A New Method for Predicting the Seismic Behavior of Concrete Dams by Considering Lift Joints

Document Type : Original Research

Authors
1 PhD Candidate, Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Zanjan, Iran.
2 Associate Professor, Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Zanjan, Iran.
Abstract
Dams are structures whose continuous evaluation is of great importance. Due to their large scale, experimental study of concrete dams is difficult and therefore, the numerical simulation is used in the dynamic analysis of such dams more effectively. Despite the widespread use of concrete, our knowledge on its exact properties and physical behavior under different conditions is still limited, and many assumptions and simplifications are made to study the concrete behavior in most studies. This is especially complicated in mass concrete structures such as concrete dams. The presence of joints in most concrete structures is common and inevitable. Lift joints in dams cause different characteristics in vertical and horizontal planes. In fact, this is a special type of anisotropy that follows axial symmetry with respect to any vertical axis, which means that the mechanical behavior is the same in all horizontal planes. The mechanical behavior in all vertical planes passing through the axis of symmetry is also the same, however, it is different from the behavior of horizontal planes. Since the lift joints are usually ignored in the numerical analyzes of concrete dams, in the present paper, taking into account the orthotropic behavior of concrete, the concreting joints that cause weakness in specific positions and directions of the dam body are included. First, non-linear seismic analyzes were performed using FEAP finite element software, then a Fortran program was coded to predict the time history of displacement. The proposed method draws upon evolutionary algorithms inspired by Darwinian biology, which are increasingly utilized as surrogate models for various analyses. This approach relies on data-driven learning, wherein algorithms, based on training or sample data, generate a mathematical model for making predictions. The Pine Flat dam was modeled and analyzed under the Taft earthquake loading over a 20 second time interval with 0.02 second time steps. After successful training and learning, the model was compared and tested for other anisotropy ratios. The purpose of developing the program was to reduce the time required for analyzes so that by analyzing the initial seconds of seismic loading, by importing training inputs to the program, a proper prediction of the response process for the rest of the loading time could be obtained. In addition, by training the program for the isotropic and orthotropic modes, time history diagrams could be extracted for other orthotropic modes in different anisotropy ratios. According to the obtained results, the program is acceptably able to predict the graphs in a very short time. In addition, an equation for predicting the displacements in the orthotropic mode is presented. The maximum displacement of the orthotropic analysis was more than the isotropic one, and the use of isotropic material and homogeneous modeling of the dam body caused errors in the results. Therefore, considering the orthotropic properties of concrete can lead to more realistic results. The results reveal that time history plots derived from the implemented program closely resemble those from finite element analyses. The output results are remarkable, given the significantly reduced time required for predictions generated by the implemented program.

