2017
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The Application of Particle Swarm Optimization and Artificial Neural Networks to Estimating the Strength of Reinforced Concrete Flexural Members
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The aim of this paper is determination of the shear strength of fiber reinforced polymer reinforced concrete flexural members without stirrups. For this purpose, neural network approach was used. The weights and biases of the considered network determined based on best values which was optimized from particle swarm optimization algorithm (PSO). For training the model, a collection of 108 data set which were published in literatures was applied. Six inputs including compressive strength of concrete, flexural FRP reinforcement ratio, modulus of elasticity for FRP, shear spantodepth ratio, member web width and member effective depth used for create the model while the shear strength considered as the output. The best structure for the network was obtained by a network with one hidden layer and ten nodes. The results indicated that artificial neural networks based on particle swarm optimization algorithm can be able to predict the strength of the considered RC elements.
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Reza
Farahnaki
University of Wollongong, New South Wales, Australia
University of Wollongong, New South Wales,
Australia
rf847@uowmail.edu.au
Artificial Neural Network
FRP
Shear strength
PSO
RC element
[[1] M. Mirrashid, "Earthquake magnitude prediction by adaptive neurofuzzy inference system (ANFIS) based on fuzzy Cmeans algorithm," Natural hazards, vol. 74, pp. 15771593, 2014. ##[2] M. Mirrashid, M. Givehchi, M. Miri, and R. Madandoust, "Performance investigation of neurofuzzy system for earthquake prediction " Asian journal of civil enginering (BHRC), vol. 17, pp. 213223, 2016. ##[3] H. Naderpour and M. Mirrashid, "Compressive Strength of Mortars Admixed with Wollastonite and Microsilica," in Materials Science Forum, 2017, pp. 415418. ##[4] H. Naderpour and M. Mirrashid, "Application of Soft Computing to Reinforced Concrete Beams Strengthened with Fibre Reinforced Polymers: A StateoftheArt Review," in Computational techniques for civil and structural engineering ed Stirlingshire, UK: SaxeCoburg Publications, 2015, pp. 305323. ##[5] T. Alkhrdaji, M. Wideman, A. Belarbi, and A. Nanni, "Shear strength of RC beams and slabs. CCC2001, J. Figueiras, L. Juvandes, and R. Faria, eds," ed: AA Balkema Publishers, Netherlands, 2001. ##[6] A. Ashour, "Flexural and shear capacities of concrete beams reinforced with GFRP bars," Construction and Building Materials, vol. 20, pp. 10051015, 2006. ##[7] D. Deitz, I. Harik, and H. Gesund, "Oneway slabs reinforced with glass fiber reinforced polymer reinforcing bars," Special Publication, vol. 188, pp. 279286, 1999. ##[8] N. Duranovic, K. Pilakoutas, and P. Waldron, "Tests on concrete beams reinforced with glass fibre reinforced plastic bars," Nonmetallic (FRP) reinforcement for concrete structure, vol. 2, pp. 479486, 1997. ##[9] A. ElSayed, E. ElSalakawy, and B. Benmokrane, "Shear strength of oneway concrete slabs reinforced with fiberreinforced polymer composite bars," Journal of Composites for Construction, vol. 9, pp. 147157, 2005. ##[10] A. K. ElSayed, E. F. ElSalakawy, and B. Benmokrane, "Shear capacity of highstrength concrete beams reinforced with FRP bars," ACI Structural Journal, vol. 103, p. 383, 2006. ##[11] A. K. ElSayed, E. F. ElSalakawy, and B. Benmokrane, "Shear strength of FRPreinforced concrete beams