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Online since: August 2015
Authors: Ammar Alsheghri, Rashid K. Abu Al-Rub
Upon excluding both the damaged and healed zones from the healing configuration a fictitious effective (undamaged) configuration (Fig. 1(e)) is reached which only contains the intact material without the healed zones and has an effective area less than Fig. 1(c) or Fig. 1(d).
Table 1: Input values for the model parameters Stiffness (k) 1000 [MPa] Damage History Exponent (n) 1 Constitutive Thickness (δL) 1 [mm] Damage Force Ratio Exponent (q) 0.5 Threshold Separation (δth) 0.01 [mm] Healing History Exponent (m1) 1 Damage Fluidity (Гd) 0.001[sec-1] Damage History Exponent (m2) 1 Healing Fluidity (ГH) variable Crack Closure Exponent (m3) -1 Effect of Resting Time.
The input values are similar to those in Table 1 but with Гh = 0.001/sec.
References [1] D.
Solomon, "Self-healing polymeric materials: A review of recent developments," Progress in Polymer Science, vol. 33, pp. 479-522, 2008
Table 1: Input values for the model parameters Stiffness (k) 1000 [MPa] Damage History Exponent (n) 1 Constitutive Thickness (δL) 1 [mm] Damage Force Ratio Exponent (q) 0.5 Threshold Separation (δth) 0.01 [mm] Healing History Exponent (m1) 1 Damage Fluidity (Гd) 0.001[sec-1] Damage History Exponent (m2) 1 Healing Fluidity (ГH) variable Crack Closure Exponent (m3) -1 Effect of Resting Time.
The input values are similar to those in Table 1 but with Гh = 0.001/sec.
References [1] D.
Solomon, "Self-healing polymeric materials: A review of recent developments," Progress in Polymer Science, vol. 33, pp. 479-522, 2008
Online since: March 2006
Authors: Yoon Suk Chang, Young Jin Kim, H.K. Kim, Jae Boong Choi
Chang
1,a
, H.K.
Kim 1,b , J.B.
Choi 1,c and Y.J.
References [1] ASME: B&PV Code Sec.
Hong: Proceedings of ASME PV&P Vol.479 (2004), pp. 43-48 [5] J.L.
Kim 1,b , J.B.
Choi 1,c and Y.J.
References [1] ASME: B&PV Code Sec.
Hong: Proceedings of ASME PV&P Vol.479 (2004), pp. 43-48 [5] J.L.
Online since: December 2010
Authors: Yu Mei Shan, Ren Yun Sun, Bo Wang
Study of Pavement Identification Approach Based on Wavelet Analysis
Renyun Sun 1, a, Yumei Shan 1,b and Bo Wang 1,c
1 School of Transportation and Automobile Engineering, Xihua University,
Chengdu, 610039, China
a sunry2004@163.com, b294816078 @qq.com, c 310641294@qq.com
Key words: Wavelet analysis, Pavement identification, Electronic control brake system.
(10) From the multi-resolution analysis, that is[1,5,6]:
The data in Fig.1(c) and Fig.2(c) are de-noised with soft threshold wavelet analysis.
[3] Ergun Er&celebi: Computers in Biology and Medicine(2004),pp.479–493
[11] S.Abbasion, A.Rafsanjani, A.Farshidianfar and N.Irani: Mechanical Systems and Signal Processing (2007), pp.1-13
(10) From the multi-resolution analysis, that is[1,5,6]:
The data in Fig.1(c) and Fig.2(c) are de-noised with soft threshold wavelet analysis.
[3] Ergun Er&celebi: Computers in Biology and Medicine(2004),pp.479–493
[11] S.Abbasion, A.Rafsanjani, A.Farshidianfar and N.Irani: Mechanical Systems and Signal Processing (2007), pp.1-13
Online since: February 2020
Authors: Sen Yeu Yang, Kuo Hsun Lee
and aging treatment was shown in Fig.1, 2 and 3.
References [1] J.
Koo, Materials Chemistry and Physics, Vol. 94(1), (2005)131-140
Chang, Materials Science & Engineering A, Vol. 420(1-2) ,(2006) 155-164
Koo, Materials Science & Engineering A, Vol. 398(1-2), (2005) 113-127
References [1] J.
Koo, Materials Chemistry and Physics, Vol. 94(1), (2005)131-140
Chang, Materials Science & Engineering A, Vol. 420(1-2) ,(2006) 155-164
Koo, Materials Science & Engineering A, Vol. 398(1-2), (2005) 113-127
Online since: October 2018
Authors: R.A. Shishkin, A.P. Zemlyanskaya, A.R. Beketov
Fig. 1.
In the case the filler thermal conductivity value considerable higher than binder one (λp>>λm) thermal conductivity could be predicted with a following equation: λλm=1(1-φ)3(1-α)(1+2α) (4) The third model was suggested by Hamilton R.L. and Crosser O.K. [16, 17].
