A probability prediction method for the classification of surrounding rock quality of tunnels with incomplete data using Bayesian networks.

The classification of surrounding rock quality is critical for the dynamic construction and design of tunnels. However, obtaining complete parameters for predicting the surrounding rock grades is always challenging in complex tunnel geological environment. In this study, a new method based on Bayesian networks is proposed to predict the probability for the classification of surrounding rock quality of tunnel with incomplete data.

Healthy Community-Life Circle Planning Combining Objective Measurement and Subjective Evaluation: Theoretical and Empirical Research.

Background: The world faces vast health challenges, and urban residents living in high-density areas have even greater demand for healthy lifestyles.

Methods: Based on the data of points of interest, a field survey, and an interview, we explored the healthy community-life circle in the downtown area of Chengdu, China from two perspectives: objective measurement and subjective perception of residents. We evaluated the coverage rate and convenience in accessing eight types of health service facilities within a 15-min walk using linear and logistics regression models to explore the degree of resident satisfaction with facilities and influencing factors.

Measurement of Coupling Coordination Degree and Spatio-Temporal Characteristics of the Social Economy and Ecological Environment in the Chengdu-Chongqing Urban Agglomeration under High-Quality Development.

With rapid urbanization and industrialization, ecological disorders and environmental degradation have become serious, and the promotion of the coordinated development of the social economy and ecological environment is not only a pressing problem to be solved, but also an important step towards sustainable development. The coordinated development of the social economy and eco-environment is conducive to sustainable development. Considering the Chengdu-Chongqing urban agglomeration as a case study, this paper adopts panel data and establishes an index system to evaluate the coupling coordination degree (CCD) between the social economy and ecological environment based on the concept of high-quality development.

Multi-Classification of Complex Microseismic Waveforms Using Convolutional Neural Network: A Case Study in Tunnel Engineering.

Due to the complexity of the various waveforms of microseismic data, there are high requirements on the automatic multi-classification of such data; an accurate classification is conducive for further signal processing and stability analysis of surrounding rock masses. In this study, a microseismic multi-classification (MMC) model is proposed based on the short time Fourier transform (STFT) technology and convolutional neural network (CNN). The real and imaginary parts of the coefficients of microseismic data are inputted to the proposed model to generate three classes of targets.