without transverse reinforcement," ACI Structural Journal, vol. 103, p. 235, 2006. ##[12] S. Gross, D. Dinehart, J. Yost, and P. Theisz, "Experimental tests of highstrength concrete beams reinforced with CFRP bars," in Proceedings of the 4th International Conference on Advanced Composite Materials in Bridges and Structures (ACMBS4), Calgary, Alberta, Canada (quoted from Razaqpur and Isgor, 2006), 2004. ##[13] S. P. Gross, J. R. Yost, D. W. Dinehart, E. Svensen, and N. Liu, "Shear strength of normal and high strength concrete beams reinforced with GFRP bars," in High performance materials in bridges, ed, 2003, pp. 426437. ##[14] A. Lubell, T. Sherwood, E. Bentz, and M. Collins, "Safe shear design of large wide beams," Concrete International, vol. 26, pp. 6678, 2004. ##[15] C. R. Michaluk, S. H. Rizkalla, G. Tadros, and B. Benmokrane, "Flexural behavior of oneway concrete slabs reinforced by fiber reinforced plastic reinforcements," ACI Structural Journal, vol. 95, pp. 353365, 1998. ##[16] Y. Mizukawa, Y. Sato, T. Ueda, and Y. Kakuta, "A study on shear fatigue behavior of concrete beams with FRP rods," Nonmetallic (FRP) reinforcement for concrete structure, vol. 2, pp. 309316, 1997. ##[17] A. G. Razaqpur, B. O. Isgor, S. Greenaway, and A. Selley, "Concrete contribution to the shear resistance of fiber reinforced polymer reinforced concrete members," Journal of Composites for Construction, vol. 8, pp. 452460, 2004. ##[18] N. Swamy and M. Aburawi, "Structural implications of using GFRP bars as concrete reinforcement," in Proceedings of 3rd International Symposium, FRPRCS, 1997, pp. 503510. ##[19] M. Tariq and J. Newhook, "Shear testing of FRP reinforced concrete without transverse reinforcement," in Proceedings, Annual Conference of the Canadian Society for Civil Engineering, 2003, pp. 13301339. ##[20] J. R. Yost, S. P. Gross, and D. W. Dinehart, "Shear strength of normal strength concrete beams reinforced with deformed GFRP bars," Journal of composites for construction, vol. 5, pp. 268275, 2001. ##[21] W. Zhao, K. Maruyama, and H. Suzuki, "Shear behavior of concrete beams reinforced by FRP rods as longitudinal and shear reinforcement," in RILEM PROCEEDINGS, 1995, pp. 352352. ##]
Calculation of Torsion Capacity of the Reinforced Concrete Beams Using Artificial Neural Network
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This paper presents a model for calculation of torsion capacity of the reinforced concrete beams using the artificial neural network. Considering the complex reaction of reinforced concrete beams under torsion moments, torsion strength of these beams is depended on different parameters; therefore using the artificial neural network is a proper method for estimating the torsion capacity of the beams. In the presented model the beam's dimensions, concrete compressive strength and longitudinal and traverse bars properties are the input data, and torsion capacity of the reinforced concrete beam is the output of the model. Also considering the neural network results, a sensitivity analysis is performed on the network layers weight, and the effect of different parameters is evaluated on torsion strength of the reinforced concrete beams. According to the sensitivity analysis, properties of traverse steel have the most effect on torsion capacity of the beams.
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Mohammad Hosein
Ilkhani
Ph.D. Candidate, Faculty of Civil Engineering, Semnan University, Semnan, Iran
Ph.D. Candidate, Faculty of Civil Engineering,
Iran
m.m.ilkhani@semnan.ac.ir@semnan.ac.ir


