ORIGINAL_ARTICLE
Fuzzy Vibration Suppression of a Smart Elastic Plate using Graphical Computing Environment
A nonlinear model for the vibration suppression of a smart composite elastic plate using graphical representation involving fuzzy control is presented. The plate follows the von Kármán and Kirchhoff plate bending theories and the oscillations are caused by external transversal loading forces, which are applied directly on it. Two different control forces, one continuous and one located at discrete points, are considered. The mechanical model is spatially discretized by using the time spectral Galerkin and collocation methods. Our aim is to suppress vibrations through a simulation process within a modern graphical computing environment. Here we use MATLAB/SIMULINK, while other similar packages can be used as well. The nonlinear controller is designed, based on an application of a Mamdani-type fuzzy inference system. A computational algorithm, proposed and tested here is not only effective, but robust as well. Furthermore, all elements of the study can be replaced or extended, due to the flexibility of the used SIMULINK environment.
http://www.jsoftcivil.com/article_50651_cf628d88c4bab9c5b680c3e7c5a60b5c.pdf
2018-01-01T11:23:20
2020-07-09T11:23:20
1
17
10.22115/scce.2018.50651
Smart plate model
Spatial discretization
fuzzy control
Computational algorithm
SIMULINK diagrams
Aliki
Muradova
aliki@mred.tuc.gr
true
1
School of Production Engineering and Management, Technical University of Crete, Chania, Greece
School of Production Engineering and Management, Technical University of Crete, Chania, Greece
School of Production Engineering and Management, Technical University of Crete, Chania, Greece
AUTHOR
Georgios
Tairidis
tairidis@gmail.com
true
2
School of Production Engineering and Management, Technical University of Crete, Chania, Greece
School of Production Engineering and Management, Technical University of Crete, Chania, Greece
School of Production Engineering and Management, Technical University of Crete, Chania, Greece
AUTHOR
Georgios
Stavroulakis
gestavr@dpem.tuc.gr
true
3
School of Production Engineering and Management, Technical University of Crete, Chania, Greece
School of Production Engineering and Management, Technical University of Crete, Chania, Greece
School of Production Engineering and Management, Technical University of Crete, Chania, Greece
LEAD_AUTHOR
[1] Tairidis G. K., Stavroulakis G. E., Marinova D. G., & Zacharenakis E. C. Classical and Soft Robust Active Control of Smart Beams, In: Papadrakis, M., Charmpis, D. C. Lagaros, N. D., Tsompanakis, Y. (Eds), Computat. Struct. Dynamics and Earthquake Engineer, CRC Press/Balkema and Taylor & Francis Group, London, UK, 2009, Ch. 11, pp. 165–178.
1
[2] Tavakolpour A. R., Mailah M., Darus I. Z. M., & Tokhi O. Selflearning active vibration vibration control of a flexible plate structure with piezoelectric actuator. Simul. Model. Prac. and Theory 18, 2010, 516–532.
2
[3] Fisco N.R. & Adeli H. Smart structures: Part II: Hybrid control systems and control control strategies. Scientia Iranica 18, 2011, 285–295.
3
[4] Precup R.-E., & Hellendoorn H. A survey on industrial applications of fuzzy control. Computers in Industry 62, 2011, 213–226.
4
[5] Korkmaz S. A review of active structural control: challenges for engineering informatics. Comput. and Struct. 89, 2011, 2113–2132.
5
[6] Preumont A. Vibration Control of Active Structures, Springer, 2002.
6
[7] Driankov D., Hellendoorn H., & Reinfrank M. An Introduction to Fuzzy Control, 2nd ed., Springer-Verlag, Berlin, Heidelberg, New York, 1996.
7
[8] Zeinoun I. J., & Khorrami I. J. An Adaptive Control Scheme Based on Fuzzy Logic and its Application to Smart Structures. Smart Mater. Struct. 3, 1994, 266–276.
8
[9] Wenzhonga Q., Jincaib S., & Yangc Q. Active control of vibration using a fuzzy control method. J. of Sound and Vibration, 275, 2004, 917–930.
9
[10] Shirazi A. H. N., Owji H. R., & Rafeeyan M. Active vibration control of an FGM rectangular plate using fuzzy logic controllers. Procedia Engineering 14, 2011, 3019–3026.
10
[11] Ciarlet Ph. G. Mathematical Elasticity, v. II:Theory of Plates. Elsevier, Amsterdam, 1997.
11
[12] Ciarlet P., & Rabier P. Les equations de von Kármán. Springer-Verlag, Berlin, Heidelberg, New York, 1980.
12
[13] Duvaut G., & Lions J. L. (1972). Les inequations en mecaniques et en physiques, Dunod.
13
[14] Muradova A. D. A time spectral method for solving the nonlinear dynamic equations of a rectangular elastic plate. J. Eng. Math. 92, 2015, 83–101.
14
[15] Muradova A. D. & Stavroulakis G. E. Hybrid control of vibrations of smart von Kármán plate. Acta Mechanica 226, 2015, 3463–3475.
15
[16] Muradova A. D., & Stavroulakis G. E. Fuzzy vibration control of a smart plate. Int. J. Comput. Meth. in Eng. Sci. Mech.14, 2013, 212–220.
16
[17] Tairidis G. Foutsitzi G., Koutsianitis P., Stavroulakis G.E. Fine tuning of a fuzzy controller for vibration suppression of smart plates using genetic algorithms. Advances in Engineering Software, 101, 2016, 123–135.
17
[18] Marinaki M., Marinakis Y., Stavroulakis G. T. Fuzzy control optimized by a Multi-Objective Particle Swarm Optimization algorithm for vibration suppression of smart structures. Structural and Multidisciplinary Optimization, 43, 2011, 29–42.
18
[19] Koutsianitis P., Tairidis G. K., Drosopoulos G. A., Foutsitzi G. A., Stavroulakis G. E. (2017). Effectiveness of optimized fuzzy controllers on partially delaminated piezocomposites, Acta Mechanica, 228, 1373–1392.
19
[20] Muradova A. D., Tairidis G. K., Stavroulakis G. T. Adaptive Neuro-Fuzzy Vibration Control of a Smart Plate. Numerical Algebra, Control and Optimization, 7, 2017, 251–271.
20
[21] Destuynder Ph., & Salaun M.. Mathematical Analysis of Thin Plate Models., Springer, 1996.
21
[22] Reddy J. N. Theory and Analysis of Elastic Plates and Shells, CRC Press, 2007, Taylor & Francis.
