The Cambrian–Ordovician boundary unconformity in the southern part of the South China Craton is spatially and temporally related to coeval orogenic activity along the Indian margin of east Gondwana. ...Detrital zircon age spectra from strata above and below the unconformity range in age from 3580–450Ma, with peaks in the late Mesoproterozoic and Neoproterozoic. The patterns are similar to time equivalent sequences elsewhere in South China and together with regional facies relationships and paleocurrent data indicate derivation from a Gondwana source. The disconformity at the base of the Ordovician succession forms part of a regional break that has also been documented in the Himalaya, Qiangtang, Lhasa, Sibumasu, and Western Australia. All these successions have similar detrital zircon age spectra suggesting derivation from common source(s). In South China the effects of this tectonic event are relatively mild and are represented by a local disconformity at the base of the Ordovician succession, but elsewhere in north Gondwana this event is marked by an angular unconformity with metamorphism of older units and relatively widespread magmatic activity. South China was likely located in a distal location to the northeast of the pulse of tectonic activity, which was focused in the Himalaya region, and was close to the continent–ocean boundary between northern Gondwana and the proto-Tethys.
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•South China was located along the northernmost margin of East Gondwana.•Early Paleozoic orogenic activity in north Gondwana affected South China.•Detritus from the Himalayan region accumulated in South China.•South China represents the distal portion of the northward facing synorogenic basin.
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.
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•A database including 42400 images of tunnel face over 150 sections are collected from a highway tunnel project in southwest of China.•A framework for classifying multiple rock structures from the images of tunnel face is proposed by using the convolutional neural networks (CNN).•The framework is able to interpret 5 types of rock structures with high efficiency and accuracy.
Development of efficient photocatalysts with both photoinduced oxidation and reduction properties is of great importance for environmental and energy applications. Herein, we report the fabrication ...of CeO
2
/g-C
3
N
4
hybrid materials by a simple
in situ
co-pyrolysis method using Ce(IO
3
)
3
and melamine as precursors. The CeO
2
/g-C
3
N
4
composite catalysts possess outstanding photocatalytic activity for phenol degradation and NO removal under visible light irradiation. The degradation efficiency reaches up to 68.5 and 17.3 times higher than that of pure CeO
2
and g-C
3
N
4
, respectively. Significantly, it simultaneously exhibits an enhanced hydrogen production rate, which is 1.5 times that of the pure g-C
3
N
4
. The highly enhanced photo-induced oxidation and reduction activity could be attributed to the construction of a CeO
2
/g-C
3
N
4
n-n type heterojunction established by our
in situ
co-pyrolysis route, which enables intimate interaction across the phase interfaces; this facilitates separation and transfer of photoexcited charge carriers. This study could not only provide a facile and general approach to the fabrication of high-performance carbon-nitride-based photocatalytic materials, but also increase our understanding further on designing new hybrid composite photocatalysts for multi-functional applications.
CeO
2
/g-C
3
N
4
n-n type heterojunction was successfully constructed
via
a facile
in situ
co-pyrolysis route by employing Ce(IO
3
)
3
and melamine as precursors. It exhibits high photo-induced oxidation and reduction properties for degradation of phenol, NO removal and hydrogen evolution.
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.
•A novel diatomite-immobilized BiOI hybrid photocatalyst has been prepared by a facile one-step deposition process for the first time.•The diatomite-immobilized BiOI hybrid photocatalyst exhibits ...much better photocatalytic performance.•This enhancement should be attributed to that diatomite can play as an excellent carrier platform to increase the reactive sites and promote the separation of photogenerated electron–hole pairs.•This work shed new light on facile fabrication of novel composite photocatalyst based on natural mineral.
A novel diatomite-immobilized BiOI hybrid photocatalyst has been prepared by a facile one-step deposition process for the first time. The structure, morphology and optical property of the products were characterized by X-ray powder diffraction (XRD), scanning electron microscopy (SEM) and UV–vis diffuse reflectance spectroscopy (DRS). The photocatalytic performance of the as-prepared BiOI/diatomite photocatalysts was studied by photodegradation of Rhodamine B (RhB) and methylene blue (MB) and monitoring photocurrent generation under visible light (λ>420nm). The results revealed that BiOI/diatomite composites exhibit enhanced photocatalytic activity compared to the pristine BiOI sample. This enhancement should be attributed to that diatomite can play as an excellent carrier platform to increase the reactive sites and promote the separation of photogenerated electron–hole pairs. In addition, the corresponding photocatalytic mechanism was proposed based on the active species trapping experiments. This work shed new light on facile fabrication of novel composite photocatalyst based on natural mineral.
Quantifying water inflow information from rock tunnel faces is critical for field engineers to assess the rock mass rating and subsequently make appropriate construction management decisions. This ...paper proposes a novel convolutional neural network (CNN)-based water inflow evaluation method that emulates a typical field engineer's inspection process. It is integrated by a classification step and a semantic segmentation step: the first one is to classify the non-damaged regions and the damaged regions; and the second one is to segment the detailed water inflow damage to the rock tunnel faces. An image database of water inflow in rock tunnel faces was applied for comprehensive training, validation and testing. The experiments on the testing data demonstrate an ideal performance in terms of convergence speed and classification accuracy, as well as quantitative water inflow segmentation. The proposed automatic quantification approach significantly reduces the ergodic damage segmentation procedure through the early exclusion of undamaged samples during the classification process.
