Magnetic nickel shells can be grown on gold nanorods with platinum tips by reduction of Ni2+ with hydrazine in aqueous cetyl‐trimethylammonium bromide (CTAB) solution, using Pt tips as catalysts. The plasmon absorption of the starting gold nanorods can be totally quenched by the metallic layer of nickel, while the magnetic character of these anisotropic, hybrid nanocrystals is demonstrated through alignment under an external magnetic field. Quasi‐epitaxial growth is demonstrated by HRTEM and electron diffraction (see figure).

Bridge weigh-in-motion systems are based on the measurement of strain on a bridge and the use of the measurements to estimate the static weights of passing traffic loads. Traditionally, commercial systems employ a static algorithm and use the bridge influence line to infer static axle weights. This paper describes the experimental testing of an algorithm based on moving force identification theory. In this approach the bridge is dynamically modeled using the finite element method and an eigenvalue reduction technique is employed to reduce the dimension of the system. The inverse problem of finding the applied forces from measured responses is then formulated as a least squares problem with Tikhonov regularization. The optimal regularization parameter is solved using the Lcurve method. Finally, the static axle loads, impact factors and truck frequencies are obtained from a complete time history of the identified moving forces.Keywords: Bridge, Weigh-in-motion, Force identification, Regularization, Dynamic programming, Traffic loads Moving Force Identification Bridge Weigh-in-Motion AlgorithmThe traditional static bridge weigh-in-motion (B-WIM) algorithm provides static axle weights from minimizing the sum of squares of differences between measured total bridge strain and theoretical static strain [1]. Although the deviations with respect to the static response that vehicle and bridge dynamics introduce in the measured response tend to be averaged out during the minimization process, they remain a significant source of inaccuracy [2]. This paper proposes an alternative B-WIM algorithm that calculates the time history of moving forces as they cross the bridge, based on moving force identification (MFI) theory. The MFI algorithm implemented here was developed by the authors [3][4][5], and is an extension of the one-dimensional algorithm by Law and Fang [6] to two dimensions. The mathematics behind general inverse theory is available in the literature [7][8][9][10][11]. The MFI algorithm requires a finite element (FE) mathematical model that accurately represents the static and dynamic behavior of the bridge structure. The method of dynamic programming requires that the equilibrium equation of motion be converted to a discrete time integration scheme. In this case the equilibrium equation of motion is reduced to an equation in modal coordinates defined by:where [Φ] is the modal matrix of normalized eigenvectors and n z is the number of modes to be used in the inverse analysis.[Ω] is a diagonal matrix containing the natural frequencies and ζ is the percentage damping. [L(t)] is a time varying location matrix relating the n g applied vehicle forces of the vector g(t) to the degrees of freedom, n dof , of the original FE model. Tikhonov regularization [12] is applied to provide a bound to the error and 'smoother' solutions to the ill-conditioning nature of the MFI problem [13,14]. The final part of the solution lies in the calculation of the optimal regularization parameter; the L-curve has been chosen to obtain...

Publication information Abstract:The traditional approach to Bridge Weigh-in-Motion (WIM) developed by Moses, gives good accuracy for estimating gross vehicle weights but is less accurate for individual axle weights. In this paper, Tikhonov regularisation is applied to the original Moses' equations to reduce some of the inaccuracies inherent within the algorithm. The optimal regularisation parameter is calculated using the L-curve criterion. The new regularised solution is numerically tested using simulations of moving vehicles on a bridge. Results show that the regularised solution performs significantly better than the original approach of Moses and is insensitive to road surface roughness.

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