Permanent deformation response parameters of asphalt mixtures for a new mix-confined repeated load test

The main objective of this work is to propose new mixture response parameters and to compare correlations with rut depths and sensitivity of permanent deformation response parameters based on field extracted cores and lab-mixed duplicates. A new "mix-confined" test is developed and four new parameters for this test are proposed. Correlation coefficients with rut depths and coefficients of variation (sensitivity) are compared between the four new and two existing parameters. Some parameters are recommended to be used for the newly developed test. The results show that, newly developed test can capture the changes of permanent deformation of asphalt mixtures. Only one new parameter (D1 of Stephen Price model) and one existing parameter (flow number, Fn ) have strong correlations with rut depths of asphalt pavements (R2 greater than 0.7) and have relative small sensitivity (coefficient of variation, COV, less than 30%). For polymer modified asphalt mixtures, the parameter D1 rather than Fn should be used. These findings can be used to check the permanent deformation of asphalt mixture during the mix design.

Static response analysis of structures with interval parameters using the second-order Taylor series expansion and the DCA for QB

In this paper, based on the second-order Taylor series expansion and the difference of convex functions algo- rithm for quadratic problems with box constraints （the DCA for QB）, a new method is proposed to solve the static response problem of structures with fairly large uncertainties in interval parameters. Although current methods are effective for solving the static response problem of structures with interval parameters with small uncertainties, these methods may fail to estimate the region of the static response of uncertain structures if the uncertainties in the parameters are fairly large. To resolve this problem, first, the general expression of the static response of structures in terms of structural parameters is derived based on the second-order Taylor series expansion. Then the problem of determining the bounds of the static response of uncertain structures is transformed into a series of quadratic problems with box constraints. These quadratic problems with box constraints can be solved using the DCA approach effectively. The numerical examples are given to illustrate the accuracy and the efficiency of the proposed method when comparing with other existing methods.

Research on the Q-value, Site Response and Seismic Source Parameters in Jiangsu and Its Adjacent Areas

Based on 49 digital seismograms recorded by 73 seismic stations in the Jiangsu Telemetered Seismic Network,the paper uses Atkinson's method to calculate the inelastic attenuation coefficient of the Jiangsu area. We find that the frequency-dependent Q in the Jiangsu region is Q( f) = 272. 1·f^(0. 5575). We also use Moya's method to invert the 63 stations' site responses. The results show that the site responses of the 25 stations in Jiangsu are approximately 1 at a range between 1Hz and 20 Hz, which is consistent with their basements on rocks. The response curves of the site responses of the 14 underground stations are similar to each other. Their site responses show an amplification at low frequencies and minimization at high frequencies. The calculation of the Brune model on the waveform data of M_L≥2. 5 earthquakes from Jiangsu Digital Seismic Network between October 2010 and May 2015 in terms of seismic source parameters of 58 seismic waves shows that there are good correlations between seismic magnitude and other source parameters such as seismic moment, source radius and corner frequency, while the correlations between seismic magnitude and stress drop,and stress drop and source radius are not so good.

Introducing the nth-Order Features Adjoint Sensitivity Analysis Methodology for Nonlinear Systems (nth-FASAM-N): II. Illustrative Example

This work highlights the unparalleled efficiency of the “nth-Order Function/ Feature Adjoint Sensitivity Analysis Methodology for Nonlinear Systems” (nth-FASAM-N) by considering the well-known Nordheim-Fuchs reactor dynamics/safety model. This model describes a short-time self-limiting power excursion in a nuclear reactor system having a negative temperature coefficient in which a large amount of reactivity is suddenly inserted, either intentionally or by accident. This nonlinear paradigm model is sufficiently complex to model realistically self-limiting power excursions for short times yet admits closed-form exact expressions for the time-dependent neutron flux, temperature distribution and energy released during the transient power burst. The nth-FASAM-N methodology is compared to the extant “nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems” (nth-CASAM-N) showing that: (i) the 1st-FASAM-N and the 1st-CASAM-N methodologies are equally efficient for computing the first-order sensitivities;each methodology requires a single large-scale computation for solving the “First-Level Adjoint Sensitivity System” (1st-LASS);(ii) the 2nd-FASAM-N methodology is considerably more efficient than the 2nd-CASAM-N methodology for computing the second-order sensitivities since the number of feature-functions is much smaller than the number of primary parameters;specifically for the Nordheim-Fuchs model, the 2nd-FASAM-N methodology requires 2 large-scale computations to obtain all of the exact expressions of the 28 distinct second-order response sensitivities with respect to the model parameters while the 2nd-CASAM-N methodology requires 7 large-scale computations for obtaining these 28 second-order sensitivities;(iii) the 3rd-FASAM-N methodology is even more efficient than the 3rd-CASAM-N methodology: only 2 large-scale computations are needed to obtain the exact expressions of the 84 distinct third-order response sensitivities with respect to the Nordheim-Fuchs model’s parameters when applying the 3rd-FASAM-N methodology, while the application of the 3rd-CASAM-N methodology requires at least 22 large-scale computations for computing the same 84 distinct third-order sensitivities. Together, the nth-FASAM-N and the nth-CASAM-N methodologies are the most practical methodologies for computing response sensitivities of any order comprehensively and accurately, overcoming the curse of dimensionality in sensitivity analysis.

Physical Parameter Identification Method Based on Modal Analysis for Two-axis On-road Vehicles:Theory and Simulation

Physical parameters are very important for vehicle dynamic modeling and analysis.However,most of physical parameter identification methods are assuming some physical parameters of vehicle are known,and the other unknown parameters can be identified.In order to identify physical parameters of vehicle in the case that all physical parameters are unknown,a methodology based on the State Variable Method（SVM） for physical parameter identification of two-axis on-road vehicle is presented.The modal parameters of the vehicle are identified by the SVM,furthermore,the physical parameters of the vehicle are estimated by least squares method.In numerical simulations,physical parameters of Ford Granada are chosen as parameters of vehicle model,and half-sine bump function is chosen to simulate tire stimulated by impulse excitation.The first numerical simulation shows that the present method can identify all of the physical parameters and the largest absolute value of percentage error of the identified physical parameter is 0.205%;and the effect of the errors of additional mass,structural parameter and measurement noise are discussed in the following simulations,the results shows that when signal contains 30 d B noise,the largest absolute value of percentage error of the identification is 3.78%.These simulations verify that the presented method is effective and accurate for physical parameter identification of two-axis on-road vehicles.The proposed methodology can identify all physical parameters of 7-DOF vehicle model by using free-decay responses of vehicle without need to assume some physical parameters are known.