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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.

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