Modeling of Seepage Flow Through Concrete Face Rockfill and Embankment Dams Using Three Heuristic Artificial Intelligence Approaches: a Comparative Study

Rehamnia, Issam; Benlaoukli, Bachir; Heddam, Salim

doi:10.1007/s40710-019-00414-6

Cited by 25 publications

(3 citation statements)

References 60 publications

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“…Besides and variables, each hazard contains a list of potentially affected DRS’ subsystems. This attribute is assessed using historical data if there are documented historical failures, and/or detailed numerical and theoretical analyses of the DRSs behavior (Rehamnia et al 2020 ; Chen et al 2021 ; Rakić et al 2022 ; Nafchi et al 2021a , b ; Tang et al 2022 ). It should be noted that the hazard database contains an event to describe normal conditions (no hazard), which has the highest occurrence probability.…”

Section: Methodsmentioning

confidence: 99%

Failure Conditions Assessment of Complex Water Systems Using Fuzzy Logic

Milašinović

Ivetić

Stojković³

et al. 2023

Water Resour Manage

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Climate change, energy transition, population growth and other natural and anthropogenic impacts, combined with outdated (unfashionable) infrastructure, can force Dam and Reservoir Systems (DRS) operation outside of the design envelope (adverse operating conditions). Since there is no easy way to redesign or upgrade the existing DRSs to mitigate against all the potential failure situations, Digital Twins (DT) of DRSs are required to assess system’s performance under various what-if scenarios. The current state of practice in failure modelling is that failures (system’s not performing at the expected level or not at all) are randomly created and implemented in simulation models. That approach helps in identifying the riskiest parts (subsystems) of the DRS (risk-based approach), but does not consider hazards leading to failures, their occurrence probabilities or subsystem failure exposure. To overcome these drawbacks, this paper presents a more realistic failure scenario generator based on a causal approach. Here, the novel failure simulation approach utilizes fuzzy logic reasoning to create DRS failures based on hazard severity and subsystems’ reliability. Combined with the system dynamics (SD) model this general failure simulation tool is designed to be used with any DRS. The potential of the proposed method is demonstrated using the Pirot DRS case study in Serbia over a 10-year simulation period. Results show that even occasional hazards (as for more than 97% of the simulation there were no hazards), combined with outdated infrastructure can reduce DRS performance by 50%, which can help in identifying possible “hidden” failure risks and support system maintenance prioritization. Graphical Abstract

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Section: Methodsmentioning

confidence: 99%

Failure Conditions Assessment of Complex Water Systems Using Fuzzy Logic

Milašinović

Ivetić

Stojković³

et al. 2023

Water Resour Manage

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“…Statistical models consist of hydrostatic, temperature, and aging (time effect) components, as well as an error (residual) term which is the deviation of estimated deformation from the measured data. The parameters of respective component (e.g., regression coefficients) can be determined by minimizing the error term using various mathematical algorithms, such as multiple linear regression, 6,7 stepwise regression, 8,9 partial least square regression, 10,11 kernel function partial least square regression, 12,13 and so on. Statistical models have been developing to improve prediction accuracy of dam deformation.…”

Section: Introductionmentioning

confidence: 99%

Separate modeling technique for deformation monitoring of concrete dams

Yin

2022

Structural Health Monitoring

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Deformation monitoring is an important aspect of safety control for concrete dams. Deformation monitoring models (such as statistical models and hybrid models) are extensively applied to predict concrete dam deformation and derive confidence interval of normal deformation for anomaly detection. Deformation monitoring models for concrete dams mainly consist of hydrostatic component, temperature component, and aging component. The optimum parameters of individual components are simultaneously determined by least square method for monitoring data fitting. Thus, significant over-fitting of deformation monitoring models may be induced by mutual compensation among the parameters of model components. In this paper, the Separate Modeling Technique (SMT) is proposed for mitigating the over-fitting problem of deformation monitoring models for concrete dams. Firstly, the Empirical Mode Decomposition (EMD) is adopted to extract and visualize the aging component of displacement sequence. Proper mathematical formulation of the aging component can be established, and the problem of improperly presupposing the mathematical form of aging component in the process of constructing traditional deformation monitoring models is well addressed. In this study, the hydrostatic component is represented by the Hybrid Response Surface (HRS), which is formulated using numerical simulation with varying water levels and material parameters. The displacement variation caused by water level fluctuation is identified in terms of isothermal conditions and is used to calibrate the material parameters in the HRS. The temperature component is separated through subtracting the hydrostatic and aging components from displacement time series and then is expressed with proper mathematical formulations. Finally, hybrid models for displacement monitoring of concrete dams are established by combining the separately formulated components. The Separate modeling technique is applied to formulate crest displacement of the YL concrete gravity dam. The false alarm rate of displacement monitoring and a new model selection criterion (namely over-fitting coefficient) are adopted to compare various deformation monitoring models. It is shown that the over-fitting levels of deformation monitoring models can be effectively reduced using the SMT. The deformation monitoring models constructed with the SMT are of better accuracy in displacement prediction and present no false alarm of displacement monitoring for the tested period.

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“…Recently, many studies using Support Vector Regression (SVR) for environmental predictions have emerged, for example, wind speed prediction (Santamaria et al, 2016), predict evaporation (Moazenzadeh et al, 2018Qasem et al, 2019), air pollutant concentration (Li et al, 2019), and predicting the daily levels of groundwater (Guzman et al, 2019). The SVR has also been used in dams, as in Rehamnia et al (2020) and Tabari & Sanayei (2019). More specifically, for the dam rupture flow problem in Seyedashraf et al (2018), the formation and propagation of shock and rarefaction waves are modeled using artificial neural networks of the type MLP and SVR via radial bases function kernel (RBF).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Computational Model to Assist in the Location of Victims Buried in the Tragedy of Brumadinho

Pinheiro¹,

Bustos²,

Turrin³

et al. 2021

J. Env. Anal. Progr.

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This paper presents a hybrid computational model based on regression techniques, machine learning and physicomathematical algorithms developed for assistance in locating victims in the Brumadinho tragedy in 2019. The physicomathematical model, which provided results to help search teams, is based on integral and vector calculus, and fluid mechanics concepts. In addition, from data provided by the physicomathematical algorithm, two hybrid model were developed. One of them uses regression statistical and the other one uses support vector regression which is a type of machine learning. With good prospects of the advances in research, it is expected in future work, a more accurate model that can be used in other possible situations of dam-break. Moreover the model can be applied to situations involving computational fluid dynamics in general

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Modeling of Seepage Flow Through Concrete Face Rockfill and Embankment Dams Using Three Heuristic Artificial Intelligence Approaches: a Comparative Study

Cited by 25 publications

References 60 publications

Failure Conditions Assessment of Complex Water Systems Using Fuzzy Logic

Failure Conditions Assessment of Complex Water Systems Using Fuzzy Logic

Separate modeling technique for deformation monitoring of concrete dams

Hybrid Computational Model to Assist in the Location of Victims Buried in the Tragedy of Brumadinho

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