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The wheel tread wear of heavy haul freight car in operation leads to shortened wheel turning period, reduced operation life, and poor train operation performance. In addition, wheel rail wear is a complex non-linear problem that integrates multiple disciplines. Thus, using a single physical or mathematical model to accurately describe and predict it is difficult. How to establish a model that could accurately predict wheel tread wear is an urgent problem and challenge that needs to be solved. In this paper, a tread wear prediction and optimization method based on chaotic quantum particle swarm optimization (CQPSO)-optimized derived extreme learning machine (DELM), namely CQPSO-DELM, is proposed to overcome this problem. First, an extreme learning machine model with derivative characteristics is proposed (DELM). Next, the chaos algorithm is introduced into the quantum particle swarm optimization algorithm to optimize the parameters of DELM. Then, through the CQPSO-DELM prediction model, the vehicle dynamics model simulates the maximum wheel tread wear under different test parameters to train and predict. Results show that the error performance index of the CQPSO-DELM prediction model is smaller than that of other algorithms. Thus, it could better reflect the influence of different parameters on the value of wheel tread wear. CQPSO is used to optimize the tread coordinates to obtain a wheel profile with low wear. The optimized wheel profile is fitted and reconstructed by the cubic non-uniform rational B-spline (NURBS) theory, and the optimized wear value of the tread is compared with the original wear value. The optimized wear value is less than the original wear value, thus verifying the effectiveness of the optimization model. The CQPSO-DELM model proposed in this paper could predict the wear value of different working conditions and tree shapes and solve the problem that different operating conditions and complex environment could have a considerable effect on the prediction of tread wear value. The optimization of wheel tread and the wear prediction of different tread shapes are realized from the angle of artificial intelligence for the first time.
Meiqi Wang; Sixian Jia; Enli Chen; Shaopu Yang; Pengfei Liu; Zhuang Qi. Research and application of neural network for tread wear prediction and optimization. Mechanical Systems and Signal Processing 2021, 162, 108070 .
AMA StyleMeiqi Wang, Sixian Jia, Enli Chen, Shaopu Yang, Pengfei Liu, Zhuang Qi. Research and application of neural network for tread wear prediction and optimization. Mechanical Systems and Signal Processing. 2021; 162 ():108070.
Chicago/Turabian StyleMeiqi Wang; Sixian Jia; Enli Chen; Shaopu Yang; Pengfei Liu; Zhuang Qi. 2021. "Research and application of neural network for tread wear prediction and optimization." Mechanical Systems and Signal Processing 162, no. : 108070.
The extreme learning machine (ELM) requires a large number of hidden layer nodes in the training process. Thus, random parameters will exponentially increase and affect network stability. Moreover, the single activation function affects the generalization capability of the network. This paper proposes a derived least square fast learning network (DLSFLN) to solve the aforementioned problems. DLSFLN uses the inheritance of some functions to obtain various activation functions through continuous differentiation of functions. The types of activation functions were increased and the mapping capability of hidden layer neurons was enhanced when the random parameter dimension was maintained. DLSFLN randomly generates the input weights and hidden layer thresholds and uses the least square method to determine the connection weights between the output and the input layers and that between the output and the input nodes. The regression and classification experiments show that DLSFLN has a faster training speed and better training accuracy, generalization capability, and stability compared with other neural network algorithms, such as fast learning network(FLN).
Meiqi Wang; Sixian Jia; Enli Chen; Shaopu Yang; Pengfei Liu; Zhuang Qi. A derived least square fast learning network model. Applied Intelligence 2020, 50, 4176 -4194.
AMA StyleMeiqi Wang, Sixian Jia, Enli Chen, Shaopu Yang, Pengfei Liu, Zhuang Qi. A derived least square fast learning network model. Applied Intelligence. 2020; 50 (12):4176-4194.
Chicago/Turabian StyleMeiqi Wang; Sixian Jia; Enli Chen; Shaopu Yang; Pengfei Liu; Zhuang Qi. 2020. "A derived least square fast learning network model." Applied Intelligence 50, no. 12: 4176-4194.