22
ORIGINAL_ARTICLE
Development of MLR, ANN and ANFIS Models for Estimation of PCUs at Different Levels of Service
Passenger car unit (PCU) of a vehicle type depends on vehicular characteristics, stream characteristics, roadway characteristics, environmental factors, climate conditions and control conditions. Keeping in view various factors affecting PCU, a model was developed taking volume to capacity ratio and percentage share of particular vehicle type as independent parameters. A microscopic traffic simulation model VISSIM has been used in present study for generating traffic flow data which some time very difficult to obtain from field survey. A comparison study was carried out with the purpose of verifying when the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and multiple linear regression (MLR) models are appropriate for prediction of PCUs of different vehicle types. From the results observed that ANFIS model estimates were closer to the corresponding simulated PCU values compared to MLR and ANN models. It is concluded that the ANFIS model showed greater potential in predicting PCUs from v/c ratio and proportional share for all type of vehicles whereas MLR and ANN models did not perform well.
http://www.jsoftcivil.com/article_50036_14d480c4b6c292771f565646ab8f5687.pdf
2018-01-01T11:23:20
2020-07-09T11:23:20
18
35
10.22115/scce.2018.50036
PCU
MLR
ANN
ANFIS
Seelam
Srikanth
ssseelamsrikanth@gmail.com
true
1
Research Scholar, Department of Civil Engineering, National Institute of Technology, Warangal, India
Research Scholar, Department of Civil Engineering, National Institute of Technology, Warangal, India
Research Scholar, Department of Civil Engineering, National Institute of Technology, Warangal, India
LEAD_AUTHOR
Arpan
Mehar
arpanmehr400@gmail.com
true
2
Assistant Professor, Department of Civil Engineering, National Institute of Technology, Warangal, India
Assistant Professor, Department of Civil Engineering, National Institute of Technology, Warangal, India
Assistant Professor, Department of Civil Engineering, National Institute of Technology, Warangal, India
AUTHOR
[1] S. Anand, S.V.C. Sekhar and M.R. Karim, Development of Passenger Car Unit (PCU) values for Malaysia, Journal of the Eastern Asia Society for Transportation Studies3(3), 1999, 73-80.
1
[2] J.A. Tracey, J. Zhu and K.R. Crooks, Modeling and inference of animal movement using artificial neural networks, Environmental and Ecological Statistics 18, 2011, 393–410.
2
[3] A. Moghaddamnia, M. Ghafari Gousheh, J. Piri, S. Amin and D. Han, Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system technique, Adv Water Resource 32(1), 2009, 88–97.
3
[4] J.S.R. Jang, ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans Syst Man Cybern 23(3), 1993, 665–685.
4
[5] C.T. Lin and C.S.G. Lee, Neural fuzzy system. Prentice Hall, Englewood Cliffs, 1995.
5
[6] J.H. Tay and X. Zhang, Neural fuzzy modelling of anaerobic biological waste water treatment systems. Journal of Environmental Engineering – ASCE 125, 1999, 1149–1159.
6
[7] H.M. Azamathulla, C.K. Chang, A.A. Ghani, J. Ariffin, N.A. Zakaria and Z. Abu Hasan, An ANFIS-based approach for predicting the bed load for moderately sized rivers, Journal of Hydro-Environment Research 3, 2009, 35–44.
7
[8] E.L Keller, and G.J Saklas, Passenger Car Equivalents from Network Simulation, Journal of Transportation Engineering, 110, 1984, 397-411.
8
[9] T.V. Ramanayya, Highway capacity under mixed traffic conditions, Traffic Engineering and Control, 29(5), 1988, 284-287.
9
[10] H.S.L Fan, Passenger Car Equivalent for Vehicles on Singapore Expressways, Transportation Research-A (GB), 24 A(5), 1990, 391-396.
10
[11] L. Elefteriadou, D. Torbic, D and N. Webster, Development of Passenger Car Equivalents for freeways, two-lane highways, and arterials, Transportation Research Record. 1572. Transportation Research Board, Washington, D.C., 1997, 51-58.
11
[12] S. Chandra and U. Kumar, Effect of lane width on capacity under Mixed Traffic Conditions in India, Journal of Transportation Engineering, ASCE, 129(2), 2003, 155-160.
12
[13] V.T. Arasan and S.S. Arkatkar, Micro-simulation Study of Effect of Volume and Road Width on PCU of Vehicles under Heterogeneous Traffic, Journal of Transportation Engineering, ASCE, 136(12), 2010, 1110-1119.
13
[14] M.S. Bians, B. Ponnu and S.S. Arkatkar, Modeling of Traffic Flow on Indian Expressways using Simulation Technique, International Conference on Traffic and Transportation Studies Changsha, China, 2012.
14
[15] Mehar, S. Chandra and S. Velmurugan, Passenger car units at different levels-of-service for capacity analysis of multilane divided highways, ASCE, Journal of Transportation Engineering140 (1), 2013, 81-88.
15
[16] Mehar, S. Chandra and S. Velmurugan, Effect of Traffic Composition on Capacity of Multilane Highways, KSCE Journal of Civil Engineering, 2015, 1-8.
16
[17] F. Khademi, M. Akbari and M. Nikoo, Displacement determination of concrete reinforcement building using data-driven models, International Journal of Sustainable Built Environment, 2017.
17
[18] G. Jiang, J. Keller, P.L. Bond and Z. Yuan, Predicting concrete corrosion of sewers using artificial neural network, Water Res., 92, 2016, 52-60.
18
[19] H. Mashhadban, S.S. Kutanaei and M.A. Sayarinejad, Prediction and modeling of mechanical properties in fiber reinforced self-compacting concrete using particle swarm optimization algorithm and artificial neural network, Constr. Build. Mater., 119, 2016, 277-287.
19
[20] Q. Zhou, F. Wang and F. Zhu, Estimation of compressive strength of hollow concrete masonry prisms using artificial neural networks and adaptive neuro-fuzzy inference systems, Constr. Build. Mater., 125, 2016, 417-426.
20
[21] K. Behfarnia and F. Khademi, A comprehensive study on the concrete compressive strength estimation using artificial neural network and adaptive neuro-fuzzy inference system, Int. J. Opt. Civ. Eng., 7 (1), 2017, 71-80.
21
[22] S. Biswas, S. Chandra and I. Ghosh, Estimation of Vehicular Speed and Passenger Car Equivalent Under Mixed Traffic Condition Using Artificial Neural Network, Arabian Journal for Science and Engineering, 2017, 1-12.
22
[23] Guisan, T.C. Edwards, T. Hastie, Generalized linear and generalized additive models in studies of species distributions: setting the scene, Ecological Modelling 157, 2002, 89–100.
23
[24] F. Khademi, M. Akbari, S.M. Jamal, and M. Nikoo, Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete, Frontiers of Structural and Civil Engineering, 11(1), 2017, 90-99.
24
[25] F. Khademi, S.M. Jamal, N. Deshpande and S. Londhe, Predicting strength of recycled aggregate concrete using artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression, International Journal of Sustainable Built Environment, 5(2), 2016, 355-369.
25
[26] Mehar, S. Chandra and S. Velmurugan, Highway capacity through VISSIM calibrated for mixed traffic conditions, KSCE Journal of Civil Engineering 18(2), 2014.
