The key to detecting damage to civil engineering structures is to find an effective damage indicator. The damage indicator should promptly reveal the location of the damage and accurately identify the state of the structure. We propose to use the distance measures of low-order AR models as a novel damage indicator. The AR model has been applied to parameterize dynamical responses, typically the acceleration response. The premise of this approach is that the distance between the models, fitting the dynamical responses from damaged and undamaged structures, may be correlated with the information about the damage, including its location and severity. Distance measures have been widely used in speech recognition. However, they have rarely been applied to civil engineering structures. This research attempts to improve on the distance measures that have been studied so far. The effect of varying the data length, number of parameters, and other factors was carefully studied.

This paper describes the development and evaluation of an innovative TDR distributed moisture sensor. This sensor features advantages of being responsive to the spatial variations of the soil moisture content. The geometry design of the sensor makes it rugged for field installation. Good linear calibration is obtained between the sensor measured dielectric constant and soil physical properties. Simulations by the finite element method (FEM) are conducted to assist the design of this sensor and to determine the effective sampling range. Compared with conventional types of moisture sensor, which only makes point measurement, this sensor possesses distributed moisture sensing capability. This new sensor is not only easy to install, but also measures moisture distribution with much lower cost. This new sensor holds promise to significantly improve the current field instruments. It will be a useful tool to help study the influence of a variety of moisture-related phenomena on infrastructure performance.

Measuring displacement response for civil structures is very important for assessing their performance, safety and integrity. Recently, video-based techniques that utilize low-cost high-resolution digital cameras have been developed for such an application. These techniques however have relatively low sampling frequency and the results are usually contaminated with noises. In this study, an integrated visual-inertial measurement method that combines a monocular videogrammetric displacement measurement technique and a collocated accelerometer is proposed for displacement and velocity measurement of civil engineering structures. The monocular videogrammetric technique extracts three-dimensional translation and rotation of a planar target from an image sequence recorded by one camera. The obtained displacement is then fused with acceleration measured from a collocated accelerometer using a multi-rate Kalman filter with smoothing technique. This data fusion not only can improve the accuracy and the frequency bandwidth of displacement measurement but also provide estimate for velocity. The proposed measurement technique is illustrated by a shake table test and a pedestrian bridge test. Results show that the fusion of displacement and acceleration can mitigate their respective limitations and produce more accurate displacement and velocity responses with a broader frequency bandwidth.

This paper investigates the possibility of a strategy for an automatic full recover of a structural component undergoing loading-unloading (fatigue) cycles: full recover means here that no replacement is required at the end of the mission. The goal is to obtain a material capable of self healing earlier before the damage becomes irreversible. Attention is focused on metallic materials, and in particular on shape memory alloys, for which the recovering policy just relies on thermal treatments. The results of several fatigue tests are first reported to acquire a deep understanding of the physical process. Then, for cycles of constant amplitude, the self-healing objective is achieved by mounting, on the structural component of interest, a suitable microcontroller. Its input, from suitable sensors, covers the current stress and strain in the alloy. The microcontroller elaborates from the input the value of a decisional parameter and activates the thermal process when a threshold is overcome.

An early detection of structural damages is critical for the decision making of repair and replacement maintenance in order to guarantee a specified structural reliability. Consequently, the structural damage detection, based on vibration data measured from the structural health monitoring (SHM) system, has received considerable attention recently. The traditional time-domain analysis techniques, such as the least square estimation (LSE) method and the extended Kalman filter (EKF) approach, require that all the external excitations (inputs) be available, which may not be the case for some SHM systems. Recently, these two approaches have been extended to cover the general case where some of the external excitations (inputs) are not measured, referred to as the adaptive LSE with unknown inputs (ALSE-UI) and the adaptive EKF with unknown inputs (AEKF-UI). Also, new analysis methods, referred to as the adaptive sequential non-linear least-square estimation with unknown inputs and unknown outputs (ASNLSE-UI-UO) and the adaptive quadratic sum-squares error with unknown inputs (AQSSE-UI), have been proposed for the damage tracking of structures when some of the acceleration responses are not measured and the external excitations are not available. In this paper, these newly proposed analysis methods will be compared in terms of accuracy, convergence and efficiency, for damage identification of structures based on experimental data obtained through a series of laboratory tests using a scaled 3-story building model with white noise excitations. The capability of the ALSE-UI, AEKF-UI, ASNLSE-UI-UO and AQSSE-UI approaches in tracking the structural damages will be demonstrated and compared.

Railway transport of goods and passengers is effective in terms of energy conservation and travel time savings. Safety and ride quality have become important issues as train speeds have increased. Due to increased speeds, minor damage to railway structures and abnormal interactions between trains and structures have given rise to increasingly serious accidents. Therefore, structural health and operational conditions must be monitored continuously in all service environments. Currently, various health and operation management systems are being developed and these are reducing both maintenance frequency and costs associated with disassembly. In this review, major damage and malfunctions and their locations are first analyzed based on numerous references. Then advanced train health and operation management technologies are classified into wayside detection methods and advanced integrated sensor methods and their operating principle and functions are reviewed and analyzed.