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Applications of Soft Computing in Civil Engineering: A Review

by Vinay Chandwani, Vinay Agrawal, Ravindra Nagar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 81 - Number 10
Year of Publication: 2013
Authors: Vinay Chandwani, Vinay Agrawal, Ravindra Nagar
10.5120/14047-2210

Vinay Chandwani, Vinay Agrawal, Ravindra Nagar . Applications of Soft Computing in Civil Engineering: A Review. International Journal of Computer Applications. 81, 10 ( November 2013), 13-20. DOI=10.5120/14047-2210

@article{ 10.5120/14047-2210,
author = { Vinay Chandwani, Vinay Agrawal, Ravindra Nagar },
title = { Applications of Soft Computing in Civil Engineering: A Review },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 81 },
number = { 10 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 13-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume81/number10/14047-2210/ },
doi = { 10.5120/14047-2210 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:55:42.593834+05:30
%A Vinay Chandwani
%A Vinay Agrawal
%A Ravindra Nagar
%T Applications of Soft Computing in Civil Engineering: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 81
%N 10
%P 13-20
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The field of engineering is a creative one. The problems encountered in this field are generally unstructured and imprecise influenced by intuitions and past experiences of a designer. The conventional methods of computing relying on analytical or empirical relations become time consuming and labor intensive when posed with real life problems. To study, model and analyze such problems, approximate computer based Soft Computing techniques inspired by the reasoning, intuition, consciousness and wisdom possessed by a human beings are employed. In contrast to conventional computing techniques which rely on exact solutions, soft computing aims at exploiting given tolerance of imprecision, the trivial and uncertain nature of the problem to yield an approximate solution to a problem in quick time. Soft Computing being a multi-disciplinary field uses a variety of statistical, probabilistic and optimization tools which complement each other to produce its three main branches viz. , Neural Networks, Genetic Algorithms and Fuzzy Logic. The review paper presents the applications of two major Soft Computing techniques viz. , Artificial Neural Networks and Genetic Algorithms in the field of Civil Engineering, which to some extent has replaced the time consuming conventional techniques of computing with intelligent and time saving computing tools.

References
  1. Chaturvedi, D. K. 2008. Soft Computing Techniques and its applications in Electrical Engineering, Studies in Computational Intelligence, 103, 1-10.
  2. Mitchell, T. 1997. Machine Learning, McGraw Hill Education, India.
  3. Rafiq, M. , Bugmann, G. and Easterbrook, D. 2001. Neural Network Design for Engineering Applications, Journal of Computers and structures, 79(17), 1541-1552.
  4. Zadeh, L. H. 1965. Fuzzy Sets, Information and Control, 8, 338-353.
  5. Haykin, S. 2009. Neural Networks A Comprehensive Foundation, 8th Edn. , Pearson Prentice Hall, India.
  6. Flood, I. and Kartam, N. 1994. Neural Networks in Civil Engineering. I: Principles and Understanding, Journal of Computing in Civil Engineering, 8(2), 131-148.
  7. Vanluchene, R. D. and Sun, R. 1990. Neural Networks in Structural Engineering, Computer-Aided Civil and Infrastructure Engineering, 5(3), 207-215.
  8. Hajela, P. and Berke, L. 1991. Neurobiological Computational Models in Structural Analysis and Design, Computers and Structures, 41(4), 657-667.
  9. Rogers, J. L. 1994. Simulating Structural Analysis with Neural Network, Journal of Computing in Civil Engineering, 8(2), 252-265.
  10. Mukherjee, A. and Despande, J. M. 1995. Modeling Initial design process using Artificial Neural Networks, Journal of Computing in Civil Engineering, 9(3), 194-200.
  11. Adeli, H. and Park, H. S. 1995. A neural dynamics model for structural optimization-Theory, Computers and Structures, 57(3), 383-390.
  12. Elazouni, A. M. , Nosair, I. A. , Mohieldin, Y. A. and Mohamed. A. G. 1997. Estimating Resource Requirements at Conceptual Stage using Neural Networks, Journal of Computing in Civil Engineering, 11(4), 217-223.
  13. Sanad, A. and Saka, M. 2001. Prediction of Ultimate Shear Strength of Reinforced-Concrete Deep Beams Using Neural Networks, Journal of Structural Engineering, 127(7), 818–828.
