Special Emergency Resources Preallocation Concerning Demand Time for Tunnel Collapse

Lacking timely access to rescue resources is one of the main causes of casualties in tunnel collapse.To provide timely rescue,this study proposed a multi-objective preallocation model of special emergency resources for tunnel collapse based on demand time.Efficiency,multiple coverage,and cost-effectiveness are taken as the key objectives of the model;the demand time service range is used as a coverage decision factor considering the unique nature of tunnel collapse.The weight of potential disaster-affected points and other general factors are also considered in this model in order to thoroughly combine the distribution of disaster points and service areas.Further,we take 15 main tunnel projects under construction in China as an example.When the relative proximity to the ideal point of the selected optimal schemeε_(a)is smaller than 0.5,we will adjust the weight of three objectives and reselect the optimal scheme untilε_(a)>0.5.Compared with the not preallocated case,the number of rescue rigs needed is reduced by 8.3%,the number of covered potential disaster-affected points is increased by 36.36%,the weighted coverage times are increased from 0.853 to 1.383,and the weighted distance is significantly reduced by 99%when the rescue rigs are preallocated,verifying the feasibility and superiority of the proposed model.

Feedback on a shared big dataset for intelligent TBM Part Ⅱ:Application and forward look

This review discusses the application scenarios of the machine learning-supported performance prediction and the optimization effi-ciency of tunnel boring machines(TBMs).The rock mass quality ratings,which are based on the Chinese code for geological survey,were used to provide"labels"suitable for supervised learning.As a result,the generation of machine prediction for rock mass grades reason-ably agreed with the ground truth documented in geological maps.In contrast,the main operational parameters,i.e.,thrust and torque,can be reasonably predicted based on historical data.Consequently,18 collapse sections of the Yinsong project have been successfully predicted by several researchers.Preliminary studies on the selection of the optimal penetration rate and cost were conducted.This review also presents a summary of the main achievements in response to the initiatives of the Lotus Pool Contest in China.For the first time,large and well-documented TBM performance data has been shared for joint scientific research.Moreover,the review discusses the technical problems that require further study and the perspectives in the future development of intelligent TBM construction based on big data and machine learning.