Ehsan
Moradi
Ph.D. Candidate, Faculty of Civil Engineering, Semnan University, Semnan, Iran
Ph.D. Candidate, Faculty of Civil Engineering,
Iran
ehsanmoradi68@yahoo.com


Mohammad
Lavasani
Ph.D., Florida International University, Miami, FL, USA
Ph.D., Florida International University,
United States
ssada006@fiu.edu
Neural Network
Torsion
RC Beam
[1. HaoJan Chiu, et al., Behavior of reinforced concrete beams with minimum torsional reinforcement. Engineering Structures, 2007. 29: p. 21932205. ##2. Victor DJ and M. R, Effect of stirrups on ultimate torque of reinforced concrete beams. ACI J, 1973: p. 3270. ##3. Rasmussen LJ and B. G, Assessment of torsional strength in reinforced normal and highstrength concrete beams. Aust Civil Eng Trans, 1994. 36: p. 71165. ##4. Rasmussen LJ and B. G, Torsion in reinforced normal and high strength concrete beams. PartI: an experimental test series. ACI Struct J, 1995. 92: p. 5662. ##5. McMullen AE and R. BV., Pure torsion in rectangular sectionA reexamination. ACI J, 1978. 75: p. 9511. ##6. Collins CP and M. D, Shear and torsion design of prestressed and nonprestressed concrete beams. PCI J, 1980. 25: p. 32100. ##7. TTC, H., Torsion of structural concretebehavior of reinforced concrete rectangular members. Torsion Struct Concr, 1968(261–306). ##8. Hong Guang, N., JiZong, W. , Prediction of Compressive Strength of Confined Concrete by Neural Networks. Cement and Concrete Research, 2000. 30: p. 1245–1250. ##9. Naderpour, H., Kheyroddin, A., Ghodrati Amiri, G, Prediction of FRPConfined Compressive Strength of Concrete using Artificial Neural Networks. Compos. Struct., 2010. 92(12): p. 2817–2829. ##10. Perera, R., Arteaga, A., Diego, A. D. , Artificial intelligence techniques for prediction of the capacity of RC beams strengthened in shear with external FRP reinforcement. Composite Structures, 2010. 92: p. 1169–1175. ##11. Perera. R., B., M., Arteaga, A., De Diego, A., Prediction of the Ultimate Strength of Reinforced Concrete Beams FRPStrengthened in Shear using Neural Networks. Composites: Part B, 2010. 41(4): p. 287–298. ##12. Jodaei, A., M. Jalal, and M.H. Yas, Free Vibration Analysis of Functionally Graded Annular Plates by StateSpace based Differential Quadrature method and Comparative Modeling by ANN. Composites: Part B, 2012. 43(2): p. 340–53. ##13. Bimal, B., B.B. Adhikary, and H. Mutsuyoshi, Artificial neural networks for the prediction of shear capacity of steel plate strengthened RC beams. Construction and Building Materials, 2004. 18: p. 409–417. ##14. Leonhardt F, S.G., Torsionsversuche a Stahl Betonbalken. Deutscher Ausschuss fur Stahlbeton, 1974: p. 122. ##15. Mitchell D, C.M., Diagonal compression field theorya rational model for structural concrete in pure torsion. ACI Struct, 1974. 71(8): p. 396–408. ##16. Koutchoukali NE, B.A., Torsion of highstrength reinforced concrete beams and minimum reinforcement requirements. ACI Struct J 2001. 98(4): p. 462–9. ##17. Fang IK, S.J., Torsional behavior of normal and highstrength concrete beams. ACI Struct J 2004. 101(3): p. 304–13. ##18. Chiu HJ, F.I., Young WT, Shiau JK, Behavior of reinforced concrete beams with minimum torsional reinforcement. Eng Struct 2007. 29(9): p. 2193–205. ##19. Peng XN, W.Y., Behavior of reinforced concrete walls subjected to monotonic pure torsion – an experimental study. Eng Struct 2011. 33(9): p. 2495–508. ##20. (ACI), A.C.I., Guide for the design and construction of externally bonded FRP systems for strengthening concrete structures, in Rep. No. 440 2R08. 2008: Farmington Hills. ##21. B, F.I., Design and use of externally bonded fibre reinforced polymer reinforcement (FRP EBR) for reinforced concrete structures, in fib Bulletin 14. 2001: Switzerland Lausanne. ##22. Garson, G.D., Interpreting NeuralNetwork Connection Weights. AI Expert, 1991. 39(2): p. 47–51. ##]
Optimisation of Recycled Thermoplastic Plate (Tile)
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2
The purpose of this paper is to perform a structural optimisation of a flat thermoplastic plate (tile). This task is developed computationally through the interface between an optimisation algorithm and the finite element method with the goal of minimising the equivalent stress with a specified target stress of 2 MPa when applied with a load intensity of 1000N. A 300 x 300 x 20 mm thermoplastic plate was selected for the optimisation, which was performed with a tool in MATLAB R2012b known as genetic algorithm accompanied with a static analysis in ANSYS 15. The results produced the optimum equivalent stress (δopt) of 2.136 MPa with the optimum dimensions of 305 x 302 x 20 mm. Also, the dimensions of the plate with the optimum value of the equivalent stress were discovered to be within the lower and upper bound dimensions of the plate. The thermoplastic plate object of the optimization was a square plate of 300 x 300mm and 20 mm thick with isotropic properties and a particular load and boundary conditions were applied on the entire plate.
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Lonping
Olivier
Department of Civil Engineering, University of Ilorin, Ilorin, Nigeria
Department of Civil Engineering, University
Nigeria
oliviertazou@gmail.com


Alao
Jimoh
Department of Civil Engineering, University of Ilorin, Ilorin, Nigeria
Department of Civil Engineering, University
Nigeria
aajimoh4real@yahoo.com


