Dynamic structural health monitoring for concrete gravity dams based on the Bayesian inference

The preservation of concrete dams is a key issue for researchers and practitioners in dam engineering because of the important role played by these infrastructures in the sustainability of our society. Since most of existing concrete dams were designed without considering their dynamic behaviour, monitoring their structural health is fundamental in achieving proper safety levels. Structural Health Monitoring systems based on ambient vibrations are thus crucial. However, the high computational burden related to numerical models and the numerous uncertainties affecting the results have so far prevented structural health monitoring systems for concrete dams from being developed. This study presents a framework for the dynamic structural health monitoring of concrete gravity dams in the Bayesian setting. The proposed approach has a relatively low computational burden, and detects damage and reduces uncertainties in predicting the structural behaviour of dams, thus improving the reliability of the structural health monitoring system itself. The application of the proposed procedure to an Italian concrete gravity dam demonstrates its feasibility in real cases.

[1]  Ashutosh Bagchi,et al.  A New Iterative Procedure for Deconvolution of Seismic Ground Motion in Dam-Reservoir-Foundation Systems , 2014, J. Appl. Math..

[2]  Anna De Falco,et al.  Simulation of concrete crack development in seismic assessment of existing gravity dam , 2017 .

[3]  Victor E. Saouma,et al.  Anatomy of the vibration characteristics in old arch dams by random field theory , 2019, Engineering Structures.

[4]  J. F. Hall.  The dynamic and earthquake behaviour of concrete dams: review of experimental behaviour and observational evidence , 1988 .

[5]  Li Min Zhang,et al.  Dam Failure Mechanisms and Risk Assessment , 2016 .

[6]  Anil K. Chopra,et al.  Earthquake analysis of arch dams including dam-water-foundation rock interaction , 1995 .

[7]  Hermann G. Matthies,et al.  Parameter estimation via conditional expectation: a Bayesian inversion , 2016, Adv. Model. Simul. Eng. Sci..

[8]  Pierre Léger,et al.  Seasonal Thermal Displacements of Gravity Dams Located in Northern Regions , 2009 .

[9]  Junjie Li,et al.  Structural inverse analysis by hybrid simplex artificial bee colony algorithms , 2009 .

[10]  Yong Huang,et al.  State-of-the-art review on Bayesian inference in structural system identification and damage assessment , 2018, Advances in Structural Engineering.

[11]  Giacomo Sevieri,et al.  The seismic assessment of existing concrete gravity dams: FE model uncertainty quantification and reduction , 2021 .

[12]  Massimo Ruzzene,et al.  Computational Techniques for Structural Health Monitoring , 2011 .

[13]  J. Hadamard,et al.  Lectures on Cauchy's Problem in Linear Partial Differential Equations , 1924 .

[14]  José Sá da Costa,et al.  Constructing statistical models for arch dam deformation , 2014 .

[15]  Bruno Sudret,et al.  Global sensitivity analysis using polynomial chaos expansions , 2008, Reliab. Eng. Syst. Saf..

[16]  Meng Yang,et al.  A novel model of dam displacement based on panel data , 2018 .

[17]  De Falco,et al.  Modelling issues in the structural analysis of existing concrete gravity dams , 2017 .

[18]  Jeeho Lee,et al.  Plastic-Damage Model for Cyclic Loading of Concrete Structures , 1998 .

[19]  Jiang Hu,et al.  Anomaly identification of foundation uplift pressures of gravity dams based on DTW and LOF , 2018 .

[20]  Nicola Cavalagli,et al.  Calibration of finite element models of concrete arch-gravity dams using dynamical measures: the case of Ridracoli , 2017 .

[21]  Victor E. Saouma,et al.  Random finite element method for the seismic analysis of gravity dams , 2018, Engineering Structures.

[22]  Guido De Roeck,et al.  The influence of environmental parameters on the dynamic behaviour of the San Frediano bell tower in Lucca , 2018 .

[23]  Massimiliano Lucchesi,et al.  Masonry Constructions: Mechanical Models and Numerical Applications , 2008 .

[24]  Jerry Nedelman,et al.  Book review: “Bayesian Data Analysis,” Second Edition by A. Gelman, J.B. Carlin, H.S. Stern, and D.B. Rubin Chapman & Hall/CRC, 2004 , 2005, Comput. Stat..

[25]  S. Timoshenko,et al.  Theory of Elasticity (3rd ed.) , 1970 .

[26]  Sriram Narasimhan,et al.  Initial service life data towards structural health monitoring of a concrete arch dam , 2018 .

