Summary
Advent of computationally efficient smartphones, inexpensive high‐resolution cameras, drones, and robotic sensors has brought a new era of next‐generation intelligent monitoring systems for civil infrastructure. Vibration‐based condition assessment has garnered as a prominent method of evaluating the health of large‐scale infrastructure. The use of contact‐based sensors for acquiring vibration data becomes uneconomical and tedious due to their instrumentation cost, centralized nature, and densification required to collect sufficient data for system identification of modern complex structures. A need to advance and develop alternative methods for efficient sensing system results in next‐generation measurement technology of structural health monitoring. The abundance of handheld smartphones with easily programmable framework has helped in modifying relevant software to acquire vibration data using embedded sensors in the smartphone. The inexpensive cameras have been used to capture images and videos that are utilized to understand the structural behavior with the aid of advanced signal processing techniques. The inaccessible components of structures require noncontact sensors such as unmanned aerial vehicles (UAVs) or so‐called drones and mobile sensors to acquire structural data. To the authors' knowledge, this paper first time presents a comprehensive review of a suite of next‐generation smart sensing technology that has been developed in recent years within the context of structural health monitoring. The state‐of‐the‐art methods have been presented by conducting a detailed literature review of the recent applications of smartphones, UAVs, cameras, and robotic sensors used in acquiring and analyzing the vibration data for structural condition monitoring and maintenance.
Ambient system identification in noisy environments, in the presence of low-energy modes or closely-spaced modes, is a challenging task. Conventional blind source separation techniques such as second-order blind identification (SOBI) and Independent Component Analysis (ICA) do not perform satisfactorily under these conditions. Furthermore, structural system identification for flexible structures require the extraction of more modes than the available number of independent sensor measurements. This results in the estimation of a non-square modal matrix that is spatially sparse. To overcome these challenges, methods that integrate blind identification with time-frequency decomposition of signals have been previously presented. The basic idea of these methods is to exploit the resolution and sparsity provided by time-frequency decomposition of signals, while retaining the advantages of second-order source separation methods. These hybrid methods integrate two powerful time-frequency decompositions-wavelet transforms and empirical mode decomposition-into the framework of SOBI. In the first case, the measurements are transformed into the time-frequency domain, followed by the identification using a SOBI-based method in the transformed domain. In the second case, a subset of the operations are performed in the transformed domain, while the remaining procedure is conducted using the traditional SOBI method. A new method to address the underdetermined case arising from sparse measurements is proposed. Each of these methods serve to address a particular situation: closely-spaced modes or low-energy modes. The proposed methods are verified by applying them to extract the modal information of an airport control tower structure located near Toronto in Canada.
SummaryThis paper presents a statistical framework to monitor the performance of an operational concrete arch dam using sensory data acquired during its initial service life.One of the major challenges in dealing with a newly constructed dam is to predict its long-term behaviour by forecasting appropriate thresholds using limited data exhibiting nonstationarity. In this paper, a hybrid model is implemented to predict dam responses using environmental-hydrostatic, seasonal, and temperature-as well as age-related variables. The data from multiple sensors are first analyzed using principal component analysis to incorporate overall dam behaviour into a prediction model. The proposed prediction framework is then employed to estimate the residuals and control limits required to calculate thresholds under nonstationary operating conditions during its initial service life. The dam performance is then monitored using statistical control charts and anomalies are detected by comparing the test statistics, square prediction error, and Hotelling T-squared, calculated from the residuals with the preset control limits. The issue of limited data is addressed by updating the model parameters and thresholds periodically, which is aimed at minimizing the false alarm rate. The proposed method is demonstrated using a 130-m-high double-arch concrete dam located in Bulgaria.
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