Eng

Traditional underground space evaluation systems often employ 2D GIS methods to represent 3D information, leading to issues such as the loss of 3D spatial data and insufficient resolution in depth. To address the practical needs and methodological steps of 3D geological suitability evaluation for underground space (3D UGEE) development, this study adopts an integrated secondary development approach to design and implement a software system capable of conducting quantitative geological suitability evaluation in three dimensions using multivariate data. The system incorporates the latest methods and achievements in 3D UGEE, featuring functional modules such as multidimensional data conversion, 3D statistical analysis, 3D spatial distance analysis, and 3D comprehensive evaluation, which enable the integration and analytical assessment of multivariate geoscientific data. In comparison with existing 3D-UGEE systems, the proposed 3D-UGEE system integrates a broader range of functional modules, conducts in-depth integration and mining of multi-source geological data, boasts robust 3D graphical display and interactive capabilities, and achieves more efficient operational performance. This study elaborates on the system’s overall architecture, development approach, and the design and implementation processes of its functional modules. Application results from a case study in Qingdao demonstrate that the system not only provides a suite of 3D spatial analysis and comprehensive evaluation tools for integrating multivariate geoscientific data but also offers robust support for enhancing 3D UGEE practices.

This study presents an integrated approach combining environmental risk assessment and experimental performance evaluation for asphalt production plants incorporating reclaimed asphalt pavement (RAP). Unlike previous studies, which focus separately on mechanical performance or environmental impact, our methodology applies a semi-quantitative Environmental Impact Score (EIS), calculated using legal requirements (L), pollutant characteristics (P), and control measure effectiveness (C). The EIS framework is based on ISO 14001 and ISO 31000 principles. The results indicate that significant impacts are mainly associated with high-temperature processes and hazardous materials, while mitigation measures effectively reduce residual risks. The experimental investigation compared conventional asphalt mixtures with mixtures containing 9.71% RAP across different bitumen contents. Key quantitative findings include a 3-point increase in EIS for RAP mixtures due to higher volatile organic compound (VOC) emissions and a 3–8% improvement in Marshall stability and stiffness at lower bitumen contents (3.8–4.2%). The results demonstrate that RAP can enhance mechanical performance while supporting circular economy objectives, provided that environmental risks are actively managed through process control and mitigation measures. This work highlights the novel integration of quantitative environmental scoring with laboratory validation, providing a reproducible framework for sustainable and risk-informed asphalt production.

The reliability and efficiency of induction motors in Industry 4.0 environments critically depend on advanced fault detection systems capable of real-time monitoring and diagnosis. This paper presents a novel deep learning approach combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks for automated detection and classification of inter-turn short-circuit faults in three-phase induction motors. Our methodology processes three-phase current signals through a sophisticated CNN-LSTM architecture that extracts both spatial and temporal fault patterns. The proposed system classifies seven distinct motor conditions: healthy operation, three levels of high-impedance faults (HI-1 to HI-3), and three levels of low-impedance faults (LI-1 to LI-3). Experimental validation demonstrates exceptional performance, with the CNN-LSTM model achieving 97.2% accuracy, significantly outperforming traditional machine learning approaches, including SVM (66.3%), Random Forest (67.4%), and KNN (78.1%). The system provides real-time fault classification with inference times under 3 ms, making it suitable for continuous monitoring in smart manufacturing environments.