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  • 1.
    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.

  • 2.
    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.
    Karoumi, Raid
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges. KTH, School of Engineering Sciences (SCI), Centres, The KTH Railway Group.
    González Silva, Ignacio
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Cost-based optimization of the performance of a damage detection system2019In: Life-Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision - Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018, CRC Press/Balkema , 2019, p. 2103-2112Conference paper (Refereed)
    Abstract [en]

    Situations such as the collapse of civil engineering structures can be avoided if Structural Health Monitoring (SHM) systems can detect early potential failures and timely withdraw the structure from service ahead of a likely disaster. Structural safety is the leading reason for the implementation of SHM but also noteworthy is the cost reduction associated with routine maintenance and inspection. One of the remaining obstacles to fully implement SHM systems in our infrastructure deals with justifying their economic advantage. This paper proposes a rational framework for the use of SHM in the decision making process regarding the maintenance of civil engineering structures, based on the optimal setup of the damage detection system that yields the minimum associated deployment cost. Concepts such as Bayesian Theorem, Damage Index and Receiver Operating Characteristic curve are used in the proposed framework.

  • 3.
    Neves, André
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    González, I.
    Leander, John
    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.
    Structural health monitoring of bridges: a model-free ANN-based approach to damage detection2017In: Journal of Civil Structural Health Monitoring, ISSN 2190-5452, Vol. 7, no 5, p. 689-702Article in journal (Refereed)
    Abstract [en]

    As civil engineering structures are growing in dimension and longevity, there is an associated increase in concern regarding the maintenance of such structures. Bridges, in particular, are critical links in today’s transportation networks and hence fundamental for the development of society. In this context, the demand for novel damage detection techniques and reliable structural health monitoring systems is currently high. This paper presents a model-free damage detection approach based on machine learning techniques. The method is applied to data on the structural condition of a fictitious railway bridge gathered in a numerical experiment using a three-dimensional finite element model. Data are collected from the dynamic response of the structure, which is simulated in the course of the passage of a train, considering the bridge in healthy and two different damaged scenarios. In the first stage of the proposed method, artificial neural networks are trained with an unsupervised learning approach with input data composed of accelerations gathered on the healthy bridge. Based on the acceleration values at previous instants in time, the networks are able to predict future accelerations. In the second stage, the prediction errors of each network are statistically characterized by a Gaussian process that supports the choice of a damage detection threshold. Subsequent to this, by comparing damage indices with said threshold, it is possible to discriminate between different structural conditions, namely between healthy and damaged. From here and for each damage case scenario, receiver operating characteristic curves that illustrate the trade-off between true and false positives can be obtained. Lastly, based on the Bayes’ Theorem, a simplified method for the calculation of the expected total cost of the proposed strategy, as a function of the chosen threshold, is suggested.

  • 4.
    Neves, Cláudia
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    Structural Health Monitoring of Bridges: Model-free damage detection method using Machine Learning2017Licentiate thesis, monograph (Other academic)
    Abstract [en]

    This is probably the most appropriate time for the development of robust and reliable structural damage detection systems as aging civil engineering structures, such as bridges, are being used past their life expectancy and beyond their original design loads. Often, when a significant damage to the structure is discovered, the deterioration has already progressed far and required repair is substantial. This is both expensive and has negative impact on the environment and traffic during replacement. For the exposed reasons the demand for efficient Structural Health Monitoring techniques is currently extremely high. This licentiate thesis presents a two-stage model-free damage detection approach based on Machine Learning. The method is applied to data gathered in a numerical experiment using a three-dimensional finite element model of a railway bridge. The initial step in this study consists in collecting the structural dynamic response that is simulated during the passage of a train, considering the bridge in both healthy and damaged conditions. The first stage of the proposed algorithm consists in the design and unsupervised training of Artificial Neural Networks that, provided with input composed of measured accelerations in previous instants, are capable of predicting future output acceleration. In the second stage the prediction errors are used to fit a Gaussian Process that enables to perform a statistical analysis of the distribution of errors. Subsequently, the concept of Damage Index is introduced and the probabilities associated with false diagnosis are studied. Following the former steps Receiver Operating Characteristic curves are generated and the threshold of the detection system can be adjusted according to the trade-off between errors. Lastly, using the Bayes’ Theorem, a simplified method for the calculation of the expected cost of the strategy is proposed and exemplified.

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