Adeola
Adedeji
Department of Civil Engineering, University of Ilorin, Ilorin, Nigeria
Department of Civil Engineering, University
Nigeria
aaadeji@unilorin.edu.ng
Genetic algorithm (GA)
Finite Element (FE)
ANSYS 15
Matlab R2012b
Local Sensitive Curve (LSC)
[[1] Achilias, D. S., Antonakou, A., Roupakiasi, C., Megalokonomosi, P., & Lappas, A. (2007). Chemical Recycling of Plastic Wastes made from polyethylene (LDPE and HDPE) and polypropylene (PP). Journal of Hazardous Materials, vol. 149, no. 3, pp. 536  542. ##[2] Adetunji, M. B., & Ilias, B. M. (2010). Externality Effects of Sachet Water Consumption and the Choice of Policy Instruments in Nigeria: Evidence from Kwara State. Journal of Economics, vol. 1, No. 2, pp. 113  131. ##[3] Aguado, J., Serrano, D. P., & Miguel, G. S. (2007). European Trends in the Feedstock Recycling of plastic Wastes. Global Nest Journal, vol. 9 No. 1, 12  19. ##[4] Alter, L. (2007). Africa wages war on scourge of plastic bags. Retrieved from http://www.treehugger.com/files/2007/08/africa wages wa.php#ch01 ##[5] Chen, G. C., & Yu, J. S. (2005). Particle Swarm Optimisation Neural Network and its Application in SoftSensing Modelling. Lecture Notes in Computer Science, Vol. 3611, pp. 610617. ##[6] Ding, S. F., Xu, L., & Su, C. Y. (2010). Using Genetic Algorithm to Optimize Artificial Neural Networks. Journal of Convergence Information Technology, Vol. 5, pp. 5462. ##[7] Edoga, M. O., Onyeji, L. L., & Oguntosin, O. O. (2008). Achieving Vision 2020 through Waste Management. Journal Of Engineering and Applied Sciences, vol. 3, No. 8, pp. 642  646. ##[8] He, A., Bae, K. K., Newman, T. R., Gaeddert, J., Kim, K., Menon, R., et al. (2010). A Survey of artificial intelligence for cognitive radios. IEEE Trans. Veh. Technol, Vol. 59, No. 4, pp. 15781592. ##[9] Hussein, A. A., Sultan, A. A., & Matoq, Q. A. (2011). Mechanical Behaviour of Loe Density polyethylene/Shrimp Shells Composite. Journal od Basrah Research Sciences, vol. 37, No. 3A. ##[10] Jimenez, A., & Zaikov, G. (2008). Recent Advances in Research Biodegradable Polymers and Sustenable Composites. In Hauppauge (p. 315). ##[11] Olanrewaju, O. O., & Ilemobade. (2009). Waste to Wealth: A case Study of the Ondo State Integrated Waste recycling and treatment project, Nigeria. European Journal of Social Sciences, vol. 8, No. 1, pp. 7  16. ##[12] Olesya, P. (2007). Global Optimisation Genetic Algorithms. McMaster Unversity Hamilton, Ontaria ppt presentation, pp 25. ##[13] Paul, T. W., & Edward, S. (2006). Analysis of Products From the Pyrolysis and liquefaction of Single Plastics and Waste Plastic Mixtures. Resources, Conservation and Recycling, vol. 51, pp. pp. 754  769. ##[14] Rajib, K. B. (2012). Introduction To Genetic algorithms. Department of Civil Engineering. Indian Institute of Technology Guwahati. ##[15] Sarker, M., Rashid, M. M., & Rahman, M. S. (2011). Agricultural Waste Plastics Conversion into High Energ Liquid Hydrocarbon Fuel by Thermal Degredation Process. Journal of Petroleum Technology and Alternative Fuels, vol. 2, No. 8, pp. 141  145. ##[16] Simon, N., Olayide, O., & Chinyere, O. (2010). occurrence and Recalcitrance of Polyethylene Bag Waste in Nigerian Soils. African Journal Of Biotechnology, vol. 9, No. 37, pp. 6096  6104. ##[17] Subbol, W. K., & Moindi , N. M. (2008). Recycling of Wastes as a Strategy for Environmental conservation in the lake Victoria basin: The case of Women Groups in Kisumu, Kenya. African Journal of Environmental Science and technology, vol. 2, No. 10, pp. 318  325. ##[18]Tamboli, S. M., Mhaske, S. T., & Kale, D. D. (2004). Crosslinked Polymer. Indian Journal of Chemical Technology, vol 11, 853  864.##]
Seismic Analysis and Design of a MultiStorey Building Located in Haql City, KSA
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Recently the design of RC building to mitigate seismic loads has received a great attention. Since Saudi Arabia has low to moderate seismicity, most of buildings were designed only for gravity load. The objective of this paper is to analysis design RC building located in the most active seismic zone region in Saudi Arabia to mitigate seismic loads. A multistorey reinforced concrete building, in Haql city, was seismically analyzed and designed using the Equivalent Lateral Force Procedure with the aid of SAP200 software. The chosen buildings which was Ordinary Moment Resisting Frame (OMR), was analyzed and designed by using SBC 301 (2007) Saudi Building Code [1], SAP2000 (structural analysis software) [2] and ISACOL "Information Systems Application on Reinforced Concrete Columns" [3]. The results showed that the current design of RC buildings located in the most active seismic zone region in Saudi Arabia, Haql city was found unsafe, inadequate and unsatisfied to mitigate seismic loads.
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Mohammed
Ismaeil
Assistant Professor, Department of Civil Engineering, King Khalid University, KSA. On leave from Sudan University for Science and Technology, Khartoum, Sudan
Assistant Professor, Department of Civil
Saudi Arabia
abunama79@hotmail.com


