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Danial Jahed Armaghani

Bio: Danial Jahed Armaghani is an academic researcher from Duy Tan University. The author has contributed to research in topics: Artificial neural network & Support vector machine. The author has an hindex of 55, co-authored 212 publications receiving 8400 citations. Previous affiliations of Danial Jahed Armaghani include Universiti Teknologi Malaysia & Islamic Azad University.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
TL;DR: In this article, the authors developed new intelligent prediction models for estimating the tunnel boring machine performance (TBM) by means of the rate pf penetration (PR) of the Pahang-Selangor Raw Water Transfer (PSRWT) tunnel in Malaysia.

286 citations

Journal ArticleDOI
TL;DR: Comparison between the coefficients of determination, R2, obtained through conventional ANN and PSO-based ANN techniques reveal the superiority of the PSO -based ANN model in predicting UCS.

255 citations

Journal ArticleDOI
TL;DR: It was found that the PSO–ANN technique can predict FOS with higher performance capacities compared to ANN and R2 values of testing datasets equal to 0.915 and 0.986 suggest the superiority of thePSO– ANN technique.
Abstract: One of the main concerns in geotechnical engineering is slope stability prediction during the earthquake. In this study, two intelligent systems namely artificial neural network (ANN) and particle swarm optimization (PSO)---ANN models were developed to predict factor of safety (FOS) of homogeneous slopes. Geostudio program based on limit equilibrium method was utilized to obtain 699 FOS values with different conditions. The most influential factors on FOS such as slope height, gradient, cohesion, friction angle and peak ground acceleration were considered as model inputs in the present study. A series of sensitivity analyses were performed in modeling procedures of both intelligent systems. All 699 datasets were randomly selected to 5 different datasets based on training and testing. Considering some model performance indices, i.e., root mean square error, coefficient of determination (R2) and value account for (VAF) and using simple ranking method, the best ANN and PSO---ANN models were selected. It was found that the PSO---ANN technique can predict FOS with higher performance capacities compared to ANN. R2 values of testing datasets equal to 0.915 and 0.986 for ANN and PSO---ANN techniques, respectively, suggest the superiority of the PSO---ANN technique.

248 citations

Journal ArticleDOI
TL;DR: In this article, a support vector machine (SVM) was applied and developed to predict ground vibration in blasting operations of Bakhtiari Dam, Iran, where 80 blasting works were investigated and results of peak particle velocity (PPV) as a vibration index, distance from the blast-face and maximum charge per delay were measured and monitored to utilize in the modeling.

216 citations

Journal ArticleDOI
TL;DR: In this article, a hybrid artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) was proposed to predict peak particle velocity (PPV) resulting from quarry blasting.
Abstract: This paper presents a new hybrid artificial neural network (ANN) optimized by imperialist competitive algorithm (ICA) to predict peak particle velocity (PPV) resulting from quarry blasting. For this purpose, 95 blasting works were precisely monitored in a granite quarry site in Malaysia and PPV values were accurately recorded in each operation. Furthermore, the most influential parameters on PPV were measured and used to train the ICA-ANN model. Considering the measured data from the granite quarry site, a new empirical equation was developed to predict PPV. For comparison, a pre-developed ANN model was developed for PPV prediction. The results demonstrated that the proposed ICA-ANN model is able to predict blasting-induced PPV better than other presented techniques.

205 citations


Cited by
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Journal ArticleDOI
Wengang Zhang1, Chongzhi Wu1, Haiyi Zhong1, Yongqin Li1, Lin Wang1 
TL;DR: Novel data-driven extreme gradient boosting (XGBoost) and random forest ensemble learning methods are applied for capturing the relationships between the USS and various basic soil parameters to predict undrained shear strength of soft clays.
Abstract: Accurate assessment of undrained shear strength (USS) for soft sensitive clays is a great concern in geotechnical engineering practice. This study applies novel data-driven extreme gradient boosting (XGBoost) and random forest (RF) ensemble learning methods for capturing the relationships between the USS and various basic soil parameters. Based on the soil data sets from TC304 database, a general approach is developed to predict the USS of soft clays using the two machine learning methods above, where five feature variables including the preconsolidation stress (PS), vertical effective stress (VES), liquid limit (LL), plastic limit (PL) and natural water content (W) are adopted. To reduce the dependence on the rule of thumb and inefficient brute-force search, the Bayesian optimization method is applied to determine the appropriate model hyper-parameters of both XGBoost and RF. The developed models are comprehensively compared with three comparison machine learning methods and two transformation models with respect to predictive accuracy and robustness under 5-fold cross-validation (CV). It is shown that XGBoost-based and RF-based methods outperform these approaches. Besides, the XGBoost-based model provides feature importance ranks, which makes it a promising tool in the prediction of geotechnical parameters and enhances the interpretability of model.

367 citations

Journal ArticleDOI
TL;DR: An overview of some soft computing techniques as well as their applications in underground excavations is presented and a case study is adopted to compare the predictive performances ofsoft computing techniques including eXtreme Gradient Boosting, Multivariate Adaptive Regression Splines, and Support Vector Machine in estimating the maximum lateral wall deflection induced by braced excavation.
Abstract: Soft computing techniques are becoming even more popular and particularly amenable to model the complex behaviors of most geotechnical engineering systems since they have demonstrated superior predictive capacity, compared to the traditional methods. This paper presents an overview of some soft computing techniques as well as their applications in underground excavations. A case study is adopted to compare the predictive performances of soft computing techniques including eXtreme Gradient Boosting (XGBoost), Multivariate Adaptive Regression Splines (MARS), Artificial Neural Networks (ANN), and Support Vector Machine (SVM) in estimating the maximum lateral wall deflection induced by braced excavation. This study also discusses the merits and the limitations of some soft computing techniques, compared with the conventional approaches available.

287 citations

Journal ArticleDOI
TL;DR: In this article, the authors developed new intelligent prediction models for estimating the tunnel boring machine performance (TBM) by means of the rate pf penetration (PR) of the Pahang-Selangor Raw Water Transfer (PSRWT) tunnel in Malaysia.

286 citations

Journal ArticleDOI
TL;DR: This paper addresses the development of a flood susceptibility assessment that uses intelligent techniques and GIS and an adaptive neuro-fuzzy inference system (ANFIS) was coupled with a genetic algorithm and differential evolution for flood spatial modelling.

266 citations