26
ORIGINAL_ARTICLE
Structural Response of Reinforced Self-Compacting Concrete Deep Beam using Finite Element Method
Analysis of reinforced concrete deep beam is based on simplified approximate method due to the complexity of the exact analysis. The complexity is due to a number of parameters affecting its response. To evaluate some of this parameters, finite element study of the structural behaviour of reinforced self-compacting concrete deep beam was carried out using Abaqus finite element modeling tool. The model was validated against experimental data from literature. The parametric effects of varied concrete compressive strength, vertical web reinforcement ratio and horizontal web reinforcement ratio on the beam were tested on eight (8) different specimens under four point loads. The results of the validation work showed good agreement with the experimental studies. The parametric study revealed that the concrete compressive strength most significantly influenced the specimens’ response with the average of 41.1% and 49 % increment in the diagonal cracking and ultimate load respectively due to doubling of concrete compressive strength. Although increase in horizontal web reinforcement ratio from 0.31 % to 0.63 % lead to average of 6.24 % increment on the diagonal cracking load, it does not influence the ultimate strength and the load deflection response of the beams. Similar variation in vertical web reinforcement ratio lead to average of 2.4 % and 15 % increment in cracking and ultimate load respectively with no appreciable effect on the load deflection response.
http://www.jsoftcivil.com/article_50115_8d2518e02c8b9f6efc7419cb1e7f3753.pdf
2018-01-01T11:23:20
2020-07-09T11:23:20
36
61
10.22115/scce.2018.50115
Self-Compacting Concrete
Deep beam
Concrete damage plasticity
FEM
Mutiu
Akinpelu
mutiu.akinpelu@kwasu.edu.ng
true
1
Lecturer, Department of Civil Engineering, College of Engineering and Technology, Kwara State University, Malete, Kwara State, Nigeria
Lecturer, Department of Civil Engineering, College of Engineering and Technology, Kwara State University, Malete, Kwara State, Nigeria
Lecturer, Department of Civil Engineering, College of Engineering and Technology, Kwara State University, Malete, Kwara State, Nigeria
LEAD_AUTHOR
Adeola
Adedeji
aaadeji@unilorin.edu.ng
true
2
Department of Civil Engineering, University of Ilorin, Ilorin, Nigeria
Department of Civil Engineering, University of Ilorin, Ilorin, Nigeria
Department of Civil Engineering, University of Ilorin, Ilorin, Nigeria
AUTHOR
ACI 318, Building Code Requirements for Structural Concrete and Commentary, American Concrete Institute (ACI)., 2014.
1
Eurocode 2, "Design of concrete structures - Part 1-1: General," 2004.
2
Russo,G.,Venir,R.,and Pauletta, M., "Reinforced Concrete Deep Beams-Shear Strength Model and Design Formula.," ACI Structural Journal, vol. 102, no. 3, pp. 429-437, 2005.
3
Al-Khafaji J., Al-Shaarbaf I., and Sultan W. H., "Shear Behavior of Self Compacting Concrete Deep Beams.," Journal of Engineering and Development, vol. 18, no. 2, pp. 36-58, 2014.
4
Okamura H. and Ouchi M., "Self-compacting concret," Journal of Advance Concrete Technology, vol. 1, no. 1, pp. 1-15, 2003.
5
EFNARC: European Federation Dedicated to Specialist Construction Chemicals and Concrete Systems, Surrey, 2002.
6
Mutiu A. Akinpelu, Samson O. Odeyemi, Oladipupo S. Olafusi and Fatimah Z. Muhammed, "Evaluation of splitting tensile and compressive," Journal of King Saud University – Engineering Sciences, 2017.
7
Choi Y. W., Lee H. K., Chu S. B., Cheong S. H. and Jung W. Y., "Shear Behavior and Performance of Deep Beams Made with Self-Compacting Concrete," International Journal of Concrete Structures and Materials, vol. 6, no. 2, p. 65–78., 2012.
8
Itterbeeck P. V., Cauberg N., Parmentier B., Ann Van Gysel A. V.and Vandewalle L., "Shear Capacity of Self-Compacting Concrete," in Proceedings of the Fifth North American Conference on the Design and Use of Self-Consolidating Concrete, Chicago, Illinois, 2013.
9
Yaw L. T., Osei J. B. and Adom-Asamoah M., "On The Non-Linear Finite Element Modelling of Self-Compacting Concrete Beams," Journal of Structural and Transportation Studies, vol. 2, no. 2, pp. 1-18, 2017.
10
Anjitha M. S., and Kumar K. R., "Analytical Studies on Hybrid Self Compacting Concrete Deep Beam Using Fem Software.," International Journal of Innovative Research in Science, Engineering and Technology, vol. 4 , no. 5, pp. 71-77, 2015.
11
Schlaich J., Schafer K. and Jannewin M, " Toward a Consistent Design of Structural Concrete. Special Report, CEB (Comité Euro International du Béton), 77-150.," 1987.
12
Liang Q. Q., Uy B. and Steven G. P. , "Performance-Based Optimization for Strut-Tie Modelling of Structural Concrete," Journal of Structural Engineering, vol. 128, no. 6, pp. 815-823, 2002.
13
Abaqus Analysis User’s Manual, Version 6.12, Volume III: Materials (2012). Dassault Systèmes Simulia Corp., Providence, RI, USA.
14
Abaqus Theory Manual, Version 6.12 (2012). Dassault Systèmes Simulia Corp., Providence, RI, USA.
15
Mohamed A. R., Shoukry M. S. and Saeed J. M., "Prediction of the behavior of reinforced concrete deep beams with web openings using the ﬁnite element method," Alexandria Engineering Journal, vol. 53, pp. 329-339, 2014.
16
Metwally I. M., "Nonlinear analysis of concrete deep beam reinforced with gfrp bars using finite element method," Malaysian Journal of Civil Engineering, vol. 26, no. 2, pp. 224-250, 2014.
17
Genikomsou A. S. and Polak M. A., "Damaged plasticity modelling of concrete in finite element analysis of reinforced concrete slabs. ,," in 9th International Conference on Fracture Mechanics of Concrete and Concrete Structures University of California, Berkeley, United State May 22- 25, 2016.
18
Comité Euro-International du Béton, CEB-FIP-model Code , "Design code, 1990," London, Thomas Telford,, 1993.
19
Sümer Y. and Aktas M, "Defining parameters for concrete damage plasticity model," Challenge Journal of Structural Mechanics, vol. 1, no. 3, p. 149–155, 2015.
20
Demir A., Ozturk H., and Dok G., "3D Numerical Modeling of RC Deep Beam Behavior by Nonlinear Finite Element Analysis," Disaster Science and Engineering, vol. 2, no. 1, pp. 13-18, 2016.
21
Michal Szczecina and Andrzej Winnicki, "Calibration of the CDP model parameters in Abaqus.," in World Congress on Advances in structural Engineering and Mechanics (ASEM 15), Incheon Korea, August 25 -29.
22
Jankowiak T. and Odygowski T., "Identification of Parameters of Concrete," Foundation of Civil and Environmental Engineering, no. 6, pp. 53-69, 2005.
23
Mohammadhassani M., Jumaat M., Ashour A., Jameel M., "Failure modes and serviceability of high strength self compacting concrete deep beams," Engineering Failure Analysis, vol. 18, pp. 2271-2281, 2011.