•A CNN-based approach emulating the human inspection process was developed.•An image database of water inflow in rock tunnel faces was applied.•Automatic quantification of water inflow information is achieved.•The ergodic segmentation procedure is significantly reduced.
The efficient charge transfer at the interface of g-C3N4/Bi2O2CO3 hetrojunction composites leads to an effective photoexcited electron–hole separation and promotes the photocatalytic activity.
...•G-C3N4/Bi2O2CO3 composites were synthesized by a simple mixed calcinations method.•The 1:2 g-C3N4/Bi2O2CO3 sample shows the most improved photocatalytic performance.•Highly improved photocatalytic activity is attributed to heterojunction mechanism.•Active species h+ and •O2− are detected by species trapping experiment.
Novel composite photocatalyst g-C3N4/Bi2O2CO3 has been synthesized by a simple mixed-calcinations method. The photocatalysts were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and diffuse reflection spectroscopy (DRS). The g-C3N4/Bi2O2CO3 composites showed high efficiency for the degradation of Rhodamine B (RhB) under visible light. The optimum photocatalytic activity of g-C3N4/Bi2O2CO3 with molar ratio of 1:2 under visible light irradiation was almost 3.1 and 14.2 times as high as those of the pure g-C3N4 and Bi2O2CO3, respectively. The enhancement of visible light photocatalytic activity in g-C3N4/Bi2O2CO3 should be assigned to the effective separation and transfer of photogenerated charges originating from the well-matched overlapping band-structures. The supposed photocatalytic mechanism was verified by the result of photoluminescence spectroscopy (PL) and active species trapping experiments.
The longitudinally inclined layered rock masses under seepage flow conditions have an important impact on tunnel face stability. To solve this issue, a novel equivalent analytical model is proposed ...in this paper. This model settles the puzzle that the traditional model of limit analysis cannot directly determine the hydraulic head by treating the distribution hydraulic head at the anterior aspect of the tunnel face with the equivalent method, that is, without the tedious calculation by the numerical software. The seepage force is directly considered as a body force in this analytical model. Then, combined with the mirror-image method, layered discretization technique, limit analysis method, and Hoek-Brown yield criterion, an analytical expression for stability determination can be derived. The main factors affecting the stability of tunnel face are analyzed, such as the inclination angle of the flow line, the inclination angle of the rock layer, hydraulic conductivity, rock quality, excavation disturbed zone (EDZ), and the groundwater table height. The calculated diagrams have a certain guiding effects for tunnel construction.
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•A probabilistic framework for tunnel longitudinal performance analysis is proposed.•A conditional random field theory-based site characterization method is outlined.•A tunnel ...longitudinal performance analysis model is presented.•The significance of the proposed framework is demonstrated through an illustrative example.
Because of the inherent spatial variability of soil properties and the limited number of boreholes that can be afforded in a typical project, the soil properties at given geotechnical sites could not be known with certainty, which leads to an uncertainty in the predicted performance of a geotechnical system. For such uncertain system, probabilistic analysis is often used to assess its performance considering uncertainty. This paper presents a new framework for the probabilistic analysis of tunnel longitudinal performance. Within this framework, the conditional random field theory is adopted to simulate the spatial variation of soil properties along the tunnel longitudinal direction, in which the soil properties at borehole locations can be explicitly considered. Then, the tunnel longitudinal performance is analyzed with an advanced tunnel performance model, in which the influence of tunnel longitudinal behavior on the circumferential behavior of the tunnel cross section can be explicitly considered. With the aid of Monte Carlo simulation (MCS), tunnel longitudinal performance can readily be analyzed in a probabilistic manner; and, the variation of the tunnel performances (i.e., the structural safety and serviceability of the cross section) along the tunnel longitudinal direction could be assessed. The novelty and significance of this proposed framework, compared to the existing methods, are demonstrated through an illustrative example. Further, the influence of the borehole density (i.e., the number of boreholes per tunnel length) on the prediction of the tunnel longitudinal performance is analyzed through a parametric study.
Photocatalytic CO2 reduction into solar fuels illustrates huge charm for simultaneously settling energy and environmental issues. The photoreduction ability of a semiconductor is closely correlated ...to its conduction band (CB) position. A homogeneous-phase solid-solution with the same crystal system always has a monotonously changed CB position, and the high CB level has to be sacrificed to achieve a benign photoabsorption. Herein, we report the fabrication of heterogeneous-phase solid-solution ZnXCa1-XIn2S4 between trigonal ZnIn2S4 and cubic CaIn2S4. The ZnXCa1-XIn2S4 solid solutions with orderly tuned photoresponsive range from 540 to 640 nm present a more negative CB level and highly enhanced charge-separation efficiency. Profiting from these merits, all of these ZnXCa1-XIn2S4 solid solutions exhibit remarkably strengthened photocatalytic CO2 reduction performance under visible light (λ > 420 nm) irradiation. Zn0.4Ca0.6In2S4, bearing the most negative CB position and highest charge-separation efficiency, casts the optimal photocatalytic CH4 and CO evolution rates, which reach 16.7 and 6.8 times higher than that of ZnIn2S4 and 7.2 and 3.9 times higher than that of CaIn2S4, respectively. To verify the crucial role of the heterogeneous-phase solid solution in promoting the band structure and photocatalytic performance, another heterogeneous-phase solid-solution ZnXCd1-XIn2S4 has been synthesized. It also displays an upshifted CB level and promoted charge separation. This work may provide a new perspective into the development of an efficient visible-light driven photocatalyst for CO2 reduction and other photoreduction reactions.
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