Data-Based Models for the Prediction of Dam Behaviour: A Review and Some Methodological Considerations

Salazar, Fernando; Morán, R.; Toledo, Miguel Ángel; Oñate, Eugenio

doi:10.1007/s11831-015-9157-9

Cited by 254 publications

(187 citation statements)

References 45 publications

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“…In the recent years, non-parametric techniques have emerged as an alternative to HST for building data-based behaviour models [8], e.g. support vector machines (SVN) [9], neural networks (NN) [10], adaptive neuro-fuzzy systems (ANFIS) [11], among others [8].…”

Section: Introductionmentioning

confidence: 99%

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Interpretation of dam deformation and leakage with boosted regression trees

Salazar

Toledo

Oñate

et al. 2016

Engineering Structures

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101

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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.

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Section: Introductionmentioning

confidence: 99%

“…support vector machines (SVN) [9], neural networks (NN) [10], adaptive neuro-fuzzy systems (ANFIS) [11], among others [8]. In general, these tools are more suitable to model non-linear cause-effect relations, as well as interaction among external variables, as that previously mentioned between hydrostatic load and temperature.…”

Section: Introductionmentioning

confidence: 99%

Interpretation of dam deformation and leakage with boosted regression trees

Salazar

Toledo

Oñate

et al. 2016

Engineering Structures

Self Cite

101

View full text Add to dashboard Cite

show abstract

“…On the basis of the analysis in Section 3.1 for model construction, the preset factor sets are selected as follows. Temperature subset: T 0-1 , T 1-2 , T 3-7 , T [8][9][10][11][12][13][14][15] , and T [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] ; Aging subset: On the basis of the qualitative analysis of the process line of measured internal settlement at TA1-5, the variation is consistent with the characteristics of the combination of linear change and logarithmic change. Therefore, t 1 and ln(t 1 + 1) are selected as the preset aging factors from the six types of factors in Equation 7.…”

Section: Validating the Improved Statistical Modelmentioning

confidence: 99%

“…These new concepts and methods can improve CFRD deformation analysis and deformation prediction. [17][18][19][20] Mathematical models of CFRD deformation monitoring are mainly associated with data observation during the runup period when the impoundment of the sluice is complete. Such models generally disregard deformation during construction.…”

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confidence: 99%

A statistical model of deformation during the construction of a concrete face rockfill dam

Yuqun

Yang

et al. 2017

Struct Control Health Monit

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SummaryMonitoring data collected during dam construction are important in complete series of monitoring data. These data play a significant role in dam safety monitoring and the analysis of structural conditions. The traditional statistical model of the deformation of a concrete face rockfill dam (CFRD) with filling height and time factors is associated with serious multicollinearity issues during the construction phase. This study uses the Longbeiwan CFRD as an | INTRODUCTIONA concrete face rockfill dam (CFRD) is a type of dam that uses rockfill as the support structure and an upstream surface concrete face as the anti-seepage structure. CFRDs are effective because of their adaptability to poor topographical, geological, and climatic conditions. In addition, they have excellent safety features and economic efficiency. Currently, CFRDs are one of the most commonly used and cost competitive dam types. [1,2] Deformation and seepage control are two key technical problems in CFRD construction. Deformation (such as that of the surface, interior, and foundation of dams) and various joint deformations can be monitored using various technologies. [3] For example, the horizontal displacement of the dam surface can be monitored by line of sight or torsion, whereas the surface settlement (vertical displacement) of the dam can be monitored using the geometric method. Moreover, the horizontal displacement of the rockfill body can be monitored by a meter or inclinometer, whereas theThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The statistical analysis also provides an index that measures the relative importance of each component in the structural behavior evolution. The most popular statistical model for dam monitoring analysis is the hydrostatic‐seasonal‐time (HST) model . It was first proposed to predict displacements in concrete dams, and then has been widely applied other variables such as piezometric tube levels in earth dams and embankments.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive investigation of leakage problems for concrete gravity dams with penetrating cracks based on detection and monitoring data: A case study

2017

Struct Control Health Monit

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Summary The leakage amount is an important indicator evaluating the seepage behavior of a dam. Due to the complex behavior, crack development judgment and leakage behavior identification for concrete gravity dams remain a challenging task. The paper is concerned with comprehensive investigation of leakage problems for concrete gravity dams with penetrating cracks. A case study on Shimantan Reservoir Dam was taken to examine the utilization of the comprehensive investigation method. Deficiency investigations were undertaken to identify the nature and sources of defects responsible for the serious leakage. Based on the information acquired from field visual inspections and ground‐penetrating radar tests, it is apparent that several transverse cracks penetrate the dam crest and propagate downward into the dam body. The results of the remedial grouting, X‐ray diffraction analyses and water injection tests reflect the appearance of penetrating leakage paths in the dam body. Subsequently, 2 behavior models including the special hydrostatic‐seasonal‐time model and the inverse analysis model were conducted to distinguish the contribution of penetrating cracks to leakage amount. The special hydrostatic‐seasonal‐time model was modified to address the influence of leakage flow in real microcracks in concrete dams based on the Navier–Stokes equation. The result shows that the proposed models provide an acceptable accuracy in analyzing leakage monitoring data for gravity dams with penetrating cracks. Leakage flow in penetrating cracks plays a dominant role in the dam seepage field. The execution flow in this paper could be readily employed in investigations of similar problems of concrete dams.

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Data-Based Models for the Prediction of Dam Behaviour: A Review and Some Methodological Considerations

Cited by 254 publications

References 45 publications

Interpretation of dam deformation and leakage with boosted regression trees

Interpretation of dam deformation and leakage with boosted regression trees

A statistical model of deformation during the construction of a concrete face rockfill dam

Comprehensive investigation of leakage problems for concrete gravity dams with penetrating cracks based on detection and monitoring data: A case study

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