24
ORIGINAL_ARTICLE
Application of ANN and ANFIS Models in Determining Compressive Strength of Concrete
Concrete compressive strength is recognized as one of the most important mechanical properties of concrete and one of the most significant mechanical properties in determining the quality of the produced concrete. Since the traditional procedures of determining the compressive strength of concrete require time and cost, scholars have always been looking for new methods to replace them with existing traditional methods. In this paper, soft computing methods are investigated for determining the compressive strength of concrete. To be specific, 150 different concrete specimens with various mix design parameters have been built in the laboratory, and the compressive strength of them have been measured after 28 days of curing in the water. Five different concrete mix parameters, (i.e. cement, water to cement ratio, gravel, sand, and microsilica) were considered as input variables. In addition, two soft computing techniques have been used in this study which are Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference (ANFIS) System. Results have shown that both of ANN and ANFIS models are successful models for predicting the compressive strength of concrete. Also, results have shown that ANFIS is more capable than ANN in predicting the compressive strength of concrete.
http://www.jsoftcivil.com/article_51114_7e1f30e53b1d69cbf043f67adaf0121b.pdf
2018-01-01T11:23:20
2020-07-09T11:23:20
62
70
10.22115/scce.2018.51114
Compressive strength
Artificial Neural Network
Adaptive Neuro Fuzzy Inference System
Prediction models
Zahra
Keshavarz
zahrakeshavarz.88@gmail.com
true
1
Graduate Student, Department of Civil Engineering, Islamshahr Branch, Islamic Azad University Islamshahr, Iran
Graduate Student, Department of Civil Engineering, Islamshahr Branch, Islamic Azad University Islamshahr, Iran
Graduate Student, Department of Civil Engineering, Islamshahr Branch, Islamic Azad University Islamshahr, Iran
LEAD_AUTHOR
Hojjatollah
Torkian
hodjat.torkian@gmail.com
true
2
Faculty Member, Department of Civil Engineering, Islamshahr Branch, Islamic Azad University Islamshahr, Iran
Faculty Member, Department of Civil Engineering, Islamshahr Branch, Islamic Azad University Islamshahr, Iran
Faculty Member, Department of Civil Engineering, Islamshahr Branch, Islamic Azad University Islamshahr, Iran
AUTHOR
[1] Krishna, M. S. V., Begum, K. M. S., &Anantharaman, N. (2017). Hydrodynamic studies in fluidized bed with internals and modeling using ANN and ANFIS. Powder Technology, 307, 37-45.
1
[2] Hamdia, K. M., Lahmer, T., Nguyen-Thoi, T., &Rabczuk, T. (2015). Predicting the fracture toughness of PNCs: A stochastic approach based on ANN and ANFIS. Computational Materials Science, 102, 304-313.
2
[3] Naderpour, H., Kheyroddin, A., &Ghodrati Amiri, G. (2010). Prediction of FRP-confined compressive strength of concrete using artificial neural networks. Composite Structures, 92 (12), 2817-2829.
3
[4] Khademi, F., Akbari, M., &Nikoo, M. (2017). Displacement Determination of Concrete Reinforcement Building using Data-Driven models. International Journal of Sustainable Built Environment.
4
[5] Gupta, A. K., Kumar, P., Sahoo, R. K., Sahu, A. K., & Sarangi, S. K. (2017). Performance measurement of plate fin heat exchanger by exploration: ANN, ANFIS, GA, and SA. Journal of Computational Design and Engineering, 4(1), 60-68.
5
[6] Talebizadeh, M., &Moridnejad, A. (2011). Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models. Expert Systems with Applications, 38(4), 4126-4135.
6
[7] Khademi, F., Akbari, M., Jamal, S. M., &Nikoo, M. (2017). Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. Frontiers of Structural and Civil Engineering, 11(1), 90-99.
7
[8] You, H., Ma, Z., Tang, Y., Wang, Y., Yan, J., Ni, M., ... & Huang, Q. (2017). Comparison of ANN (MLP), ANFIS, SVM, and RF models for the online classification of heating value of burning municipal solid waste in circulating fluidized bed incinerators. Waste Management.
8
[9] Güneyisi, E. M., D'Aniello, M., Landolfo, R., & Mermerdaş, K. (2014). Prediction of the flexural overstrength factor for steel beams using artificial neural network. Steel and Composite Structures, 17(3), 215-236.
9
[10] Nasrollahi, Amir. Optimum shape of large-span trusses according to AISC-LRFD using Ranked Particles Optimization. Journal of Constructional Steel Research, 134, 92-101.
10
[11] Keshavarz, Z. (2017). Predicting the Civil Engineering Characteristics through Soft Computing Models. Civil Engineering Research Journal, 1 (3), 555563.
11
[12] Khademi, F., Akbari, M., & Jamal, S. M. (2016). Predictia Rezistentei La Compresiune A Betonului Prin Testare Upv (Ultrasonic Pulse Velocity) Si Modelare Cu Retele Neuronale Artificiale/Prediction of Concrete Compressive Strength Using Ultrasonic Pulse Velocity Test and Artificial Neural Network Modeling. Revista Romana de Materiale, 46(3), 343.
12
[13] Yaman, M. A., Elaty, M. A., &Taman, M. (2017). Predicting the ingredients of self compacting concrete using artificial neural network. Alexandria Engineering Journal.
13
[14] Khademi, F., Jamal, S. M., Deshpande, N., &Londhe, S. (2016). Predicting strength of recycled aggregate concrete using artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression. International Journal of Sustainable Built Environment, 5(2), 355-369.
14
[15] Sitton, J. D., Zeinali, Y., & Story, B. A. (2017). Rapid soil classification using artificial neural networks for use in constructing compressed earth blocks. Construction and Building Materials, 138, 214-221.
15
[16] Khademi, F., Akbari, M., & Jamal, S. M. (2015). Prediction of compressive strength of concrete by data-driven models. i-manager's Journal on Civil Engineering, 5(2), 16.
16
[17] Karthika, B. S., &Deka, P. C. (2015). Prediction of air temperature by hybridized model (Wavelet-ANFIS) using wavelet decomposed data. Aquatic Procedia, 4, 1155-1161.
17
[18] Akib, S., Mohammadhassani, M., &Jahangirzadeh, A. (2014). Application of ANFIS and LR in prediction of scour depth in bridges. Computers & Fluids, 91, 77-86.
18
[19] Deka, P. C., &Diwate, S. N. (2011). Modeling Compressive Strength of Ready Mix Concrete Using Soft Computing Techniques. International Journal of Earth Sciences and Engineering, 4(6), 793-796.
19
[20] Pathak, S. S., Sharma, S., Sood, H., &Khitoliya, R. K. (2012). Prediction of Compressive Strength of Self compacting Concrete with Flyash and Rice Husk Ash using Adaptive Neuro-fuzzy Inference System. Editorial Preface, 3(10).
20
[21] Vishnuvaradhan, S., Chandrasekhar, N., Vasudevan, M., & Jayakumar, T. (2013). Intelligent modeling using adaptive neuro fuzzy inference system (ANFIS) for predicting weld bead shape parameters during A-TIG welding of reduced activation ferritic-martensitic (RAFM) steel. Transactions of theIndianInstitute of Metals, 66(1), 57-63.
