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Deep Underground Science and Engineering 2024年第2期3卷 231-246页

作者： Xin Han Fei Ye Xingbo Han Chao Ren Jing Song Ruliang Zhao School of Highway Chang'an UniversityXi'anChina shandong province transportation planning and design institute JinanChina China Water Resources Beifang Investigation Design and Research Co.Ltd.TianjinChina

To understand the mechanical response pattern of the existing structure and ground due to the construction of metro tunnels underneath,the finite difference method is adopted to study the torsional deformation and stress variation of the existing structure and the effect of underground carriageway structures on the surface *** curves of the maximum differential subsidence,torsion angle,and distortion of the cross-section of the existing structure show two peaks in succession during traversing of two metro tunnels beneath *** torsion angle of the existing structure changes when the two tunnels traverse beneath it in opposite *** first traversing of the shield tunnel mainly induces the magnitude variation in torsional deformation of the existing structure,but the second traversing of the subsurface tunnel may cause a dynamic change in the magnitude and form of torsional deformation in the existing *** shielding effect can reduce the surface subsidence caused by metro tunnel excavation to a certain extent,and the development trend of subsidence becomes slower as the excavation continues.

关键词： metro tunnel numerical simulation structural deformation surface subsidence underground carriageway structure

Short-term prediction of on-street parking occupancy using multivariate variable based on deep learning

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Journal of Traffic and transportation Engineering(English Edition) 2024年第1期11卷 28-40页

作者： Mengqi Lyu Yanjie Ji Chenchen Kuai Shuichao Zhang School of transportation Southeast UniversityNanjing 211189China shandong Provincial Communications planning and design institute Group Co. Ltd.Jinan 250101China School of Civil and transportation Engineering Ningbo University of TechnologyNingbo 315211China

Short-term prediction of on-street parking occupancy is essential to the ITS system,which can guide drivers in finding vacant parking *** the spatial dependencies and exogenous dependencies need to be considered simultaneously,which makes short-term prediction of on-street parking occupancy ***,this paper proposes a deep learning model for predicting block-level parking ***,the importance of multiple points of interest(POI)in different buffers is sorted by Boruta,used for feature *** results show that different types of POI data should consider different buffer *** based on the real on-street parking data,long short-term memory(LSTM)that can address the time dependencies is applied to predict the parking *** results demonstrate that LSTM considering POI data after Boruta selection(LSTM(+BORUTA))outperforms other baseline methods,including LSTM,with an average testing MAPE of 11.78%.The selection process of POI data helps LSTM reduce training time and slightly improve the prediction performance,which indicates that complex correlations among the same type of POI data in different buffer zones will also affect the prediction accuracy of *** there are more restaurants on both sides of the street,the prediction performance of LSTM(+BORUTA)is significantly better than that of LSTM.

关键词： Smart parking Parking occupancy Short-term prediction Long short-term memory Boruta

Persistence of fertilization effects on soil organic carbon in degraded alpine wetlands in the Yellow River source region

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Journal of Mountain Science 2024年第4期21卷 1358-1371页

作者： DUAN Peng WEI Rongyi WANG Fangping LI Yongxiao SONG Ci HU Bixia YANG Ping ZHOU Huakun YAO Buqing ZHAO Zhizhong Qinghai University Xining 810016China Qinghai transportation planning and design institute Co.Ltd Xining 810000China Key Laboratory of Adaptation and Evolution of Plateau Biota Chinese Academy of SciencesXining 810008China Key Laboratory of Restoration Ecology of Cold Area in Qinghai province Northwest Institute of Plateau BiologyChinese Academy of SciencesXining 810008China Qinghai University of Science and Technology Xining 810016China

In the restoration of degraded wetlands,fertilization can improve the vegetation-soil-microorganisms complex,thereby affecting the organic carbon ***,it is currently unclear whether these effects are *** study employed Biolog-Eco surveys to investigate the changes in vegetation characteristics,soil physicochemical properties,and soil microbial functional diversity in degraded alpine wetlands of the source region of the Yellow River at 3 and 15 months after the application of nitrogen,phosphorus,and organic mixed *** following results were obtained:The addition of nitrogen fertilizer and organic compost significantly affects the soil organic carbon content in degraded *** months after fertilization,nitrogen addition increases soil organic carbon in both lightly and severely degraded wetlands,whereas after 15 months,organic compost enhanced the soil organic carbon level in severely degraded *** equation modeling indicates that fertilization decreases the soil pH and directly or indirectly influences the soil organic carbon levels through variations in the soil water content and the aboveground biomass of *** months after fertilization,nitrogen fertilizer showed a direct positive effect on soil organic ***,organic mixed fertilizer indirectly reduced soil organic carbon by increasing biomass and decreasing soil *** 15 months,none of the fertilizers significantly affected the soil organic carbon *** summary,it can be inferred that the addition of nitrogen fertilizer lacks sustainability in positively influencing the organic carbon content.

