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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, Ignacio
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Application of monitoring to dynamic characterization and damage detection in bridges2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The field of bridge monitoring is one of rapid development. Advances in sensor technologies, in data communication and processing algorithms all affect the possibilities of Structural Monitoring in Bridges. Bridges are a very critical part of a country’s infrastructure, they are expensive to build and maintain, and many uncertainties surround important factors determining their serviceability and deterioration state. As such, bridges are good candidates for monitoring. Monitoring can extend the service life and avoid or postpone replacement, repair or strengthening works. The amount of resources saved, both to the owner and the users, by reducing the amount of non-operational time can easily justify the extra investment in monitoring.

    This thesis consists of an extended summary and five appended papers. The thesis presents advances in sensor technology, damage identification algorithms, Bridge Weigh-In-Motion systems, and other techniques used in bridge monitoring. Four case studies are presented. In the first paper, a fully operational Bridge Weigh-In-Motion system is developed and deployed in a steel railway bridge. The gathered data was studied to obtain a characterization of the site specific traffic. In the second paper, the seasonal variability of a ballasted railway bridge is studied and characterized in its natural variability. In the third, the non-linear characteristic of a ballasted railway bridge is studied and described stochastically. In the fourth, a novel damage detection algorithm based in Bridge Weigh-In-Motion data and machine learning algorithms is presented and tested on a numerical experiment. In the fifth, a bridge and traffic monitoring system is implemented in a suspension bridge to study the cause of unexpected wear in the bridge bearings.

    Some of the major scientific contributions of this work are: 1) the development of a B-WIM for railway traffic capable of estimating the load on individual axles; 2) the characterization of in-situ measured railway traffic in Stockholm, with axle weights and train configuration; 3) the quantification of a hitherto unreported environmental behaviour in ballasted bridges and possible mechanisms for its explanation (this behaviour was shown to be of great importance for monitoring of bridges located in colder climate) 4) the statistical quantification of the nonlinearities of a railway bridge and its yearly variations and 5) the integration of B-WIM data into damage detection techniques.

     

  • 4.
    Gonzalez, Ignacio
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Design and Bridges.
    Karoumi, Raid
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Design and Bridges.
    A review of recent development in structural health monitoring of bridgesManuscript (preprint) (Other academic)
    Abstract [en]

    Bridges are expensive to manage as they require thorough inspection and maintenance operations. Meanwhile the development of Structural Health Monitoring (SHM) techniques in recent years have demonstrated the potential for this to become a tool that bridge managers can use to optimise inspection and maintenance procedures. This paper reviews recent development in SHM and damage detection techniques. The paper focuses on recent algorithms developed or adapted for damage detection and structural health monitoring in bridge structures, although some of the algorithms presented might be of a more general nature or developed for other kind of structures. The algorithms presented are classified according to the method they use for damage detection and location into Acoustic Emission, Modal Analysis and Statistical pattern recognition based.

     

  • 5.
    Gonzalez, Ignacio
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Karoumi, Raid
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Analysis of the annual variations in the dynamic behavior of a ballasted railway bridge using Hilbert transform2014In: Engineering structures, ISSN 0141-0296, E-ISSN 1873-7323, Vol. 60, p. 126-132Article in journal (Refereed)
    Abstract [en]

    In this paper the variations in dynamic properties (eigenfrequency and damping) due to seasonal effects of a single span, ballasted railway bridge are studied. It is demonstrated that both the eigenfrequency and characteristic damping vary importantly with environmental conditions and amplitude of vibration. For this, acceleration signals corresponding to roughly a year of monitoring are analyzed with the Hilbert transform and the instantaneous frequency and equivalent viscous damping ratio are calculated during the free vibrations. Over 1000 trains passages were analyzed, with temperatures ranging from -30 to +30°C and amplitudes of vibration varying from 0.5m/s2 to 0. The location of the accelerometers allowed for separation of the signals into their bending and torsional components. It was found that during the cold season, with months of temperatures below 0°C, the dynamic properties varied the most. Not only did the frequencies (for small vibrations) differ more than 9% even for a given temperature, but the non-linearity present in the structure did also change in a matter of hours. These findings are important in the context of Structural Health Monitoring. Any system that aims at warning early in the onset of damage by analyzing changes in the dynamic characteristic of a structure needs to first fully understand and account for the natural variability of these parameters, often much larger than what could be expected from reasonable levels of damage.

