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Neves, A. C., Leander, J., Gonzalez, I. & Karoumi, R. (2019). An approach to decision-making analysis for implementation of structural health monitoring in bridges. Structural Control and Health Monitoring: The Bulletin of ACS, 26(6), Article ID e2352.
Open this publication in new window or tab >>An approach to decision-making analysis for implementation of structural health monitoring in bridges
2019 (English)In: 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) Published
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.

Place, publisher, year, edition, pages
JOHN WILEY & SONS LTD, 2019
Keywords
Bayesian decision analysis, bridge structural health monitoring, event trees, expected utility theory, value of information
National Category
Civil Engineering
Identifiers
urn:nbn:se:kth:diva-252589 (URN)10.1002/stc.2352 (DOI)000467742400005 ()2-s2.0-85063567974 (Scopus ID)
Note

QC 20190611

Available from: 2019-06-11 Created: 2019-06-11 Last updated: 2019-06-25Bibliographically approved
Neves, A. C., Leander, J., Karoumi, R. & González Silva, I. (2019). Cost-based optimization of the performance of a damage detection system. In: 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. Paper presented at 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018, 28 October 2018 through 31 October 2018 (pp. 2103-2112). CRC Press/Balkema
Open this publication in new window or tab >>Cost-based optimization of the performance of a damage detection system
2019 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
CRC Press/Balkema, 2019
Keywords
Cost reduction, Damage detection, Decision making, Life cycle, Structural health monitoring, Structures (built objects), Civil engineering structures, Cost-based optimization, Damage detection systems, Decision making process, Economic advantages, Receiver operating characteristic curves, Routine maintenance, Structural health monitoring (SHM), Cost engineering
National Category
Construction Management
Identifiers
urn:nbn:se:kth:diva-252102 (URN)000471120402048 ()2-s2.0-85063957299 (Scopus ID)
Conference
6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018, 28 October 2018 through 31 October 2018
Note

QC 20190802

Available from: 2019-08-02 Created: 2019-08-02 Last updated: 2019-08-02Bibliographically approved
Neves, A. C., Gonzalez, I., Leander, J. & Karoumi, R. (2018). A New Approach to Damage Detection in Bridges Using Machine Learning. In: Conte, JP Astroza, R Benzoni, G Feltrin, G Loh, KJ Moaveni, B (Ed.), EXPERIMENTAL VIBRATION ANALYSIS FOR CIVIL STRUCTURES: TESTING, SENSING, MONITORING, AND CONTROL. Paper presented at International Conference on Experimental Vibration Analysis for Civil Engineering Structures (EVACES), JUL 12-14, 2017, Univ California San Diego, San Diego, CA (pp. 73-84). SPRINGER INTERNATIONAL PUBLISHING AG
Open this publication in new window or tab >>A New Approach to Damage Detection in Bridges Using Machine Learning
2018 (English)In: 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. 73-84Conference paper, Published paper (Refereed)
Abstract [en]

At the same time that civil engineering structures are increasing in number, size and longevity, there is a conforming increasing preoccupation regarding the monitoring and maintenance of such structures. In this sense the demand for new reliable Structural Health Monitoring systems and damage detection techniques is high. A model-free damage detection approach based on Machine Learning is presented in this paper. The method performs on the collected feature measurements on a railway bridge, which for this study were gathered in a numerical experiment using a three dimensional finite element model. The first step of the approach consists in collecting the dynamic response of the structure, simulated during the passage of a train over the bridge, in both the healthy and damage states of the structure. The next step consists in the design and unsupervised training of Artificial Neural Networks that use as input accelerations and axle loads and compute a novelty index, called prediction error, based on a novelty detection approach. The distribution of the obtained prediction errors is statistically evaluated by means of a Gaussian Process and, after this process, damage indexes can be defined. Finally, the efficiency of the method is assessed in terms of Type I (false positive) and Type II (false negative) errors using Receiver Operating Characteristic curves. The promising results obtained in the case study demonstrate the capability of the presented method.

