**Appendix**

$$f\_1 = 12.856 + \frac{T}{V} + \frac{T}{Dt} \times \left( 35.3 + D + \frac{6.43}{2D + \frac{2T^2}{Vt} - \frac{t}{D + \frac{4.74T^3}{t^2} + \frac{T(T - D)}{t}}} \right)$$

2 2 2 2 2 0.0727 12.856 *T Vt f DT Vt <sup>T</sup>* 1 3 0.5 35.3 <sup>7</sup> , 28.86 4.1739 4.1739 0.5 *<sup>t</sup> <sup>D</sup> V t <sup>T</sup> f T TV V T Tt T tD t V VT* 2 2 4 2 2 2 2 (28.86 2.37 ) 19.3 0.034 6.43 , ( ) 7.43 4.1739 *T T T t f T V t Vt T Dt D Tt Vt* 2 2 <sup>2</sup> 5 6 2 2(35.3 ) 35.3 2.37 77 4 , 4.1739 3.3 28.86 0.98 28.86 *T DT T <sup>V</sup> <sup>D</sup> <sup>T</sup> V t <sup>t</sup> f T <sup>D</sup> <sup>f</sup> <sup>T</sup> <sup>t</sup> <sup>T</sup> T Vt TVt T Vt V* 2 3 2 *T t*

The Usage of Genetic Methods for Prediction of Fabric Porosity 199

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[8] M. Brezočnik and J. Balič, »A Genetic-based Approach to Simulation of Self-Organizing Assembly«, *Robot. Comput. Integrat. Manufact.,* Vol. 17, pp. 113-120, 2001.

[11] P. Bajaj and A. Sengupta, »Protective Clothing«, in *Textile Progress*, Manchester, The

[12] Y. Shoshani and Y. Yakubov, »A Model for Calculating the Noise Absorption Capacity of Nonwoven Fiber Webs«, *Textile Research Journal,* Vol. 69, pp. 519-526, 1999. [13] M. Mohammadi and P. Banks-Lee, »Determing Effective Thermal Conductivity of Multilayered Nonwoven Fabrics«, *Textile Research Journal,* Vol. 73, pp. 802-808, 2003. [14] N. Pan and P. Gibson, Thermal and moisture transport in fibrous materials,

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*Computational Materials Science* , Vol. 45, pp. 1-7, 2009.

Textile Institute, 1992, pp. 1-94.

**6. References** 

39, 2010.

$$f\_{7} = \frac{\frac{2T}{Vt} - \frac{t^{-}}{T^{2}(2T+t) + D(Tt - 2T^{2} + t^{2})}}{7T + 2D - Vt}$$

$$f\_8 = 41.7137 + 2D + \frac{35.3 + D - \frac{4.1739T}{T^2 - 8.393t}}{V} + \frac{t}{35.3 + \frac{T}{V} - \frac{6.88T^2(35.3 + T)}{DVt^2} + t}\prime$$

$$f\_{\theta} = \frac{35.3 + D}{V} + \left(\frac{\frac{28.86}{V} + t + \frac{(T + t)(T - 0.29Vt)}{V^2 t}}{T - \frac{4.1739T}{V} + t}\right)$$

$$\left(\frac{3.43 + \frac{28.86T}{DV} - \frac{8.35(35.29 + 2D)T}{t}}{\frac{28.86}{V}}\right)$$

$$f\_{10} = \frac{28.86 + t + \frac{(28.86 - D)t - T^2}{2T^2} \left(6.43 + \frac{Vt}{T - 0.24D} \left(D - \frac{4.1739T}{V} + 0.034TV + t\right)\right)}{41.7137 + D - \frac{16.786T^3}{DVt^2} - \frac{8.393T^2}{V^2t} + \frac{Vt^2}{T(2.37T + t(V - 8.393))}}$$

#### **6. References**

198 Genetic Programming – New Approaches and Successful Applications

3

  2 2

*V VT*

2 2 <sup>2</sup>

0.5 35.3 <sup>7</sup> , 28.86 4.1739 4.1739 0.5

 

