Probabilistic analysis of tunnel face seismic stability in layered rock masses using Polynomial Chaos Kriging metamodel

Face stability is an essential issue in tunnel design and construction.Layered rock masses are typical and ubiquitous;uncertainties in rock properties always exist.In view of this,a comprehensive method,which combines the Upper bound Limit analysis of Tunnel face stability,the Polynomial Chaos Kriging,the Monte-Carlo Simulation and Analysis of Covariance method(ULT-PCK-MA),is proposed to investigate the seismic stability of tunnel faces.A two-dimensional analytical model of ULT is developed to evaluate the virtual support force based on the upper bound limit analysis.An efficient probabilistic analysis method PCK-MA based on the adaptive Polynomial Chaos Kriging metamodel is then implemented to investigate the parameter uncertainty effects.Ten input parameters,including geological strength indices,uniaxial compressive strengths and constants for three rock formations,and the horizontal seismic coefficients,are treated as random variables.The effects of these parameter uncertainties on the failure probability and sensitivity indices are discussed.In addition,the effects of weak layer position,the middle layer thickness and quality,the tunnel diameter,the parameters correlation,and the seismic loadings are investigated,respectively.The results show that the layer distributions significantly influence the tunnel face probabilistic stability,particularly when the weak rock is present in the bottom layer.The efficiency of the proposed ULT-PCK-MA is validated,which is expected to facilitate the engineering design and construction.

Rock mass quality prediction on tunnel faces with incomplete multi-source dataset via tree-augmented naive Bayesian network

Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.

Centrifuge modeling of a large-scale surcharge on adjacent foundation

This study investigates the ground and structural response of adjacent raft foundations induced by largescale surcharge by ore in soft soil areas through a 130g centrifuge modeling test with an innovative layered loading device.The prototype of the test is a coastal iron ore yard with a natural foundation of deep soft soil.Therefore,it is necessary to adopt some measures to reduce the influence of the large-scale surcharge on the adjacent raft foundation,such as installing stone columns for foundation treatment.Under an acceleration of 130 g,the model conducts similar simulations of iron ore,stone columns,and raft foundation structures.The tested soil mass has dimensions of 900 mm×700 mm×300 mm(lengthwidthdepth),which is remodeled from the soil extracted from the drilling holes.The test conditions are consistent with the actual engineering conditions and the effects of four-level loading conditions on the composite foundation of stone columns,unreinforced zone,and raft foundations are studied.An automatic layer-by-layer loading device was innovatively developed to simulate the loading process of actual engineering more realistically.The composite foundation of stone columns had a large settlement after the loading,forming an obvious settlement trough and causing the surface of the unreinforced zone to rise.The 12 m surcharge loading causes a horizontal displacement of 13.19 cm and a vertical settlement of 1.37 m in the raft foundation.The stone columns located on both sides of the unreinforced zone suffered significant shear damage at the sand-mud interface.Due to the reinforcement effect of stone columns,the sand layer below the top of the stone columns moves less.Meanwhile,the horizontal earth pressure in the raft foundation zone increases slowly.The stone columns will form new drainage channels and accelerate the dissipation of excess pore pressure.

Metal-Free 2D/2D van der Waals Heterojunction Based on Covalent Organic Frameworks for Highly Efficient Solar Energy Catalysis