Effective thermal conductivity could be calculated according to the formula: λλm=γ+n-1-(n-1)(1-γ)φγ+n-1+(1-γ)φ (5) where γ is thermal conductivity ratio of particles to binder (matrix) thermal conductivity γ = λp/λm, n – particle shape factor (3 or 6).
References [1] D.D.L.
A, 4 (50) (2008) 471-479.
In the case the filler thermal conductivity value considerable higher than binder one (λp>>λm) thermal conductivity could be predicted with a following equation: λλm=1(1-φ)3(1-α)(1+2α) (4) The third model was suggested by Hamilton R.L. and Crosser O.K. [16, 17].
Effective thermal conductivity could be calculated according to the formula: λλm=γ+n-1-(n-1)(1-γ)φγ+n-1+(1-γ)φ (5) where γ is thermal conductivity ratio of particles to binder (matrix) thermal conductivity γ = λp/λm, n – particle shape factor (3 or 6).
References [1] D.D.L.
A, 4 (50) (2008) 471-479.
Online since: August 2023
Authors: Muamer Abuzwidah, Samer Barakat, Abdulrauf Khetrish
Modeling Crash Frequency Using Crash and Geometric Data
at Freeways
Abdulrauf Khetrish1,a*, Muamer Abuzwidah2,b and Samer Barakat3,c
1-3Department of Civil and Environmental Engineering, University of Sharjah, Sharjah, UAE
au19104466@sharjah.ac.ae, bmabuzwidah@sharjah.ac.ae, csbarakat@sharjah.ac.ae
Keywords: Crash data analysis, Crash Frequency, Poisson Regression, Negative Binomial Regression.
Figure 1: Trend of crashes Table 1: Total number of crashes per year year Number of crashes Increase 2014 1260 ------- 2015 1435 13.89 2016 1401 -2.37 2017 1421 1.43 2018 1917 34.90 Table 2: Summary of the Data N Minimum Maximum Mean Std.
Table 5: Parameter estimate for Poisson regression Parameter B Wald Chi-Square df P-value Exp(B) Segment length [miles] 2.894 6499.066 1 .000 18.074 AADT 7.988E-6 160.893 1 .000 1.000 Speed limit [mph] -.244 358.961 1 .000 .784 Number of lanes .008 .586 1 .444 1.008 Right shoulder width [ft] .013 6.886 1 .009 1.013 Left shoulder width [ft] .005 1.995 1 .158 1.005 Median Width [ft] -.003 11.702 1 .001 .997 In contrast to the Poisson model, as shown in Table 6 negative binomial model show significance only in three variables e segment length, AADT, and the speed limit, the rest are not significant based on p-value, similar to Poisson the coefficient estimate B sign for the speed was negative.
Exp(B) Segment length [miles] 5.869 510.982 1 .000 353.825 AADT 7.836E-6 20.429 1 .000 1.000 Speed limit [mph] -.175 37.562 1 .000 .840 Number of lanes -.020 .502 1 .479 .980 Right shoulder width [ft] .005 .196 1 .658 1.005 Left shoulder width [ft] -.002 .044 1 .834 .998 Median Width [ft] -.003 2.665 1 .103 .997 Conclusion In this paper , two widely used statistical models were utilized to analyze crash data from the North Carolina I-40 highway these models are Poisson regression and Negative Binomial, both models showed the importance of exposure factors the segment length and AADT, it was also concluded from the models that the increase of the speed decrease the number of crashes, although statistical advanced over the years there is still many limitations and issues associated with them for example in dealing with time-varying variables, unobserved heterogony, and the bias of under-reporting, so these limitations could be an opportunity to future research to utilize artificial techniques
References [1] P.P.
Figure 1: Trend of crashes Table 1: Total number of crashes per year year Number of crashes Increase 2014 1260 ------- 2015 1435 13.89 2016 1401 -2.37 2017 1421 1.43 2018 1917 34.90 Table 2: Summary of the Data N Minimum Maximum Mean Std.
Table 5: Parameter estimate for Poisson regression Parameter B Wald Chi-Square df P-value Exp(B) Segment length [miles] 2.894 6499.066 1 .000 18.074 AADT 7.988E-6 160.893 1 .000 1.000 Speed limit [mph] -.244 358.961 1 .000 .784 Number of lanes .008 .586 1 .444 1.008 Right shoulder width [ft] .013 6.886 1 .009 1.013 Left shoulder width [ft] .005 1.995 1 .158 1.005 Median Width [ft] -.003 11.702 1 .001 .997 In contrast to the Poisson model, as shown in Table 6 negative binomial model show significance only in three variables e segment length, AADT, and the speed limit, the rest are not significant based on p-value, similar to Poisson the coefficient estimate B sign for the speed was negative.