Keywords

Subjects


[1] Committee of Concrete dams (ICOLD), "The Physical Properties of Hardened Conventional Concrete in dams", 2008.
[2] Alliard PM, Léger P, "Earthquake safety evaluation of gravity dams considering aftershocks and reduced drainage efficiency", Journal of engineering mechanics, 2008, 134(1), 12-22.
[3] Alembagheri M, Ghaemian M, "Incremental dynamic analysis of concrete gravity dams including base and lift joints", Earthquake Engineering and Engineering Vibration, 2013, 12, 119-34.
[4] Hesari MA, Ghaemian M, Shamsai A, "Advanced nonlinear dynamic analysis of arch dams considering joints effects", Advances in Mechanical Engineering, 2014, 6, 587263.
[5] Al-Suhaili RH, Ali AA, Behaya SA, "Artificial neural network modeling for dynamic analysis of a dam-reservoir-foundation system", International Journal of Engineering Research and Applications, 2014, 4(1), 121-43.
[6] Yazdani Y, Alembagheri M, "Effects of base and lift joints on the dynamic response of concrete gravity dams to pulse-like excitations", Journal of Earthquake Engineering, 2017, 21(5), 840-60.
[7] Dizaji MS, Dizaji FS, Taghizadeh E, "Nonlinear Adaptive Simulation of Concrete Gravity Dams using Generalized Prandtl Neural Networks", International Research Journal of Engineering and Technology (IRJET), 2018, 5(6), 1990-4.
[8] Cheng L, Tong F, Li Y, Yang J, Zheng D, "Comparative study of the dynamic back-analysis methods of concrete gravity dams based on multivariate machine learning models", Journal of Earthquake Engineering, 2021, 25(1), 1-22.
[9] Ganji HT, Alembagheri M, Khaneghahi MH, "Evaluation of seismic reliability of gravity dam-reservoirinhomogeneous foundation coupled system", Frontiers of Structural and Civil Engineering, 2019, 13, 701-15.
[10] Hariri-Ardebili MA, "Uncertainty quantification of heterogeneous mass concrete in macro-scale", Soil Dynamics and Earthquake Engineering, 2020, 137, 106137.
[11] Pan J, "Seismic damage behavior of gravity dams under the effect of concrete inhomogeneity" Journal of Earthquake Engineering, 2021, 25(7), 1438-58.
[12] Liu P, Chen J, Fan S, Xu Q, "Uncertainty quantification of the effect of concrete heterogeneity on nonlinear seismic response of gravity dams including record-to-record variability", InStructures, 2021, 34, 1785-1797.
[13] Li Z, Wu Z, Chen J, Pei L, Lu X, "Fuzzy seismic fragility analysis of gravity dams considering spatial variability of material parameters", Soil Dynamics and Earthquake Engineering, 2021, 140, 106439.
[14] Mata J, Salazar F, Barateiro J, Antunes A, "Validation of machine learning models for structural dam behaviour interpretation and prediction", Water, 2021, 13(19), 2717.
[15] Hariri-Ardebili MA, Pourkamali-Anaraki F, "An automated machine learning engine with inverse analysis for seismic design of dams", Water, 2022, 14, 3898.
[16] Salazar F, Hariri-Ardebili MA, "Coupling machine learning and stochastic finite element to evaluate heterogeneous concrete infrastructure", Engineering Structures, 2022, 260, 114190.
[17] Puzrin A, "Constitutive modelling in geomechanics: introduction", Springer Science & Business Media, 2012.
[18] Fronteddu L, Léger P, Tinawi R, "Static and dynamic behavior of concrete lift joint interfaces", Journal of structural engineering, 1998, 124(12), 1418-30.
[19] Vaseghi Amiri J, "Nonlinear dynamic analysis of shear tensile failure of concrete gravity dams subjected to earthquake, considering reservoir interaction', PhD Thesis, Tarbiat Modares University, Tehran, Iran, 1997.( In Persian).
[20] Balan TA, Spacone E, Kwon M, "A 3D hypoplastic model for cyclic analysis of concrete structures", Engineering Structures, 2001, 23(4), 333-42.
[21] Bono GF, Campos Filho A, Pacheco AR, "A 3D finite element model for reinforced concrete structures analysis" Revista IBRACON de Estruturas e Materiais, 2011, 4, 548-60.
[22] Hariri-Ardebili MA, Mirzabozorg H, "Orthotropic material and anisotropic damage mechanics approach for numerically seismic assessment of arch dam–reservoir–foundation system", Strength of Materials, 2013, 45, 648-65.
[23] Penado FE, "Fracture parameter determination for the orthotropic interface crack with friction", Engineering Fracture Mechanics, 2018, 204, 542-56.
[24] Santillán D, Fraile-Ardanuy J, Toledo MÁ, "Dam seepage analysis based on artificial neural networks: The hysteresis phenomenon", InThe 2013 international joint conference on neural networks (IJCNN), IEEE, 4 Aug, 2013.

[25] Ganesh A, Balasubramanian G, Jena SK, Pradhan N, "Fourier Approach to Function Approximation", International Journal of Mathematical Archive, 2011, 2(4).
[26] Nicolau M, Agapitos A, "Choosing function sets with better generalisation performance for symbolic regression models", Genetic programming and evolvable machines, 2021, 22(1), 73-100.
[27] Willam KJ, Warnke EP, "Constitutive model for the triaxial behaviour of concrete", IABSE Seminar on concrete structures subjected to triaxial stress, 1975.
[28] Su W, Qiu YX, Xu YJ, Wang JT, "A scheme for switching boundary condition types in the integral static-dynamic analysis of soil-structures in Abaqus", Soil Dynamics and Earthquake Engineering, 2021, 141, 106458.