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 Thesis

[2020]  Stochastic Modelling of Infrastructures Deterioration and Interventions based

            on Network-Scale Visual Inspections

            Hamida, Z.

            Ph.D. Thesis, Polytechnique Montréal

            [PDF]

 

[2019]  Real-time Anomaly Detection in the Behaviour of Structures

            Nguyen, L.H.

            Ph.D. Thesis, Polytechnique Montréal

            [PDF]


Journal

publications

[2021]  Tractable Approximate Gaussian Inference for Bayesian Neural Networks

            Goulet, J.-A., Nguyen, L.H. and Amiri, S.

            Journal of Machine Learning Research, 20-1009, In press 

            [PDF] [ ] [ ] []

[2021]  Quantifying the Effects of Interventions Based on Visual Inspections from a Network of Bridges

            Hamida, Z. and Goulet, J.-A.

            Structure and Infrastructure Engineering. In press

            [PDF] [ ] [ ] [DOI link] []

[2021]  Anomaly detection using state-space models and reinforcement learning

            Khazaeli, S., Nguyen, L.H. and Goulet, J.-A.

            Structural Control and Health Monitoring. Volume 28, Issue 6, e2720.

            [PDF] [EndNote] [BibTeX] [DOI link] [ ]

[2021]  Network-scale deterioration modelling of bridges based on visual inspections and structural attributes

            Hamida, Z. and Goulet, J.-A.

            Structural Safety. Volume 88, January 2021, 102024.

            [PDF] [EndNote] [BibTeX] [DOI link] []

 

[2020]  Modeling Infrastructure Degradation from Visual Inspections Using Network-Scale State-Space Models

            Hamida, Z. and Goulet, J.-A.

            Structural Control and Health Monitoring. Volume 27, Issue 9, e2582.

            [PDF] [EndNote] [BibTeX] [DOI link] []

 

[2019]  Real-time Anomaly Detection with Bayesian Dynamic Linear Models

            Nguyen, L.H. and Goulet, J.-A.

            Structural Control and Health Monitoring. Volume 26, Issue 9

            [PDF] [EndNote] [BibTeX] [DOI link]


[2019]  A Kernel-based Method for Modeling Non-Harmonic Periodic Phenomena in

            Bayesian Dynamic Linear Models

            Nguyen, L.H., Gaudot, I., Shervin Khazaeli and Goulet, J.-A.

            Frontiers in Built Environment. Vol.5, i.d.:434090

            [PDF] [EndNote] [BibTeX] [DOI link]


[2019]  Uncertainty quantification for model parameters and hidden state variables in

            bayesian dynamic linear models

            Nguyen, L.H., Gaudot, I., and Goulet, J.-A.

            Structural Control and Health Monitoring. Vol. 26, Issue 3, pp.e2136

            [PDF] [EndNote] [BibTeX] [DOI link]


[2018]  Anomaly Detection with the Switching Kalman Filter for Structural Health Monitoring

            Nguyen, L.H. and Goulet, J.-A.

            Structural Control and Health Monitoring. Vol. 24, Issue 4, pp.e2136

            [PDF] [EndNote] [BibTeX] [DOI link]


[2018]  Structural health monitoring with dependence on non-harmonic periodic hidden covariates
            Nguyen, L.H. and Goulet, J.-A.

            Engineering Structures, 166:187 – 194.

            [PDF] [EndNote] [BibTeX] [DOI link]


[2018]  Empirical validation of Bayesian Dynamic Linear Models in the context of Structural Health Monitoring

            Goulet, J.-A. and Koo, K.

            Journal of Bridge Engineering. Vol. 23, Issue 2, pp. 05017017

            [PDF] [EndNote] [BibTeX] [DOI link]


[2018]  Probabilistic Modeling of Heteroscedastic Laboratory Experiments Using Gaussian Process

            Regression

            Tabor, L. Goulet, J.-A., Charron, J.-P., Desmettre C

            Journal of Engineering Mechanics. Vol. 44, Issue 6, pp. 04018038

            [PDF] [EndNote] [BibTeX] [DOI link]


[2017]  Bayesian dynamic linear models for structural health monitoring

            Goulet, J.-A.

            Structural Control and Health Monitoring. Vol. 24, Issue 12, pp.e2025

            [PDF] [EndNote] [BibTeX] [DOI link]


[2017]  A machine learning approach for characterizing soil contamination in the presence of physical site     

            discontinuities and aggregated samples

            Quach, A., Tabor, L., Dumont, D., Courcelles, B., and Goulet, J.-A.

            Advanced Engineering Informatics., vol.66, pp. 60-67.

            [PDF] [EndNote] [BibTeX] [DOI link]


[2016]  Measurement system design using expected utility

            R. Pasquier, J.-A. Goulet and I. Smith,

            Advanced Engineering Informatics, vol. 32, pp. 40–51.