  14. Hadi, M. N. S. 2003. Neural Network applications in Concrete Structures, Computers and Structures, 81(6), 373-381.
  15. Oreta, A. W. C. and Kawashima, K. 2003. Neural Network Modeling of Confined Compressive Strength and strain of Circular Concrete Columns, Journal of Structural Engineering, 129(4), 554-561.
  16. Cladera, A. and Mari, A. R. 2004. Shear design procedure for reinforced normal and high-strength concrete beams using artificial neural networks Part I: beams without stirrups, Engineering Structures, 26, 917-926.
  17. Cladera, A. and Mari, A. R. 2004. Shear design procedure for reinforced normal and high-strength concrete beams using artificial neural networks Part II: beams with stirrups, Engineering Structures, 26, 927-936.
  18. Ahmadkhanlou, F. and Adeli, H. 2005. Optimum cost design of reinforced concrete slabs using neural dynamics model, Engineering Applications of Artificial Intelligence, 18, 65-72.
  19. Pendharkar, U. , Chaudhary, S. and Nagpal, A. K. 2007. Neural network for bending moment in continuous composite beams considering cracking and time effects in concrete, Engineering Structures, 29(9), 2069-2079.
  20. Erdem, H. 2010. Prediction of moment capacity of reinforced concrete slabs in fire using artificial neural networks, 41(2), 270-276.
  21. Bagci, M. 2010. Neural Network Model for Moment-Curvature relationship of Reinforced Concrete sections, Mathematical and Computational Applications, 15(1), 66-78.
  22. Jasim, N. A. and Mohammed, M. Y. 2011. Prediction of Ultimate Torsional Strength of Spandrel beams using Artificial Neural Networks, Basrah Journal for Engineering Science, 11(1), 88-100.
  23. Jakubek, M. 2012. Neural network prediction of load capacity for eccentrically loaded reinforced concrete columns, Computer Assisted Methods in Engineering and Science, 19, 339-349.
  24. Yeh, I. 1999. Design of High Performance Concrete Mixture using Neural Networks and Non-linear programming, Journal of Computing in Civil Engineering, 13(1), 36-42.
  25. Guang, N. H. and Zong, W. J. 2000. Prediction of compressive strength of concrete by neural networks, Cement and Concrete Research, 30, 1245-1250.
  26. Gupta, R. , Kewalramani, M. and Goel, A. 2006. Prediction of Concrete Strength using Neural-Expert System, Journal of Materials in Civil Engineering, 18(3), 462-466.
  27. Yeh, I. (2006). Exploring Concrete Slump Model Using Artificial Neural Networks, Journal of Computing in Civil Engineering, 20(3), 217–221.
  28. Ozturan, M. , Birgul, K. and Ozturan, T. 2008. Comparison of Concrete Strength Prediction Techniques with Artificial Neural Network Approach, Building Research Journal, 56(1), 23-36.
  29. Alshihri, M. A. , Azmy, A. M. and El-Bisy, M. S. 2009. Neural networks for predicting compressive strength of structural light weight concrete, Construction and Building Materials, 23, 2214-2219.
  30. Aggarwal, Y. and Aggarwal, P. 2011. Prediction of Compressive Strength of SCC containing Bottom Ash using Artificial Neural Networks, World Academy of Science, Engineering and Technology, 53, 735-740.
  31. Shahin, M. A. , Maier, H. R. and Jaksa, M. B. 2002. Predicting settlement of Shallow Foundations using Neural Networks, Journal of Geotechnical and Geoenvironmental Engineering, 128(9), 785-793.
  32. Young-Su, K. and Byung-Tak, K. 2006. Use of Artificial Neural Networks in the Prediction of Liquefaction Resistance of Sands, Journal of Geotechnical and Geoenvironmental Engineering, 132(11), 1502–1504.
  33. Das, S. , Manna, B. and Baidya, D. K. 2011. Prediction of Dynamic Soil-Pile Interaction under coupled vibration using Artificial Neural Network Approach, Geo Frontiers, 1-10.
  34. Nath, U. K. and Hazarika, P. J. 2011. Study of Pile Cap Lateral resistance using Artificial Neural Networks, International Journal Of Computer Applications, 21(1), 20-25.