Khalid
Elhadi
Assistant Professor, Department of Civil Engineering, King Khalid University, KSA. On leave from Structural Engineering Department, Zagazig University, Zagazige, Egypt
Assistant Professor, Department of Civil
Saudi Arabia
kalhdi@kku.edu.sa


Yasser
Alashker
Assistant Professor, Department of Civil Engineering, King Khalid University, KSA. On leave from Structural Engineering Department, Zagazig University, Zagazige, Egypt
Assistant Professor, Department of Civil
Saudi Arabia
yalashgr@kku.edu.sa


Isam Eldin
Yousef
Lecturer, Department of Civil Engineering, King Khalid University, KSA
Lecturer, Department of Civil Engineering,
Saudi Arabia
ieyousef@kku.edu.sa
SAP2000
SBC 301 (2007)
Active seismic zone region
Saudi Arabia
Equivalent static method
Seismic loads
[[1] Saudi Building Code SBC3012007: Loads and Forces Requirements, Saudi Buildings Code National Committee (2007). ##[2] Computers and Structures. (2001). SAP2000: Three Dimensional Static and Dynamic Finite ElementAnalysisand Design of Structures, Computers and Structures Inc., Berkeley, California, U.S.A ##[3] Shehata , A .Y. "Information Systems Application On Reinforced Concrete Columns", M.Sc. Thesis, Faculty of Engineering , Department of Structural Engineering , Cairo University , Giza , Egypt , 1999 . ##[4] https://en.wikipedia.org/wiki/1995_Gulf_of_Aqaba_earthquake ##[5] Saleh Mahmoud A. Attar ‘Evaluation of the seismic performance of a typical school building’, Thesis (M.Sc.), King AbdulAziz University, 2003. ##[6] ALHaddad, M., Siddiqi, G.S., AlZaid, R., Arafah, A., Necioglu, A., and Turkelli, N., " A Study Leading to a Preliminary Seismic Design Criteria, for the Kingdom," Final Report, KACST project No. AR931, Riyadh, 1992. ##[7] AlHaddad, M., Siddiqi, G.S., AlZaid, R., Arafah, A., Necioglu, A., and Turkelli, N., “A Basis for Evaluation of Seismic Hazard and Design Criteria for Saudi Arabia”, Journal of Earthquake Engineering Research Institute, EERI, Spectra, Vol. 10, No. 2, May 1994, Okland, California. ##[8] M. A. Ismaiel et.al. (2017) Seismic Analysis of a TenStorey Reinforced Concrete Building in Jazan Area, KSA. Open Journal of Civil Engineering, 7, PP. 252266. http://www.scirp.org/journal/ojce/. DOI: 10.4236/ojce.2017.72016 ##[9] BS 8110. (1997). the Structural Use of Concrete, British Standard Institution, London.. ##[10] Mosley, W. H. and Bungey, J. H. (1997): Reinforced Concrete Design; BS 8110:Part 1, 2nd Ed. Macmillan , London.##]
Development of Intelligent Systems to Predict Diamond Wire Saw Performance
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2
Assessment of wear rate is an inseparable section of the sawability of dimension stone and an essential task to optimization in diamond wire saw performance. The aim of this research is to provide an accurate, practical and applicable model for predicting the wear rate of diamond bead based on rock properties using applications and performances of intelligent systems. In order to reach this purpose, 38 cutting test results with 38 different rocks were used from andesites, limestones and real marbles quarries located in eleven areas in Turkey. Prediction of wear rate is determined by optimization techniques like Multilayer Perceptron (MLP) and hybrid Genetic algorithm –Artificial neural network (GAANN) models that were utilized to build two estimation models by MATLAB software. In this study, 80% of the total samples were used randomly for training dataset and the remaining 20% was considered as testing data for GAANN model. Further, accuracy and performance capacity of models established were investigated using root mean square error (RMSE), coefficient of determination (R2) and standard deviation (STD). Finally, a comparison was made among performances of these soft computing techniques for predicting and the results obtained clearly indicated hybrid GAANN model with coefficient of determination (R2) of training = 0.95 and testing = 0.991 can get more accurate predicting results in comparison with MLP models.
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Reza
Mikaeil
Department of Mining and Metallurgical Engineering, Urmia University of Technology, Urmia, Iran
Department of Mining and Metallurgical Engineering
Iran
reza.mikaeil@uut.ac.ir