2 2 (28.86 2.37 ) 19.3 0.034 6.43 , ( ) 7.43 4.1739 *T T T t f T V t Vt T Dt D Tt*

2.37 77 4 , 4.1739 3.3 28.86 0.98 28.86

2 2 2

8.393 41.7137 2 , 6.88 (35.3 ) 35.3

35.3 4.1739

*<sup>T</sup> T t <sup>D</sup> <sup>V</sup> <sup>f</sup> <sup>V</sup> T D <sup>T</sup>*

2 2

(28.86 ) 4.1739 28.86 6.43 0.034

*T Vt TD V*

2

2 99.2 0.24

*t <sup>V</sup> <sup>T</sup> <sup>f</sup> T T Vt <sup>D</sup>*

10 32 2

16.786 8.393 41.7137

28.86 8.35(35.29 2 ) 6.43

*DV t*

*Dt T Vt <sup>T</sup> t D TV <sup>t</sup>*

*DVt V t T T tV*

*<sup>D</sup> <sup>V</sup> V t*

*T t <sup>t</sup> f D <sup>V</sup> TT T <sup>t</sup>*

(2 ) ( 2 ) , 77 2

*DT T <sup>V</sup> <sup>D</sup>*

*<sup>T</sup> V t <sup>t</sup> f T <sup>D</sup> <sup>f</sup> <sup>T</sup> <sup>t</sup> <sup>T</sup> T Vt TVt T Vt V*

*T*

1

*Vt*

2

*V DVt*

2

(2.37 ( 8.393))

*T t T Vt <sup>t</sup>*

28.86 ( )( 0.29

2 2 2 0.0727 12.856 *T Vt f DT Vt <sup>T</sup>* 

*<sup>t</sup> <sup>D</sup> V t <sup>T</sup> f T TV V T Tt T tD t*

2 2 4 2 2

2(35.3 ) 35.3

2 3

*Vt T T t D Tt T t <sup>f</sup> D Vt* 

*T t*

2 8 2

*<sup>T</sup> <sup>D</sup>*

4.1739 35.3

5 6 2

2

7

9

35.3

2 2

	- [22] Y. L. Hsieh, »Liquid Transport in Fabric Structures«, *Textile Research Journal,* Vol. 65, pp. 299-307, 1995.

**Chapter 9** 

© 2012 Londhe and Dixit, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Londhe and Dixit, licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The application of soft computing techniques in the field of Civil Engineering started since early nineties and since encompassed almost all fields of Civil Engineering namely Structural Engineering, Construction Engineering and Management, Geotechnical

**Genetic Programming: A Novel Computing** 

The use of artificial intelligence in day to day life has increased since late 20th century as seen in many home appliances such as microwave oven, washing machine, camcorder etc which can figure out on their own what settings to use to perform their tasks optimally. Such intelligent machines make use of the soft computing techniques which treat human brain as their role model and mimic the ability of the human mind to effectively employ modes of reasoning that are approximate rather than exact. The conventional hard computing techniques require a precisely stated analytical model and often a lot of computational time. Premises and guiding principles of Hard Computing are precision, certainty, and rigor [1]. Many contemporary problems do not lend themselves to precise solutions such as recognition problems (handwriting, speech, objects and images), mobile robot coordination, forecasting, combinatorial problems etc. This is where soft computing techniques score over the conventional hard computing approach. Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. The guiding principle of soft computing is to exploit the tolerance for imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost [1]. The principal constituents, i.e., tools, techniques of Soft Computing (SC) are Fuzzy Logic (FL), Neural Networks (NN), Evolutionary Computation (EC), Machine Learning (ML) and Probabilistic Reasoning (PR). Soft computing many times employs NN, EC, FL etc, in a complementary rather than a competitive way resulting into hybrid techniques like Adaptive Neuro-Fuzzy Interface

**Approach in Modeling Water Flows** 

Shreenivas N. Londhe and Pradnya R. Dixit

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/48179

**1. Introduction** 

System (ANFIS).