21
ORIGINAL_ARTICLE
Flexural Analysis of Deep Aluminum Beam
Many parts of spacecrafts, airplane are made up of aluminum, which are thick or deep in section. For the analysis of deep or thick beams, a trigonometric shear deformation theory is used, taking into account transverse shear deformation effects, is developed. To represent the shear deformation effects, a sinusoidal function is used in displacement field in terms of thickness coordinate. The important feature of this theory is that the transverse shear stresses can be obtained directly from the use of constitutive relations with excellent accuracy, satisfying the shear stress conditions on the end surfaces of the beam. Hence, the theory obviates the need of shear correction factor. Using the principle of virtual work governing differential equations and boundary conditions are obtained. The thick aluminum beam is considered for the numerical study to show the accuracy of the theory. The cantilever beam subjected to cosine loads is examined using the present theory. Results obtained are discussed with those of other theories.
http://www.jsoftcivil.com/article_49679_0e4ca02007a3c2724d84dcc111d7d45f.pdf
2018-01-01T11:23:20
2020-07-09T11:23:20
71
84
10.22115/scce.2018.49679
Deep beam
Trigonometric shear deformation
Principle of virtual work
Equilibrium equations
Aluminum
Ajay
Dahake
ajaydahake@gmail.com
true
1
Maharashtra Institute of technology, Aurangabad (Maharashtra), India
Maharashtra Institute of technology, Aurangabad (Maharashtra), India
Maharashtra Institute of technology, Aurangabad (Maharashtra), India
LEAD_AUTHOR
Pravin
Kapdis
pravinskapdis73@gmail.com
true
2
Shreeyash College of Engineering, Aurangabad, India
Shreeyash College of Engineering, Aurangabad, India
Shreeyash College of Engineering, Aurangabad, India
AUTHOR
Uttam
Kalwane
kalwane62@gmail.com
true
3
Shreeyash College of Engineering, Aurangabad, India
Shreeyash College of Engineering, Aurangabad, India
Shreeyash College of Engineering, Aurangabad, India
AUTHOR
Umesh
Salunkhe
umeshcivil@gmail.com
true
4
Shreeyash College of Engineering, Aurangabad, India
Shreeyash College of Engineering, Aurangabad, India
Shreeyash College of Engineering, Aurangabad, India
AUTHOR
[1] Timoshenko S. P., “On the Correction for Shear of the Differential Equation for Transverse Vibrations of Prismatic Bars,” Philosophical Magazine, Series 6, Vol. 41, 1921, pp. 742-746.
1
[2] Cowper G. R., “The Shear Coefficients in Timoshenko Beam Theory,” ASME Journal of Applied Mechanics, Vol. 33, 1966, pp. 335-340.
2
[3] Cowper G. R., “On The Accuracy of Timoshenko’s Beam Theory,” ASCE journal of the Engineering Mechanics Division, Vol. 94, No. EM 6, 1968, pp. 1447-1453.
3
[4] Murty A. V. K., 1970, “Vibration of Short Beams,” AIAA Journal, Vol. 8, 1970, pp. 3438.
4
[5] Baluch M. H., Azad A. K. and Khidir, M. A., “Technical Theory of Beams with Normal Strain,” Journal of the Engineering Mechanics, Proceedings of ASCE, Vol. 110, 1984, pp. 1233-1237.
5
[6] Bhimaraddi A. and Chandrashekhara K., “Observations on Higher-Order Beam Theory,” Journal of Aerospace Engineering, Proceedings of ASCE, Technical Note., Vol. 6, 1993, pp. 408-413.
6
[7] Kant T. and Gupta A., “A Finite Element Model for a Higher order Shear Deformable Beam Theory,” Journal of Sound and Vibration, Vol. 125, No.2, 1988, pp. 193-202.
7
[8] Heyliger P. R., and Reddy J. N., “A Higher Order Beam Finite Element for Bending and Vibration Problems,” Journal of Sound and Vibration, Vol. 126, No. 2, 1988, pp. 309-326.
8
[9] Ghugal Y. M., “A Simple Higher Order Theory for Beam with Transverse Shear and Transverse Normal Effect,” Departmental Report, No. 4, Applied of Mechanics Department, Government College of Engineering, Aurangabad, India, 2006, pp. 1-96.
9
[10] Averill R.C. and Reddy J. N., “An assessment of four-noded plate finite elements based on a generalized third order theory”, International Journal of Numerical Methods in Engineering, 33, 1992, pp. 1553-1572.
10
[11] Dahake A. G. and Ghugal Y. M., “Flexure of Thick Simply Supported Beam Using Trigonometric Shear Deformation Theory”, International Journal of Scientific and Research Publications, 2(11), ISSN 2250-3153, 2012, pp. 1-7
11
[12] Ghugal Y. M. and Dahake A. G., “Flexural Analysis of Deep Beam Subjected to Parabolic Load Using Refined Shear Deformation Theory”, Applied and Computational Mechanics, 6(2), 2012, pp. 163-172
12
[13] Ghugal Y. M. and Dahake A. G., “Flexure of thick beams using refined shear deformation theory”, International Journal of Civil and Structural Engineering, 3(2), 2012, pp. 321-335
13
[14] Sawant M. K. and Dahake A. G., “A New hyperbolic Shear Deformation Theory for Analysis of thick Beam”, International Journal of Innovative research in Science, engineering and technology, ISSN: 2319-8753, 3(2), 2014, pp. 9636-9643
14
[15] Chavan V. B. and Dahake A. G., “Analysis of Thick Beam Bending Problem by Using a New Hyperbolic Shear Deformation Theory”, International Journal of Engineering Research and General Science, 2(5), 2014, ISSN 2091-2730, pp. 209-215
15
[16] Chavan V. B. and Dahake A. G., “ A Refined Shear Deformation Theory for Flexure of Thick Beam”, International Journal of Pure and Applied Research in Engineering and Technology, Impact Factor: 4.226, ISSN: 2319-507X, 3(9), 2015, pp. 109-119.
