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Summary The extended Kalman filter (EKF) is a powerful tool used to assess structural damage in the time domain. A method combining EKF and l2‐norm regularization, such as Tikhonov regularization, is found to improve the solution of ill‐posed inverse problems. However, the l2‐norm regularization process may lead to an over‐smooth damage identification solution, which is contradictory to the sparse and concentrated distribution of local damages. This paper presents a new damage identification algorithm based on EKF with l1‐norm regularization via free vibration responses. The l1‐norm regularization item is used to enhance the identification accuracy of local damages while restraining the interference of measurement noise. Afterward, the constrained minimization problem is solved by EKF endowed with a pseudomeasurement equation. The numerical and experimental examples confirm that the proposed algorithm shows good robustness and excellent accuracy of damage identification with the unknown initial structural state.

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A numerical study on the role of microstructure in the thermomechanical behavior of shape memory alloy (SMA) composites under uniaxial tension is performed. The simulation is based on the micromechanics model established recently by the authors. The influence of the shape and volume fraction of SMA on the overall behavior of the composite as well as on the internal stress and strain evolution is investigated. The strengthening effect of SMA on ductile matrix is illustrated. The obtained results demonstrate several interesting features of the new composite and may serve as a quantitative basis for the microstructure design of this composite in the future.

Surface modification by means of nanostructures is of interest to enhance boiling heat transfer in various applications including the organic Rankine cycle (ORC). With the goal of obtaining rough and dense aluminum oxide (Al 2 O 3 ) nanofilms, the optimal combination of process parameters for electrophoretic deposition (EPD) based on the uniform design (UD) method is explored in this paper. The detailed procedures for the EPD process and UD method are presented. Four main influencing conditions controlling the EPD process were identified as: nanofluid concentration, deposition time, applied voltage and suspension pH. A series of tests were carried out based on the UD experimental design. A regression model and statistical analysis were applied to the results. Sensitivity analyses of the effect of the four main parameters on the roughness and deposited mass of Al 2 O 3 films were also carried out.The results showed that Al 2 O 3 nanofilms were deposited compactly and uniformly on the substrate. Within the range of the experiments, the preferred combination of process -4 g/cm 2 , respectively. A verification experiment was carried out at these conditions and gave values of roughness and deposited mass within 8% error of the expected ones as determined from the UD approach. It is concluded that uniform design is useful for the optimization of electrophoretic deposition requiring only 7 tests compared to 49 using the orthogonal design method.

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An innovative damage detection method for bridge structures under moving vehicular load is proposed on the basis of extended Kalman filter (EKF) and l1-norm regularization. An augmented state vector includes structural damage parameters and motion state variables of bridge and vehicle. Through a recursive process of the EKF, the structural damage parameters and state variables of a bridge are updated continually to obtain an optimal estimate using bridge responses due to a moving vehicle. The distribution of element stiffness reduction of a structure with local damages is sparse. Thus, l1-norm regularization is introduced into the updating process of the EKF using pseudo-measurement (PM) technology to improve the ill-posedness of the inverse problem. Numerical studies on a simple-supported and continuous beam bridge deck, with a smooth road surface that is subject to a moving vehicle, are performed to test the proposed approach. Furthermore, using the robustness of the EKF, the proposed algorithm is applied as a simplified method to the case where a bridge deck with road roughness is considered. Results show that the proposed identification algorithm is robust and effective for different vehicle speeds and measurement noises under smooth and good road conditions.