Multi-parameter numerical simulation of dynamic monitoring of rock deformation in deep mining

The level of deformation development of surrounding rocks is a vital predictor to evaluate impending coal mine disasters and it is important to establish accurate measurements of the deformed status to ensure coal mine safety. Traditional deformation monitoring methods are mostly based on single parameter, in this paper, multiple approaches are integrated: firstly, both electric and elastic models are established,from which electric field distribution and seismic wave recording are calculated and finally, the resistivity profiles and source position information are determined using inversion methods, from which then the deformation and failure of mine floor are evaluated. According to the inversion results of both electric and seismic field signals, multiple-parameter dynamic monitoring of surrounding rock deformation in deep mine can be performed. The methodology is validated using numerical simulation results which shows that the multi-parameter dynamic monitoring methods have better results for surrounding rock deformation in deep mine monitoring than single parameter methods.

Numerical simulation for recognition of coalfield fire areas by Rayleigh waves

Effective recognition of a coalfield fire area improves fire-fighting efficiency and helps avoid potential geological hazards. Coalfield fire areas are hard to detect accurately using general geophysical methods. This paper describes simulations of shallow, buried coalfield fires based on real geological conditions. Recognizing the coalfield fire by Rayleigh wave is proposed. Four representative geological models are constructed, namely; the non-burning model, the pseudo-burning model, the real-burning model, and the hidden-burning model. Numerical simulation using these models shows many markedly different characteristics between them in terms of Rayleigh wave dispersion and Eigen displacement. These characteristics, as well as the shear wave velocity obtained by inverting the fundamental dispersion, make it possible to distinguish the type of the coalfield fire area and indentify the real and serious coalfield fire area. The results are very helpful for future application of Rayleigh waves for the detection of coalfield fire area.

Integrated Learning and Research:The Accelerated Construction Self-Sensing Project

To minimize travel delay impact,the US Department of Transportation(USDOT) has been pushing for Accelerated Construction(AC) techniques for public transportation construction.Contrast to traditional construction techniques,AC technology involves extensive applications of pre-fabricated components.This change in construction philosophy offers a great opportunity to introduce the concept of integrating full monitoring of structural construction/aging processes with AC technology via embedded sensors.The resulting structure can be quickly built and is able to self-sense health conditions throughout its service life.In order to introduce the concept and encourage synergistic learning,a joint course between Construction Management course in Engineering Technology(ET) department and Nondestructive Testing course in Civil Engineering(CE) department, has been developed with the course content designed to engage students to research on the Smart Monitoring of Accelerated Construction(SMAC ) in a two-stage(ET-CE-ET) project.This innovative approach generates synergies amongst students and encourages them to self-learn new techniques.

Effective LiDAR Damage Detection:Comparing Two Detection Algorithms

The health conditions of highway bridges is critical for sustained transportation operations.US federal government mandates that all bridges built with public funds are to be inspected visually every two years. There is a growing consensus that additional rapid and non-intrusive methods for bridge damage evaluation are needed.This paper explores the potential of applying ground-based laser scanners for bridge damage evaluation. LiDAR has the potential of providing high-density,full-field surface static imaging.Hence,it can generate volumetric quantification of concrete corrosion or steel erosion.By recording object surface topology,LiDAR can detect different damages on the bridge structure and differentiate damage types according to the surface flatness and smoothness.To determine the effectiveness of LiDAR damage detection,two damage detection algorithms are presented and compared using scans on actual bridge damages.The results demonstrate and validate LiDAR damage quantification,which can be a powerful tool for bridge condition evaluation.