Predicting damping ratio of fine-grained soils using soft computing methodology
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ORIGINAL PAPER
Predicting damping ratio of fine-grained soils using soft computing methodology Hamed Javdanian & Yaser Jafarian & Abdolhosein Haddad
Received: 18 October 2013 / Accepted: 3 June 2014 / Published online: 14 June 2014 # Saudi Society for Geosciences 2014
Abstract Accurate prediction of dynamic soil properties is very important to basic understanding of soil behavior and also practical soil modeling. Shear modulus and damping ratio play a vital role in the design of geotechnical structures subjected to dynamic loads. In this study, artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) were employed for prediction of damping ratio of fine-grained soils. Most effective factors that affect this parameter include shear strain, plasticity index, and effective confining pressure. A wide-ranging database of soil element tests was used to develop an advanced model, capable of predicting soil damping ratio accurately. Results of geotechnical centrifuge tests were also involved during the training process for adequate generalization of the algorithm for future predictions. Contributions of the effective variables were evaluated through a parametric study. It was found that the ANN model developed with feed-forward back propagation (FFBF) algorithm exhibits higher performance in prediction of soil damping ratio than those developed by radial basis function (RBF) and ANFIS. The results indicate that the soft computing-based model could provide accurate and reasonable predictions, compared with the available practical charts.
Keywords Fine-grained soils . Dynamic properties . Damping ratio . Artificial neural network
H. Javdanian : A. Haddad Department of Civil Engineering, Semnan University, Semnan, Iran Y. Jafarian (*) Geotechnical Engineering Research Center, International Institute of Earthquake Engineering and Seismology, Tehran, Iran e-mail: [email protected]
Introduction Dynamic properties of geotechnical materials are commonly expressed in terms of shear modulus and damping ratio (D). Capturing the dynamic response of geotechnical structures to dynamic excitations requires such dynamic properties. D is defined as the proportion of dissipated strain energy to the maximum recoverable energy during each cycle at a given strain amplitude as shown in Fig. 1. The energy dissipated over a loading cycle is represented by the gray area within the hysteresis loop (AL), and the maximum recoverable strain energy is represented by the triangular area (AT), which is calculated using peak shear stress and strain. Material damping is a result of friction between soil particles, strain rate effects, and nonlinearity of the stress–strain relationship in soils. Shear modulus (G) represents the shear stiffness of the soil. It is essentially the slope of the relationship between shear stress (τ) and shear strain (γ). The secant G can also be approximated for the case of dynamic loading over a cycle of loading at a given strain amplitude as shown in Fig. 1. D and G of soils are commonly dete
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