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The mechanical behavior of a fiber-reinforced concrete after extensive thermal damage is studied in this paper. Undulated steel fibers have been used for reinforcement. After being exposed to direct fire action at the temperature of 850°C, specimens were subjected to bending and compression in order to determine the loss of strength and stiffness in comparison to intact specimens and between the two types. The fire damage was assessed using nondestructive evaluation techniques, specifically ultrasonic pulse velocity (UPV) and acoustic emission (AE). Apart from the strong, well known, correlation of UPV to strength (both bending and compressive), AE parameters based mainly on the frequency and duration of the emitted signals after cracking events showed a similar or, in certain cases, better correlation with the mechanical parameters and temperature. This demonstrates the sensitivity of AE to the fracture incidents which eventually lead to failure of the material and it is encouraging for potential in-situ use of the technique, where it could provide indices with additional characterization capability concerning the mechanical performance of concrete after it subjected to fire.

We present investigations into the collision of co-travelling solitary waves in a granular chain. Impulses are injected into the system by means of a piezo stack and the results are compared to a numerical model of discrete masses connected by non-linear springs. Similar to other solitary wave-carrying systems, a phase shift in both interacting solitary waves is observed due to their collision. Additionally, the formation of small secondary waves is observed in both numerical and experimental results. Insight into solitary wave interactions will be important for high-frequency excitation of a granular crystal, which may allow for improved Non-Destructive Evaluation (NDE) and Structural Health Monitoring (SHM) methods.

In structural health monitoring (SHM) systems for civil structures, signal compression is often important to reduce the cost of data transfer and storage because of the large volumes of data generated from the monitoring system. Compressive sensing is a novel data compressing method whereby one does not measure the entire signal directly but rather a set of related ("projected") measurements. The length of the required compressive-sensing measurements is typically much smaller than the original signal, therefore increasing the efficiency of data transfer and storage. Recently, a Bayesian formalism has also been employed for optimal compressive sensing, which adopts the ideas in the relevance vector machine (RVM) as a decompression tool, such as the automatic relevance determination prior (ARD). Recently publications illustrate the benefits of using the Bayesian compressive sensing (BCS) method. However, none of these publications have investigated the robustness of the BCS method. We show that the usual RVM optimization algorithm lacks robustness when the number of measurements is a lot less than the length of the signals because it can produce sub-optimal signal representations; as a result, BCS is not robust when high compression efficiency is required. This induces a tradeoff between efficiently compressing data and accurately decompressing it. Based on a study of the robustness of the BCS method, diagnostic tools are proposed to investigate whether the compressed representation of the signal is optimal. With reliable diagnostics, the performance of the BCS method can be monitored effectively. The numerical results show that it is a powerful tool to examine the correctness of reconstruction results without knowing the original signal.