Place, publisher, year, edition, pages
SPRINGER INTERNATIONAL PUBLISHING AG, 2018
Series
Lecture Notes in Civil Engineering, ISSN 2366-2557 ; 5
Keywords
Structural Health monitoring, Machine Learning, Damage detection, Model-free based method, Artificial Neural Networks
National Category
Infrastructure Engineering
Identifiers
urn:nbn:se:kth:diva-242272 (URN)10.1007/978-3-319-67443-8_5 (DOI)000455235800005 ()2-s2.0-85060236672 (Scopus ID)978-3-319-67443-8 (ISBN)978-3-319-67442-1 (ISBN)
Conference
International Conference on Experimental Vibration Analysis for Civil Engineering Structures (EVACES), JUL 12-14, 2017, Univ California San Diego, San Diego, CA
Note

QC 20190201

Available from: 2019-02-01 Created: 2019-02-01 Last updated: 2019-08-20Bibliographically approved
Leander, J. (2018). Fatigue life prediction of steel bridges using a small scale monitoring system. Stockholm
Open this publication in new window or tab >>Fatigue life prediction of steel bridges using a small scale monitoring system
2018 (English)Report (Other academic)
Abstract [en]

With an increasing number of bridges approaching their expected service life, improved and new methods for accurate assessment methods are called for. Economical restraints and sustainability reasons will not allow bridge managers to replace the numerous bridges that theoretically will be judged unsafe. As a method for refined assessment, in-service monitoring can be used to accurately determine the actual structural response. This will enable an alleviation of conservative estimates and facilitate accurate service life predictions.For fatigue assessment, the well established technique for strain measurements using electrical strain gauges can provide accurate estimations of the actual structural response. It is, however, not possible to mount gauges at all positions with critical details for large structures as bridges. The possibility of using a small scale monitoring system with few sensors has been investigated and a review of methods for predicting the response at unmeasured locations is presented in this report. A few selected methods, like multivariate regression and artificial neural networks (ANN), have been tested and evaluated on measured data from the Rautasjokk Bridge.The use of an ANN for time history prediction is demonstrated and promising results are presented. However, the predictions are sensitive to the input data and questionable results were attained when the input deviated from the training set. For predictions based on stress range spectra, multivariate linear regression constitute a robust tool and provided a high accuracy for an example from the Rautasjokk Bridge.This report also contains a presentation of the monitoring campaign of the Rautasjokk Bridge. The setup of the system and the management of data are described. The bridge is used for demonstrating the prediction methods and an advanced assessment approach based on linear elastic fracture mechanics. It enables a consideration of the measured response and a reliability based updating considering inspection results.

Place, publisher, year, edition, pages
Stockholm: , 2018. p. 55
Keywords
Fatigue, Monitoring, Bridges
National Category
Civil Engineering
Identifiers
urn:nbn:se:kth:diva-238718 (URN)
Funder
Swedish Transport Administration, TRV 2015/50535
Note

QC 20181119

Available from: 2018-11-08 Created: 2018-11-08 Last updated: 2018-11-19Bibliographically approved
Leander, J. (2018). Gamla Lidingöbron: Sammanfattning av mätningar från mars 2017 t.o.m. november 2018. Stockholm: KTH Royal Institute of Technology
Open this publication in new window or tab >>Gamla Lidingöbron: Sammanfattning av mätningar från mars 2017 t.o.m. november 2018
2018 (Swedish)Report (Other academic)
Abstract [sv]

Gamla Lidingöbron bär spårvagnstrafiken längs Lidingöbanan, gående och cyklister över sundet mellan Ropsten och Lidingö i Stockholm. Brons tillstånd och bärförmåga har under flera år ifrågasatts då korrosion och skador har påvisats under inspektioner. Ett mätsystem har installerats som ett led i att säkerställa brons bärförmåga fram till dess att den nya bron, Lilla Lidingöbron, är färdigställd. Föreliggande rapport sammanfattar de mätningar som påbörjades 15 mars 2017 och pågår fortlöpande. Rapporten omfattar mätresultat fram till och med 19 november 2018.

