Risk warning technologies and emergency response mechanisms in Sichuan-Tibet Railway construction

Safety is one of the most critical themes in any large-scale railway construction project.Recognizing the importance of safety in railway engineering,practitioners and researchers have proposed various standards and procedures to ensure safety in construction activities.In this study,we first review four critical research areas of risk warning technologies and emergency response mechanisms in railway construction,namely,(i)risk identification methods of large-scale railway construction projects,(ii)risk management of large-scale railway construction,(iii)emergency response planning and management,and(iv)emergency response and rescue mechanisms.After reviewing the existing studies,we present four corresponding research areas and recommendations on the Sichuan-Tibet Railway construction.This study aims to inject new significant theoretical elements into the decision-making process and construction of this railway project in China.

Flood Risk Forecasting and Early Warning Technology for Medium and Small Rivers in the Yellow River Basin Induced by Heavy Rain

The Yellow River Basin is one of the important sand-producing and sediment-transporting areas in China,and one of the three most important sand-producing areas in the world.The amount of sand and dust days in the“Three Norths”(Dongbei,Xibei,and Huabei)area has increased,and regional sand and dust storms have occurred frequently.There are generally more serious hidden danger points of debris flow geological disasters in small and medium-sized river basins.The technical achievements of flood risk forecasting and early warning for medium and small rivers in the Yellow River Basin based on rainstorm-induced floods are important technical supports for flood forecasting and early warning for medium and small rivers.Based on this,a case study was carried out on the problems such as the weak forecasting and early warning ability of flood disasters induced by heavy rain and the low accuracy of flood disaster loss assessment in the flood disasters of medium and small rivers,for the reference of relevant personnel.

Real-time lane departure warning system based on principal component analysis of grayscale distribution and risk evaluation model

A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.

An Artificial Intelligence Algorithm for the Real-Time Early Detection of Sticking Phenomena in Horizontal Shale Gas Wells

Sticking is the most serious cause of failure in complex drilling operations.In the present work a novel“early warning”method based on an artificial intelligence algorithm is proposed to overcome some of the known pro-blems associated with existing sticking-identification technologies.The method is tested against a practical case study(Southern Sichuan shale gas drilling operations).It is shown that the twelve sets of sticking fault diagnostic results obtained from a simulation are all consistent with the actual downhole state;furthermore,the results from four groups of verification samples are also consistent with the actual downhole state.This shows that the pro-posed training-based model can effectively be applied to practical situations.