Author
Ali Ghorbani
Other affiliations: Iran University of Science and Technology
Bio: Ali Ghorbani is an academic researcher from University of Gilan. The author has contributed to research in topics: Compressive strength & Liquefaction. The author has an hindex of 12, co-authored 28 publications receiving 454 citations. Previous affiliations of Ali Ghorbani include Iran University of Science and Technology.
Papers
More filters
TL;DR: In this article, a neural network model is developed to predict the horizontal ground displacement in both ground slope and free face conditions due to liquefaction-induced lateral spreading, which is the one compiled by Youd and his colleagues in their revised MLR model.
99 citations
TL;DR: In this article, industrial wastes such as Granulated Blast Furnace Slag (GBFS) and Basic Oxygen Furnace SLag (BOFS) activated with calcium oxide (CaO) and medium reactive magnesia (MgO) are used for chemical stabilization of a soft clay.
81 citations
TL;DR: In this article, the results of 90 Unconfined Compressive Strength (UCS) and California Bearing Ratio (CBR) tests on sulfate silty sand stabilized with different lime and microsilica percentages as the two main stabilizers.
62 citations
TL;DR: In this article, a comprehensive dynamic three dimensional finite element model, which includes the effect of lots of important parameters on the micropiles seismic performance, has been presented and validated using remodeling a single degree of freedom shaking table test done by Mc Manus at the University of Canterbury.
38 citations
Journal Article•
TL;DR: In this paper, the bearing-settlement behavior of combined pile-raft foundations on medium dense sand was investigated using a 1g physical model test was performed on a circular rigid raft underpinned with four model piles and numerical simulation was also carried out on the model test, using FLAC-3D to show compatibility of the numerical analysis with the test.
Abstract: The pile-raft foundation is a combination of a raft foundation with piles Pile-raft foundation has been widely designed, assuming all structure loads to be transferred to piles without considering contribution of the load taken by contact surface between raft and soil Methods of analysis currently used in practice are based upon relatively conservative assumptions of soil behavior or on the less realistic soil-structure interaction In this study the bearing -settlement behavior of combined pile-raft foundations on medium dense sand was investigated 1g physical model test was performed on a circular rigid raft underpinned with four model piles Numerical simulation was also carried out on the model test, using FLAC-3D, to show compatibility of the numerical analysis with the test The obtained results showed very good accuracy of the numerical method used in this study as long as the applied load does not exceed the working load, while the performance of numerical model was relatively good for the loads beyond working load
37 citations
Cited by
More filters
31 Oct 2001
TL;DR: The American Society for Testing and Materials (ASTM) as mentioned in this paper is an independent organization devoted to the development of standards for testing and materials, and is a member of IEEE 802.11.
Abstract: The American Society for Testing and Materials (ASTM) is an independent organization devoted to the development of standards.
3,792 citations
TL;DR: The state-of-the-art review indicates to what extent ML has been applied in four topic areas of earthquake engineering, including seismic hazard analysis, system identification and damage detection, seismic fragility assessment, and structural control for earthquake mitigation.
Abstract: Machine learning (ML) has evolved rapidly over recent years with the promise to substantially alter and enhance the role of data science in a variety of disciplines. Compared with traditional appro...
185 citations
Journal Article•
TL;DR: A state-of-the-art examination of ANNs in geotechnical engineering and insights into the modeling issues ofANNs are presented.
Abstract: Over the last few years, artificial neural networks (ANNs) have been used successfully for modeling almost all aspects of geotechnical engineering problems. Whilst ANNs provide a great deal of promise, they suffer from a number of shortcomings such as knowledge extraction, extrapolation and uncertainty. This paper presents a state-of-the-art examination of ANNs in geotechnical engineering and provides insights into the modeling issues of ANNs. The paper also discusses current research directions of ANNs that need further attention in the future.
167 citations
TL;DR: In this paper, a new approach based on genetic programming (GP) for determination of liquefaction induced lateral spreading is presented, which is trained and validated using a database of SPT-based case histories.
141 citations
TL;DR: In this article, the authors investigated the potential of ANN and ANFIS models in predicting the groundwater level of Bastam Plain in Iran. And they found that the ANN model (with root-mean-square-error (RMSE) 0.02m and determination coefficient (R2) of 0.96) performed better than the AN Fuzzy Inference System (ANFIS) with RMSE 1.06m and R2 0.83m.
Abstract: Prediction of the groundwater level (GWL) fluctuations is very important in the water resource management. This study investigates the potential of two intelligence models namely, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) in the forecasting of the groundwater level of Bastam Plain in Iran. For this purpose, 9 years data-sets including hydrological and hydrogeological parameters like rainfall recharge, irrigation returned flow and also pumping rates from water wells were used as input data to predict groundwater level. The results showed that ANN and ANFIS models can predict GWL accurately. Also, it was found that the ANFIS model (with root-mean-square-error (RMSE) 0.02 m and determination coefficient (R2) of 0.96) performed better than the ANN model with RMSE = 1.06 m and R2 = 0.83. Finally, three scenarios were considered to predict the groundwater level in the next 2 years as follows 1- The rainfall recharge and pumping rate of water wells will be constant, 2- The rainfall recharge will be constant, but the pumping rate of water wells will be reduced equal to the water deficit of the aquifer, 3- The pumping rate of water wells will be constant but the rainfall recharge will be reduced 30 %. The prediction with these scenarios showed that the groundwater level has the minimum reduction when the pumping rate of water wells is equal to the water deficit of the aquifer.
139 citations