Temperature-seepage-stress coupling exists in asphalt pavement and directly affects force and failure mechanisms in the pavement structure. The objective of this paper is to propose a systematic approach to simulate the dynamic characteristics of asphalt pavement, under the combined effects of vehicle dynamic load and environmental factors. In the simulation, the asphalt viscoelastic constitutive equation was established using Burger’s model, to derive the nonlinear control equation of thermal-hydraulic-mechanical coupling of asphalt pavement. Then, a finite element model of asphalt pavement was built using the software ABAQUS. The thermal stress and pore water pressure in pavement structure were analyzed under the condition of rainfall infiltration. Moreover, vertical stress, longitudinal stress, transverse stress and shear stress of different structural layers were analyzed and computed under the multi-field condition. These stresses were then compared to the stresses in the single stress field. The results show that when seepage and temperature are considered, stress change of the subbase is insignificant. However, the transverse stress in the modified fine-grained asphalt concrete layer (AC-1) and the vertical shear stress in the asphalt macadam layer (AM) increased 30% and 53% respectively, compared to the single stress field. This would lead to the occurrence of rutting and cracks in asphalt pavement, which accordingly seriously affects the road performance in hot and rainy weather. The field test was carried out in the DaGuang Highway, and the validity of the simulation model was verified.
Chundi Si; Enli Chen; Zhanping You; Ran Zhang; Peng Qiao; Yang Feng. Dynamic response of temperature-seepage-stress coupling in asphalt pavement. Construction and Building Materials 2019, 211, 824 -836.
AMA StyleChundi Si, Enli Chen, Zhanping You, Ran Zhang, Peng Qiao, Yang Feng. Dynamic response of temperature-seepage-stress coupling in asphalt pavement. Construction and Building Materials. 2019; 211 ():824-836.
Chicago/Turabian StyleChundi Si; Enli Chen; Zhanping You; Ran Zhang; Peng Qiao; Yang Feng. 2019. "Dynamic response of temperature-seepage-stress coupling in asphalt pavement." Construction and Building Materials 211, no. : 824-836.
In order to systematically study the rutting resistance performance of High-Modulus Asphalt Concrete (HMAC) pavements, a finite element method model of HMAC pavement was established using ABAQUS software. Based on the viscoelasticity theory of asphalt, the stress and deformation distribution characteristics of HMAC pavement were studied and compared to conventional asphalt pavement under moving loads. Then, the pavement temperature field model was established to study the temperature variation and the thermal stress in HMAC pavement. Finally, under the condition of continuous temperature variation, the creep behavior and permanent deformation of HMAC pavement were investigated. The results showed that under the action of moving loads, the strain and displacement generated in HMAC pavement were lower than those in conventional asphalt pavement. The upper surface layer was most obviously affected by outside air temperature, resulting in maximum thermal stress. Lastly, under the condition of continuous temperature change, HMAC pavement could greatly reduce the deformation of asphalt material in each surface layer compared to conventional asphalt pavement.
Chundi Si; Hang Cao; Enli Chen; Zhanping You; Ruilan Tian; Ran Zhang; Junfeng Gao. Dynamic Response Analysis of Rutting Resistance Performance of High Modulus Asphalt Concrete Pavement. Applied Sciences 2018, 8, 2701 .
AMA StyleChundi Si, Hang Cao, Enli Chen, Zhanping You, Ruilan Tian, Ran Zhang, Junfeng Gao. Dynamic Response Analysis of Rutting Resistance Performance of High Modulus Asphalt Concrete Pavement. Applied Sciences. 2018; 8 (12):2701.
Chicago/Turabian StyleChundi Si; Hang Cao; Enli Chen; Zhanping You; Ruilan Tian; Ran Zhang; Junfeng Gao. 2018. "Dynamic Response Analysis of Rutting Resistance Performance of High Modulus Asphalt Concrete Pavement." Applied Sciences 8, no. 12: 2701.