Covalent organic frameworks(COFs)have emerged as a kind of rising star materials in photocatalysis.However,their photocatalytic activities are restricted by the high photogenerated electron-hole pairs recombination rate.Herein,a novel metal-free 2D/2D van der Waals heterojunction,composed of a two-dimensional(2D)COF with ketoenamine linkage(TpPa-1-COF)and 2D defective hexagonal boron nitride(h-BN),is successfully constructed through in situ solvothermal method.Benefitting from the presence of VDW heterojunction,larger contact area and intimate electronic coupling can be formed between the interface of TpPa-1-COF and defective h-BN,which make contributions to promoting charge car-riers separation.The introduced defects can also endow the h-BN with porous structure,thus providing more reactive sites.Moreover,the TpPa-1-COF will undergo a structural transformation after being integrated with defective h-BN,which can enlarge the gap between the conduction band position of the h-BN and TpPa-1-COF,and suppress electron backflow,corroborated by experimental and density functional theory calculations results.Accordingly,the resulting porous h-BN/TpPa-1-COF metal-free VDW heterojunction displays out-standing solar energy catalytic activity for water splitting without co-catalysts,and the H_(2) evolution rate can reach up to 3.15 mmol g^(−1) h^(−1),which is about 67 times greater than that of pristine TpPa-1-COF,also surpassing that of state-of-the-art metal-free-based photocatalysts reported to date.In particular,it is the first work for constructing COFs-based heterojunctions with the help of h-BN,which may provide new avenue for designing highly efficient metal-free-based photocatalysts for H_(2) evolution.

A hybrid attention deep learning network for refined segmentation of cracks from shield tunnel lining images

This research developed a hybrid position-channel network (named PCNet) through incorporating newly designed channel and position attention modules into U-Net to alleviate the crack discontinuity problem in channel and spatial dimensions. In PCNet, the U-Net is used as a baseline to extract informative spatial and channel-wise features from shield tunnel lining crack images. A channel and a position attention module are designed and embedded after each convolution layer of U-Net to model the feature interdependencies in channel and spatial dimensions. These attention modules can make the U-Net adaptively integrate local crack features with their global dependencies. Experiments were conducted utilizing the dataset based on the images from Shanghai metro shield tunnels. The results validate the effectiveness of the designed channel and position attention modules, since they can individually increase balanced accuracy (BA) by 11.25% and 12.95%, intersection over union (IoU) by 10.79% and 11.83%, and F1 score by 9.96% and 10.63%, respectively. In comparison with the state-of-the-art models (i.e. LinkNet, PSPNet, U-Net, PANet, and Mask R–CNN) on the testing dataset, the proposed PCNet outperforms others with an improvement of BA, IoU, and F1 score owing to the implementation of the channel and position attention modules. These evaluation metrics indicate that the proposed PCNet presents refined crack segmentation with improved performance and is a practicable approach to segment shield tunnel lining cracks in field practice.

熔融共混制备PLA-g-MAH/EP对PLA/PBAT复合材料性能的影响

以过氧化二异丙苯(DCP)为引发剂,马来酸酐(MAH)、环氧树脂(EP)为共同接枝单体,在转矩流变仪中通过熔融法制备聚乳酸(PLA)双接枝马来酸酐/环氧树脂(PLA-g-MAH/EP),以作为聚乳酸/聚己二酸-对苯二甲酸丁二酯(PBAT)复合材料的增容剂。使用双螺杆挤出机制备了PLA/PBAT复合材料。结果表明,当MAH含量为2%时,PLA-g-MAH接枝率最大为0.48%。EP的加入使得PLA-g-MAH接枝率获得极大地提升,当加入3%EP后,PLA-g-MAH(2%)-EP-3接枝物接枝率最大为1.81%。差示扫描量热分析结果表明添加PLA-g-MAH/EP接枝物作为增容剂后,复合材料的双重熔融峰消失。动态力学热分析曲线表明复合材料加入相容剂后的界面强度增加。力学性能测试发现,接枝物作为PLA/PBAT复合材料相容剂能有效地提升复合材料的拉伸强度和缺口冲击强度。同时扫描电镜图像显示,添加相容剂后PBAT在PLA基体相获得了有效分散,PLA与PBAT两相之间的相容性得到提高。

