Modern structures incorporating lightweight, low-stiffness floors face challenges for lowfrequency impact noise transmission. Using spring isolators or resilient layers (e.g., floating floors) to improve isolation in light weight floor can introduce variability over time and increase structural complexity, making the system more sensitive to construction errors. An alternative approach is reviewed in this work, using internal floor cavities that contain Granular Materials (GM). Previous studies describe GM particle dampers in different applications where large movements between particles result in significant energy losses. However, a review of the experimental methods used in those studies is needed to be able to quantify the energy losses in relation to the type and degree of impact excitation. Modelling approaches are reviewed comparing their computational demand and which properties of GM are included, motion regimes and container properties. These studies span both destructive and non-destructive testing methods and give some pointers to both the geometrical and mechanical properties of granules which influence dissipation. This review goes beyond structural damping to include airborne sound absorption provided by a granular bed. This additional attenuation can be significant over a wide frequency range. A small number of practical studies of GM integrated with light weight floors show improvement in impact sound insulation. However, the lack of more detailed knowledge of GM damping mechanisms and a better understanding of GM bed interactions with containers prevents optimization of their use for insulating floors against sound transmission. This review proposes a general framework for future GM research to guide the selection of appropriate GM and addresses what is needed for optimizing lightweight floor impact sound insulation.

This article presents an active acoustic excitation method for leak detection of buried gas pipelines based on cavity resonance reflection. The principles of gas leakage in pipelines are analyzed, including the gas passage model and the gas cavity model. The principle of Helmholtz resonator is employed to establish the cavity model. For the cavity model, the relationships between cavity resonance frequency, acoustic impedance, sound pressure amplification, and leakage damage size are derived. The resonant effect of the gas cavity on the acoustic signal is considered in this study to solve the problem that the echo signal after long distance propagation and reflection becomes very weak. Numerical simulations are conducted to demonstrate the relationships between acoustic reflection coefficient of the leak hole size, cavity volume, and pipe wall thickness. In order to verify the effectiveness of the proposed method, a pipeline experimental rig with a length of 100 m is constructed. Sound waves are generated by a speaker and reflected echoes are received by a microphone. The cavity resonance reflection and echo characteristics of different leak hole size, different transmitting acoustic frequency, and different cavity volume are analyzed. The empirical mode decomposition (EMD) algorithm is used to decompose and reconstruct the echo signals to eliminate the noise interference in the pipeline system. An echo time-distance conversion method is used to visualize the locations of the leak hole and welds. Experimental results show that the proposed method can effectively detect the leak holes and welds in the pipeline.

In this article, we propose a method using T(0,1) guided waves combined with coil coding technique to detect defects in buried liquid-filled pipes implemented by an electromagnetic acoustic transducer (EMAT). Due to its non-dispersive properties and the fact that there is no energy loss in nonviscoelastic fluids, the T(0,1) mode is selected for pipe defects detection. The electromagnetic device that generates the circumferential magnetic field is optimized to excite the pure T(0,1) mode. To realize energy enhancement and defect location identification, the electromagnetic acoustic coil is spatially encoded by 11-bit Barker code and the receiver coil is multiplexed consisting of a spatial coded coil and a unit coil. The defect detection is accomplished through time-of-flight (TOF) time-frequency analysis, and the defect location identification is achieved by digital signal processing methods (cross correlation and convolution). The feasibility of this method is verified by the finite element (FE) model and experimental analysis, indicating the defect locating error in a liquid-filled pipes is less than 1%. Overall, the proposed method achieves a high-precision flaw detection and location identification.