Sina
Shaffiee Haghshenas
Young Researchers and Elite Club, Rasht Branch, Islamic Azad University, Rasht, Iran
Young Researchers and Elite Club, Rasht Branch,
Iran
s.shaffiee@yahoo.com


Yilmaz
Ozcelik
Department of Mining Engineering, Hacettepe University, Ankara, Turkey
Department of Mining Engineering, Hacettepe
Turkey
yilmaz@hacettepe.edu.tr


Sami
Shaffiee Haghshenas
Graduate of Civil Engineering, Department of Civil Engineering, Islamic Azad University, Astara Branch, Astara, Iran
Graduate of Civil Engineering, Department
Iran
sami.shaffiee@yahoo.com
Diamond wire saw
Wear rate
Soft Computing
Hybrid GAANN Model
Multilayer Perceptron
[[1] Ozcelik, Y., Polat, E., Bayram, F., & Ay, A. M. (2004). Investigation of the effects of textural properties on marble cutting with diamond wire. International Journal of Rock Mechanics and Mining Sciences, 41, 228234. ##[2] Luo, S. Y., & Liao, Y. S. (1993). Effects of diamond grain characteristics on sawblade wear. International Journal of Machine Tools and Manufacture, 33(2), 257266. ##[3] Luo, S. Y., & Liao, Y. S. (1995). Study of the behaviour of diamond sawblades in stone processing. Journal of materials processing technology, 51(1), 296308. ##[4] Luo, S. Y. (1996). Characteristics of diamond sawblade wear in sawing. International Journal of Machine Tools and Manufacture, 36(6), 661672. ##[5] Luo, S. Y. (1997). Investigation of the worn surfaces of diamond sawblades in sawing granite. Journal of materials processing technology, 70(1), 18. ##[6] Xu, X. (2001). Study on the thermal wear of diamond segmented tools in circular sawing of granites. Tribology Letters, 10(4), 245250. ##[7] Wei, X., Wang, C. Y., & Zhou, Z. H. (2003). Study on the fuzzy ranking of granite sawability. Journal of materials processing technology, 139(1), 277280. ##[8] Ersoy, A., Buyuksagic, S., & Atici, U. (2005). Wear characteristics of circular diamond saws in the cutting of different hard abrasive rocks. Wear, 258(9), 14221436. ##[9] Özçelik, Y., Kulaksız, S., (2000). Investigation of the relationship between cutting angles and wear on beads in diamond wire cutting method, 9th Mine Planning and Equipment Selection Symposium, Panagiotou & Michalakopoulos (Ed.), pp. 661666. ##[10] Özçelik, Y., Kulaksız, S., & Çetin, M. C. (2002). Assessment of the wear of diamond beads in the cutting of different rock types by the ridge regression. Journal of Materials Processing Technology, 127(3), 392400. ##[11] Özçelik, Y. (2003). Multivariate statistical analysis of the wear on diamond beads in the cutting of andesitic rocks. In Key Engineering Materials (Vol. 250, pp. 118130). Trans Tech Publications. ##[12] Mikaeil, R., Ozcelik, Y., Ataei, M., & Shaffiee Haghshenas, S. (2016). Application of harmony search algorithm to evaluate performance of diamond wire saw. Journal of Mining and Environment. ##[13] 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, 115. ##[14] Mikaeil, R., Haghshenas, S.S., Ataei, M., Haghshenas, S.S., & Shafiee Haghshenas, A., (2017). Ranking and Assessment of Tunneling Projects Risks Using Fuzzy MCDM (Case Study: Toyserkan Doolayi Tunnel). 25th International Mining Congress and Exhibition of Turkey, P. 122128. ##[15] Haghshenas, S. S., Neshaei, M. A. L., Pourkazem, P., & Haghshenas, S. S. (2016). The Risk Assessment of Dam Construction Projects Using Fuzzy TOPSIS (Case Study: Alavian Earth Dam). Civil Engineering Journal, 2(4), 158167. ##[16] Haghshenas, S. S., Haghshenas, S. S., Barmal, M., & Farzan, N. (2016). Utilization of Soft Computing for Risk Assessment of a Tunneling Project Using Geological Units. Civil Engineering Journal, 2(7), 358364. ##[17] Rad, M. Y., Haghshenas, S. S., & Haghshenas, S. S. (2014). Mechanostratigraphy of cretaceous rocks by fuzzy logic in East Arak, Iran. In The 4th International Workshop on Computer Science and EngineeringSummer, WCSE. ##[18] Onyari, E. K., & Ilunga, F. M. (2013). Application of MLP neural network and M5P model tree in predicting streamflow: A case study of Luvuvhu catchment, South Africa. International Journal of Innovation, Management and Technology, 4(1), 11. ##[19] Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural networks, 2(5), 359366. ##[20] Daniels, H., & Kamp, B. (1999). Application of MLP networks to bond rating and house pricing. Neural Computing & Applications, 8(3), 226234. ##[21] Khotanzad, A., & Chung, C. (1998). Application of multilayer perceptron neural networks to vision problems. Neural Computing & Applications, 7(3), 249259. ##[22] Sonmez, H., Gokceoglu, C., Nefeslioglu, H. A., & Kayabasi, A. (2006). Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. International Journal of Rock Mechanics and Mining Sciences, 43(2), 224235. ##[23] HechtNielsen R (1987) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the first IEEE international conference on neural networks, San Diego, CA, pp 11–14. ##[24] Hush DR (1989) Classification with neural networks: a performance analysis. In: Proceedings of the IEEE international conference on systems engineering. Dayton, OH, pp 277–280 ##[25] Kaastra, I., & Boyd, M. (1996). Designing a neural network for forecasting financial and economic time series. Neurocomputing, 10(3), 215236. ##[26] Kanellopoulos, I., & Wilkinson, G. G. (1997). Strategies and best practice for neural network image classification. International Journal of Remote Sensing, 18(4), 711725. ##[27] Ripley, B. D. (1993). Statistical aspects of neural networks. Networks and chaos—statistical and probabilistic aspects, 50, 40123. ##[28] Paola, J. D. (1994). Neural network classification of multispectral imagery. Master Tezi, The University of Arizona, USA. ##[29] Wang C (1994) A theory of generalization in learning machines with neural application. PhD thesis, The University of Pennsylvania. ##[30] Masters T (1994) Practical neural network recipes in C++. Academic Press, Boston MA. ##[31] Hagan, M. T., & Menhaj, M. B. (1994). Training feedforward networks with the Marquardt algorithm. IEEE transactions on Neural Networks, 5(6), 989993. ##[32] Armaghani, D. J., Hasanipanah, M., & Mohamad, E. T. (2016). A combination of the ICAANN model to predict airoverpressure resulting from blasting. Engineering with Computers, 32(1), 155171. ##[33] Zorlu, K., Gokceoglu, C., Ocakoglu, F., Nefeslioglu, H. A., & Acikalin, S. (2008). Prediction of uniaxial compressive strength of sandstones using petrographybased models. Engineering Geology, 96(3), 141158. ##[34] Mikaeil, R., Haghshenas, S. S., Shirvand, Y., Hasanluy, M. V., & Roshanaei, V. (2016). Risk Assessment of Geological Hazards in a Tunneling Project Using Harmony Search Algorithm (Case Study: ArdabilMianeh Railway Tunnel). Civil Engineering Journal, 2(10), 546554. ##[35] Mikaeil, R., Haghshenas, S. S., Haghshenas, S. S., & Ataei, M. (2016). Performance prediction of circular saw machine using imperialist competitive algorithm and fuzzy clustering technique. Neural Computing and Applications, 110. ##[36] Haghshenas,S.S., Haghshenas, S.S., Mikaeil, R., Sirati Moghadam, P., & HaghshenasA.S., “A New Model for Evaluating the Geological Risk Based on Geomechanical Properties —Case Study: The Second Part of Emamzade Hashem Tunnel” Electronic Journal of Geotechnical Engineering, 2017. (22.01), pp 309320. Available at ejge.com. ##[37] Holland, J. H. (1975). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. Ann Arbor, MI: University of Michigan Press, 441393. ##[38] Terranova, O. G., Gariano, S. L., Iaquinta, P., & Iovine, G. G. (2015). GA SAKe: forecasting landslide activations by a geneticalgorithmsbased hydrological model. Geoscientific Model Development, 8(7), 19551978. ##[39] Woodward, M., Kapelan, Z., & Gouldby, B. (2014). Adaptive flood risk management under climate change uncertainty using real options and optimization. Risk Analysis, 34(1), 7592. ##[40] Saemi, M., Ahmadi, M., & Varjani, A. Y. (2007). Design of neural networks using genetic algorithm for the permeability estimation of the reservoir. Journal of Petroleum Science and Engineering, 59(1), 97105. ##[41] Khandelwal, M., & Armaghani, D. J. (2016). Prediction of drillability of rocks with strength properties using a hybrid GAANN technique. Geotechnical and Geological Engineering, 34(2), 605620. ##[42] Momeni, E., Nazir, R., Armaghani, D. J., & Maizir, H. (2014). Prediction of pile bearing capacity using a hybrid genetic algorithmbased ANN. Measurement, 57, 122131. ##[43] Monjezi, M., Khoshalan, H. A., & Varjani, A. Y. (2012). Prediction of flyrock and backbreak in open pit blasting operation: a neurogenetic approach. Arabian Journal of Geosciences, 5(3), 441448. ##[44] Aghajanloo, M. B., Sabziparvar, A. A., & Talaee, P. H. (2013). Artificial neural network genetic algorithm for estimation of crop evapotranspiration in a semiarid region of Iran. Neural Computing and Applications, 23(5), 13871393.##]
Artificial Neural Networks for Construction Management: A Review
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2
Construction Management (CM) has to deal with a variety of uncertainties related to Time, Cost, Quality and Safety, to name a few. Such uncertainties make the entire construction process highly unpredictable. It therefore falls under the purview of artificial neural networks (ANNs) in which the given hazy information can be effectively interpreted in order to arrive at meaningful conclusions. This paper reviews application of ANNs in construction activities related to prediction of costs, risk and safety, tender bids, as well as labor and equipment productivity. The review suggests that the ANN’s had been highly beneficial in correctly interpreting an inadequate input information. It was seen that most of the investigators used feed forward back propagation type of the network; however if a single ANN architecture was found to be insufficient then hybrid modeling in association with other machine learning tools such as genetic programming and support vector machines were much useful. It was however clear that the authenticity of data and experience of the modeler are important in obtaining good results.
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70
88


