Multiparameter Sensitivity Analysis of Flexural-tensile Property of Basalt Fiber-reinforced Asphalt Mixture
柳力1,2, 杨程程1, 刘朝晖1
1. School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha Hunan 410114, China;
2. State Engineering Laboratory of Highway Maintenance Technology, Changsha University of Science & Technology, Changsha Hunan 410114, China
Multiparameter Sensitivity Analysis of Flexural-tensile Property of Basalt Fiber-reinforced Asphalt Mixture
LIU Li1,2, YANG Cheng-cheng1, LIU Zhao-hui1
1. School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha Hunan 410114, China;
2. State Engineering Laboratory of Highway Maintenance Technology, Changsha University of Science & Technology, Changsha Hunan 410114, China
摘要To evaluate the sensitivity of basalt fiber content, length-diameter ratio, and modulus to the flexural-tensile properties of fiber-reinforced asphalt mixture, the spatial random distribution of basalt fibers was realized by using the rand function in MATLAB and VBA in AutoCAD. A three-point bending beam with randomly distributed basalt fibers was modeled by ABAQUS finite element software. Basalt fiber content W (0.1%, 0.2%, 0.3%), length-diameter ratio Ld (23, 35, 47), and modulus E (8×104, 9×104, 1×105 MPa) were set for the experiment. Through the orthogonal experiment, the maximum tensile stress at the bottom of the specimens under 0.7 MPa wheel load was calculated, and the sensitivity of the influence of basalt fiber parameters on the flexural-tensile properties of asphalt mixture was analyzed by using range and variance analyses. Results show that the significance of variance analysis is consistent with that of range analysis. The order of sensitivity of parameters is W>Ld>E, which shows that basalt fiber content has the greatest influence on the flexural-tensile properties of asphalt mixture, followed by the length-diameter ratio, and the influence of fiber modulus on the flexural-tensile properties of asphalt mixture can be ignored. With the increase in basalt fiber and length-diameter ratio, the maximum tensile stress of asphalt mixture decreases, indicating the improvement of flexural-tensile performance. With the increase in the fiber modulus, the flexural-tensile properties of asphalt mixture are almost unchanged. The research results are believed to provide a reference for the application of basalt fibers in asphalt mixtures.
Abstract：To evaluate the sensitivity of basalt fiber content, length-diameter ratio, and modulus to the flexural-tensile properties of fiber-reinforced asphalt mixture, the spatial random distribution of basalt fibers was realized by using the rand function in MATLAB and VBA in AutoCAD. A three-point bending beam with randomly distributed basalt fibers was modeled by ABAQUS finite element software. Basalt fiber content W (0.1%, 0.2%, 0.3%), length-diameter ratio Ld (23, 35, 47), and modulus E (8×104, 9×104, 1×105 MPa) were set for the experiment. Through the orthogonal experiment, the maximum tensile stress at the bottom of the specimens under 0.7 MPa wheel load was calculated, and the sensitivity of the influence of basalt fiber parameters on the flexural-tensile properties of asphalt mixture was analyzed by using range and variance analyses. Results show that the significance of variance analysis is consistent with that of range analysis. The order of sensitivity of parameters is W>Ld>E, which shows that basalt fiber content has the greatest influence on the flexural-tensile properties of asphalt mixture, followed by the length-diameter ratio, and the influence of fiber modulus on the flexural-tensile properties of asphalt mixture can be ignored. With the increase in basalt fiber and length-diameter ratio, the maximum tensile stress of asphalt mixture decreases, indicating the improvement of flexural-tensile performance. With the increase in the fiber modulus, the flexural-tensile properties of asphalt mixture are almost unchanged. The research results are believed to provide a reference for the application of basalt fibers in asphalt mixtures.
基金资助:Supported by the National Key R&D Program of China (No. 2018YFB1600200); National Natural Science Foundation Program of China (51678078); Natural Science Foundation of Hunan Province (No. 2020JJ5578); Open Fund for National Engineering Laboratory of Highway Maintenance Technology, Changsha University Science & Technology (No. kfj180101)
通讯作者:
LIU Li
E-mail: 805296712@qq.com
引用本文:
柳力, 杨程程, 刘朝晖. Multiparameter Sensitivity Analysis of Flexural-tensile Property of Basalt Fiber-reinforced Asphalt Mixture[J]. Journal of Highway and Transportation Research and Development, 2021, 15(1): 14-21.
