Abstract

Predictive models are essential in dam safety assessment. They have been traditionally based on simple statistical tools such as the hydrostatic-season-time (HST) model. These tools are well known to have limitations in terms of accuracy and reliability. In the recent years, the examples of application of machine learning and related techniques are becoming more frequent as an alternative to HST. While they proved to feature higher flexibility and prediction accuracy, they are also more difficult to interpret. As a consequence, the vast majority of the research is limited to prediction accuracy estimation. In this work, one of the most popular machine learning techniques (boosted regression trees), was applied to model 8 radial displacements and 4 leakage flows at La Baells Dam. The possibilities of model interpretation were explored: the relative influence of each predictor was computed, and the partial dependence plots were obtained. Both results were analysed to draw conclusions on dam response to environmental variables, and its evolution over time. The results show that this technique can efficiently identify dam performance changes with higher flexibility and reliability than simple regression models.

Abstract

Predictive models are essential in dam safety assessment. Both deterministic and statistical models applied in the day-to-day practice have demonstrated to be useful, although they show relevant limitations at the same time. On another note, powerful learning algorithms have been developed in the field of machine learning (ML), which have been applied to solve practical problems. The work aims at testing the prediction capability of some state-of-the-art algorithms to model dam behaviour, in terms of displacements and leakage. Models based on random forests (RF), boosted regression trees (BRT), neural networks (NN), support vector machines (SVM) and multivariate adaptive regression splines (MARS) are fitted to predict 14 target variables. Prediction accuracy is compared with the conventional statistical model, which shows poorer performance on average. BRT models stand out as the most accurate overall, followed by NN and RF. It was also verified that the model fit can be improved by removing the records of the first years of dam functioning from the training set.

Abstract

The availability of machine learning techniques opens up possibilities in different fields of civil engineering. Their application in conjunction with numerical simulations overcomes the limitations in traditional approaches and pave the road for some new horizons. This communication presents several applications of such a hybrid tool in design and safety assessment of dams and hydraulic structures. They include the generation of behavior prediction models from monitoring data, the identification of behavior patterns with classification models, the analysis of the seismic response of gravity dams with heterogeneous concrete in a probabilistic framework, the investigation of the performance of arch dams, and the estimation of the discharge capacity of arched labyrinth spillways.

Abstract

The advances in information and communication technologies led to a general trend towards the availability of more detailed information on dam behaviour. This allows applying advanced data‐based algorithms in its analysis, which has been reflected in an increasing interest in the field. However, most of the related literature is limited to the evaluation of model prediction accuracy, whereas the ulterior objective of data analysis is dam safety assessment. In this work, a machine‐learning algorithm (boosted regression trees) is the core of a methodology for early detection of anomalies. It also includes a criterion to determine whether certain discrepancy between predictions and observations is normal, a procedure to compute a realistic estimate of the model accuracy, and an original approach to identify extraordinary load combinations. The performance of causal and noncausal models is assessed in terms of their ability to detect different types of anomalies, which were artificially introduced on reference time series generated with a numerical model of a 100‐m‐high arch dam. The final approach was implemented in an online application to visualise the results in an intuitive way to support decision making.

Abstract

The most recent report of the Intergovernmental Panel on Climate Change (IPPC) presented medium confidence in projected increases of precipitation and run-off in some regions, while in others these are projected to decrease. However, the intensification of the climate is more generally projected to increase, which may lead to larger floods. Other studies have assessed the projected precipitation and have agreed with the IPPC assessment. These changes in climate present a concern about the risk of failure of large dams, given that most of these structures were built during the second half of the 20th century with different methods and often with limited hydrological data, resulting in uncertainty on how the design discharge capacity of the spillways can provide sufficient safety levels against overtopping in both the current and future climate. This study analyses the impacts of climate change on the failure of dams due to overtopping at a global scale The analysis is based on current and projected hydrological data obtained from the a PCR-GLOBWB Global Hydrological Model and five Global Climate Models (GCMs) selected from CMIP5 (Climate Model Intercomparison Project, phase 5). In this research an analysis of the design flood for a sample of about 1400 dams across the world under current, historical and future scenarios of climate change is made, and compared to the original design flood, by building a synthetic spillway design (using re-analysis data from CRU TS 3.2 and ERA-Interim datasets). Results from this study show that changes can be expected for the spillway discharge capacity. A consistent trend of increasing difference of the spillway capacity between the RCP4.5 and RCP8.5 scenarios and the baseline runs shows that there is a direct impact of climate change on the increase of dam failures rates due to overtopping. East Asia, South Asia, Central North America and Western North America are the regions facing the biggest rise in spillway discharge.