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  • 1.
    Chalouhi, Elisa K.
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Gonzalez, Ignacio
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Gentile, C.
    Karoumi, Raid
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Damage detection in railway bridges using Machine Learning: Application to a historic structure2017In: X International Conference on Structural Dynamics, EURODYN 2017, Elsevier, 2017, Vol. 199, p. 1931-1936Conference paper (Refereed)
    Abstract [en]

    This paper presents a method that uses machine learning to detect and localize damage in railway bridges. Results of the method application to a historical bridge are presented and used to validate the proposed algorithm. For the application of this technique, both air temperature and deck accelerations data, measured under railway traffic at several locations on the bridge, are needed. The method consists of four stages: (1) collection of such data in both reference condition (i.e. when the state of preservation of the structure is known) and current one; (2) pre-processing of acceleration time histories aimed at extracting characteristics of the crossing train (i.e. running direction, speed and number of axles); (3) training of Artificial Neural Networks and Gaussian Processes using data collected in reference condition and (4) health classification of the bridge in current condition through the comparison between predicted and measured responses. During stage 3, a set of neural networks is trained to predict deck accelerations under every environmental and operational condition (i.e. air temperature and crossing vehicle characteristics, respectively) assuming the reference state of preservation. Then, in stage 4, the current response is compared with accelerations predicted under current environmental and operational conditions. Changes in the behavior of the structure due to damage are thus detected as a discrepancy between predicted and measured responses. The application of the proposed technique to data collected on San Michele Bridge (1889), in Northern Italy, has shown good agreement with results from previous studies based on mode shape variation. This shows the potential and confirms the possibility of applying the proposed technique to real bridges. This method can thus be used to detect anomalous responses that can be flagged as possible damage as well as give an indication of the location of the decayed structural region.

  • 2.
    Chalouhi, Elisa Khouri
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Gonzalez, Ignacio
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Gentile, Carmelo
    Politecn Milan, Milan, Italy..
    Karoumi, Raid
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Vibration-Based SHM of Railway Bridges Using Machine Learning: The Influence of Temperature on the Health Prediction2018In: EXPERIMENTAL VIBRATION ANALYSIS FOR CIVIL STRUCTURES: TESTING, SENSING, MONITORING, AND CONTROL / [ed] Conte, JP Astroza, R Benzoni, G Feltrin, G Loh, KJ Moaveni, B, SPRINGER INTERNATIONAL PUBLISHING AG , 2018, p. 200-211Conference paper (Refereed)
    Abstract [en]

    Civil engineering structures continuously undergo environmental conditions changes that can lead to temporary variations of their dynamic characteristics. Therefore, damage detection techniques have to be able to distinguish abnormal changes in the response due to damage from those normally related to environmental conditions variability. This paper addresses this issue by presenting a damage detection method that uses machine learning to detect and localize damage in railway bridges under varying environmental conditions (i.e. temperature). Results of the application to simulated data are shown with validation purposes. The first stage of the proposed algorithm consists in training a set of Artificial Neural Networks (ANNs) to predict deck accelerations during train passages assuming the bridge to be undamaged (or in a known state of preservation). In the second stage, the currently measured response is compared with that predicted by the trained ANNs. Since possible changes in the bridge state of preservation (damage) decrease the predictive accuracy of the ANNs, this comparison allows for the damage detection. During both stages, air temperature is given as input to the networks together with the train characteristics (i.e. speed and load per axle). The application results in the paper prove the ability of the algorithm to detect and localize damage. Furthermore, when the same procedure was applied neglecting the environmental factor, a noticeable decrease of the prediction power was met. This proves that changes in structural properties due to temperature variation can mask the damage occurrence and prevent its detection. The importance of accounting for environmental variations in damage detection is thus highlighted.

  • 3.
    Gonzalez, I.
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Khouri, Elisa
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Gentile, C.
    Karoumi, Raid
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Novel AI-based railway SHM, its behaviour on simulated data versus field deployment2018In: Proceedings of the 7th Asia-Pacific Workshop on Structural Health Monitoring, APWSHM 2018, NDT.net , 2018, p. 802-807Conference paper (Refereed)
    Abstract [en]

    A new damage detection method for railway bridges is presented. The proposed method uses raw acceleration data, avoiding the difficult step of designing a-priori a damage sensitive feature and leaving that task to the powerful pattern-recognition capacity of Artificial Intelligence, in particular an Artificial Neural Networks (ANN). The proposed method is applied to data from a numerical experiment and a field deployment and demonstrates good novelty detection capabilities in both cases. Closer examination of the results reveals, however, that the features automatically extracted by the ANN are qualitatively different in the two case studies. The numerical data, obtained by simulating moving point forces, is dominated by the modal behaviour of the structure and, consequently, whatever feature the ANN learns to evaluate it must be based on that modal information. The data measured in the field deployment is dominated by non-modal vibration (vibrations induced by rail-roughness, sleeper-distance and train-bridge interactions), which are of higher frequency than the modal data. Experiments with low- And high-pass filtered real data are performed to discern if the ANN extract features from the modal data (low-frequency), other high-frequency phenomena or a mix of both. These reveal that it is mainly the high-frequency data that informs the ANN novelty detection. The fact that the damage detection of the proposed algorithm is based mainly the high-frequency content of the input signals raises important questions about the validity/efficacy of numerical validations of this type of damage detection methods (and of modal-based approaches in general). These are normally confined to modalsimulations of moving point forces and thus only contain modal data, which seems to be of lesser importance when a machine learning approach is used. 

  • 4.
    Khouri Chalouhi, Elisa
    KTH, School of Architecture and the Built Environment (ABE).
    Optimal design solutions of concrete bridges considering environmental impact and investment cost2019Licentiate thesis, monograph (Other academic)
    Abstract [en]

    The most used design approach for civil engineering structures is a trial and error procedure; the designer chooses an initial configuration, tests it and changes it until all safety requirements are met with good material utilization. Such a procedure is time consuming and eventually leads to a feasible solution, while several better ones could be found. Indeed, together with safety, environmental impact and investment cost should be decisive factors for the selection of structural solutions. Thus, structural optimization with respect to environmental impact and cost has been the subject of many researches in the last decades. However, design techniques based on optimization haven’t replaced the traditional design procedure yet. One of the reasons might be the constructive feasibility of the optimal solution. Moreover, concerning reinforced concrete beam bridges, to the best of the author knowledge, no study in the literature has been published dealing with the optimization of the entire bridge including both the structural configuration and cross-section dimensions.

    In this thesis, a two-steps automatic design and optimization procedure for reinforced concrete road beam bridges is presented. The optimization procedure finds the solution that minimizes the investment cost and the environmental impact of the bridge, while fulfilling all requirements of Eurocodes. In the first step, given the soil morphology and the two points to connect, it selects the optimal number of spans, type of piers-deck connections and piers location taking into account any obstacle the bridge has to cross. In the second and final step, it finds the optimal dimensions of the deck cross-section and produces the detailed reinforcement design. Constructability is considered and quantified within the investment cost to avoid a merely theoretical optimization. The wellknown Genetic Algorithm (GA) and Pattern Search optimization algorithms have been used. However, to reduce the computational effort and make the procedure more user-friendly, a memory system has been integrated and a modified version of GA has been developed. Moreover, the design and optimization procedure is used to study the relationship between the optimal solutions concerning investment cost and environmental impact.

    One case study concerning the re-design of an existing road bridge is presented. Potential savings obtained using the proposed method instead of the classic design procedure are presented. Finally, parametric studies on the total bridge length have been carried out and guidelines for designers have been produced regarding the optimal number of spans.

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