Chelang A.Arslan
@uokirkuk.edu.iq
University of Kirkuk
EDUCATION
doctor of philosophy in water resources engineering
RESEARCH INTERESTS
civil engineering , water resources engineering, Dam engineering, soft computing
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Noor Nazar Al-Bayati, Chelang A. Arslan, and Waqed H. Hassan
Walter de Gruyter GmbH
Abstract Due to a variety of reasons, including the water retained inside and the intrinsic weight of the dam itself, dam constructions have the ability to move both horizontally and vertically. If these displacements exceed a crucial limit, a dam’s structural integrity is jeopardized. Concrete buttress dams in particular may be susceptible to high-frequency vibrations because of their slender structure, especially when the flow of water is involved. The Khassa Chi Dam, which is located northeast of Kirkuk City, is the subject of this study’s attempt to offer an alternative since the constructed dam is an embankment dam. In this research, a concrete buttress dam design was studied as an alternative dam to the constructed one. Such designs exemplify one form of gravity dams widely implemented on diverse soil types. Finite element model (FEM) was employed to simulate the behavior of the dam. The simulation utilized DIANA FEA, which relies on governing equations. There are several steps involved in developing an accurate FEM that faithfully simulates the actual behavior of a dam and predicts its future responses. The model is evaluated in later analyses in terms of stress and displacement. In this context, RSA was conducted on the modeled buttress dam. The outcome of the displacement analysis of the buttress dam exhibited its safety across all load combinations after undergoing linear dynamic analysis. This analysis included Eigenvalue Analysis and RSA. The response remained low at seismic frequencies below 3 Hz, and the extent of displacement correlated with the frequency values.
Jian Zhou, Chuanqi Li, Chelang A. Arslan, Mahdi Hasanipanah, and Hassan Bakhshandeh Amnieh
Springer Science and Business Media LLC
Accurately predicting the particle size distribution of a muck-pile after blasting is always an important subject for mining industry. Adaptive neuro-fuzzy inference system (ANFIS) has emerged as a synergic intelligent system. The main contribution of this paper is to optimize the premise and consequent parameters of ANFIS by firefly algorithm (FFA) and genetic algorithm (GA). To the best of our knowledge, no research has been published that assesses FFA and GA with ANFIS for fragmentation prediction and no research has tested the efficiency of these models to predict the fragmentation in different time scales as of yet. To show the effectiveness of the proposed ANFIS-FFA and ANFIS-GA models, their modelling accuracy has been compared with ANFIS, support vector regression (SVR) and artificial neural network (ANN). Intelligence predictions of fragmentation by ANFIS-FFA, ANFIS-GA, ANFIS, SVR and ANN are compared with observed values of fragmentation available in 88 blasting event of two quarry mines, Iran. According to the results, both ANFIS-FFA and ANFIS-GA prediction models performed satisfactorily; however, the lowest root mean square error (RMSE) and the highest correlation of determination (R2) values were obtained from ANFIS-GA model. The values of R2 and RMSE obtained from ANFIS-GA, ANFIS-FFA, ANFIS, SVR and ANN models were equal to (0.989, 0.974), (0.981, 1.249), (0.956, 1.591), (0.924, 2.016) and (0.948, 2.554), respectively. Consequently, the proposed ANFIS-GA model has the potential to be used for predicting aims on other fields.
Jian Zhou, Atefeh Nekouie, Chelang A. Arslan, Binh Thai Pham, and Mahdi Hasanipanah
Springer Science and Business Media LLC
Air overpressure (AOp) produced by blasting is one of the environmental hazards of mining operations. Accordingly, the accurate prediction of AOp is very important, and this issue requires the application of appropriate prediction models. With this in view, this paper aims to propose a new data-driven model in the prediction of AOp using a hybrid model of fuzzy system (FS) and firefly algorithm (FA). This combination is abbreviated as FS-FA model. The used data-sets in the proposed FS-FA model were arranged in a format of three input parameters. In total, 86 sets of the mentioned parameters were prepared. To avoid over-fitting, the data-sets were divided into two parts of training (80%) and test sets (20%). Three quantitative standard statistical performance evaluation measures, variance account for (VAF), coefficient correlation ( R 2 ) and root mean squared error (RMSE), were used to check the accuracy of the FS-FA model. According to the results, the R 2 and RMSE values obtained from the proposed FS-FA model were equal to 0.977 and 1.241 (for testing phase), respectively, which clearly demonstrate the merits of the proposed FS-FA model. In other words, the obtained R 2 and RMSE show that FS-FA model has high prediction level in the modeling of blast-induced AOp.
Wenchao Jiang, Chelang A. Arslan, Mehran Soltani Tehrani, Majid Khorami, and Mahdi Hasanipanah
Springer Science and Business Media LLC
Ground vibration is an adverse effect induced by rock blasting in civil and mining projects. Peak particle velocity is the most important descriptor to evaluate the ground vibration in the blasting sites. The present paper proposes an adaptive neuro-fuzzy inference system (ANFIS) for simulating the PPV in Shur River Dam area, Iran. For checking the ANFIS performance in simulating the PPV, a linear regression model is also used. To achieve the objective of this research, 90 blasting operations were monitored in the mentioned site and the values of weight charge per delay and distance between the blasting face and the installed seismograph, as the most effective parameters on the PPV, were measured. Using magnitude of three error indices, i.e., coefficient of correlation, variance account for and root mean square error, we proved that the proposed ANFIS model can simulate the PPV with a high degree of accuracy and reliability. The values of the coefficient of correlation obtained from the ANFIS and linear regression models were 0.983 and 0.876, respectively, that indicate the ANFIS outperforms the linear regression model.