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Thermal Analysis of Roller Compacted Concrete Dam Utilizing a Probabilistic Model
Author(s) -
Seyed Mohammad Mousavi,
Majid Pasbani Khiavi
Publication year - 2022
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2022/3781989
Subject(s) - latin hypercube sampling , sensitivity (control systems) , structural engineering , roller compacted concrete , finite element method , workbench , gravity dam , thermal , probabilistic logic , geotechnical engineering , engineering , materials science , mathematics , monte carlo method , statistics , mechanical engineering , composite material , visualization , physics , meteorology , cement , electronic engineering
Roller compacted concrete (RCC) dam safety evaluation requires a realistic definition of thermal condition during construction and operation. Seasonal variations of temperature induce significant tensile stress. Therefore, the required preparations should be made to control and limit the thermal stresses. In this study, the effect of thermal and mechanical parameters on RCC dam behavior is evaluated using a probabilistic model. ANSYS Workbench software based on the finite element method has been utilized. The Latin hypercube sampling (LHS) method was used for probabilistic and sensitivity analysis, in which the density of concrete, Young's modulus, Poisson ratio, thermal conductivity, and specific heat have been selected as input variables. Dam body temperature, total deformation, and maximum principal stress in the body were considered as output variables. To evaluate the structural performance of the case model of the RCC dam under thermal loading, the model sensitivity to selected input variables is investigated. Considering to obtained curves, it can be concluded that the mechanical and thermal parameters have different effects on the performance of the RCC dam under variation of body temperature. Also, the results show that the sensitivity curves of output parameters are linear or bilinear relative to input variables.

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