Exp(B) Segment length [miles] 5.869 510.982 1 .000 353.825 AADT 7.836E-6 20.429 1 .000 1.000 Speed limit [mph] -.175 37.562 1 .000 .840 Number of lanes -.020 .502 1 .479 .980 Right shoulder width [ft] .005 .196 1 .658 1.005 Left shoulder width [ft] -.002 .044 1 .834 .998 Median Width [ft] -.003 2.665 1 .103 .997 Conclusion In this paper , two widely used statistical models were utilized to analyze crash data from the North Carolina I-40 highway these models are Poisson regression and Negative Binomial, both models showed the importance of exposure factors the segment length and AADT, it was also concluded from the models that the increase of the speed decrease the number of crashes, although statistical advanced over the years there is still many limitations and issues associated with them for example in dealing with time-varying variables, unobserved heterogony, and the bias of under-reporting, so these limitations could be an opportunity to future research to utilize artificial techniques
References [1] P.P.
Online since: December 2023
Authors: M. Kanmani, R. Jaanaki Raman, N. Jerome Festus, J. Pradeep Kumar, R. Arun Prakash
Welded specimens
Fig. 1.
(a) Experimental setup Fig. 1.
Rank 1 is TSN and Rank 2 is WC and Rank 3 is DNW.
[12] Filippo Montevecchi, Niccolò Grossi, Hisataka Takagi, Antonio Scippa, Hiroyuki Sasahara, Gianni Campatelli, Cutting Forces Analysis in Additive Manufactured AISI H13 Alloy, Procedia CIRP, 46 (2016) 476-479
Sādhanā 47, (2022)1-14.
(a) Experimental setup Fig. 1.
Rank 1 is TSN and Rank 2 is WC and Rank 3 is DNW.
[12] Filippo Montevecchi, Niccolò Grossi, Hisataka Takagi, Antonio Scippa, Hiroyuki Sasahara, Gianni Campatelli, Cutting Forces Analysis in Additive Manufactured AISI H13 Alloy, Procedia CIRP, 46 (2016) 476-479
Sādhanā 47, (2022)1-14.
Online since: June 2014
Authors: Aibin Ma, Guan Guo Liu, Lun Wang, Ping Zhang, Hong Gen Qin, Chao Ming Pang
The detector of the computed tomography (CT) system was a flat-panel type with 1 024 × 1 024 detector elements and 1 080 projections.
References [1] W.
Nucl Eng Des 2002; 212(1-3):221-231
Cement Concrete Comp 2001; 23(6): 479-484
Industrial Construction 2005; 35(1): 5-7
References [1] W.
Nucl Eng Des 2002; 212(1-3):221-231
Cement Concrete Comp 2001; 23(6): 479-484
Industrial Construction 2005; 35(1): 5-7
Online since: October 2014
Authors: Mahadzir Mahadzir, Md. Rafiqul Islam
ER308L-Si wire of 1 mm diameter was used as filler and 100% CO2 was used as shielding gas.
From the welded plates, ASTM E8M-04 standard tensile and ASTM D5045 standard impact toughness specimens were prepared by electrical discharge machine as shown in Fig. 1(b) and Fig. 1(c).
Table 1: DOE, yield strength, absorbed impact energy, calculated FT and failure locations Exp.
References [1] L.M.
Charact. 59, 4 (2008) 479-483
From the welded plates, ASTM E8M-04 standard tensile and ASTM D5045 standard impact toughness specimens were prepared by electrical discharge machine as shown in Fig. 1(b) and Fig. 1(c).
Table 1: DOE, yield strength, absorbed impact energy, calculated FT and failure locations Exp.
References [1] L.M.
Charact. 59, 4 (2008) 479-483
Online since: October 2014
Authors: Qiu Sheng Yan, Jia Bin Lu, Lei Wang, Xiao Lan Xiao
The components of chemical polishing solutions are shown in Table 1.
Fig. 1 The photography of CMP machine Results and discussion Corrosion on the SiC surface.
But the concentration of ionized Fe2+ ions is very low due to the slow ionization process of Eq.(1).
It can be found that the best polishing surface is obtained using Fe powder as catalyst of Fenton reaction, and the surface roughness reaches Ra0.479 nm and Rq0.658 nm, but some shallow scratches exist on the polished surface (see Fig. 6a).
References [1] B.Z.
Fig. 1 The photography of CMP machine Results and discussion Corrosion on the SiC surface.
But the concentration of ionized Fe2+ ions is very low due to the slow ionization process of Eq.(1).
It can be found that the best polishing surface is obtained using Fe powder as catalyst of Fenton reaction, and the surface roughness reaches Ra0.479 nm and Rq0.658 nm, but some shallow scratches exist on the polished surface (see Fig. 6a).
References [1] B.Z.