  35. Tokar, S. A. and Johnson, P. A. 1999. Rainfall-Runoff modeling using Artificial Neural Networks, 4(3), 232-239.
  36. Wilby, R. L. Abrahart, R. J. and Dawson, C. W. 2003. Detection of conceptual model rainfall-runoff processes inside an artificial neural network, Hydrological Sciences Journal, 48(2), 163-181.
  37. Jeong, D. and Kim, Y. 2005. Rainfall-runoff models using artificial neural networks for ensemble streamflow prediction, Hydrological Processes, 19, 3819-3835.
  38. Abrahart, R. J. and See, L. M. 2007. Neural network modeling of non-linear hydrological relationships, Hydrology and Earth System Sciences, 11, 1563-1579.
  39. Golabi, M. , Radmanesh, F. , Akhondali, A. and Kashefipoor, M. 2013. Prediction of Seasonal Precipitation using Artificial Neural Networks (Case Study: Selected Stations of (Iran) Khozestan Province), Journal of Basic and Applied Scientific Research, 3(1), 589-595.
  40. Nagy, H. , Watanabe, K. , and Hirano, M. 2002. Prediction of Sediment Load Concentration in Rivers using Artificial Neural Network Model. " Journal Hydraulic Engineering, 128(6), 588–595.
  41. Cigizoglu, H. K. 2002. Suspended sediment estimation for rivers using Artificial Neural Networks and sediment rating curves, Turkish Journal of Engineering and Environmental Sciences, 26, 27-36.
  42. Rai, R. K. and Mathur, B. S. 2008. Event based sediment yield modeling using Artificial Neural Networks, 22(4), 423-441.
  43. Heng, S. and Suetsugi, T. 2013. Using Artificial Neural Networks to estimate sediment in ungauged catchment of the Tonle Sap River Basin Cambodia, Journal of Water Resources and Protection, 5, 111-123.
  44. Kumar, M. , Raghuwanshi, N. , Singh, R. , Wallender, W. , and Pruitt, W. 2002. Estimating Evapotranspiration using Artificial Neural Network, Journal of Irrigation and Drainage Engineering, 128(4), 224–233.
  45. Tabari, H. , Marofi, S. and Sabziparvar, A. 2010. Estimation of daily pan evaporation using artificial neural networks and multivariate non-linear regression, 28(5), 399-406.
  46. Karasekreter, N. , Basciftci, F. and Fidan, U. 2013. A new suggestion for an irrigation schedule with an artificial neural network, Journal of Experimental and Theoretical Artificial Intelligence, 25(1), 93-104.
  47. Lee, S. C. and Han, S. W. 2002. Neural network based models for generating artificial earthquakes and response spectra, Computers and Structures, 80(20-21), 1627-1638.
  48. Cho, H. C. , Fadali, M. S. , Saiidi, S. M. and Soon, L. K. 2005. Neural network active control of structures with earthquake excitation, International Journal of Control, Automation and Systems, 3(2), 202-210.
  49. Ghaboussi, J. and Elnashai, A. S. 2007. Development of neural network based hysteretic models for steel beam-column connections through self learning simulation, Journal of Earthquake Engineering, 11, 453-467.
  50. Arslan, H. M. 2010. An evaluation of effective design parameters on the earthquake performance of RC buildings using neural networks, Engineering Structures, 32, 1888-1898.
  51. Xie, J. , Qiu, J. , Wei, L. and Wang, J. W. 2011. The application of neural network in earthquake prediction in East China, Advances in Intelligent and Soft Computing, 106, 79-84.
  52. Robles, C. M. A. and Becerril, H. R. A. 2012. Seismic alert system based on artificial neural networks, World Academy of science, Engineering and Technology, 66, 813-818.
  53. Koza, J. R. 1992. Genetic programming: On the Programming of Computers by Means of Natural Selection, MIT press.
  54. Ravindran, A. , Ragsdell, K. M. and Reklaitis, G. V. 2006. Engineering Optimization: Methods and Applications, John Wiley & Sons Inc, Hoboken, New Jersey, USA.
  55. Goldberg, D. E. and Samtani, M. P. 1986, Engineering optimization via genetic algorithm, Proceedings 9th Conference on Electronic Computation, ASCE, 471-482.