蓖麻油基预聚物原位反应增韧聚乳酸

以4,4′-二苯基甲烷二异氰酸酯(MDI)为原料,在转矩流变仪中进行原位共混,制备出具有良好拉伸韧性的聚乳酸(PLA)/蓖麻油基聚氨酯预聚物(COPUP)共混物。对PLA/COPUP共混物的熔体扭矩、微观形貌、结晶性、热稳定性、流变行为和力学性能进行了系统分析,探讨了COPUP含量对共混物性能的影响。研究结果表明,随着COPUP含量增加,熔体扭矩值增大,MDI在PLA基体中与蓖麻油之间原位反应形成了化学交联结构。扫描电镜分析得知,共混物PLA/COPUP的微观相形态呈相分离结构,COPUP颗粒作为分散相均匀分散在PLA基体中。随着COPUP含量的增加,共混物的结晶能力和热稳定性降低,交联程度增大,断裂伸长率呈增大的趋势。当COPUP质量分数为30%时,拉伸强度为42.2 MPa,断裂伸长率达到最大值(断裂伸长率为180%),约为纯PLA(断裂伸长率为5%)的34倍。

聚乳酸/聚丁二酸丁二醇酯/二苯基甲烷二异氰酸酯共混体系的结构与性能

聚乳酸(PLA)的韧性差、稳定性不好使产业化生产受到局限,文中通过引入聚丁二酸丁二醇酯(PBS)来改善材料的性能,但是其相容性比较差,所以寻找了一种增容剂二苯基甲烷二异氰酸酯(MDI)来改善共混体系相容性。实验结果显示,聚乳酸性能得到了极大改善。通过红外测试发现共混体系生成了聚氨酯;由扫描电镜可以看出,共混体系分散性得到了改善;从扭矩图可以看出,共混体系流动性得到了改善,共混体系塑化时间比纯PLA/PBS和纯PLA相比分别缩短33s和53s;通过力学性能测试,得知其断裂伸长率得到了极大提升,共混体系断裂伸长率比纯PLA/PBS和纯PLA分别提高了1.57倍和66倍。

经济增长压力与环境保护的经济代价--基于我国“两控区”的实证分析

围绕经济增长的晋升锦标赛给地方政府领导带来了巨大的经济增长压力,这被认为是环境污染的重要原因。然而,本文认为,如果将完成环境目标作为增长竞争的前提条件,那么地方竞争不仅能够有效地达成环境目标,而且能够减少环境目标的经济代价。本文利用我国的环境目标一票否决制下的“两控区”政策实践,用双重差分DID方法对此进行了实证分析。结果表明:尽管环境保护存在明显的经济增长代价,但地方政府的经济增长竞争的确有利于促使地方政府鼓励企业进行清洁的技术创新,减少环境保护对经济增长的不利影响。该研究的政策含义是,既要保护环境又要快速增长,必须有外在的压力,环境目标约束下的增长竞争是一种有效的激励制度。

Deep learning based classification of rock structure of tunnel face

The automated interpretation of rock structure can improve the efficiency,accuracy,and consistency of the geological risk assessment of tunnel face.Because of the high uncertainties in the geological images as a result of different regional rock types,as well as in-situ conditions(e.g.,temperature,humidity,and construction procedure),previous automated methods have limited performance in classification of rock structure of tunnel face during construction.This paper presents a framework for classifying multiple rock structures based on the geological images of tunnel face using convolutional neural networks(CNN),namely Inception-ResNet-V2(IRV2).A prototype recognition system is implemented to classify 5 types of rock structures including mosaic,granular,layered,block,and fragmentation structures.The proposed IRV2 network is trained by over 35,000 out of 42,400 images extracted from over 150 sections of tunnel faces and tested by the remaining 7400 images.Furthermore,different hyperparameters of the CNN model are introduced to optimize the most efficient algorithm parameter.Among all the discussed models,i.e.,ResNet-50,ResNet-101,and Inception-v4,Inception-ResNet-V2 exhibits the best performance in terms of various indicators,such as precision,recall,F-score,and testing time per image.Meanwhile,the model trained by a large database can obtain the object features more comprehensively,leading to higher accuracy.Compared with the original image classification method,the sub-image method is closer to the reality considering both the accuracy and the perspective of error divergence.The experimental results reveal that the proposed method is optimal and efficient for automated classification of rock structure using the geological images of the tunnel face.