            [PDF] [EndNote] [BibTeX] [DOI link]


[2016]  Measurement, data interpretation and uncertainty propagation for fatigue assessments of structures

            R. Pasquier, L. D'Angelo, J.-A. Goulet, C. Acevedo, A. Nussbaumer, and I. Smith,

            Journal of Bridge Engineering, 10.1061/(ASCE)BE.1943-5592.0000861, 04015087.

            [PDF] [EndNote] [BibTeX] [DOI link]


[2015]  Data-driven post-earthquake rapid structural safety assessment

            Goulet, J.-A., Michel C., and Der Kiureghian, A.

            Earthquake Engineering and Structural Dynamics, vol. 44, no. 4, pp. 549-562.

            [PDF] [EndNote] [BibTeX] [DOI link]


[2015]  Pre-Posterior Optimization of Sequence of Measurement and Intervention Actions Under Structural

            Reliability Constraint

            Goulet, J.-A., Der Kiureghian, A., and Li, B.

            Structural Safety, vol. 52, Part A, pp. 1-9.

            [PDF] [EndNote] [BibTeX] [DOI link]


[2015]  Improving Fatigue Evaluations of Structures Using In-service Behavior Measurement Data

            Pasquier, R., Goulet, J.-A., Acevedo, C., and Smith, I.F.C.

            Journal of Bridge Engineering, vol. 19, no. 11, p. 04014045

            [PDF] [EndNote] [BibTeX] [DOI link]


[2014]  Quantifying the effects of modeling simplifications for structural identification of bridges

             Goulet, J.-A., Texier, M., Michel, C., Smith, I.F.C. and Chouinard, L.

             Journal of Bridge Engineering, ASCE, Vol. 19, no. 1, pp. 59-71.

             [PDF] [EndNote] [BibTeX] [DOI link]

[2014] 
Exploring approaches to improve the performance of autonomous monitoring with imperfect data in

            location-aware wireless sensor networks

            Luo, X., O'Brien, W. J., Leite, F., and Goulet, J.-A.

            Advanced Engineering Informatics, vol. 28, no. 4, pp. 287-296.

            [PDF] [EndNote] [BibTeX] [DOI link]   


[2013]  Structural Identification with Systematic Errors and Unknown Uncertainty Dependencies

            Goulet, J.-A. and Smith, I.F.C.

            Computers and Structures, vol. 128, pp. 251-258

            [PDF] [EndNote] [BibTeX] [DOI link]


[2013]  Model falsification diagnosis and sensor placement for leak detection in pressurized pipe networks

            Goulet, J.-A., Coutu, S. and Smith I.F.C.

            Advanced Engineering Informatics. vol. 27, no. 2, pp. 261-269.

            [PDF] [EndNote] [BibTeX] [DOI link]

[2012]  Performance-driven measurement system design for structural identification

            Goulet, J.-A. and Smith I.F.C.

            Journal of Computing in Civil Engineering. 10.1061/(ASCE)CP.1943-5487.0000250, 427-436.

            [PDF] [EndNote] [BibTeX] [DOI link]

[2012]  Hybrid probabilities and error-domain structural identification using ambient vibration monitoring

            Goulet, J.-A. et Michel, C. and Smith, I.F.C.

            Mechanical Systems and Signal Processing, Volume 37, Issues 1-2, May-June 2013, Pages 199-212

            [PDF] [EndNote] [BibTeX] [DOI link]

[2012]  Predicting the usefulness of monitoring for identifying the behaviour of structures

            Goulet, J.-A. and Smith, I.F.C.

           Journal of Structural Engineering, 10.1061/(ASCE)ST.1943-541X.0000577, 1716-1727.

            [PDF] [EndNote] [BibTeX] [DOI link]

[2010]  Multimodel structural performance monitoring

            Goulet, J.-A., Kripakaran, P., and Smith, I.F.C.

            Journal of Structural Engineering, 136(10):13091318.

            [PDF] [EndNote] [BibTeX] [DOI link]


 Preprints

[2021]  Analytically Tractable Hidden-States Inference in Bayesian Neural Networks

            Nguyen, L.H. and Goulet, J.-A.,

            arXiv:2107.03759

            [PDF] [ ] [ ] [ ]

 

[2021]  Analytically Tractable Bayesian Deep Q-Learning

            Nguyen, L.H. and Goulet, J.-A.,

            arXiv:2106.11086

            [PDF] [ ] [ ] [ ]

 

[2021]  Analytically Tractable Inference in Deep Neural Networks

            Nguyen, L.H. and Goulet, J.-A.,

            arXiv:2103.05461

            [PDF] [ ] [ ] [ ]