Preeti
Kulkarni
Associate Professor, Vishwakarma Institute of Information Technology, Pune, India
Associate Professor, Vishwakarma Institute
India
preeti.kulkarni@viit.ac.in


Shreenivas
Londhe
Professor, Vishwakarma Institute of Information Technology, Pune, India
Professor, Vishwakarma Institute of Information
India
shreenivas.londhe@viit.ac.in


Makarand
Deo
Professor, Indian Institute of Technology, Mumbai, India
Professor, Indian Institute of Technology,
India
mcdeo@civil.iitb.ac.in
Construction management
Artificial Neural Networks
Training algorithm
Sensitivity analysis
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Predicting Budget from Transportation Research Grant Description: An Exploratory Analysis of Text Mining and Machine Learning Techniques
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2
Funding agencies such as the U.S. National Science Foundation (NSF), U.S. National Institutes of Health (NIH), and the Transportation Research Board (TRB) of The National Academies make their online grant databases publicly available which document a variety of information on grants that have been funded over the past few decades. In this paper, based on a quantitative analysis of the TRB’s Research In Progress (RIP) online database, we explore the feasibility of automatically estimating the appropriate funding level, given the textual description of a transportation research project. We use statistical Text Mining (TM) and Machine Learning (ML) technologies to build this model using the 14,000 or more records of the TRB’s RIP research grants big data. Natural Language Processing (NLP) based text representation models such as the Latent Dirichlet Allocation (LDA), Latent Semantic Indexing (LSI) and the Doc2Vec are used to vectorize the project descriptions and generate semantic vectors. Each of these representations are then used to train supervised regression models such as Random Forest (RF) regression. Out of the three latent feature generation models, we found LDA gives the least Mean Absolute Error (MAE). However, based on the correlation coefficients, it was found that it is not very feasible to accurately predict the funding level directly from the unstructured project abstract, given the large variations in source agencies, subject areas, and funding levels. By using separate prediction models for different types of funding agencies, funding levels were better correlated to the project abstract.
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89
102


Ayush
Singhal
R&D, Contata Solutions, LLC, Minneapolis, Minnesota, USA
R&D, Contata Solutions, LLC, Minneapolis,
United States
ayush@cs.umn.edu


Kasthurirangan
Gopalakrishnan
Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA
Department of Electrical Engineering and
United States
rangan@northwestern.edu


Siddhartha
Khaitan
Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA
Department of Electrical and Computer Engineering,
United States
skhaitan@iastate.edu
Text Mining
Transportation research
Natural Language Processing (NLP)
Big Data
Deep Learning
Soft Computing
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