Detection of Internal Defects in Concrete Members Using Global Vibration Characteristics

Rock-pocket and honeycomb defects impair overall stiffness, accelerate aging, reduce service life, and cause structural problems in hardened concrete members. Traditional methods for detecting such deficient volumes involve visual observations or localized nondestructive methods, which are labor-intensive, time-consuming, highly sensitive to test conditions, and require knowledge of and accessibility to defect locations. The authors propose a vibration response-based nondestructive technique that combines experimental and numerical methodologies for use in identifying the location and severity of internal defects of concrete members. The experimental component entails collecting mode shape curvatures from laboratory beam specimens with size-controlled rock pocket and honeycomb defects, and the numerical component entails simulating beam vibration response through a finite element (FE) model parameterized with three defect-identifying variables indicating location (x, coordinate along the beam length) and severity of damage (alpha, stiffness reduction and beta, mass reduction). Defects are detected by comparing the FE model predictions to experimental measurements and inferring the low number of defect-identifying variables. This method is particularly well-suited for rapid and cost-effective quality assurance for precast concrete members and for inspecting concrete members with simple geometric forms.

Corpus-Based Speech Enhancement With Uncertainty Modeling and Cepstral Smoothing

We present a new approach for corpus-based speech enhancement that significantly improves over a method published by Xiao and Nickel in 2010. Corpus-based enhancement systems do not merely filter an incoming noisy signal, but resynthesize its speech content via an inventory of pre-recorded clean signals. The goal of the procedure is to perceptually improve the sound of speech signals in background noise. The proposed new method modifies Xiao's method in four significant ways. Firstly, it employs a Gaussian mixture model (GMM) instead of a vector quantizer in the phoneme recognition front-end. Secondly, the state decoding of the recognition stage is supported with an uncertainty modeling technique. With the GMM and the uncertainty modeling it is possible to eliminate the need for noise dependent system training. Thirdly, the post-processing of the original method via sinusoidal modeling is replaced with a powerful cepstral smoothing operation. And lastly, due to the improvements of these modifications, it is possible to extend the operational bandwidth of the procedure from 4 kHz to 8 kHz. The performance of the proposed method was evaluated across different noise types and different signal-to-noise ratios. The new method was able to significantly outperform traditional methods, including the one by Xiao and Nickel, in terms of PESQ scores and other objective quality measures. Results of subjective CMOS tests over a smaller set of test samples support our claims.