Machine learning-based classification of rock discontinuity trace:SMOTE oversampling integrated with GBT ensemble learning

This paper presents a hybrid ensemble classifier combined synthetic minority oversampling technique(SMOTE),random search(RS)hyper-parameters optimization algorithm and gradient boosting tree(GBT)to achieve efficient and accurate rock trace identification.A thirteen-dimensional database consisting of basic,vector,and discontinuity features is established from image samples.All data points are classified as either‘‘trace”or‘‘non-trace”to divide the ultimate results into candidate trace samples.It is found that the SMOTE technology can effectively improve classification performance by recommending an optimized imbalance ratio of 1:5 to 1:4.Then,sixteen classifiers generated from four basic machine learning(ML)models are applied for performance comparison.The results reveal that the proposed RS-SMOTE-GBT classifier outperforms the other fifteen hybrid ML algorithms for both trace and nontrace classifications.Finally,discussions on feature importance,generalization ability and classification error are conducted for the proposed classifier.The experimental results indicate that more critical features affecting the trace classification are primarily from the discontinuity features.Besides,cleaning up the sedimentary pumice and reducing the area of fractured rock contribute to improving the overall classification performance.The proposed method provides a new alternative approach for the identification of 3D rock trace.

Recent advances on Bi_(2)WO_(6)‐based photocatalysts for environmental and energy applications

Bismuth tungstate(Bi_(2)WO_(6))has become a research hotspot due to its potential in photocatalytic energy conversion and environmental purification.Nevertheless,the limited light absorption and fast recombination of photogenerated carriers hinder the further improvement of the photocatalytic performance of Bi_(2)WO_(6).Herein,we provide a systematic review for the recent advances on Bi_(2)WO_(6)‐based photocatalysts.It starts with the crystal structure,optical properties and photocatalytic fundamentals of Bi_(2)WO_(6).Then,we focus on the modification strategies of Bi_(2)WO_(6)based on morphology control,atomic modulation and composite fabrication for diverse photocatalytic applications,such as organic synthesis,water splitting,CO2 reduction,water treatment,air purification,bacterial inactivation,etc.Finally,some current challenges and future development prospects are proposed.We expect that this review can provide a useful reference and guidance for the development of efficient Bi_(2)WO_(6)photocatalysts.

Role of transition metal oxides in g-C_(3)N_(4)-based heterojunctions for photocatalysis and supercapacitors

g-C_(3)N_(4) emerges as a star 2D photocatalyst due to its unique layered structure,suitable band structure and low cost.However,its photocatalytic application is limited by the fast charge recombination and low photoabsorption.Rationally designing g-C_(3)N_(4)-based heterojunction is promising for improving photocatalytic activity.Besides,g-C_(3)N_(4) exhibits great potentials in electrochemical energy storage.In view of the excellent performance of typical transition metal oxides(TMOs)in photocatalysis and energy storage,this review summarized the advances of TMOs/g-C_(3)N_(4) heterojunctions in the above two areas.Firstly,we introduce several typical TMOs based on their crystal structures and band structures.Then,we summarize different kinds of TMOs/g-C_(3)N_(4) heterojunctions,including type Ⅰ/Ⅱ heterojunction,Z-scheme,p-n junction and Schottky junction,with diverse photocatalytic applications(pollutant degradation,water splitting,CO_(2) reduction and N_(2) fixation)and supercapacitive energy storage.Finally,some promising strategies for improving the performance of TMOs/g-C_(3)N_(4) were proposed.Particularly,the exploration of photocatalysis-assisted supercapacitors was discussed.

Machine learning-based automatic control of tunneling posture of shield machine

For a tunnel driven by a shield machine,the posture of the driving machine is essential to the construction quality and environmental impact.However,the machine posture is controlled by the experienced driver of shield machine by setting hundreds of tunneling parameters empirically.Machine learning(ML)algorithm is an alternative method that can let the computer to learn from the driver’s operation and try to model the relationship between parameters automatically.Thus,in this paper,three ML algorithms,i.e.multi-layer perception(MLP),support vector machine(SVM)and gradient boosting regression(GBR),are improved by genetic algorithm(GA)and principal component analysis(PCA)to predict the tunneling posture of the shield machine.A set of the parameters for shield tunneling is extracted from the construction site of a Shanghai metro.In total,53,785 pairwise data points are collected for about 373 d and the ratio between training set,validation set and test set is 3:1:1.Each pairwise data point includes 83 types of parameters covering the shield posture,construction parameters,and soil stratum properties at the same time.The test results show that the averaged R2 of MLP,SVM and GBR based models are 0.942,0.935 and 0.6,respectively.Then the automatic control for the posture of shield tunnel is illustrated with an application example of the proposed models.The proposed method is proved to be helpful in controlling the construction quality with optimized construction parameters.

