No abstract

This paper presents a recursive stochastic subspace identification (RSSI) technique for on-line and almost real-time structural damage diagnosis using output-only measurements. Through RSSI the time-varying natural frequencies of a system can be identified. To reduce the computation time in conducting LQ decomposition in RSSI, the Givens rotation as well as the matrix operation appending a new data set are derived. The relationship between the size of the Hankel matrix and the data length in each shifting moving window is examined so as to extract the time-varying features of the system without loss of generality and to establish on-line and almost real-time system identification. The result from the RSSI technique can also be applied to structural damage diagnosis. Off-line data-driven stochastic subspace identification was used first to establish the system matrix from the measurements of an undamaged (reference) case. Then the RSSI technique incorporating a Kalman estimator is used to extract the dynamic characteristics of the system through continuous monitoring data. The predicted residual error is defined as a damage feature and through the outlier statistics provides an indicator of damage. Verification of the proposed identification algorithm by using the bridge scouring test data and white noise response data of a reinforced concrete frame structure is conducted.

This study aims to validate a piezoelectric driven-rod scour monitoring system that can sense changes in scour depth along the entire rod at its instrumented location. The proposed sensor is a polymeric slender rod with a thin strip of polyvinylidene fluoride that runs through its midline. Extraction of the fundamental frequency allows the direct calculation of the exposed length (or scour depth) of the slender rod undergoing fluid flow excitation. First, laboratory validation in dry conditions is presented. Second, hydrodynamic testing of the sensor system in a soil-bed flume is discussed. Each rod was installed using a three-dimensional-printed footing designed for ease of installation and stabilization during testing. The sensors were installed in a layout designed to capture symmetric scour conditions around a scaled pier. In order to analyze the system out of steady-state conditions, water velocity was increased in stages during testing to induce different degrees of scour. As ambient water flow excited the portion of the exposed rods, the embedded piezoelectric element outputted a time-varying voltage signal. Different methods were then employed to extract the fundamental frequency of each rod, and the results were compared. Further testing was also performed to characterize the relationship between frequency outputs and flow velocity, which were previously thought to be independent. In general, the proposed driven-rod scour monitoring system successfully captured changing frequencies under varied flow conditions.

In this paper, a wireless sensing system is designed to realize on-line damage localization and quantification of a structure using a frequency response function change method (FRFCM). Data interrogation algorithms are embedded in the computational core of the wireless sensing units to extract the necessary structural features, i.e. the frequency spectrum segments around eigenfrequencies, automatically from measured structural response for the FRFCM. Instead of the raw time history of the structural response, the extracted compact structural features are transmitted to the host computer. As a result, with less data transmitted from the wireless sensors, the energy consumed by the wireless transmission is reduced. To validate the performance of the proposed wireless sensing system, a six-story steel building with replaceable bracings in each story is instrumented with the wireless sensors for on-line damage detection during shaking table tests. The accuracy of the damage detection results using the wireless sensing system is verified through comparison with the results calculated from data recorded of a traditional wired monitoring system. The results demonstrate that, by taking advantage of collocated computing resources in wireless sensors, the proposed wireless sensing system can locate and quantify damage with acceptable accuracy and moderate energy efficiency.