Modelling and Characterizing a Concrete Gravity Dam for Fragility Analysis

Abstract

Most gravity dams have been designed and built during the past century with methods of analysis that are now considered inadequate. In recent decades, knowledge of seismology, structural dynamics and earthquake engineering has greatly evolved, leading to the evaluation of existing dams to ensure public safety. This study proposes a methodology for the proper modelling and characterisation of the uncertainties to assess the seismic vulnerability of a dam-type structure. This study also includes all the required analyses and verifications of the numerical model prior to performing a seismic fragility analysis and generating the corresponding fragility curves. The procedure presented herein also makes it possible to account for the uncertainties associated with the modelling parameters as well as the randomness in the seismic solicitation. The methodology was applied to a case study dam in Eastern Canada, whose vulnerability was assessed against seismic events with characteristics established by the current safety guidelines.

Soft Computing and Machine Learning in Dam Engineering

“…However, such methods, in addition to being computationally expensive, can produce very different solutions, depending on the input parameters, which can greatly influence the decision making. The importance of probabilistic methods in dam engineering has been discussed previously in [31][32][33]. In the paper by Segura et al [34], "Accounting for Uncertainties in the Safety Assessment of Concrete Gravity Dams: A Probabilistic Approach with Sample Optimization", the authors proposed a probabilistic-based methodology for assessing the safety of dams under usual, unusual, and extreme loading conditions.…”

Expected seismic performance of gravity dams using machine learning techniques

“…The rows of this Latin Hypercube experimental design matrix X are then paired with a suite of n ground motions with varying intensity measures. Hence, the dimensions of the original Latin Hypercube experimental design matrix are n × m. The set of considered parameters was selected by taking into account the input parameters in the numerical model and random variables (RV) frequently considered for probabilistic analyses in the literature [5,9,30]. Table 1 presents the modelling parameters that were considered as random variables in the analysis of the dam response and for which the uncertainty was properly included through the probability distribution function (PDF).…”

“…Hence, the tallest monolith of the dam was considered representative of the system. A numerical model was developed following the recommendations of Segura et al (2019) [9] and the United States Bureau of Reclamation (USBR) [33]. The dam-reservoirfoundation (DRF) system was modelled using the computer software LS-Dyna, and the different model features are shown in Figure 1(b).…”

“…The recorded horizontal displacement time series of a node at the crest of the block was then used to estimate the fundamental period of the system through the calculation of the Fourier spectrum and the global damping was approximated using the logarithmic decrement. Further details of the modeling assumptions and validation of the numerical model can be found in Segura et al ( 2019) [9] and Bernier et al ( 2016) [44].…”

“…Thus, there is a need to move towards more refined methods to consider uncertainties inherent in the problem of dam safety assessment under extreme events. For these reasons, probabilistic-based methods have arisen as a useful tool in dam safety, and the results have been promising in recent studies [5][6][7][8][9]. However, the use of probabilistic methods for dams within a normative framework has not been well developed, although increasing research is being conducted.…”