16
[17] Nimbalkar V. N. and Dahake A. G., “Displacement and Stresses for Thick Beam using New Hyperbolic Shear Deformation Theory”, International Journal of Pure and Applied Research in Engineering and Technology, Impact Factor: 4.226, ISSN: 2319-507X, 3(9), May 2015, pp. 120-130
17
[18] Jadhav V. A. and Dahake A. G., “Bending Analysis of Deep Beam Using Refined Shear Deformation Theory”, International Journal of Engineering Research, ISSN: e2319-6890, p2347-5013, 5(3), doi:10.17950/ijer/v513/003, Feb. 2016, pp. 526-531
18
[19] Manal S. S., Sawant R. M. and Dahake A. G., “A New Trigonometric Shear Deformation Theory for Thick Fixed Beam”, International Journal of Engineering Research, ISSN: e2319-6890, p2347-5013, 5(3), doi:10.17950/ijer/v513/004, Feb. 2016, pp. 532-536
19
[20] Patil P. B. and Dahake A. G., “Finite Element Analysis Using 2D Plane Stress Elements for Thick Beam”, Journal of Aerospace Engineering and Technology, ISSN: 2231-038X (Online), ISSN: 2348-7887 (Print),STM, 6(2), 2016, pp. 1-8
20
[21] Dahake A. G., Manal S. S. and Sawant R. M., “Flexure of Fixed Thick Beam using Trigonometric Shear Deformation Theory”, Proceedings of 6th International Congress on Computational Mechanics and Simulation, Indian Institute of Technology, (IIT) Powai (Bombay), Maharashtra, India, 27th June to 1st July 2016, pp. 1112-1115
21
[22] Tupe D. H., Dahake A. G. and Gandhe G. R., “Comparison of various displacement fields for static analysis of thick isotropic beams”, Structural Engineering Convention (SEC-2016), CSIR-SERC, Chennai, INDIA, 21-23 December 2016
22
[23] Properties of Aluminum 6061-T6, 6061-T651, http://www.aerospacemetals.com
23
ORIGINAL_ARTICLE
Cone Penetration Based Probabilistic Assessment of Shallow Foundation Settlement
Probabilistic (Reliability or safety) analysis, as a measure of structural performance, was expressed in terms of reliability indices which were calculated for total settlement of shallow foundations in a Site in Abuja, the Federal Capital of the Federal Republic of Nigeria based on the Burland and Burbidge settlement prediction method. Reliability indices were calculated with the objective of developing a risk analysis procedure specifically for prediction of settlement of foundations lying on soils. This research was aimed at the development of a method that will assist in the process of calibration of load and resistance factors (reliability-based design (RBD)) for service limit state based on cone penetration test (CPT) results. The CPT data were obtained from four test holes (CPT1 - 4) at three foundation embedment depths of 0.6, 1.2 and 1.8 m and analysis was done using applied foundation pressures of 50, 100, 200, 300 and 500 kN/m2. Reliability analysis, expressed in the form of reliability index (β) and probability of failure (Pf) was performed for foundation settlement using First Order Reliability Method (FORM) in MATLAB. The footings were designed for a 25 mm allowable settlement value as recommended in Eurocode 7 for serviceability limit state (SLS) design which is a conventional approach. Sensitivity study indicated that the applied foundation pressure and coefficient of variation (COV) of CPT tip resistance significantly affected the magnitude of foundation settlements and the variability of the geotechnical parameters is highly influenced and has a significant effect on the settlement and safety of any structure. The use of COV value of 30 % of CPT tip resistance which corresponds to target reliability index (βT) of 4.52 and target probability of failure (PfT) of 0.000677% based on the Burland and Burbidge method for SLS design is recommended for RBD of footings total settlement on soils in Abuja, Nigeria.
http://www.jsoftcivil.com/article_51115_3c88c026c889adc9329c5392349599cf.pdf
2018-01-01T11:23:20
2020-07-09T11:23:20
85
100
10.22115/scce.2018.51115
Foundation settlement
reliability analysis
Safety index
probability of failure
Reliability-based design
Cone penetration test
Salahudeen
Bunyamin
basalahudeen@gmail.com
true
1
Samaru College of Agriculture, Division of Agricultural Colleges, Ahmadu Bello University, Zaria, Nigeria
Samaru College of Agriculture, Division of Agricultural Colleges, Ahmadu Bello University, Zaria, Nigeria
Samaru College of Agriculture, Division of Agricultural Colleges, Ahmadu Bello University, Zaria, Nigeria
LEAD_AUTHOR
[1] Foye, K. C., Salgado, R. and Scott, B. (2006). Assessment of Variable Uncertainties for Reliability-Based Design of Foundations. Journal of Geotechnical and Geoenvironmental Engineering,Vol. 132, No. 9, pp. 1197-1207. DOI: 10.1061/ASCE109002412006132:91197
1
[2] Scott, B., Kim, B. J., and Salgado, R. (2003). “Assessment of current load
2
factors for use in geotechnical LRFD.” Journal of Geotechnical and Geoenvironmental. Engineering, Issue 1294, pp. 287–295.
3
[3] Lacasse, S. and Nadim, F. (1996). Uncertainties in characterizing soil properties. In Proceedings of Uncertainty ‘96. Geotechnical Special Publication 58, Vol. 1, pp. 49–75.
4
[4] El-Ramly, H., Morgenstern, N. R. and Cruden, D. M. (2002). Probabilistic slope stability analysis for practice. Canadian Geotechnical Journal, Vol. 39 pp. 665 – 683, DOI: 10.1139/T02-034.
5
[5] Phoon, K. K., Kulhawy, F. H., and Grigoriu, M. D.(1995). “Reliability-based foundation design for transmission line structures.”Report No. TR-105000, Electric Power Research Institute, Palo Alto, California.
6
[6] Liu, Y., Zheng, J. J. and Guo, J. (2007). “Reliability-based design methodology of multi-pile composite foundation.” First International Symposium on Geotechnical Safety and Risk (ISGSR), Tongji University, Shanghai, China, pp. 461-470.
7
[7] Haddad, A., Eidgahee, D. R. and Naderpour, H. (2017). "A probabilistic study on the geometrical design of gravity retaining walls", World Journal of Engineering, Vol. 14 Issue: 5, pp.414-422, https://doi.org/10.1108/WJE-07-2016-0034.
8
[8] Salahudeen A. B. and Kaura, J. M. (2017). Reliability based analysis of foundation settlement. Leonardo Electronic Journal of Practices and Technologies (LEJPT), Issue 30, Pp. 127-148.
9
[9] Bowles, J.E. (1996). Foundation Analysis and Design, 5th Edition., McGraw-Hill, USA.
10
[10] Cherubini, C. (2007). “Shallow Foundation Reliability Design.” First International Symposium on Geotechnical Safety & Risk, Tongji University, Shanghai, China.
11
[11] Honjo, Y. (2011). Challenges in Geotechnical Reliability Based Design. ISGSR 2011 - Vogt, Schuppener, Straub & Bräu (edition) Bundesanstalt für Wasserbau pp. 11-27. ISBN 978-3-939230-01-4.
12
[12] ASTM, (1994) Designation: D 3441-94 Standard Test Method for Deep, Quasi-Static, Cone and Friction-Cone Penetration Tests: American Society for Testing and Materials: Annual Book of Standards, Vol. 4.08 Soil and Rock (I): D 420–D-4914, pp. 348–354.
13
[13] MATLABR2014a(8.3.0.532) Program (2014). MATLAB Documentation.
14
[14] Baecher G. B and Christian J. T. (2003). Reliability and statistics in geotechnical engineering. John Wiley and Sons Edition, California. USA.
15
[15] Salahudeen, A.B., Akiije, I. and Usman, G.M. (2013). Effect of sectional modulus on universal and hollow steel columns subjected to flexure. International Journal of Engineering Research and Technology (IJERT). Vol. 2 issue 9, pp 1848-1864.
16
[16] Hasofer, A. M. and Lind, N. C. (1974). “An exact and invariant first order reliability format.” Journal of Engineering Mechanics Division, ASCE, 100(EM1), pp. 111–21.
17
[17] Eurocode 7: European Committee for Standardization (CEN). (1994) Geotechnical design-Part 1, general rules. Eurocode 7, No. CEN/TC250, Brussels, Belgium.
18
[18] Burland, J.B. and Burbidge, M.C. (1985). “Settlement of foundations on sand and gravel.”Proceedings of Institution of Civil Engineers, Part 1, Vol. 78, 1325-1381.