  56. Deb, Kalyanmoy. 1991. Optimal design of a welded beam via genetic algorithms, AIAA Journal, 29, 2013-2015.
  57. Jenkins, W. 1992. Plane Frame Optimum Design Environment Based on Genetic Algorithm, Journal of Structural Engineering, 118(11), 3103–3112.
  58. Rajeev, S. and Krishnamoorthy, C. 1992. Discrete Optimization of Structures Using Genetic Algorithms, Journal of Structural Engineering, 118(5), 1233–1250.
  59. Koumousis, V. K. , and Arsenis, S. J. 1994. Genetic algorithms in a multi-criterion optimal detailing of reinforced concrete members, Advances in structural optimization, CIVIL-COMP Ltd, Edinburgh, Scotland, 233-240.
  60. Sarma, K. and Adeli, H. 1998. Cost Optimization of Concrete Structures. Journal of Structural Engineering, 124(5), 570–578.
  61. Chau, K. W. and Albermani, F. 2002. Genetic Algorithms for design of Liquid Retaining Structures, Lecture Notes in Artificial Intelligence, 2358, 119-128.
  62. Camp, C. , Pezeshk, S. , and Hansson, H. 2003. Flexural Design of Reinforced Concrete Frames Using a Genetic Algorithm, Journal of Structural Engineering, 129(1), 105–115.
  63. Fu, K. , Zhai, Y. , and Zhou, S. 2005. Optimum Design of Welded Steel Plate Girder Bridges Using a Genetic Algorithm with Elitism, Journal of Bridge Engineering, 10(3), 291–301.
  64. Guerra, A. , and Kiousis, D. P. 2006. Design optimization of reinforced concrete structures, Computers and Concrete, 3(5), 313-334.
  65. Senouci, B. A. , and Al-Ansari, S. M. 2009. Cost optimization of composite beams using genetic algorithms, Advances in Engineering Software, 40, 1112-1118.
  66. Alqedra, M. , Arafa, M. , and Ismail, M. 2011. Optimum cost of Prestressed and Reinforced Concrete Beams using Genetic Algorithms, Journal of Artificial Intelligence, 4(1), 76-88.
  67. Aydin, Z. and Ayvaz, Y. 2013. Overall cost optimization of Prestressed concrete bridges using Genetic Algorithm, KSCE Journal of Civil Engineering, 17(4), 769-776.
  68. Noguchi, T. and Maruyama, I. 2003. Performance based design system for concrete with Multi-optimizing Genetic Algorithm, Proceedings of 11th International Congress on Chemistry of Cement, Durban, South Africa.
  69. Amirjanov, A. and Sobolev, K. 2005. Optimal proportioning of concrete aggregates using a self adaptive genetic algorithm, Computers and Concrete, 2(5), 1-11.
  70. Jayaram, M. A. , Nataraja, M. C. and Ravikumar, C. N. 2009. Elitist Genetic Algorithm Models:Optimization of High Performance Concrete Mixes, Materials and Manufacturing Processes, 24(2), 225-229.
  71. Xie, X. , Yan, D. and Zheng, Y. 2011. Optimization Design of High Performance concrete based on Genetic algorithm toolbox of Matlab, Advanced Materials Research, 250-253, 2672-2677.
  72. Rahman, M. M. and Jumaat, M. Z. 2012. Cost minimum proportioning of Non-Slump concrete mix using Genetic Algorithms, Advanced Materials Research, 468-471, 50-54.
  73. Cui, L. and Sheng, D. 2005. Genetic Algorithms in probabilistic finite element analysis of geotechnical problems, Computers and Geotechnics, 32, 555-563.
  74. Liu, M. , Qie, Z. , Wu, X. , Dong, W. and Zheng, H. 2008. Model optimization of load-bearing capacity of macadam pile composite foundation based on Genetic Algorithms, IEEE Control and Decision Conference, Yantai, 3903-3907.
  75. Liu, X. , Cheng, G. , Wang, B. and Lin, S. 2012. Optimum Design of Pile Foundation by Automatic Grouping Genetic Algorithms, ISRN Civil Engineering, 2012.
  76. Manouchehrian, A. , Gholamnejad and Sharifzadeh, M. 2013. Development of a model for analysis of slope stability of circular mode failure using genetic algorithms, Environmental and Earth Sciences.
