Utilising recycled materials, such as construction and demolition waste (C&DW), into soil improvement projects offers a promising solution to reduce the environmental impact of the C&DW industry. This approach helps address issues related to waste generation, resource depletion, and environmental degradation, while enhancing the overall sustainability and resilience of soil stabilisation efforts. This study investigates the effectiveness of incorporating recycled C&DW into cement-treated peat and clayey soils to enhance their strength and stiffness. To achieve this goal, laboratory experiments were conducted on over 296 soil specimens to assess their Unconfined Compressive Strength (UCS), small-strain Young's modulus (E0) and shear modulus (G0). These tests included varying curing times (28, 60, 90, and 120 days), different cement and recycled material content, and water-to-cement ratios. Moreover, laboratory testing methods for determining geotechnical parameters are often time-consuming and prone to challenges. In this context, reliable predictive models, such as artificial neural networks (ANNs), offer an efficient alternative for accurately assessing these parameters. The findings of this research reveal that, along with cement content, the water-to-cement ratio (w/c) and curing time are key factors influencing the strength and stiffness of treated soft soils, underscoring their critical role in soil stabilisation. Additionally, while minimizing cement content and increasing RM yield improvements in both peat and clay, the effect is more pronounced in peat due to the time-dependent nature of pozzolanic reactions. This suggests that achieving optimal performance with increased strength and stiffness requires a carefully balanced RM content. Finally, the study demonstrates that ANN-based models can accurately predict the strength and mechanical properties of soft soils, offering a viable alternative to traditional UCS and FFR tests.

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.