribution of the model parameters. The proposed framework yields reasonable uncertainty bounds for the model parameters, insensitive to the redundant information contained in the measured data due to closely spaced sensors. In contrast, conventional Bayesian formulations which use probabilistic models to characterize the components of the discrepancy vector between the measured and model-predicted mode shapes result in unrealistically thin uncertainty bounds for the model parameters for a large number of sensors

A Vision-Based Motion Control Framework for Water Quality Monitoring Using an Unmanned Aerial Vehicle

In this paper, we present a vision-aided motion planning and control framework for the efficient monitoring and surveillance of water surfaces using an Unmanned Aerial Vehicle (UAV). The ultimate goal of the proposed strategy is to equip the UAV with the necessary autonomy and decision-making capabilities to support First Responders during emergency water contamination incidents. Toward this direction, we propose an end-to-end solution, based on which the First Responder indicates visiting and landing waypoints, while the envisioned strategy is responsible for the safe and autonomous navigation of the UAV, the refinement of the way-point locations that maximize the visible water surface area from the onboard camera, as well as the on-site refinement of the appropriate landing region in harsh environments. More specifically, we develop an efficient waypoint-tracking motion-planning scheme with guaranteed collision avoidance, a local autonomous exploration algorithm for refining the way-point location with respect to the areas visible to the drone’s camera, water, a vision-based algorithm for the on-site area selection for feasible landing and finally, a model predictive motion controller for the landing procedure. The efficacy of the proposed framework is demonstrated via a set of simulated and experimental scenarios using an octorotor UAV

A Vision-Based Motion Control Framework for Water Quality Monitoring Using an Unmanned Aerial Vehicle

In this paper, we present a vision-aided motion planning and control framework for the efficient monitoring and surveillance of water surfaces using an Unmanned Aerial Vehicle (UAV). The ultimate goal of the proposed strategy is to equip the UAV with the necessary autonomy and decision-making capabilities to support First Responders during emergency water contamination incidents. Toward this direction, we propose an end-to-end solution, based on which the First Responder indicates visiting and landing waypoints, while the envisioned strategy is responsible for the safe and autonomous navigation of the UAV, the refinement of the way-point locations that maximize the visible water surface area from the onboard camera, as well as the on-site refinement of the appropriate landing region in harsh environments. More specifically, we develop an efficient waypoint-tracking motion-planning scheme with guaranteed collision avoidance, a local autonomous exploration algorithm for refining the way-point location with respect to the areas visible to the drone’s camera, water, a vision-based algorithm for the on-site area selection for feasible landing and finally, a model predictive motion controller for the landing procedure. The efficacy of the proposed framework is demonstrated via a set of simulated and experimental scenarios using an octorotor UAV

Developing case studies for implementing COST TU1406 quality control plan procedure for typical highway bridges

An extensive work was done by COST TU1406 working groups (WG) 1,2 and 3 for preparing a guidance document for Quality Control Plan (QCP) of road bridges. WG 1, 2 and 3 reports named 'Performance Indicators for Roadway Bridges', 'Performance Goals for Roadway Bridges' and 'Establishment of a quality control plan' are already published. Based on these documents and the work done to-date, a new procedure for implementing the developed guidelines for the preparation of QCP for roadway bridges was developed by WG4 members in order to unify the method used and to validate the outcomes of the developed QCP. At the first stage, a set of common highway bridge prototypes were identified including girder, frame, arch and truss bridges. A database was created where each participating country has identified local bridges for developing of the case studies. Nine out of sixty bridges where selected for the first stage of preparing an example of QCP and the case study reports were compared with an objective to validate the outcomes. A guideline document was prepared with unified instruction on how to develop the national case study per country. The typical case study includes few stages which are defined based on the work done by WG1, 2 and 3. The stages includes data collection, element identification and grouping, defining vulnerable zones, damage processes and failure modes, selecting and evaluating performance indicators (PIs) and calculating key performance indicators (KPIs), establishing demands, creating QCP scenarios and comparing them by spider diagrams. First outcomes of the prototypes case study reports are now being updated to reflect the final version of WG3 report and together with the guidelines document will be distributed among participating countries to enable the benchmarking process for the full set of bridges representing Europe common highway bridge topologies.- (undefined