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Comparison of Color Measuring Methods for Yarn Dyed Woven Fabrics Qizheng LIa and Chengyan ZHUb College of Materials and Textiles, Zhejiang Sci-Tech University, Hangzhou 310018, China aliqizheng@zstu.edu.cn, bcyzhu@zstu.edu.cn Keywords: Dyed Yarn; Woven fabric; Juxtaposed color; Color measuring Abstract.
, [] Po-Sung Hung, The Experimental Application of Rotary Color Mixture Techniques on Predicting Juxtaposition Color Mixture Patterns, A Case Study of Woven Fabrics, Business College, Chung Yuan Christian University, 2004. ], while others believed that the color mixing was in consistent with the Grassmann’s color addition principle [[] HUANG Zi juan, LI Jia lin, ZHOU Yan jiang, Research on Color Model of Monllayer Digital Emulational Colorful Jacquard Silk Fabric, Journal of Zhejiang Sci-Tech University, 24-4 (2007) 378-381. ,[] HUANG Zi juan, First exploration of Lightness model of single warp and single weft yarn dyed fabrics, Silk Textile Technology Overseas. 2008 (4) 33-34. ,[] WEN Xiao li, The Study on the Surface Color Mixing Effect of Fabric with Different Colors of Warp and Weft, College of Textile and Clothing Engineering, SOOCHOW University, 2010. ].
, [] Kiyosawa Takeshi, Color prediction of union fabrics considering mutual reflection between yarns-A new color prediction model using apposed color mixture theory, Society of Fiber Science and Technology. 62-9 (2006) 212-217
, [] Krste Dimitrovski & Helena Gabrijelčič, Correction of colour values of woven fabrics using changes to constructional parameters, AUTEX Research Journal. 09-4 (2004) 187-193. ].

International Journal of Greenhouse Gas Control, V. 3, p. 2-3, 2003
International Journal of Greenhouse Gas Control, V. 5, p. 749-756, 2011
Materials & Design, v. 24, p. 561-575, 2003
Corrosion Science, V. 49, n. 12, p. 4308-4338, 2007

Materials and Methods Surface sediment samples were collected at 10 stations near Love River mouth (Fig. 1) in May, 2009 with Ekman Dredge Grab aboard a fishing boat.
Dong: International Journal of Environmental Science and Development 3 (2012) 66–71
Dong: International Journal of Chemical Engineering and Applications 3 (2012): 58–62

geographical environment,R11 Risk for Large projects Natural risk, R1 Technical risk, R2 Economical risk, R3 Management risk, R4 Political risk, R5 geological conditions, R12 force majeure, R13 design risk R21 construction technology,R22 materials and equipment,R23 exchange rate risk R31 price risk, R32 capital risk, R33 interest rate risk, R34 Organization R41 quality of employees, R42 contract risk, R43 claim risk, R44 Industry policy, R51 macro economic, R52 social security R53 Fig. 1 Hierarchy structure of risk evaluation for projects Calculation of Risk factors’ weights Based on the hierarchy structure of risk factors, experts evaluate the fuzzy weights of factors with the linguistic variables, and the average fuzzy weights can be calculated according to the fuzzy number of each linguistic variable.
Journal Of Tianjin University, 2002(2):162-166.
In- ternational Journal Forecast, 1996, 2(1): 155-161
Beijing: Science Press, 2004.

