Engaging soft computing in material and modeling uncertainty quantification of dam engineering problems

Abstract

Due to complex nature of nearly all infrastructures (and more specifically concrete dams), the uncertainty quantification is an inseparable part of risk assessment. Uncertainties might be propagated in different aspects depending on their relative importance such as epistemic and aleatory, or spatial and temporal. The objective of this paper is to focus on the material and modeling uncertainties, and to couple them with soft computing techniques aiming to reduce the computational burden of the conventional Monte Carlo-based finite element simulations. Several scenarios are considered in which the concrete and foundation material properties, the water level, and the dam geometry are assumed as random variables. Five soft computing techniques (i.e., random forest, boosted regression trees, multi-adaptive regression splines, artificial neural networks, and support vector machines) are employed to predict various quantities of interest based on different training sizes. It is argued that the artificial neural network is the most accurate algorithm in majority of cases, with enough accuracy as to be useful in reliability analysis as a complement to numerical models. The results with 200 samples in the training set are enough for reaching useful accuracy in most cases. For the simple prediction tasks, the results were predicted with less than 1% error. It is observed that increasing the number of input parameters increases the prediction error. The partial dependence plots provided most sensitive variables in dam design, which were consistent with the physics of the problem. Finally, several practical recommendations are provided for future applications.

Anomaly Detection in Dam Behaviour with Machine Learning Classification Models

“…Applications of SVM both for regression and classification are numerous in different sectors. In hydraulics and hydrology, examples include pipe failure detection in water distribution networks [36], prediction of urban water demand [37] rainfall-runoff modelling [38], flood forecasting [39], as well as reliability analysis [40][41][42] and dam safety [4,5,8,9,43,44].…”

An Explainable Probabilistic Model for Health Monitoring of Concrete Dam via Optimized Sparse Bayesian Learning and Sensitivity Analysis

“…Based on the literature reviewed above, it is evident that most existing methods in the feld of dam health monitoring provided deterministic values (i.e., point prediction) for dam structural behaviors, without adequately accounting for the uncertainties associated with these results. Uncertainty quantifcation (UQ) plays a key role in monitoring and decision making during the dam operation period [33][34][35]. Te uncertainties in dam engineering are mainly divided into two categories, i.e., aleatoric and epistemic uncertainty, where the former is referred to as the data noise, and the latter is associated with the model uncertainty (i.e., the uncertainties of model input, structure, and parameters) [36].…”

Accurate discharge coefficient prediction of streamlined weirs by coupling linear regression and deep convolutional gated recurrent unit

“…Hence, it is possible to analyse a broader range of models and systems irrespective of the nature of the problem. In particular, ML-DL modelling is an active field of research in other engineering fields such as structural and earthquake engineering (Abasi et al, 2021;M S Barkhordari & Es-haghi, 2021;Mohammad Sadegh Barkhordari & Tehranizadeh, 2021;Esteghamati & Flint, 2021;Hariri-Ardebili & Salazar, 2020;Pourkamali-Anaraki et al, 2020;Soraghi & Huang, 2021), biomedical engineering (Alizadehsani et al, 2021;Ayoobi et al, 2021), etc. Other applications of ML-DL techniques can also be found (Aswin et al, 2018;Athira et al, 2018;Selvin et al, 2017;Vinayakumar et al, 2017).…”