It is a promising and effective method for waste treatment by using coal gangue to make lightweight aggregate concrete. However, lightweight aggregate concrete with low‐density coal gangue ceramsite is prone to cracking during volume shrinkage, which limits its application in the construction industry. In an attempt to resolve the problem of cracking in shrinkage, this study investigated the effect of prewetting time and shrinkage reducing agents on shrinkage volume and concrete strength through a series of concrete shrinkage and strength tests. The experimental results show that shrinkage volume reduced at a prewetting time of 12 hrs or with a 2% addition of D‐230 polyether amine reductant. With the optimal conditions of 12 hrs prewetting time and 2% addition of the reductant, the concrete shrinkage volume significantly decreased with a negligible impact on its strength. Appropriate amount of shrinkage reducing agent and adjustment of prewetting time of coal gangue ceramsite are necessary to reduce the shrinkage rate and improve the stability of the specimen. This is of great significance to wide application of lightweight aggregate concrete with coal gangue ceramsite.

Blasting vibration could cause dynamic instability of rock masses within a critical steady state. To control the blasting vibration, it is necessary to understand the complete dynamic response process of the rock masses under the blasting vibration. The Long Short-Term Memory (LSTM) technique uses blast monitoring data to predict the full waveform of the blast vibration. Based on the LSTM, a new full waveform prediction model is proposed in this study. To verify the feasibility of the proposed model, the sample data were constructed using the well-known linear blast wave superposition prediction formula. The full waveform prediction model is trained and the predicted waveform and the actual waveform are then evaluated and compared. The loss function is calculated and discussed, which verifies the feasibility of the prediction method. In addition to the numerical research, the actual blasting vibration data are also used for verification. The parameters, such as sequence size, training algorithm, and some hidden layer nodes, are discussed and optimized. The results show that the proposed full waveform prediction model based on LSTM can predict the full blasting waveform. This study provides a new idea for the prediction and control of blasting vibration.

The selection of the most appropriate method(s) for a specific engineering geophysical site characterisation can be challenging due to the complexity and potential ambiguity of the factors influencing the utility and cost-effectiveness of the methods. This paper, using matter-element analysis techniques with analytic hierarchy process (AHP), proposes a demonstration matter-element model for selecting the most appropriate geophysical methods, and establishes the general flow and calculation process for geophysical method selection. On the basis of the analysis of the factors influencing the utility and cost-effectiveness of geophysical methods, an evaluation criteria system is constructed. To demonstrate the application of this process, the proposed matter-element geophysical methods selection mode is applied to the selection of geophysical methods for an engineering geophysical site characterisation project that ultimately involved the acquisition of electrical resistivity tomography (ERT), multichannel analysis of surface wave data (MASW) and ground penetrating radar (GPR). The optimal results, based on the matter-element model, indicate the recommended geophysical methods are consistent with the field methods ultimately employed. The research proposes a new quantitative and objective computing approach to the selection of geophysical methods. The matter-element model can minimise subjective influences to a certain extent, and is conducive to enhancing the utility and cost-effectiveness of a geophysical survey.

Underground mining operations face increasing challenges due to their complex and hazardous environments. One key difficulty is ensuring real-time safety monitoring and disaster prevention. Traditional monitoring systems often suffer from delayed data acquisition and rely heavily on cloud-based processing. These factors limit their responsiveness during emergencies. To address these limitations, this study presents an underground Internet of Things (IoT) monitoring system based on edge computing. The system architecture is composed of three layers: a perception layer for real-time sensing, an edge gateway layer for local data processing and decision-making, and a cloud service layer for storage and analytics. By shifting computation closer to the data source, the system significantly reduces latency and enhances response efficiency. The system is tailored to actual mine-site conditions. It integrates pressure monitoring for artificial expandable pillars and roof subsidence detection in stopes. It has been successfully deployed in a field environment, and the data collected during commissioning demonstrate the system’s feasibility and reliability. Results indicate that the proposed system meets real-world demands for underground safety monitoring. It enables timely warnings and improves the overall automation level. This approach offers a practical and scalable solution for enhancing mine safety and provides a valuable reference for future smart mining systems.