Review of transparent soil model testing technique for underground construction:Ground visualization and result digitalization

In geotechnical engineering,the transparent soil(also called transparent media)technique is an effective tool for conducting experimental tests and investigating the displacement characteristics and stress distribution of soils.It plays a vital role in the observation of internal soil deformations.This study aims to briefly review the current state of some of the common materials used to formulate transparent soil models and the application of the transparent soil technique to underground construction over the last 20 years.To this end,the basic concepts of transparent soils are introduced.Then,several representative applications of transparent soil in underground construction(i.e.,soil deformations induced by the penetration of pile foundations,tunnel excavation-induced movements,and structural responses caused by braced excavations)are presented.Because some research gaps may exist,certain potential research topics are proposed.This review can serve as a guideline for researchers performing experiments using transparent soils.

Effect of pore water chemistry on anisotropic behavior of clayey soil and possible application in underground construction

The effect of pore water chemistry on anisotropic behavior of consolidation and shear strength of reconstituted Ariake clay has been investigated experimentally.Two types of chemicals added into the pore water of the soil for enhancing flocculation microstructure of soil particles are sodium chloride(salt)(NaCl),and calcium chloride(CaCl_(2));and two dispersants added are sodium triphosphate(Na_(5)-P_(3)O_(10))and sodium hexametaphosphate(Na_(6)P_(6)O_(18)),respectively.The concentrations of these chemicals in pore water were 2-3%.Degrees of anisotropy of the coefficient of consolidation and undrained shear strength decreased with adding NaCl and CaCl_(2),but increased with adding the dispersants.Degree of anisotropy also increased with one-dimensional(1D)deformation and the samples with dispersive additives had higher increase rate.It has been confirmed qualitatively by scanning electron microscopy(SEM)images that adding dispersive chemicals promoted the formation of dispersive microstructure and increased the degree of anisotropy,and the chemicals enhancing flocculent microstructure had an inverse effect.The possible application of the findings to underground construction has been discussed also.

Spatial deduction of mining-induced stress redistribution using an optimized non-negative matrix factorization model

Investigation of mining-induced stress is essential for the safety of coal production.Although the field monitoring and numerical simulation play a significant role in obtaining the structural mechanical behaviors,the range of monitoring is not sufficient due to the limits of monitoring points and the associated numerical result is not accurate.In this study,we aim to present a spatial deduction model to characterize the mining-induced stress distribution using machine learning algorithm on limited monitoring data.First,the framework of the spatial deduction model is developed on the basis of non-negative matrix factorization(NMF)algorithm and optimized by mechanical mechanism.In this framework,the spatial correlation of stress response is captured from numerical results,and the learned correlation is employed in NMF as a mechanical constrain to augment the limited monitoring data and obtain the overall mechanical performances.Then,the developed model is applied to a coal mine in Shandong,China.Experimental results show the stress distribution in one plane is derived by several monitoring points,where mining induced stress release is observed in goaf and stress concentration in coal pillar,and the intersection point between goaf and coal seam is a sensitive area.The indicators used to evaluate the property of the presented model indicate that 83%mechanical performances have been captured and the deduction accuracy is about 92.9%.Therefore,it is likely that the presented deduction model is reliable.

Artificial intelligence in tunnel construction: A comprehensive review ofhotspots and frontier topics

Application of Artificial Intelligence(AI)in tunnel construction has the potential to transform the industry by improving efficiency,safety,and cost-effectiveness.This paper presents a comprehensive literature review and analysis of hotspots and frontier topics in artificial intelligence-related research in tunnel construction.A total of 554 articles published between 2011 and 2023 were collected from the Web of Science(WOS)core collection database and analyzed using CiteSpace software.The analysis identified three main study areas:Tunnel Boring Machine(TBM)performance,construction optimization,and rock and soil mechanics.The review highlights the advancements made in each area,focusing on design and operation,performance prediction models,and fault detection in TBM performance;computer vision and image processing,neural network algorithms,and optimization and decision-making in construction optimization;and geo-properties and behaviours,tunnel stability and excavation,and risk assessment and safety management in rock and soil mechanics.The paper concludes by discussing future research directions,emphasizing the integration of AI with other advanced technologies,realtime decision-making systems,and the management of environmental impacts in tunnel construction.This comprehensive review provides valuable insights into the current state of AI research in tunnel engineering and serves as a reference for future studies in this rapidly evolvingfield.