Three-dimensional distinct element modeling of fault reactivation and induced seismicity due to hydraulic fracturing injection and backflow

This paper presents a three-dimensional fully hydro-mechanical coupled distinct element study on fault reactivation and induced seismicity due to hydraulic fracturing injection and subsequent backflow process,based on the geological data in Horn River Basin,Northeast British Columbia,Canada.The modeling results indicate that the maximum magnitude of seismic events appears at the fracturing stage.The increment of fluid volume in the fault determines the cumulative moment and maximum fault slippage,both of which are essentially proportional to the fluid volume.After backflow starts,the fluid near the joint intersection keeps flowing into the critically stressed fault,rather than backflows to the wellbore.Although fault slippage is affected by the changes of both pore pressure and ambient rock stress,their contributions are different at fracturing and backflow stages.At fracturing stage,pore pressure change shows a dominant effect on induced fault slippage.While at backflow stage,because the fault plane is under a critical stress state,any minor disturbance would trigger a fault slippage.The energy analysis indicates that aseismic deformation takes up a majority of the total deformation energy during hydraulic fracturing.A common regularity is found in both fracturing-and backflow-induced seismicity that the cumulative moment and maximum fault slippage are nearly proportional to the injected fluid volume.This study shows some novel insights into interpreting fracturing-and backflowinduced seismicity,and provides useful information for controlling and mitigating seismic hazards due to hydraulic fracturing.

Engineering piezoelectricity and strain sensitivity in CdS to promote piezocatalytic hydrogen evolution

Piezocatalytic hydrogen evolution has emerged as a promising direction for the collection and utilization of mechanical energy and the efficient generation of sustainable energy throughout the day.Hexagonal CdS,as an established semiconductor photocatalyst,has been widely investigated for splitting water into H_(2),while its piezocatalytic performance has attracted less attention,and the relationship between the structure and piezocatalytic activity remains unclear.Herein,two types of CdS nanostructures,namely CdS nanorods and CdS nanospheres,were prepared to probe the above‐mentioned issues.Under ultrasonic vibration,the CdS nanorods afforded a superior piezocatalytic H_(2) evolution rate of 157μmol g^(−1)h^(−1)in the absence of any co‐catalyst,which is nearly 2.8 times that of the CdS nanospheres.The higher piezocatalytic activity of the CdS nanorods is derived from their larger piezoelectric coefficient and stronger mechanical energy harvesting capability,affording a greater piezoelectric potential and more efficient separation and transfer of intrinsic charge carriers,as elucidated through piezoelectric response force microscopy,finite element method,and piezoelectrochemical tests.This study provides a new concept for the design of efficient piezocatalytic materials for converting mechanical energy into sustainable energy via microstructure regulation.

Deep learning-based evaluation of factor of safety with confidence interval for tunnel deformation in spatially variable soil

The random finite difference method(RFDM) is a popular approach to quantitatively evaluate the influence of inherent spatial variability of soil on the deformation of embedded tunnels.However,the high computational cost is an ongoing challenge for its application in complex scenarios.To address this limitation,a deep learning-based method for efficient prediction of tunnel deformation in spatially variable soil is proposed.The proposed method uses one-dimensional convolutional neural network(CNN) to identify the pattern between random field input and factor of safety of tunnel deformation output.The mean squared error and correlation coefficient of the CNN model applied to the newly untrained dataset was less than 0.02 and larger than 0.96,respectively.It means that the trained CNN model can replace RFDM analysis for Monte Carlo simulations with a small but sufficient number of random field samples(about 40 samples for each case in this study).It is well known that the machine learning or deep learning model has a common limitation that the confidence of predicted result is unknown and only a deterministic outcome is given.This calls for an approach to gauge the model’s confidence interval.It is achieved by applying dropout to all layers of the original model to retrain the model and using the dropout technique when performing inference.The excellent agreement between the CNN model prediction and the RFDM calculated results demonstrated that the proposed deep learning-based method has potential for tunnel performance analysis in spatially variable soils.