  • 6.
    Gonzalez, Ignacio
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Karoumi, Raid
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    BMIM Aided Damage Detection in Bridges Using Machine Learning2013Manuscript (preprint) (Other academic)
  • 7.
    Gonzalez, Ignacio
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Karoumi, Raid
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    BWIM aided damage detection in bridges using machine learning2015In: Journal of Civil Structural Health Monitoring, ISSN 2190-5452, Vol. 5, no 5, p. 715-725Article in journal (Refereed)
    Abstract [en]

    In this study, a new, model-free damage detection method is proposed and validated on a simple numerical experiment. The proposed algorithm used vibration data (deck accelerations) and bridge weigh-in-motion data (load magnitude and position) to train a two-stage machine learning setup to classify the data into healthy or damaged. The proposed method is composed in its first stage of an artificial neural network and on the second stage of a gaussian process. The proposed method is applicable to railway bridges, since it takes advantage of the fact that vehicles of known axle configuration cross the bridge regularly, that normally only one train is on the bridge at a time and that the lateral positioning of the loads does not change. The novelty of the proposed algorithm is that it makes use of the data on the load’s position, magnitude and speed that can be obtained from a Bridge Weigh-in-Motion system to improve the accuracy of the damage detection algorithm.

  • 8.
    Gonzalez, Ignacio
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Karoumi, Raid
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Continous monitoring of bearing forces and displacements in the High Coast Bridge2014Conference paper (Refereed)
    Abstract [en]

    The High Coast Bridge is a four lane, 1867 m long suspension bridge located in middle Sweden. The bridge, constructed between 1993 and 1997, comprises a multiple cell steel box girder with a main span of 1210 m. The girder is fully suspended along its entire length without any bearings at the pylons. Because of the unexpected wear in the sliding bearings at its ends a monitoring program was initiated. The program included the dynamic monitoring of the longitudinal displacement of the girder and bearing forces at both bearings in the northern abutment. It also included the monitoring of hanger forces at the hangers closest to the northern end and the monitoring of road traffic entering and leaving the bridge by means of a traffic camera located at that same end. In this study the long term behavior of the bridge, as observed by the monitoring system, is studied and presented including the effects of seasonal temperature changes. Furthermore the capabilities of the installed monitoring system for measuring traffic loads, as a Bridge Weigh in Motion system, in conjunction with the camera are explored and the result of the traffic monitoring is presented. The monitoring system was found capable of estimating the total loads of trucks crossing the bridge, as well as the total distance travelled by the bearing, both important parameters for estimating bearing wear. Furthermore, with the aid of the camera to discern between single and multiple truck events, overloaded trucks could be identified by the system.

  • 9.
    Gonzalez, Ignacio
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Karoumi, Raid
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Continuous monitoring of bearing forces and displacements in the High Coast BridgeManuscript (preprint) (Other academic)
  • 10.
    Gonzalez, Ignacio
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Design and Bridges.
    Karoumi, Raid
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Design and Bridges.
    Continuous monitoring of bearing forces and displacements  on the high coast suspension bridgeManuscript (preprint) (Other academic)
    Abstract [en]

    This paper presents results from the longitudinal girder displacement and bearing force monitoring on a highway suspension bridge located in Sweden. The monitoring programme was initiated in 2005 by the bridge owner after the discovery of unusual and intense wear in the Teflon layers of the bearings. The initial work focused mainly on a long term monitoring of the bearing forces in the northern abutment. After the first monitoring period of one year the monitoring system was enhanced with a traffic camera, a temperature sensor and accelerometers mounted on the two hangers closest to the instrumented bearing. The monitoring period presented in this paper covers ten months from February to December 2010 and focuses on the horizontal displacement of the girder and on the forces registered in the north-west bearing in conjunction with the traffic information provided by the traffic camera.

  • 11.
    Gonzalez, Ignacio
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Karoumi, Raid
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Traffic monitoring using a structural health monitoring system2014In: Proceedings of the ICE - Bridge Engineering, ISSN 1478-4629, Vol. 168, no 1, p. 13-23Article in journal (Refereed)
    Abstract [en]

    The main factors influencing the deterioration of bridges are the environmental conditions and the traffic loads. Hence, a reliable and accurate characterisation of the traffic loads can improve the results from bridge rating, and health bridge monitoring. In this study a Bridge Weigh-in-Motion algorithm is developed to monitor trains passing on a steel railway bridge. The implemented system estimates the traffic loads, speeds and axle spacings. Other valuable information such as peak and RMS vertical bridge deck accelerations are also stored. The system takes advantage of two of the strain gauges from a previously deployed sensor network, installed to mainly monitor the strains for fatigue development. In this paper the possibilities and limitation of this system are explored.

