Suffering from structural deterioration and natural disasters, the resilience of civil structures in the face of extreme loadings inevitably drops, which may lead to catastrophic structural failure and presents great threats to public safety. Earthquake-induced extreme loading is one of the major reasons behind the structural failure of buildings. However, many buildings in earthquake-prone areas of China lack safety monitoring, and prevalent structural health monitoring systems are generally very expensive and complicated for extensive applications. To facilitate cost-effective building-safety monitoring, this study investigates a method using cost-effective MEMS accelerometers for buildings’ rapid after-earthquake assessment. First, a parameter analysis of a cost-effective MEMS sensor is conducted to confirm its suitability for building-safety monitoring. Second, different from the existing investigations that tend to use a simplified building model or small-scaled frame structure excited by strong motions in laboratories, this study selects an in-service public building located in a typical earthquake-prone area after an analysis of earthquake risk in China. The building is instrumented with the selected cost-effective MEMS accelerometers, characterized by a low noise level and the capability to capture low-frequency small-amplitude dynamic responses. Furthermore, a rapid after-earthquake assessment scheme is proposed, which systematically includes fast missing data reconstruction, displacement response estimation based on an acceleration response integral, and safety assessment based on the maximum displacement and maximum inter-story drift ratio. Finally, the proposed method is successfully applied to a building-safety assessment by using earthquake-induced building responses suffering from missing data. This study is conducive to the extensive engineering application of MEMS-based cost-effective building monitoring and rapid after-earthquake assessment.

A consistent regularization technique is adopted for the inverse identification of local damages in a structure under ambient vibration. The consistent regularization method fully makes use of the information from results obtained in previous iteration steps. Some elements are identified as undamaged and others are updated with small incremental steps between iterations. The covariance of covariance matrix which are formed from the auto/cross-correlation function of acceleration responses of a structure under white noise ambient excitation are used for damage detection in this paper. The components of the covariance matrix are proved to be function of the modal parameters (modal frequency, mode shape and damping parameter) of the structure. The number of vibration modes of the structure associated with the components is only limited by the sampling frequency. A simply supported thirty-one bar plane truss structure and a seven-floor frame structure are studied where a multiple damage scenario with different noise levels are identified. Numerical results show that the consistent regularization method combined with covariance of covariance matrix is very effective in improving the results in the inverse problem with ill-condition phenomenon compared with the Tikhonov regularization.

Two techniques are proposed in a new regularization method for the inverse identification of local damages in a structure. One technique is the introduction of a new side condition, and the other technique restricts the range of variation of the regularization parameter by consistently choosing the optimal point on the L-curve. Both techniques fully make use of the information from results obtained in previous iteration steps.Aplane frame structure is studied with two damaged elements and different levels of noise and model errors. Numerical results show that the proposed consistent regularization method is very effective at improving the results of the ill-conditioned inverse-problem phenomenon, compared with the Tikhonov regularization.Department of Civil and Environmental Engineerin