Study on the jacking force of the underpinning by Pipe-jacking for the historical masonry constructions ZHANG Weixi1,a, LIU Yongtao1 1School of Science, Xi’an Univ. of Arch &Tech., Xi’an, China azhwx4587@126.com Keywords: jacking force, underpinning, pipe-jacking, historical masonry constructions.
Additionally, the features of its materials, such as the brittle nature, the poor capacity of deformation and so on, make the uneven settlement and other structure damage caused by the inadequate bearing capacity or the asymmetric support of foundation become the leading factors that endangering the security of historical masonry constructions.
(in Chinese) [6]Huang Hongwei，Hu Xin, 3D Numerical analysis on construction mechanics effect of pipe-jacking[J], Chinese Journal of Rock Mechanics and Engineering, 2003.3，400-406.
(in Chinese) [8]Fang Yinggaung, Mo Haihong, Zhang Chuanying, Theoretic and testing analysis of solid deformation in the area of disturbance caused by pipe-jacking[J], Chinese Journal of Rock Mechanics and Engineering, 2003.4，601-605.

This area belonged to the North China strata of the Ordos partition the Yanhe strata district, lie in the transition zone of the Maowusu sand land and loess hilly area of surface materials aeolian sand and loess, sparse vegetation.
Acknowledgements This work was financially supported by the National Natural Science Fund(41002129),China Ministry of Education “Chunhui Plan”(Z2009-1-01006),National Undergraduate Innovation Test Program(101012616) and Inner Mongolia University Undergraduate Innovation Test Program (2012232).
Reference [1] Wu Daihe, Zheng baoshan, Kang Wangdong:Global and environment.Vol.32(2004), p.55-59(in Chinese) [2] Yang Zhengqi, Yang Xiuhong, Bao Yan: Technology Review.Vol.23(2005), p.38-41(in Chinese) [3] Wang Xinyi, Yang Jian, Guo Huoxia:Journal of China Coal Society .Vol.31(2006), p.808-812(in Chinese) [4] J.
H:Environmental Geology.Vol.29(1997), p.58-65 [6] Liang Bing,Lu Haijun, Xiao Liping:Review of Journal of System Simulation.Vol.19(2007), p.1439-1441,1616(in Chinese)

Materials and Methods Reactor Setup and Operation.
References [1] Hibiya Kazuaki, Terada Akihiko, Tsuneda Satoshi, Hirata Akira: Journal of Biotechnol.
Yang: Biochemical Engineering Journal.
P.239 [7] Chinese NEPA: Water and wastewater monitoring methods, 4th, Chinese Environmental Science Publishing House, Beijing, 2003

The nerve unit number on input layer of BP ANN in this research is 7, representing external diameter D, thickness of steel tube T, length L, length-diameter ratio L/D, hoop coefficient ξ ( ξ=Agfy /Ahfprt) of CFT structural parts, yield limit of steel material fy and axial compression specification strength of concrete fprt.
Comparison of BP ANN output and experimental test data under 0.65 Pmax Notes:D-external diameter of steel tube; T- thickness of steel tube; L - length of CFT structural part; fy - yield limit of steel material; fprt-axial compression specification strength of concrete; ξ-hoop coefficient Conclusions 1).Employment of BP ANN prediction model makes it simpler to obtain load-strain relationship of CFT structural parts due to no need of mathematic model to reflect physical rule of system. 2).Prediction of load-strain relationship of CFT structural parts using BP ANN is relatively has high reliability and able to reduce experimental test cost and time.
Acknowledgements This work is financially supported by National Natural Science Fund of China (51008199).
Journal of Constructional Steel Research.
Journal of Constructional Steel Research.

Journal of Composite Construction, 9(1):52–62 (2005)
Journal of Engineering Mechanics ASCE, 132(9), 914-923 (2006)
Construction and Building Materials, 20(1-2), 46-58 (2006)
Cold Regions Science and Technology, 65(3), 401-412 (2011).

He, et al, Stacking optimization of boxed military materials in rear depots based on the Pareto GA, Logistics Technology, 29(8): 142-145, 2010 [3] J.
Ning, et al, A hybrid genetic algorithm for solving integer programming, Journal of Air Force Engineering University(Natural Science Edition), 5(6): 80-83, 2004 [6] C.
Wang, Optimization of cutting parameters based on Pareto genetic algorithm, Journal of Agricultural Machinery, 42(2): 220-224, 2011