Mätsystemets uppbyggnad med givare, datainsamlingsenheter och utrustning för kommunikation finns redovisat. Vid skrivandet av denna rapport fanns 16 trådtöjningsgivare, 3 accelerometrar med tre mätriktningar vardera och en inklinometer med två mätriktningar installerade på bron. Dessutom fanns ett experimentellt system för energiinsamling installerat.

Det resultat som redovisas är långtidsvariationen av uppmätta spänningar, accelerationer och rotationer. Dessutom visas tidshistorier för enstaka tågpassager och ackumulerade spänningskollektiv. Spänningsnivåerna baserade på de uppmätta töjningarna för tågpassagerna är som störst ca 36\,MPa. Det är betydligt lägre än den dimensionerande lasteffekten vilket tyder på att brons statiska bärförmåga är tillräcklig. Däremot kan den nedbrytning som pågår, t.ex. omfattande korrosion, i förlängningen orsaka en oacceptabelt låg säkerhet.

Under två nätter i november 2018 genomfördes kontrollerade tester för tågpassager med hastigheter inom intervallet 5\,km/h till 50\,km/h. En statistisk analys visar att hastigheten inte är signifikant för de uppmätta spänningarna. Det betyder att hastigheten inte har kunnat visas ge några dynamiska effekter med ökande spänningsnivåer inom det beaktade intervallet.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2018. p. 35
National Category
Civil Engineering
Identifiers
urn:nbn:se:kth:diva-252547 (URN)
Note

QC 20190603

Available from: 2019-05-31 Created: 2019-05-31 Last updated: 2019-06-03Bibliographically approved
Leander, J. & Karoumi, R. (2018). The value of monitoring on the service life prediction of a critical steel bridge. In: Life Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision: Proceedings of the Sixth International Symposium on Life-Cycle Civil Engineering (IALCCE 2018), 28-31 October 2018, Ghent, Belgium: . Paper presented at International Symposium on Life-Cycle Civil Engineering (IALCCE) (pp. 2121-2128).
Open this publication in new window or tab >>The value of monitoring on the service life prediction of a critical steel bridge
2018 (English)In: Life Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision: Proceedings of the Sixth International Symposium on Life-Cycle Civil Engineering (IALCCE 2018), 28-31 October 2018, Ghent, Belgium, 2018, p. 2121-2128Conference paper, Published paper (Refereed)
Abstract [en]

This paper is focused on the service life prediction of steel bridges subjected to a combination of corrosion and fatigue. Both deterioration mechanisms are dubious to handle in theoretical assessments due to large uncertainties, on the action effect side and the resistance side. A reliability-based assessment in combination with updating considering monitoring and inspections provide an approach to reduce the uncertainties. To evaluate what assessment actions to engage, this approach has been combined with Bayesian decision theory which provides a rational basis to evaluate the value of information (VoI). The Old Liding\"{o} Bridge in Sweden is used as a case study to demonstrate the reliability-based approach. This bridge is subjected to severe corrosion and has been deemed unfit for service. However, it provides the only link to the island Liding\"{o} for the local tram and pedestrians why the bridge owner has decided to keep it in service until a new bridge is in place. To secure the safety of the bridge, recurrent inspections are conducted and discovered defects are repaired immediately. A system for continuous monitoring of critical parts was installed during the spring 2017. The bridge, the approach for assessment, and the procedure for evaluating the VoI are presented in the paper.

