Analysis of Dam Behavior by Statistical Models: Application of the Random Forest Approach

Belmokre, Ahmed; Mihoubi, Mustapha Kamel; Santillán, David

doi:10.1007/s12205-019-0339-0

Cited by 40 publications

(21 citation statements)

References 42 publications

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“…Its final output can be obtained by taking the average of the values predicted by individual trees in the forest. Ensemble makes RFR more robust and stable, thus ensuring decent performance on test data in most scenarios (Belmokre, Mihoubi, & Santillán, 2019; B. Dai et al., 2018; X. Li, Wen, & Su, 2019). The accuracy and robustness of RFWSVR, HST, BPNN, and RFR models are hereby compared by using the dam displacement subjected to irregular water‐level changes.…”

Section: Resultsmentioning

confidence: 99%

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A robust prediction model for displacement of concrete dams subjected to irregular water‐level fluctuations

Ren

et al. 2021

Computer aided Civil Eng

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Monitoring and predicting the displacement of concrete dams is one of the most crucial considerations for ensuring their long-term safe operation. Most existing models are designed for dams subjected to common structural and environmental conditions, with little attention paid to atypical operational conditions, such as structural strengthening or sudden changes in the external environment. The motivation for this work is to develop a reliable prediction model for displacement behavior of concrete dams subjected to irregular upstream water-level fluctuations. In our study, a reconstruction method for unevenly sampled time series is presented to analyze such data. Then, an improved model, factor weighted support vector regression (FWSVR), which differentiates various factors and their effects through a weighting matrix, is theoretically derived. The weights are determined by the RReliefF algorithm, and together with FWSVR form the final RReliefF-based FWSVR (RFWSVR) model. In particular, the hyperparameters involved in the above modeling strategy are optimized by the Grey Wolf Optimizer. Eventually, the prediction robustness of the developed model was verified on the data from four representative monitoring points of a real-world dam, where its accuracy was compared to classical dam behavior modeling methods, FWSVR models using other weighting methods, and an ensemble learning algorithm. Comparative evaluation of the performance of the different methods was conducted with the help of recognized statistical indices. The evaluation results show that the overall performance of the proposed RFWSVR model is optimal for the displacement prediction at the selected points when the dam case is subjected to irregular water-level changes. This novel modeling approach may be generalized for modeling the evolution behavior of other civil or hydraulic structures.

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Section: Resultsmentioning

confidence: 99%

Section: Performance Comparison With Classical and Ensemble Modeling Methodsmentioning

confidence: 99%

A robust prediction model for displacement of concrete dams subjected to irregular water‐level fluctuations

Ren

et al. 2021

Computer aided Civil Eng

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“…In this section, the data of three pendulum monitoring points (418D17, 375D17, and 418D36) along with the environmental effects were utilized for the establishment of KELM, MLR, and random forest (RF) [42] models. RF is an intelligent algorithm that has been successfully applied to improve the prediction accuracy of the HST model in concrete dam deformation monitoring [43,44]. The excellent performance of the KELM algorithm was verified by comparing the prediction accuracy of the HST-based model with MLR and RF algorithm.…”

Section: The Traditional Statistical Modelsmentioning

confidence: 99%

Concrete arch dam behavior prediction using kernel-extreme learning machines considering thermal effect

Kang

Ma³

et al. 2021

J Civil Struct Health Monit

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Behavior prediction of concrete arch dams requires the interpretation of monitoring data from instrument measurement. Mathematical models based on artificial intelligence algorithms provide an effective approach to interpret the dam behavior from the monitoring data, which can be utilized to model dam displacement as a function of water level, irreversible time effect, and thermal. In our recent study, an improved mathematical model base on kernel-extreme learning machines (KELM) algorithm with long-term daily air temperature monitoring data series has been successfully applied to the displacement prediction modeling of concrete gravity dams. Considering the important contribution of thermal effect on concrete arch dam deformation, the present paper concerns the applicability of the proposed method in the behavior prediction of concrete arch dams. For the sake of improving the efficiency of the mathematical model, the parameters which are crucial to the performance of KELM are selected by the Jaya optimization algorithm. The performance of the proposed automatic parameter optimization-based Jaya-KELM model was compared with the traditional statistical model and other intelligent algorithms. Results demonstrate that the proposed method is a promising and powerful approach for the behavior prediction modeling of concrete arch dams.

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“…Therefore, mathematical thermal models constitute an important tool for preventing excessive temperature gradients which may lead to cracking. Moreover, the expected rise in temperature due to global warming will have consequences on dams in service [3,4], and may increase the cracking risk of RCC dams under construction. Thermal models are composed of three main ingredients.…”

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

Toledo

2020

Water

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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.

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Analysis of Dam Behavior by Statistical Models: Application of the Random Forest Approach

Cited by 40 publications

References 42 publications

A robust prediction model for displacement of concrete dams subjected to irregular water‐level fluctuations

A robust prediction model for displacement of concrete dams subjected to irregular water‐level fluctuations

Concrete arch dam behavior prediction using kernel-extreme learning machines considering thermal effect

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

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