19
[19] Akbas, S. O. and Kulhawy, F. H. (2009). “Reliability-based design approach for differential settlement of footings on cohesionless soils.” Journal of Geotechnical and Geoenvironmental Engineering, ASCE, Vol. 135, No. 12, pp. 1779-1788.
20
[20] AASHTO-LRFD Bridge Design Specifications (2012). “American Association of State Highway and Transportation Officials”, Washington, D.C.
21
[21] Ahmed, A. Y. (2013). “Reliability analysis of settlement for shallow foundations in bridges.” A published dissertation of the Faculty of Graduate College, University of Nebraska, Lincoln Nebraska. UMI dissertation publishing, USA.
22
[22] Salahudeen, A. B., Ijimdiya, T. S.,Eberemu, A. O. andOsinubi, K. J. (2016a). Reliability Analysis of Foundation Settlement in the North Central Nigeria. Proceedings of International conference on Construction Summit, Nigerian Building and Road Research Institute (NBRRI), Abuja, Nigeria, pp. 369 – 384.
23
[23] Salahudeen A. B.,Eberemu A. O., Ijimdiya T. S. and Osinubi K. J. (2016b). Reliability Analysis of Foundation Settlement in the North West of Nigeria. Proceedings of Material Science and Technology Society of Nigeria (MSN) Conference, Kaduna State chapter, NARICT, Zaria, pp. 22 – 27.
24
[24] Salahudeen, A. B., Ijimdiya, T. S., Eberemu, A. O. and Osinubi K. J. (2016c). Reliability analysis of foundation settlement in the South-South of Nigeria, Nigerian Journal of Engineering (NJE), Vol. 22 No.2, Pp. 41-49.
25
[25] Fenton, G. A., Griffiths, D. V., and Cavers, W.(2005) “Resistance factors for settlement design.”Canadian Geotechnical Journal, Vol. 42No. 5, pp. 1422–1436.
26
[26] Popescu, R., Prevost, J. H., and Deodatis, G. (2005). “3D effects in seismic liquefaction of stochastically variable soil deposits.”Geotechnique, Vol. 55No. 1, pp. 21–31.
27
[27] Zekkos, D. P., Bray, J. D., and Der-Kiureghian, A. (2004). “Reliability of shallow foundation design using the standard penetration test.”Proceedings, 2nd International Conference on Site Characterization (2), Millpress, Rotterdam, pp. 1575–1582.
28
[28] Subramaniam, P. (2011). “Reliability based analysis of slope, foundation and retaining wall using finite element method.” Published Master of Technology thesis, Department of Civil Engineering, National Institute of Technology, Rourkela, India.
29
ORIGINAL_ARTICLE
Selection of an Appropriate Method to Extract the Dimensional Stones Using FDAHP & TOPSIS Techniques
In this paper it was aimed to select a suitable method to extract the dimensional stone to increase dimensional stone quarries efficiency. The usual methods including diamond cutting-wire method, blasting method, plug and feather method, Katrock expanding material and Fract expanding material have compared using TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) method by respecting to the following criteria: grass income, safety, desirability, reduction of environmental impacts, waste and reduction of extracting time. FDAHP (Fuzzy Delphi Analytic Hierarchy Process) approach was used in determining the degree of importance of the criteria by expert decision makers. Also, those criteria performed the same impacts were not considered. Consequently, the diamond wire saw method was suggested as the most appropriate method to extract the dimensional stones. It was concluded that the extraction of dimensional stone using diamond wire saw is the best method based on the mentioned criterion compared to other methods.
http://www.jsoftcivil.com/article_53997_12e1f7d8af556801da4a2529bf5dfb42.pdf
2018-01-01T11:23:20
2020-07-09T11:23:20
101
116
10.22115/scce.2018.53997
Multi-criteria decision making
Dimensional stone
TOPSIS
Fuzzy Delphi
Akbar
Esmailzadeh
esmailzade.ak@aut.ac.ir
true
1
Mining and metallurgical department, Urmia university of Technology.
Mining and metallurgical department, Urmia university of Technology.
Mining and metallurgical department, Urmia university of Technology.
LEAD_AUTHOR
Reza
Mikaeil
reza.mikaeil@uut.ac.ir
true
2
Department of Mining and Metallurgical Engineering, Urmia University of Technology, Urmia, Iran
Department of Mining and Metallurgical Engineering, Urmia University of Technology, Urmia, Iran
Department of Mining and Metallurgical Engineering, Urmia University of Technology, Urmia, Iran
AUTHOR
Golsa
Sadegheslam
sadegheslam.uut@gmail.com
true
3
Urmia University of Technology
Urmia University of Technology
Urmia University of Technology
AUTHOR
Ahmad
Aryafar
ahariafar@yahoo.com
true
4
Birjand university
Birjand university
Birjand university
AUTHOR
Hojjat
Hosseinzadeh Gharehgheshlagh
h.hoseynzade@uut.ac.ir
true
5
Urmia University of Technology
Urmia University of Technology
Urmia University of Technology
AUTHOR
[1] Mikaeil, R., Ataei, M., Hoseinie, SH. (2008). Predicting the production rate of diamond wire saws in carbonate rocks cutting, Industrial Diamond Review. 3: 28-34.
1
[2] Mikaiel, R., Ataei, M., Yousefi, R. (2011). Application of a fuzzy analytical hierarchy process to the prediction of vibration during rock sawing, Mining Science and Technology (China) 21, 611–619.
2
[3] Mikaeil, R., Yousefi, R., Ataei, M., Abbasian, R. (2011). Development of a New Classification System for Assessing of Carbonate Rock Sawability. Arch. Min. Sci. 56: 1: 57–68.
3
[4] Ataei, M., Mikaiel, R., Sereshki, F., Ghaysari, N. (2011). Predicting the production rate of diamond wire saw using statistical analysis. Arabian Journal of Geosciences. 5, 1289-1295.
4
[5] Mikaeil, R., Ataei, M., Yousefi, R. (2011). Correlation of production rate of dimension stone with rock brittleness indexes. Arabian Journal of Geosciences. 6, 115-121.
5
[6] Mikaeil R., Yousefi R., Ataei M., (2011). Sawability Ranking of Carbonate Rock Using Fuzzy Analytical Hierarchy Process and TOPSIS Approaches. Scientia Iranica, Transactions B: Mechanical Engineering, 18, 1106–1115.
6
[7] Mikaeil R., Ataei M., Yousefi R., (2011). Evaluating the Power Consumption in Carbonate Rock Sawing Process by Using FDAHP and TOPSIS Techniques, Efficient Decision Support Systems: Practice and Challenges – From Current to Future / Book 2", ISBN 978-953-307-441-2., 478.
7
[8] Mikaeil, R., Ozcelik, Y., Ataei, M., Yousefi, R., (2011). Correlation of Specific Ampere Draw with Rock Brittleness Indexes in Rock Sawing Process. Arch. Min. Sci., Vol. 56, No 4, 741–752.
8
[9] Ataei, M., Mikaeil, R., Hoseinie, S. H., Hosseini, S. M. (2012). Fuzzy analytical hierarchy process approach for ranking the sawability of carbonate rock. International Journal of Rock Mechanics & Mining Sciences, 50, 83–93.