Keywords
Fatigue, Corrossion, Reliability, Decision, Bridges, Utility
National Category
Civil Engineering
Identifiers
urn:nbn:se:kth:diva-238717 (URN)000471120402050 ()2-s2.0-85063941376 (Scopus ID)9781138626331 (ISBN)
Conference
International Symposium on Life-Cycle Civil Engineering (IALCCE)
Note

QC 20181119

Available from: 2018-11-08 Created: 2018-11-08 Last updated: 2019-08-02Bibliographically approved
Honfi, D., Leander, J., Björnsson, Í., Ivanov, O. L., Plos, M., Zandi, K., . . . Gabrielsson, H. (2017). Decision support for maintenance and upgrading of existing bridges. In: IABSE Conference, Vancouver 2017: Engineering the Future - Report. Paper presented at 39th IABSE Symposium in Vancouver 2017: Engineering the Future, 21 September 2017 through 23 September 2017 (pp. 336-345). International Association for Bridge and Structural Engineering (IABSE)
Open this publication in new window or tab >>Decision support for maintenance and upgrading of existing bridges
Show others...
2017 (English)In: IABSE Conference, Vancouver 2017: Engineering the Future - Report, International Association for Bridge and Structural Engineering (IABSE) , 2017, p. 336-345Conference paper, Published paper (Refereed)
Abstract [en]

Maintenance of transportation infrastructure assets can be relatively expensive, since it does not only include the direct cost of interventions, but also the indirect consequences of traffic disruptions. To make optimal decisions about maintenance actions, including rehabilitation and upgrading, reliable information about the performance of existing structures is needed. However, obtaining such information might require significant efforts and can be done in various ways. The purpose of an ongoing Swedish research project BIG BRO is to develop a framework for a decision support methodology that can be used for implementing maintenance strategies for bridges on a rational basis. The present paper provides a brief overview about the project as well as describes some of the ongoing work. 

Place, publisher, year, edition, pages
International Association for Bridge and Structural Engineering (IABSE), 2017
Keywords
Bridges, Decision support, Infrastructure, Maintenance, Rehabilitation, Upgrading, Decision support systems, Patient rehabilitation, Decision supports, Existing structure, Maintenance Action, Maintenance strategies, Traffic disruption, Transportation infrastructures
National Category
Civil Engineering
Identifiers
urn:nbn:se:kth:diva-236835 (URN)2-s2.0-85050024395 (Scopus ID)9783857481536 (ISBN)
Conference
39th IABSE Symposium in Vancouver 2017: Engineering the Future, 21 September 2017 through 23 September 2017
Funder
Swedish Research CouncilVINNOVASwedish Research Council FormasSwedish Transport AdministrationSwedish Energy Agency
Note

QC 20181221

Available from: 2018-12-21 Created: 2018-12-21 Last updated: 2018-12-21Bibliographically approved
Neves, A., González, I., Leander, J. & Karoumi, R. (2017). Structural health monitoring of bridges: a model-free ANN-based approach to damage detection. Journal of Civil Structural Health Monitoring, 7(5), 689-702
Open this publication in new window or tab >>Structural health monitoring of bridges: a model-free ANN-based approach to damage detection
2017 (English)In: Journal of Civil Structural Health Monitoring, ISSN 2190-5452, Vol. 7, no 5, p. 689-702Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Springer Verlag, 2017
Keywords
Artificial neural networks, Bayes’ theorem, Damage detection, Model-free-based method, Probability-based expected cost, Receiver operating characteristic curve, Statistical model development, Structural health monitoring, Chemical sensors, Economic and social effects, Finite element method, Learning algorithms, Learning systems, Neural networks, Numerical methods, Civil engineering structures, Damage detection technique, Expected costs, Model free, Receiver operating characteristic curves, Statistical modeling, Structural health monitoring systems, Three dimensional finite element model
National Category
Infrastructure Engineering
Identifiers
urn:nbn:se:kth:diva-227063 (URN)10.1007/s13349-017-0252-5 (DOI)2-s2.0-85034638701 (Scopus ID)
Note

