SUMMARYA methodology for the simulation of quasi-static cohesive crack propagation in quasi-brittle materials is presented. In the framework of the recently proposed extended ÿnite element method, the partition of unity property of nodal shape functions has been exploited to introduce a higher-order displacement discontinuity in a standard ÿnite element model. In this way, a cubic displacement discontinuity, able to reproduce the typical cusp-like shape of the process zone at the tip of a cohesive crack, is allowed to propagate without any need to modify the background ÿnite element mesh. The e ectiveness of the proposed method has been assessed by simulating mode-I and mixed-mode experimental tests.

No abstract

An integrated strategy is proposed for the simulation of damage development and crack propagation in concrete structures. In the initial stage of damage growth, concrete is considered to be macroscopically integer and is modeled by a non-symmetric isotropic non-local damage model. The transition to the discrete cohesive crack model depends on the local mesh size and is driven by an analytical estimate of the current bandwidth. When a crack is introduced in the model an extended finite element approach is used to follow the propagation path, independent of the background mesh. The proposed methodology is tested on a notched tension specimen and then applied to the analysis of a wedge splitting test. Copyright © 2006 John Wiley & Sons, Ltd

Joint estimation of unknown model parameters and unobserved state components for stochastic, nonlinear dynamic systems is customarily pursued via the extended Kalman filter (EKF). However, in the presence of severe nonlinearities in the equations governing system evolution, the EKF can become unstable and accuracy of the estimates gets poor. To improve the results, in this paper we account for recent developments in the field of statistical linearization and propose an unscented Kalman filtering procedure. In the case of softening single degree-of-freedom structural systems, we show that the performance of the unscented Kalman filter (UKF), in terms of state tracking and model calibration, is significantly superior to that of the EKF.

Recent advances in sensor technologies and data acquisition systems opened up the era of big data in the field of structural health monitoring (SHM). Data-driven methods based on statistical pattern recognition provide outstanding opportunities to implement a long-term SHM strategy, by exploiting measured vibration data. However, their main limitation, due to big data or high-dimensional features, is linked to the complex and time-consuming procedures for feature extraction and/or statistical decision-making. To cope with this issue, in this article we propose a strategy based on autoregressive moving average (ARMA) modeling for feature extraction, and on an innovative hybrid divergence-based method for feature classification. Data relevant to a cable-stayed bridge are accounted for to assess the effectiveness and efficiency of the proposed method. The results show that the offered hybrid divergence-based method, in conjunction with ARMA modeling, succeeds in detecting damage in cases strongly characterized by big data.

No abstract

The effect of accidental drops on MEMS sensors are examined within the framework of a multi-scale finite element approach. With specific reference to a polysilicon MEMS accelerometer supported by a naked die, the analysis is decoupled into macro-scale (at die length-scale) and meso-scale (at MEMS length-scale) simulations, accounting for the very small inertial contribution of the sensor to the overall dynamics of the device. Macro-scale analyses are adopted to get insights into the link between shock waves caused by the impact against a target surface and propagating inside the die, and the displacement/acceleration histories at the MEMS anchor points. Meso-scale analyses are adopted to detect the most stressed details of the sensor and to assess whether the impact can lead to possible localized failures. Numerical results show that the acceleration at sensor anchors cannot be considered an objective indicator for drop severity. Instead, accurate analyses at sensor level are necessary to establish how MEMS can fail because of drops.

No abstract