9
[10] Ghaysari, N., Ataei, M., Sereshki, F., Mikaiel, R. (2012). Prediction of Performance of diamond wire saw with respect to texture characterestic of rock, Arch. Min. Sci., 57, 4, 887–900.
10
[11] Mikaeil, R., Ozcelik, Y., Ataei, M., Yousefi, R. (2013). Ranking the sawability of dimension stone using Fuzzy Delphi and multi-criteria decision-making techniques. International Journal of Rock Mechanics & Mining Sciences; 58, 118–126.
11
[12] Sadegheslam, G., Mikaeil, R., Rooki, R., Ghadernejad, S., Ataei, M. (2013). Predicting the production rate of diamond wire saw using multiple nonlinear regression analysis, Geosystem engineering, 275-285.
12
[13] Mikaeil, R., Ataei, M., Ghadernejad, S., Sadegheslam, G. (2014). Predicting the Relationship between System Vibration with Rock Brittleness Indexes in Rock Sawing Process. Archives of Mining Sciences, 59-1, 139-153.
13
[14] Mikaeil R.; Kamran M.; Sadegheslam G.; Ataei M. Ranking the Sawability of Dimension Stone by Using PROMETHEE, Journal of Mining and Environment. 2015, Vol 6, No. 2, 263-271.
14
[15] Mikaeil R., Dormishi A., Sadegheslam G., Haghshenas S. S. Effect of Freezing on Strength and Durability of Dimension Stones Using Fuzzy Clustering Technique and Statistical Analysis. Analytical and Numerical Methods in Mining Engineering, 2016.
15
[16] Aryafar A. and Mikaeil R., Estimation of the Ampere Consumption of Dimension Stone Sawing Machine Using the Artificial Neural Networks, Int. J. Min. & Geo-Eng. Vol.50, No.1, June 2016, pp.121-130.
16
[17] Mikaeil R., Shaffiee Haghshenas S., Shaffiee Haghshenas S., M. Ataei, (2016), Performance Prediction of Circular Saw Machine Using Imperialist Competitive Algorithm and Fuzzy Clustering Technique. Neural Computing and Applications.
17
[18] Mikaeil R., Ozcelik Y., Ataei M., Shafiee Haghshenas S. Application of Harmony Search Algorithm to Evaluate the Performance of Diamond Wire Saw. Journal of Mining and Environment, 2016.
18
[19] Almasi SN., Bagherpor R., Mikaeil R., Ozcelick Y., (2017). Developing a new rock classification based on the abrasiveness, hardness, and toughness of rocks and PA for the prediction of hard dimension stone sawability in quarrying. Geosystem Engineering, 2017, 1-16.
19
[20] Almasi S.N., Bagherpour R., Mikaeil R., Ozcelik Y., Kalhori H., (2017). Predicting the Building Stone Cutting Rate Based on Rock Properties and Device Pullback Amperage in Quarries Using M5P Model Tree. Geotechnical and Geological Engineering, 1-16.
20
[21] Almasi S. N., Bagherpour R., Mikaeil R., Ozcelik Y., (2017). Analysis of bead wear in diamond wire sawing considering the rock properties and production rate. Bulletin of Engineering Geology and the Environment.
21
[22] Akhyani M., Sereshki F., Mikaeil R., Taji M., Combining fuzzy RES with GA for predicting wear performance of circular diamond saw in hard rock cutting process Journal of Mining and Environment, 2017.
22
[23] Mikaeil R., Shaffiee Haghshenas S., Ataei M., Shaffiee Haghshenas S., The Application of Multivariate Regression Analysis to Predict the Performance of Diamond Wire Saw, 25th International Mining Congress and Exhibition of Turkey, 122-128.
23
[24] Technical report of Gazik Granit Mine.
24
[25] Fatemi, S. A. A., Economical – Technical Study of Diamond cutting weir used to granite Extraction in Gazik Mine, Birjand, Iranian Mining Engineering Conference, 2003.
25
[26] Ataei, M., Site Selection of Alomina- Cement Plantation Construction using Topsis Method, Amir Kabir Journal, Volume 16, Issues 62, Pages 84-87.
26
[27] Yoon K., Hwang C.L., Multiple Attribute Decision Making Methods and Applications. A State of the Art Survey, Verlag, Berlin, 1980.
27
[28] Yoon K., System selection by multiple attribute decision making, Ph.D. Dissertation, KansasStateUniversity, Manhattan, Kansas, 1980.
28
[29] Cheng C.T, Zhao M.Y, Chau K.W. and Wu X.Y, 2006, Using genetic algorithm and TOPSIS for Xinanjiang model calibration with a single procedure Journal of Hydrology, Volume 316, Issues 1-4, Pages 129-140.
29
[30] Shanian A. and Savadogo O., 2006, TOPSIS multiple-criteria decision support analysis for material selection of metallic bipolar plates for polymer electrolyte fuel cell Journal of Power Sources, Volume 159, Issue 2, Pages 1095-1104
30
[31] Wang Y.M and Elhag T M.S., 2006, Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment Expert Systems with Applications, Volume 31, Issue 2, Pages 309-319.
31
[32] Jahanshahloo G.R., Hosseinzadeh Lotfi F. and Izadikhah M., 2006, Extension of the TOPSIS method for decision-making problems with fuzzy data Applied Mathematics and Computation, Volume 181, Issue 2, Pages 1544-1551.
32
[33] Wang Y.J. and Lee H.S., 2007, Generalizing TOPSIS for fuzzy multiple-criteria group decision-making Computers & Mathematics with Applications, Volume 53, Issue 11, June, Pages 1762-1772.
33
[34] Lin M.C, Wang C.C, Chen M.S and Chang C. A, 2008, Using AHP and TOPSIS approaches in customer-driven product design process. Computers in Industry, Volume 59, Issue 1.
34
[35] Chen T.Y and Tsao C.Y, 2007, The interval-valued fuzzy TOPSIS method and experimental analysis Fuzzy Sets and Systems.
35
[36] Ertuğrul İ. and Karakaşoğlu N., 2007, Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methodsExpert Systems with Applications.
36
[37] Wang T.C and Chang T.H, 2007, Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment Expert Systems with Applications, Volume 33, Issue 4, November, Pages 870-880
37
[38] Önüt S. and Soner S., 2007, Transshipment site selection using the AHP and TOPSIS approaches under fuzzy environment Waste Management, Available online 4 September.
38
[39] Yurdakul M., Tansel İç Y., 2008 , Analysis of the benefit generated by using fuzzy numbers in a TOPSIS model developed for machine tool selection problems. Journal of Materials Processing Technology.
39
[40] Saaty, T. L., Decision making for leade”, RWS Publication, 315P., 2001.
40
[41] Kabassi, K. & Virvou, M. , A Technique for Preference Ordering for Advice Generation in an Intelligent Help System, In Proceedings of the 2004 IEEE International Conference on System, 2004.
41
[42] Liu YC, Chen CS (2007). A new approach for application of rock mass classification on rock slope stability assessment. Engineering Geology.;89, pp.129–43.
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