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

Giacomo Sevieri,Anna De Falco

doi:10.1007/s13349-020-00380-w

Giacomo Sevieri, Anna De Falco

Open Access

https://doi.org/10.1007/s13349-020-00380-w

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Abstract

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.

Highlights

Concrete gravity dams are key to flood control, energy production, and the industrial and agricultural supply
Of the various applications that explore the use of ambient vibrations for structural control, some studies present the calibration of dam predictive models in a deterministic setting, but none have proposed a framework for the dynamic structural health monitoring (SHM) of concrete gravity dams
During the Training phase, observations recorded by the monitoring system are used to update through the Bayesian Inference the parameters of the predictive model of the mode shapes, which are related to the healthy state of the dam

Summary

Introduction

Concrete gravity dams are key to flood control, energy production, and the industrial and agricultural supply. Sevieri et al [4] show how ambient vibrations, and in particular their elaboration through operational modal analysis (OMA), can be successfully used to update the parameters of predictive models of concrete gravity dams’ dynamic behaviour, reducing epistemic uncertainties. Dynamic measurements provide structural control, thereby improving the prediction of the dam’s structural behaviour This latter is possible thanks to the Bayesian setting, exploiting the information contained in the new observations to update the state of knowledge of the model parameters. 4. The novelty of this study lies in the probabilistic nature and the particular architecture of the proposed framework, which exploits modal information derived from ambient vibrations both to control the structural health state and to reduce the uncertainties in the prediction of the dam behaviour.

Structural health monitoring systems for concrete gravity dams

Failure mechanisms and damage development in concrete dams

General overview

The training phase

The detection phase

Dam description

Dynamic experimental campaign

Material test results and prior distributions

Construction of the FE model and gPCE surrogate model

The high‐fidelity FE model

Training phase

Detection phase