Treatment of Solar Radiation by Spatial and Temporal Discretization for Modeling the Thermal Response of Arch Dams

Struct Control Health Monit

González

et al. 2017

Summary 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.

Section: A Two-dimensional Density Function Is Computed By Meanssupporting

confidence: 68%

Early detection of anomalies in dam performance: A methodology based on boosted regression trees

Salazar

Struct Control Health Monit

González

et al. 2017

“…For example, it is based on the assumption that the hydrostatic load and the temperature are independent, whereas it is obviously not the case: the thermal field in the dam body, especially in the vicinity of the water surface, is strongly dependant on the water temperature in the upstream face [5]. In turn, the thermal load influences the stress and displacement fields.…”

Section: Introductionmentioning

confidence: 99%

Interpretation of dam deformation and leakage with boosted regression trees

Salazar

Oñate

et al. 2016

Engineering Structures

Predictive models are essential in dam safety assessment. They have been traditionally based on simple statistical tools such as the hydrostatic-seasontime (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.

“…where θ dp is the dew point temperature, which is assessed by the Clausius-Clapeyron equation as function of the relative humidity and the ambient temperature [39]. The heat fluxes due to the solar radiation and the evaporative cooling are computed following the methodology of [28]. The solar radiation generates a heat flux given by…”

Section: Heat Fluxesmentioning

confidence: 99%

“…Furthermore, the night radiative cooling should be considered when computing the long wave radiation exchange. Santillan et al [28] proposed a methodology for computing the heat fluxes in concrete dams, taking into account all the previous phenomena. The methodology was validated by the comparison between the outputs of the models and the recorded data in an arch dam using both numerical [28,29] and analytical approaches [30].…”

Section: Introductionmentioning

confidence: 99%

Thermal Simulation of Rolled Concrete Dams: Influence of the Hydration Model and the Environmental Actions on the Thermal Field

Ponce-Farfán

Santillán

2020

Water

Mathematical models for the simulation of the thermal evolution of roller-compacted concrete (RCC) dams during construction constitute an important tool for preventing excessive temperature rise, which may lead to cracking and losses of functionality. Here, we present a framework for the simulation of the thermal process. We define the boundary conditions of the problem using a careful description that incorporates the main heat exchange mechanisms. We adopt both a non-adiabatic and an adiabatic heat generation model for the simulation of the cement hydration. Our numerical framework lets us study the effect of the adopted heat generation model on the thermal field. Moreover, we study the influence of the weather conditions on the evolution of the hydration, and on the starting date of construction. Our simulations have shown that the hydration model has an important influence over the temperature field during the construction and the heat generation rate. Moreover, the hydration process and the temperature evolution are driven by the weather conditions. Once the next lift is cast, its thermal insulation effect makes the hydration take place under quasi-adiabatic conditions. As expected, dams built in cold months are prone to dissipate more heat than those built in warm seasons.