QC 20180517

Available from: 2018-05-17 Created: 2018-05-17 Last updated: 2018-05-17Bibliographically approved
Leander, J. & Al-Emrani, M. (2016). Reliability-based fatigue assessment of steel bridges using LEFM: A sensitivity analysis. International Journal of Fatigue, 93(1), 82-91
Open this publication in new window or tab >>Reliability-based fatigue assessment of steel bridges using LEFM: A sensitivity analysis
2016 (English)In: International Journal of Fatigue, ISSN 0142-1123, E-ISSN 1879-3452, Vol. 93, no 1, p. 82-91Article in journal (Refereed) Published
Abstract [en]

The lack of an established safety format prohibits a widespread use of linear elastic fracture mechanics (LEFM) for the fatigue assessment of steel bridges. The aim of this study is to facilitate a future development of a deterministic design approach. A probabilistic sensitivity analysis has been performed to study the influence of different modeling options on the resulting time variant reliability. The analyses have been performed by the first order reliability method (FORM) together with a model correction factor. The result shows the importance of modeling the crack shape in an adequate manor and, the importance of the material parameters. Other parameters as the load sequence and the option between a linear and a bi-linear crack growth law are less important. A calculation of the omission sensitivity factors shows that the uncertainties of the material parameters in the crack growth law have the most decisive influence and, thereafter, the uncertainty of the stress intensity factor.

Place, publisher, year, edition, pages
Elsevier, 2016
Keywords
Fatigue; Bridges; Reliability
National Category
Infrastructure Engineering
Identifiers
urn:nbn:se:kth:diva-191476 (URN)10.1016/j.ijfatigue.2016.08.011 (DOI)000385595900008 ()2-s2.0-84983428790 (Scopus ID)
Note

QC 20160912

Available from: 2016-08-30 Created: 2016-08-30 Last updated: 2019-02-08Bibliographically approved
Leander, J., Trillkott, S. & Kullberg, C. (2015). Götaälvbron: Töjningsmätningar för kalibrering av beräkningsmodell. Stockholm: KTH Royal Institute of Technology
Open this publication in new window or tab >>Götaälvbron: Töjningsmätningar för kalibrering av beräkningsmodell
2015 (Swedish)Report (Other academic)
Abstract [en]

The Götaälv Bridge in Gothenburg was instrumented with strain gauges in April 2015. This report is a presentation of the measurements performed until 29 April. The measurements were performed by personnel from the Division of Structural Engineering and Bridges at KTH. The project was commissioned by the City of Gothenburg – Traffic & public transport authority through ÅF Infrastructure AB and performed in collaboration with researchers from the Division of Structural Engineering at Chalmers University of Technology.

The monitoring system comprised 23 strain gauges divided between two areas. One between support 13 and 14 at the South Viaduct and one between support III and IV at the elevated river part. Measurements were performed at four arranged bridge openings and a calibration monitoring using two trucks with known weights. Continuous measurement was also performed during the afternoon Tuesday 28 April.

For the bridge openings, stresses up to 5,6MPa were recorded on the elevated river part. The recorded stresses are lower than those caused by the ordinary traffic in the instrumented locations.

The calibration measurements were performed during night using two trucks with weights of 25,0 tons and 25,1 tons, respectively. Together, they caused maximum recorded stresses of 15,4MPa for the South Viaduct and 16,8MPa for the elevated river part. Associated stress ranges were 19,4MPa and 19,8MPa, respectively. The largest values were recorded in the gauges located close to the mid spans.

Stress range spectra are presented for the short term continuous measurement. The daily traffic gives in general lower stress ranges than the trucks used for the calibration. A large portion of the cycles are distributed over small stress ranges.

For the gauges on the elevated bridge part, the analyses show a significant influence of dynamics on the results.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2015. p. 43
Keywords
Steel bridge, strain measurements, fatigue, Stålbro, töjningsmätning, utmattning
National Category
Civil Engineering
Identifiers
urn:nbn:se:kth:diva-179446 (URN)
Note

QC 20160129

Available from: 2015-12-16 Created: 2015-12-16 Last updated: 2016-01-29Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-2833-4585

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