Madhuka Jayawardhana

Statistical Damage Sensitive Feature for Structural Damage Detection Using AR Model Coefficients

Structural Health Monitoring (SHM) and damage detection techniques have captured much interest and attention of researchers and structural engineers owing to their promising ability to provide spatial and quantitative information regarding structural damage and the performance of a structure during its life-cycle. With the development of smart sensors and communication technologies, Wireless Sensor Networks (WSN) has empowered the advancement in SHM. Recently, time series models have been widely used for structural damage detection due to the sensitivity of the model coefficients and residual errors to the damages in the structure. This paper presents a simple index that is computed using the Auto-Regressive (AR) model coefficients as an effective damage sensitive feature (DSF) for the detection of structural damage. Based on this feature, a damage identification method is developed. The Fisher information criterion of the computed DSF is used to statistically decide on the location of damage. This method has been implemented in a simulation environment and the verification of its accuracy in structural damage detection has been carried out experimentally. Experimental data is obtained using wireless sensors from a series of tests performed on a steel beam. The novel damage feature combined with the Fisher criterion for statistical evaluation has shown potential in effective structural damage detection.

An Experimental Study on Damage Detection of Concrete Structures using Decentralized Algorithms

In this paper, an experimental study has been carried out to detect damage on a simply supported two-span reinforced concrete slab. Different crack damages are created by static loads on the slab and impact tests are carried out before and after removing the static loads. Two decentralized damage detection methods – Auto Correlation Function-Cross Correlation Function (ACF-CCF) method and Auto Regressive-Auto Regressive with exogenous input (AR-ARX) method, are used to localize damage from measured responses. The accuracy and sensitivity as well as the effect of sensor location and loading status of the structure were analysed with these two methods. The results show that the ACF-CCF method is more effective in detecting and locating damage than the AR-ARX method. The Novelty Index value of the ACF-CCF method could be a reliable indicator of damage in concrete structures.

Compressive sensing for structural damage detection of reinforced concrete structures

High energy consumption, excessive data storage and transfer requirements are prevailing issues associated with structural health monitoring (SHM) systems, especially with those employing wireless sensors. Data compression is one of the techniques being explored to mitigate the effects of these issues. Compressive sensing (CS) introduces a means of reproducing a signal with a much less number of samples than the Nyquists rate, reducing the energy consumption, data storage and transfer cost. This paper explores the applicability of CS for SHM, in particular for damage detection and localization. CS is implemented in a simulated environment to compress SHM data. The reconstructed signal is verified for accuracy using structural response data obtained from a series of tests carried out on a reinforced concrete (RC) slab. Results show that the reconstruction was close, but not exact as a consequence of the noise associated with the responses. However, further analysis using the reconstructed signal provided successful damage detection and localization results, showing that although the reconstruction using CS is not exact, it is sufficient to provide the crucial information of the existence and location of damage.

Damage detection of reinforced concrete structures based on the Wiener filter

This paper presents a novel decentralised structural damage detection method based on the Wiener filter. The Wiener filter is customarily used for filtering out the noise that has corrupted a signal, and it is also used for system identification by matching the output of the filter with that of the unknown system. In this study, a damage index based on the mean square error of the Wiener filter is proposed to indicate the damage in structures. The current measurement is the input of the filter and the response of the undamaged structure is the design signal. Another index calculated from the cross correlation responses of neighbouring sensors is used to determine the damage location. An experimental study has been carried out on a reinforced concrete structure. The results show that this method is effective and reliable for structural damage detection and localisation.

An experimental study on distributed damage detection algorithms for structural health monitoring

Distributed structural damage detection has become the subject of many recent studies in Structural Health Monitoring (SHM). Development of smart sensor nodes has facilitated the growth of this concept enabling decentralized data processing capabilities of nodes whose sole responsibility once was acquisition of data. An experimental study has been carried out on a two span reinforced concrete slab in this paper. Different crack damages are created by the static loads and the impact tests that are carried out on the slab. Two damage detection and localization methods, one based on Auto Correlation Function-Cross Correlation Function (ACF-CCF) and the other on Auto Regressive (AR) time series model are used to detect damage from measured responses. The results from the two methods are compared in order to determine which method has been more effective and reliable in determining the damage to the concrete structure.