  • 12.
    Gonzalez, Ignacio
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Ülker-Kaustell, Mahir
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Karoumi, Raid
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Seasonal effects on the stiffness properties of a ballasted railway bridge2013In: Engineering structures, ISSN 0141-0296, E-ISSN 1873-7323, Vol. 57, p. 63-72Article in journal (Refereed)
    Abstract [en]

    In this article it is shown empirically that ballasted bridges in cold climates can exhibit a step-like variation of their natural frequencies as the yearly season changes. The bridge under study was observed to have significantly higher natural frequencies (as much as 35%) during the winter months compared to the summer. This variation was rather discrete in nature and not proportional to temperature. Furthermore the increase in natural frequencies took place only after the temperatures had dropped below 0 °C for a number of days. It was thus hypothesized that this change in natural frequencies was due to changes in the stiffness parameters of some materials with the onset of frost. In low temperature conditions not only the mean value of the measured frequencies increased, but also their variance increased considerably. Given the large spread of the measured natural frequencies, the stiffness parameters were assumed to be stochastic variables with an unknown multivariate distribution, rather than fixed values. A Bayesian updating scheme was implemented to determine this distribution from measurements. Data gathered during one annum of monitoring was used in conjunction with a finite element model and a meta model, resulting in an estimation of the relevant stiffness parameters for both the cold and the warm condition.

  • 13.
    Gonzàlez, Ignacio
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Karoumi, Raid
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    LLorens, Andrea
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Improved bridge response evaluation based on dynamic testing2012In: Bridge Maintenance, Safety, Management, Resilience and Sustainability - Proceedings of the Sixth International Conference on Bridge Maintenance, Safety and Management, Taylor & Francis Group, 2012, p. 2584-2588Conference paper (Refereed)
    Abstract [en]

    Dynamic analysis of the Rössjö railway bridge, located on the new Swedish high speed line, is carried out. A simple 2D finite element model is developed and the dynamic response to passing trains is studied. The model is updated by modifying the support stiffness and comparing the predicted response with measurements. Results show that measured deck accelerations are in good agreement with what was predicted from the updated model. To further increase the accuracy in the predicted response, a method based on Bayesian updating is proposed.

  • 14.
    González, Ignacio
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Design and Bridges (name changed 20110630). KTH, School of Engineering Sciences (SCI), Centres, The KTH Railway Group.
    Dynamic Behaviour of the New Årsta Bridge to Moving Trains: Simplified FE ‐ Analysis and Verifications2008Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
  • 15.
    González, Ignacio
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Design and Bridges.
    Study and Application of Modern Bridge Monitoring Techniques2011Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The field of monitoring is one of rapid development. Advances in sensor technologies, in data communication paradigms and data processing algorithms all influence the possibilities of Structural Health Monitoring, damage detection, traffic monitoring and other implementations of monitoring systems. Bridges are a very critical part of a country’s infrastructure, they are expensive to build and maintain, and many uncertainties surround important factors determining the serviceability and deterioration of bridges. As such, bridges are good candidates for monitoring. Monitoring can extend the service life and avoid or postpone replacement, repair or strengthening work. Many bridges constitute a bottleneck in the transport network they serve with few or no alternative routes. The amount of resources saved, both to the owner and the users, by reducing the amount of non-operational time can easily justify the extra investment in monitoring.

    This thesis consists of an extended summary and three appended papers. The thesis presents advances in sensor technology, damage identification algorithms and Bridge Weigh-In-Motion techniques. Two case studies are carried out. In the first a bridge and traffic monitoring system is implemented in a highway suspension bridge to study the cause of unexpected wear in the bridge bearings. In the second a fully operational Bridge Weigh-In-Motion system is developed and deployed in a steel railway bridge. The gathered data was studied to obtain a characterization of the site specific traffic.

  • 16.
    González, Ignacio
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering.
    The validity of simplified dynamic analysis of the new årsta bridge response to moving trains2010In: Proceedings of the Tenth International Conference on Computational Structures Technology, 2010, Vol. 93Conference paper (Refereed)
    Abstract [en]

    Dynamic simulations are necessary to predict the behaviour of bridges loaded by high-speed trains. These simulations are expensive, require a lot of computational power and are very sensitive to input parameters that are difficult to estimate. If the dynamic behaviour of the bridge could be accurately predicted by simpler models, time and resources could be saved. In this paper, a 2D finite element model of a complex bridge is developed. The results obtained are compared with those obtained by other more complex 3D models as well as with the actual response of the bridge measured in situ by a long term monitoring system. Furthermore, based on the measured results, some model parameters are updated. The main aim of this study is to check the validity of simplified models and simplified analysis of the response of complex bridges to moving trains, as well as the capabilities of updating in enhancing the model accuracy.