LIU Li, YANG Cheng-cheng, LIU Zhao-hui. Multiparameter Sensitivity Analysis of Flexural-tensile Property of Basalt Fiber-reinforced Asphalt Mixture. Journal of Highway and Transportation Research and Development, 2021, 15(1): 14-21.
[1] WEI You-po, ZHANG Zheng-qi, SI Wei, et al. Performance Promoting Mechanism of Asphalt Mixture by Basalt Fiber[J]. Journal of Chang'an University:Natural Science Edition, 2012, 32(2):39-44. (in Chinese)
[2] MOROVA N. Investigation of Usability of Basalt Fibers in Hot Mix Asphalt Concrete[J]. Construction and Building Materials, 2013, 47:175-180.
[3] ZHANG X, GU X, LÜ J, et al. 3D Numerical Model to Investigate the Rheological Properties of Basalt Fiber Reinforced Asphalt-like Materials[J]. Construction and Building Materials, 2017, 138:185-194.
[4] LI Yun-hua. Research on Asphalt Mixture Based on Numerical Simulation[J]. Highway Engineering, 2018, 43(1):265-269. (in Chinese)
[5] LI Hai-guang, SUN Wen-zhi, QIU Qing-li, et al. Study on Flexural Toughness Property of Basalt Fiber Concrete Mixed with Steel Fiber[J]. Journal of Highway and Transportation Research and Development, 2016, 33(9):78-83. (in Chinese)
[6] ZHAO Yu-sheng, LI Hong-tao. Impact of Short-cut Basalt Fiber on Asphalt Concrete Performance[J]. Journal of Highway and Transportation Research and Development, 2012, 29(9):38-42. (in Chinese)
[7] ZHANG Hang, XU Jing-zhi, HAO Pei-wen, et al. Research on Stability of Hybrid Fiber Asphalt Mixture Based on High Temperature and Continuous Loading[J]. Acta Materiae Compositae Sinica, 2017, 34(10):2344-2355. (in Chinese)
[8] CHENG Y, YU D, GONG Y, et al. Laboratory Evaluation on Performance of Eco-friendly Basalt Fiber and Diatomite Compound Modified Asphalt Mixture[J]. Materials, 2018, 11(12):2400.
[9] ARTEMENKO S E, ARZAMASTSEV S V, SHATUNOV D A, et al. Basalt Plastics:New Materials for Road Construction[J]. Fibre Chemistry, 2008, 40(6):499-502.
[10] ZHANG Wen-gang, JI Xiao-ping, SU Xiu-li, et al. Study on Improvement Mechanism and Performance of Asphalt Mixture Adding with Mineral Fiber Used in Pavement[J]. Journal of Wuhan University of Technology, 2012, 34(8):50-54. (in Chinese)
[11] GAO Chun-mei. Research on Performance of Basalt Fiber Asphalt Concrete and Micro Analysis of Its Reinforcement Mechanism[D]. Changchun:Jilin University, 2012. (in Chinese)
[12] LÜ X, LIAO X, BO Y. A Novel Pseudo-random Number Generator from Coupled Map Lattice with Time-varying Delay[J]. Nonlinear Dynamics, 2018, 94(1):325-341.
[13] ZHANG Xiao-yuan. Research on Fatigue Damage Mechanism of Fiber Asphalt Concrete Based on Mesostructure[D]. Nanjing:Southeast University, 2018. (in Chinese)
[14] JTG E20-2011, Standard Test Methods of Bitumen and Bituminous Mixtures for Highway Engineering[S].
[15] ZHANG Gui-xue. Analysis on Mechanical Characteristics and Cracking Behavior of Asphalt Concrete Pavement on Steel Bridge Deck[D]. Chongqing:Chongqing Jiaotong University, 2018. (in Chinese)
[16] HU Xiao-di, SUN Li-jun. Measuring Tire Ground Pressure Distribution of Heavy Vehicle[J]. Journal of Tongji University:Natural Science Edition, 2005, 33(11):1443-1448. (in Chinese)
[17] LEI F H, SUN N, TANG W L. The Sensitivity Analysis of Engine Mounting System's Inertial Parameters Based on the Orthogonal Test[J]. Advanced Materials Research, 2011, 338:440-445.
[18] ENGELBRECHT A P. A New Pruning Heuristic Based on Variance Analysis of Sensitivity Information[J]. IEEE Transactions on Neural Networks, 2001, 12(6):1386-1399.
2021, Vol. 15 
