Search

Summary: This paper presents a benchmark problem for the structural health monitoring community to study tall buildings. The benchmark building is called the Green Building located at the Massachusetts Institute of Technology campus, with 21 stories above the ground (83.7 m) and a basement (3.8 m) connecting to the Massachusetts Institute of Technology tunnel system. This building was constructed as cast‐in‐place reinforced concrete and instrumented with 36 accelerometers to measure the building translational, torsional and vertical responses. The benchmark problem includes the detailed description of this building, 7 field measurement data sets (4 ambient data sets, 1 data set under an unidentified event, 1 data set under the excitation of fireworks, and 1 earthquake data set), and finite element models (both full‐scale and condensed models). The Green Building has an identifiable soil‐structure interaction behavior and the base rocking movement brings significant components into the building response. To decouple the rocking effect, storey measurement condensation and rocking response determination are discussed in this paper. A blind source separation approach is finally applied to identify the modal characteristics and quantify the rocking components. The benchmark data and models are open to the public for algorithmic development and validation. [ABSTRACT FROM AUTHOR]

In this paper, a data-based damage detection algorithm that uses a novel one-class kernel classifier for detection and localisation of damage is presented. The demands of wireless sensing are carefully considered in the development of this fully decentralised and automated methodology. The one-class kernel classifier proposed in this paper is trained through a faster and simpler to implement iterative procedure than other kernel classification methods, while retaining the same advantages over parametric methods, making it especially attractive for embedded damage detection. Acceleration time series at each sensor location are processed into autoregressive and continuous wavelet transform-based damage-sensitive features. Baseline values of these features are used to train the classifier, which can then classify features from new tests as damaged or undamaged, as well as outputting a localisation index, which can be used to identify the location of damage in the structure. This methodology is evaluated using acceleration data taken from a steel-frame laboratory structure under various damage scenarios. A number of parametric studies are also conducted to investigate the effect of sampling frequency and baseline data sample size. [ABSTRACT FROM AUTHOR]

In structural health monitoring of composite structures, one important task is to detect and identify the low-velocity impact events, which may cause invisible internal damages. This paper presents a novel approach for simultaneously identifying the impact location and reconstructing the impact force time history acting on a composite structure using dynamic measurements recorded by a sensor network. The proposed approach consists of two parts: (1) an inner loop to reconstruct the impact force time history and (2) an outer loop to search for the impact location. In the inner loop, a newly developed inverse analysis method with Bayesian inference regularization is employed to solve the ill-posed impact force reconstruction problem using a state-space model. In the outer loop, a nonlinear unscented Kalman filter (UKF) method is used to recursively estimate the impact location by minimizing the error between the measurements and the predicted responses. The newly proposed impact load identification approach is illustrated by numerical examples performed on a composite plate. Results have demonstrated the effectiveness and applicability of the proposed approach to impact load identification. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This study proposes a new algorithm for damage detection in structures. The algorithm employs an energy-based method to capture linear and nonlinear effects of damage on structural response. For more accurate detection, the proposed algorithm combines multiple damage sensitive features through a distance-based method by using Mahalanobis distance. Hypothesis testing is employed as the statistical data analysis technique for uncertainty quantification associated with damage detection. Both the distance-based and the data analysis methods have been chosen to deal with small size data sets. Finally, the efficacy and robustness of the algorithm are experimentally validated by testing a steel laboratory prototype, and the results show that the proposed method can effectively detect and localize the defects. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: Structural health monitoring (SHM) applications generally utilise high sampling rates, which low‐power wireless protocols are not well equipped to handle. Smart sensing approaches can overcome this, by using the processing capability of the sensor nodes to reduce the volume of data prior to communication. Most smart sensing approaches are preprogrammed and static. This causes two issues: First, the data processing logic cannot be easily modified, making it difficult to update and improve algorithms once deployed. Secondly, there is limited ability to adapt to changes in the environment or degradation of hardware. To address these problems, we have developed a system that allows users to remotely specify their computational logic on the fly in a MapReduce style syntax. We model these user‐specified tasks as a directed acyclic graph, and combine this model with statistics of the performance of each node in the network to formulate an optimisation problem. Solving this problem optimally allocates data processing operations to nodes in the network, such that the total time spent is minimised. We demonstrate a field deployment of this system, which illustrates the advantages of the proposed approach for a typical SHM application, and examines robustness of the system under environmental variations. [ABSTRACT FROM AUTHOR]