  • 17.
    Karoumi, Raid
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Design and Bridges.
    Gonzalez, Ignacio
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Design and Bridges.
    Continuous monitoring of the High Coast Suspension Bridge: Measurement period: February to December 20102011Report (Other academic)
  • 18.
    Mottola, Luca
    et al.
    Swedish Inst Comp Sci, Box 1263, S-16428 Kista, Sweden..
    Voigt, Thiemo
    Swedish Inst Comp Sci, Box 1263, S-16428 Kista, Sweden..
    Gonzalez, Ignacio
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Karoumi, Raid
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    From the Desk to the Field: Recent Trends in Deploying Wireless Sensor Networks for Monitoring Civil Structures2011In: 2011 IEEE SENSORS, IEEE , 2011, p. 62-65Conference paper (Refereed)
    Abstract [en]

    Wireless Sensor Networks (WSNs) leverage battery-powered embedded devices to sense from and act on the environment. Their characteristics are at odds with the lifetime requirements in monitoring of civil structures. In this paper, we briefly describe the challenges at stake and how to address them, drawing from recent literature and our own real-world experience. Hardware solutions include MEMS sensors and power-efficient memories. Duty-cycled and batch operation, as well as forms of distributed processing are software techniques to reduce the energy invested in radio communication-the major energy drain in WSNs. We then conclude with an outlook on an ongoing Indo-Swedish project on monitoring the integrity of railway bridges using WSNs.

  • 19.
    Neves, Ana C.
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Leander, John
    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.
    Karoumi, Raid
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    An approach to decision-making analysis for implementation of structural health monitoring in bridges2019In: Structural Control and Health Monitoring: The Bulletin of ACS, ISSN 1545-2255, E-ISSN 1545-2263, Vol. 26, no 6, article id e2352Article in journal (Refereed)
    Abstract [en]

    Adverse situations such as prolonged downtime of a structure, unnecessary inspections, expensive allocation of personal and equipment, deficient structural performance, or failure can be avoided by using structural health monitoring (SHM). Enhanced structural safety is the leading reason for its implementation, but one of the remaining obstacles to fully implement SHM systems deals with justifying their economic benefit. At any point in time, the preference towards one particular action depends on factors such as the probability of the triggered events and their consequences. All the possible decisions and relevant information can be illustrated by decision tree models, and the optimal decision corresponds to the one with the highest utility. Applying the Bayesian Theorem, the assumed prior probabilities of the structural state are updated in the light of new information provided by a system and the optimal decision is revised. This paper proposes a dynamic decision-making framework to manage civil engineering structures, where the ultimate goal is to achieve greater overall economy without jeopardizing safety. This paper covers a case study of a bridge where the optimal SHM and maintenance decisions are determined in the context of different scenarios in which the event probabilities and associated costs are made-up.

  • 20. Shu, Jiangpeng
    et al.
    Zhang, Ziye
    Gonzalez, Ignacio
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Karoumi, Raid
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    The application of a damage detection method using Artificial Neural Network and train-induced vibrations on a simplified railway bridge model2013In: Engineering structures, ISSN 0141-0296, E-ISSN 1873-7323, Vol. 52, p. 408-421Article in journal (Refereed)
    Abstract [en]

    A damage detection algorithm based on Artificial Neural Network (ANN) was implemented in this study using the statistical properties of structural dynamic responses as input for the ANN. Sensitivity analysis is performed to study the feasibility of using the changes of variances and covariances of the dynamic responses of the structure as input to the ANN. A finite element (FE) model of a one-span simply supported beam railway bridge was developed in ABAQUS (R), considering both single damage case and multi-damage case. A Back-Propagation Neural Network (BPNN) was built and trained to perform damage detection. A series of numerical tests on the FE model with different vehicle properties was conducted to prove the validity and efficiency of the proposed approach. The results reveal not only that the ANN, together with the statistics, can correctly estimate the location and severity of damage;but also that the identification of the damage location is more difficult than that of the damage severity. In summary, it is concluded that the use of statistical property of the structural dynamic responses as damage index along with the Artificial Neural Network as tool for damage detection for an idealized model of a bridge is reliable and effective.

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