  77. Nixon, J. B, Dandy, G. C. and Simpson, A. R. 2001. A genetic algorithm for optimizing off-farm irrigation scheduling, Journal of Hydroinformatics, 3(1), 11-22.
  78. Bhattacharjya, R. 2004. Optimal design of unit hydrographs using probability distributions and genetic algorithms, Sadhana, 29(5), 499-508.
  79. Jain, A. , Bhattacharjya, R. and Sanaga, S. 2004. Optimal design of composite channels using genetic algorithms, Journal of Irrigation and Drainage, 130(4), 286-295.
  80. Cheng, C. , Wu, X. and Chau, K. W. 2005. Multiple criteria rainfall-runoff model calibration using a parallel genetic algorithm in a cluster of computers, Hydrological Sciences Journal, 50(6), 1069-1087.
  81. Zhang, F. X. , Wai, O. W. H. and Jiang, Y. W. 2010. Prediction of sediment transportation in Deep Bay (Hong Kong) using Genetic Algorithms, Journal of Hydrodynamics Ser. B, 22(5), Supplement 1, 599-604.
  82. Sambridge, M. and Gallagher, K. 1993. Earthquake hypocenter location using Genetic Algorithms, Bulletin of the Seismological Society of America, 83(5), 1467-1491.
  83. Yu, T. , Frenandez, J. and Rundle, J. B. 1998. Inverting the parameters of an earthquake rupture fault with a genetic algorithm, Computers and Geosciences, 24(2), 173-182.
  84. Naeim, F. , Alimoradi, A. and Pezeshk, S. 2004. Selection and Scaling of Ground motion Time Histories for Structural Design using Genetic Algorithms, Earthquake Spectra, 20(2), 413-426.
  85. Wongprasert, N. and Symans, M. 2004. Application of a Genetic Algorithm for Optimal Damper Distribution within the Nonlinear Seismic Benchmark Building. Journal of Engineering Mechanics, 130, 401–406.
  86. Yilmaz, S. 2011. Ground motion predictive modeling based on genetic algorithms, Natual Hazards and Earth System Sciences, 11, 2781-2789.
  87. Hejazi, F. , Toloue, I. Jaafar, M. S. and Noorzaei, J. 2013. Optimization of Earthquake Energy Dissipation System by Genetic Algorithms, Computer Aided Civil and Infrastructure Engineering, 28(10), 796-810.
  88. Rao, H. S. and Babu, B. R. 2007. Hybrid neural network model for the design of beam subjected to bending and shear, Sadhana, 32(5), 577-586.
  89. Kim, S. , Choi, H. B. , Shin, Y. , Kin, G. H. and Seo, D. S. 2013. Optimizing the mixing proportions with neural networks based on genetic algorithms for recycled aggregate concrete, Advances in Material Sciences and Engineering, 2013.
  90. Nikoo, M. , Zarfam, P. and Nokoo, M. 2012. Determining the displacement in concrete reinforcement building using evolutionary artificial neural networks, World Applied Sciences Journal, 16(2), 1699-1708.
  91. Zhang, Q. and Wang, C. 2008. Using genetic algorithm to optimize artificial neural networks: A case study on Earthquake prediction, IEEE Second International Conference on Genetic and Evolutionary Computing, Hubei, 128-131
  92. Fu, G. and Kapelan, Z. 2011. Embedding Neural Networks in Multiobjective Genetic Algorithms for Water Distribution System Design, Water Distribution Systems Analysis 2010, 888-898.
  93. Jain, A. and Srinivasulu, S. 2004. Development of effective and efficient rainfall-runoff models using integration of deterministic, real coded genetic algorithms and artificial neural networks, Water Resources Research, 40(4).
  94. Chen, Y. W. , Cheng, L. C. , Huang, C. W. and Chu, H. J. 2013. Applying genetic algorithms and neural networks to the conjunctive use of surface and subsurface water, Water Resources Management 2013.
  95. Adib, A. and Jahanbakhshan, H. 2013. Stochastic approach to determination of suspended sediment concentration in tidal rivers by artificial neural networks and genetic algorithms, Canadian Journal of Civil Engineering, 40(4), 299-312.
Index Terms

Computer Science
Information Sciences

Keywords

Soft Computing Artificial Neural Networks Genetic Algorithms Hybrid Soft Computing.

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