Pivotal roles of artificial oxygen vacancies in enhancing photocatalytic activity and selectivity on Bi_2O_2CO_3 nanosheets

There is an increasing interest in bismuth carbonate(Bi2O2CO3,BOC)as a semiconductor photocatalyst.However,pure BOC strongly absorbs ultraviolet light,which drives a high recombination rate of charge carriers and thereby limits the overall photocatalysis efficiency.In this work,artificial oxygen vacancies(OV)were introduced into BOC(OV-BOC)to broaden the optical absorption range,increase the charge separation efficiency,and activate the reactants.The photocatalytic removal ratio of NO was increased significantly from 10.0%for pure BOC to 50.2%for OV-BOC because of the multiple roles played by the oxygen vacancies.These results imply that oxygen vacancies can facilitate the electron exchange between intermediates and the surface oxygen vacancies in OV-BOC,making them more easily destroyed by active radicals.In situ DRIFTS spectra in combination with electron spin resonance spectra and density functional theory calculations enabled unraveling of the conversion pathway for the photocatalytic NO oxidation on OV-BOC.It was found that oxygen vacancies could increase the production of active radicals and promote the transformation of NO into target products instead of toxic byproducts(NO2),thus the selectivity is significantly enhanced.This work provides a new strategy for enhancing photocatalytic activity and selectivity.

Face stability analysis of circular tunnels in layered rock masses using the upper bound theorem

An analysis of tunnel face stability generally assumes a single homogeneous rock mass.However,most rock tunnel projects are excavated in stratified rock masses.This paper presents a two-dimensional(2D)analytical model for estimating the face stability of a rock tunnel in the presence of rock mass stratification.The model uses the kinematical limit analysis approach combined with the block calculation technique.A virtual support force is applied to the tunnel face,and then solved using an optimization method based on the upper limit theorem of limit analysis and the nonlinear Hoek-Brown yield criterion.Several design charts are provided to analyze the effects of rock layer thickness on tunnel face stability,tunnel diameter,the arrangement sequence of weak and strong rock layers,and the variation in rock layer parameters at different positions.The results indicate that the thickness of the rock layer,tunnel diameter,and arrangement sequence of weak and strong rock layers significantly affect the tunnel face stability.Variations in the parameters of the lower layer of the tunnel face have a greater effect on tunnel stability than those of the upper layer.

A novel image-based approach for interactive characterization of rock fracture spacing in a tunnel face

This paper presents a novel integrated method for interactive characterization of fracture spacing in rock tunnel sections.The main procedure includes four steps:(1)Automatic extraction of fracture traces,(2)digitization of trace maps,(3)disconnection and grouping of traces,and(4)interactive measurement of fracture set spacing,total spacing,and surface rock quality designation(S-RQD)value.To evaluate the performance of the proposed method,sample images were obtained by employing a photogrammetrybased scheme in tunnel faces.Experiments were then conducted to determine the optimal parameter values(i.e.distance threshold,angle threshold,and number of fracture trace grouping)for characterizing rock fracture spacing.By applying the identified optimal parameters involved in the model,the proposed method could lead to excellent qualitative results to a new tunnel face.To perform a quantitative analysis,three methods(i.e.field,straightening,and the proposed method)were employed in the same study and comparisons were made.The proposed method agrees well with the field measurement in terms of the maximum and average values of measured spacing distribution.Overall,the proposed method has reasonably good accuracy and interactive advantage for estimating the ultimate fracture spacing and S-RQD.It can be a possible extension of existing methods for fracture spacing characterization for two-dimensional(2D)rock tunnel faces.