[27]  Bowen Wei,et al.  Modified hybrid forecast model considering chaotic residual errors for dam deformation , 2018 .

[28]  Omid Omidi,et al.  Seismic cracking of concrete gravity dams by plastic-damage model using different damping mechanisms , 2013 .

[29]  Jaap Weerheijm,et al.  Understanding the tensile properties of concrete , 2013 .

[30]  Lin Cheng,et al.  The Health Monitoring Method of Concrete Dams Based on Ambient Vibration Testing and Kernel Principle Analysis , 2015 .

[31]  George E. P. Box,et al.  Bayesian Inference in Statistical Analysis: Box/Bayesian , 1992 .

[32]  Tshilidzi Marwala,et al.  Finite-element-model Updating Using Computional Intelligence Techniques , 2010 .

[33]  Jia Liu,et al.  Concrete dam deformation prediction model for health monitoring based on extreme learning machine , 2017 .

[34]  Anil K. Chopra,et al.  Response Spectrum Analysis of Concrete Gravity Dams Including Dam-Water-Foundation Interaction , 2015 .

[35]  Tongchun Li,et al.  A deformation separation method for gravity dam body and foundation based on the observed displacements , 2018, Structural Control and Health Monitoring.

[36]  Armen Der Kiureghian,et al.  Probabilistic Capacity Models and Fragility Estimates for Reinforced Concrete Columns based on Experimental Observations , 2002 .

[37]  Hermann G. Matthies,et al.  Concrete gravity dams model parameters updating using static measurements , 2019, Engineering Structures.

[38]  Junjie Li,et al.  Prediction of long-term temperature effect in structural health monitoring of concrete dams using support vector machines with Jaya optimizer and salp swarm algorithms , 2019, Adv. Eng. Softw..

[39]  G. C. Tiao,et al.  Bayesian inference in statistical analysis , 1973 .

[40]  Jiang Hu,et al.  Comprehensive investigation of leakage problems for concrete gravity dams with penetrating cracks based on detection and monitoring data: A case study , 2018 .

[41]  Bo Dai,et al.  Statistical model optimized random forest regression model for concrete dam deformation monitoring , 2018 .

[42]  Carlos E. Ventura,et al.  Introduction to Operational Modal Analysis , 2015 .

[43]  Huaizhi Su,et al.  Multisource information fusion‐based approach diagnosing structural behavior of dam engineering , 2018 .

[44]  Huaizhi Su,et al.  Performance improvement method of support vector machine‐based model monitoring dam safety , 2016 .

[45]  Chin-Hsiung Loh,et al.  Monitoring of long‐term static deformation data of Fei‐Tsui arch dam using artificial neural network‐based approaches , 2013 .

[46]  Victor E. Saouma,et al.  Seismic fragility analysis of concrete dams: A state-of-the-art review , 2016 .

[47]  Costas Papadimitriou,et al.  Identification Methods for Structural Health Monitoring , 2016 .

[48]  Wei Xiong,et al.  Modeling method for predicting seepage of RCC dams considering time‐varying and lag effect , 2018 .

[49]  Maria Girardi,et al.  Model parameter estimation using Bayesian and deterministic approaches: the case study of the Maddalena Bridge , 2018 .

[50]  Andrew Gelman,et al.  General methods for monitoring convergence of iterative simulations , 1998 .

[51]  Pierre Léger,et al.  Reducing the Earthquake Induced Damage and Risk in Monumental Structures: Experience at Ecole Polytechnique de Montreal for Large Concrete Dams Supported by Hydro-Quebec and Alcan , 2007 .

[52]  Fei Kang,et al.  Structural health monitoring of concrete dams using long-term air temperature for thermal effect simulation , 2019, Engineering Structures.

[53]  Eugenio Oñate,et al.  Early detection of anomalies in dam performance: A methodology based on boosted regression trees , 2017 .

[54]  Pankaj Agarwal,et al.  Categorization of Damage Index of Concrete Gravity Dam for the Health Monitoring after Earthquake , 2016 .

[55]  Pilate Moyo,et al.  Health monitoring of concrete dams: a literature review , 2014 .

[56]  Carlos E. Ventura,et al.  Introduction to Operational Modal Analysis: Brincker/Introduction to Operational Modal Analysis , 2015 .

[57]  Wenbo Lu,et al.  Deterministic 3D seismic damage analysis of Guandi concrete gravity dam: A case study , 2017 .

[58]  Tshilidzi Marwala,et al.  Finite Element Model Updating Using Computational Intelligence Techniques: Applications to Structural Dynamics , 2010 .