Research on a Comprehensive Monitoring System for Tunnel Operation based on the Internet of Things and Artificial Intelligence Identification Technology

This article proposes a comprehensive monitoring system for tunnel operation to address the risks associated with tunnel operations.These risks include safety control risks,increased traffic flow,extreme weather events,and movement of tectonic plates.The proposed system is based on the Internet of Things and artificial intelligence identification technology.The monitoring system will cover various aspects of tunnel operations,such as the slope of the entrance,the structural safety of the cave body,toxic and harmful gases that may appear during operation,excessively high and low-temperature humidity,poor illumination,water leakage or road water accumulation caused by extreme weather,combustion and smoke caused by fires,and more.The system will enable comprehensive monitoring and early warning of fire protection systems,accident vehicles,and overheating vehicles.This will effectively improve safety during tunnel operation.

Information Monitoring Technology for Support Structure of Railway Tunnel During Operation

In the process of railway construction, because of the inconvenience ofgeological condition, water bursting and mud surging happen frequently, and the laterdeformation of support structure on the happening geology section would threaten thenormal running of railway. The limit difference of deformation control value of thesupport structure section where geological accidents frequently happen, is small, andartificial half-automatic supervisory technology cannot get the health condition of tunnelin time, resulting many cars speed-down accidents due to deformation of supportstructure. Through design innovation, we introduce TGMIS in the later period ofYanzishan railway construction to quickly capture the deformation of support structure,the strain of lining concrete, the strain of steel frame, stress of surrounding soil, stress ofsurrounding water, strain of second lining steel bar and other situ data. Also we setobservation prism and measuring robot device in specific position inside tunnel, androbot laser locator laser spot is projected onto reflection target surface, by graphicprocessing algorithm, the receiver calculates the measured value and standard value ofthe 3D coordinates of the laser spot. Then the information is transmitted throughtransmitting device, transducer and USB-485 to computer to predict and evaluate thehealth condition of the support structure of the tunnel so as to provide safety warninginformation. Provide timely and reliable data for the operation company to avoid theoccurrence of vicious accidents.

Causal Analysis on the Lining Exfoliation and Treatment Design of an Operational Tunnel

According to the actual situation of the secondary lining of a expressway tunnel in Chongqing,this paper analyzed the specific reasons for lining exfoliation with corresponding test reports.According to this,a quick treatment scheme for lining exfoliation is proposed,which can make the treatment timely and effective,and suggestions for treating similar diseases in tunnels are put forward,which can provide reference for similar projects.

Experimental investigation on the invert stability of operating railway tunnels with different drainage systems using 3D printing technology

In recent years, the invert anomalies of operating railway tunnels in water-rich areas occur frequently,which greatly affect the transportation capacity of the railway lines. Tunnel drainage system is a crucial factor to ensure the invert stability by regulating the external water pressure(EWP). By means of a threedimensional(3D) printing model, this paper experimentally investigates the deformation behavior of the invert for the tunnels with the traditional drainage system(TDS) widely used in China and its optimized drainage system(ODS) with bottom drainage function. Six test groups with a total of 110 test conditions were designed to consider the design factors and environmental factors in engineering practice,including layout of the drainage system, blockage of the drainage system and groundwater level fluctuation. It was found that there are significant differences in the water discharge, EWP and invert stability for the tunnels with the two drainage systems. Even with a dense arrangement of the external blind tubes, TDS was still difficult to eliminate the excessive EWP below the invert, which is the main cause for the invert instability. Blockage of drainage system further increased the invert uplift and aggravated the track irregularity, especially when the blockage degree is more than 50%. However, ODS can prevent these invert anomalies by reasonably controlling the EWP at tunnel bottom. Even when the groundwater level reached 60 m and the blind tubes were fully blocked, the invert stability can still be maintained and the railway track experienced a settlement of only 1.8 mm. Meanwhile, the on-site monitoring under several rainstorms further showed that the average EWP of the invert was controlled within 84 k Pa, while the maximum settlement of the track slab was only 0.92 mm, which also was in good agreement with the results of model test.

Real-time prediction of rock mass classification based on TBM operation big data and stacking technique of ensemble learning

Real-time prediction of the rock mass class in front of the tunnel face is essential for the adaptive adjustment of tunnel boring machines(TBMs).During the TBM tunnelling process,a large number of operation data are generated,reflecting the interaction between the TBM system and surrounding rock,and these data can be used to evaluate the rock mass quality.This study proposed a stacking ensemble classifier for the real-time prediction of the rock mass classification using TBM operation data.Based on the Songhua River water conveyance project,a total of 7538 TBM tunnelling cycles and the corresponding rock mass classes are obtained after data preprocessing.Then,through the tree-based feature selection method,10 key TBM operation parameters are selected,and the mean values of the 10 selected features in the stable phase after removing outliers are calculated as the inputs of classifiers.The preprocessed data are randomly divided into the training set(90%)and test set(10%)using simple random sampling.Besides stacking ensemble classifier,seven individual classifiers are established as the comparison.These classifiers include support vector machine(SVM),k-nearest neighbors(KNN),random forest(RF),gradient boosting decision tree(GBDT),decision tree(DT),logistic regression(LR)and multilayer perceptron(MLP),where the hyper-parameters of each classifier are optimised using the grid search method.The prediction results show that the stacking ensemble classifier has a better performance than individual classifiers,and it shows a more powerful learning and generalisation ability for small and imbalanced samples.Additionally,a relative balance training set is obtained by the synthetic minority oversampling technique(SMOTE),and the influence of sample imbalance on the prediction performance is discussed.

Modal analysis of subway tunnel in soft soil during operation

Modal parameters are of great significance in civil engineering because they can characterize the properties of structures and be used for vibration control and structural health monitoring.Subway tunnels are long linear truss structures combined with the mutual cou-pling of the surrounding soil.Therefore,the operational modal analysis of a mutual coupling tunnel is complicate,as is the modal iden-tification of shield tunnels in a time–frequency domain,and these are hot civil engineering topics.Using the shield tunnel of Shanghai metro line No.12 project as a case study,we carried out the vibration response monitoring of a subway tunnel during operation and presented methods to identify structural modal parameters.The modal parameters of lower vibration modes were estimated using response measurements.Modal frequencies and shapes were identified with high precision and accuracy using the orthogonal polynomial clustering algorithm under hammer excitation conditions and the autoregressive-moving-average model under ambient excitation con-ditions.The dynamic behavior of a mutual coupling tunnel presented obvious low frequency characteristics,and the first 9th order mode frequencies were less than 100 Hz.The diagonal values of the modal assurance criteria were all greater than 0.85.The modal parameters can be used for the health monitoring of operational subway tunnels.