关键词： Degraded alpine wetlands Fertilizer Soil organic carbon Temporal variation Vegetation aboveground biomass Yellow River source region

Machine learning of element geochemical anomalies for adverse geology identification in tunnels

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Journal of Earth Science 2024年

作者： Ruiqi Shao Peng Lin Zhenhao Xu Fumin Liu Yilong Liu School of Qilu transportation， shandong University State Key Laboratory for Tunnel Engineering Jinan institute of Geotechnical and Underground Engineering， shandong University

Geological analysis， despite being a long-term method for identifying adverse geology in tunnels， has significant limitations due to its reliance on empirical analysis. The quantitative aspects of geochemical anomalies associated with adverse geology provide a novel strategy for addressing these limitations. However， statistical methods for identifying geochemical anomalies are insufficient for tunnel engineering. In contrast， data mining techniques such as machine learning have demonstrated greater efficacy when applied to geological data. Herein， a method for identifying adverse geology using machine learning of geochemical anomalies is proposed. The method was identified geochemical anomalies in tunnel that were not identified by statistical methods. We by employing robust factor analysis and self-organizing maps to reduce the dimensionality of geochemical data and extract the anomaly elements combination (AEC). Using the AEC sample data， we trained an isolation forest model to identify the multi-element anomalies， successfully. We analyzed the adverse geological features based the multi-element anomalies. This study， therefore， extends the traditional approach of geological analysis in tunnels and demonstrates that machine learning is an effective tool for intelligent geological analysis. Correspondingly， the research offers new insights regarding the adverse geology and the prevention of hazards during the construction of tunnels and underground engineering projects.

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Deep learning CNN-APSO-LSSVM hybrid fusion model for feature optimization and gas-bearing prediction

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Petroleum Science 2024年第4期21卷 2329-2344页

作者： Jiu-Qiang Yang Nian-Tian Lin Kai Zhang Yan Cui Chao Fu Dong Zhang College of Earth Sciences and Engineering Shandong University of Science and Technology Laboratory for Marine Mineral Resources Qingdao National Laboratory for Marine Science and Technology transportation institute of Inner Mongolia University Key Laboratory of Gas Hydrate Qingdao Institute of Marine Geology Ministry of Natural Resources

Conventional machine learning(CML) methods have been successfully applied for gas reservoir prediction. Their prediction accuracy largely depends on the quality of the sample data; therefore, feature optimization of the input samples is particularly important. Commonly used feature optimization methods increase the interpretability of gas reservoirs; however, their steps are cumbersome, and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data efficiently. In contrast to CML methods, deep learning(DL) methods can directly extract the important features of targets from raw data. Therefore, this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network(CNN)and an adaptive particle swarm optimization-least squares support vector machine(APSO-LSSVM). This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes, considerably simplifying the feature optimization. A CNN was used for feature optimization to highlight sensitive gas reservoir information. APSO-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction results. The constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction, giving full play to the advantages of DL and CML methods. The prediction results obtained are better than those of a single CNN model or APSO-LSSVM model. In the feature optimization process of multicomponent seismic attribute data, CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization methods. In the prediction process, the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction accuracy. The constructed CNN-APSO-LSSVM mode

关键词： Multicomponent seismic data Deep learning Adaptive particle swarm optimization Convolutional neural network Least squares support vector machine Feature optimization Gas-bearing distribution prediction

A Case of Brucellosis Concomitant with Severe Fever with Thrombocytopenia Syndrome—Weifang City,shandong province,China,2023

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China CDC weekly 2024年第27期6卷 677-680页

作者： Qingqing Xu Cuiping Wu Hui Liu Chao Geng Jinhui Zhang Dongri Piao Hongyan Zhao Yu Fan Guozhong Tian Hai Jiang National Key Laboratory of Intelligent Tracing and Forecasting for Infectious Diseases National Institute for Communicable Disease Control and PreventionChinese Center for Disease Control and PreventionBeijingChina Department of Infectious Diseases Yidu Central Hospital of WeifangWeifang CityShandong ProvinceChina institute of Bacterial Disease Jinan Center for Disease Control and PreventionJinan CityShandong ProvinceChina Tongliao Center for Disease Control and Prevention Tongliao CityInner Mongolia Autonomous RegionChina

Summary What is already known about this topic?Brucellosis and severe fever with thrombocytopenia syndrome(SFTS)are neglected zoonoses,attributable respectively to Brucella and the SFTS virus(SFTSV).While the incidence of these diseases has been rising,instances of co-infection remain *** is added by this report?This represents the first documented case of a rare coinfection involving Brucella and SFTSV.

关键词： neglected instance shandong

A local rainfall-triggered giant landslide occurred in a region along a high-speed railway on the Qinghai‒Tibetan Plateau

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Journal of Mountain Science 2024年第9期21卷 2939-2955页

作者： LI Qingpeng LIU Wenhui HE Renjie YING Chunye LIU Hairui DOU Zengning LIU Yabing YANG Sha SONG Xianteng School of Geological Engineering Qinghai UniversityXining 810016China Key Lab of Cenozoic Resource and Environment in North Margin of the Tibetan Plateau Qinghai UniversityXining 810016China College of Eco-Environmental Engineering Qinghai UniversityXining 810016China Qinghai transportation planning and design institute Co Ltd Xining 810000China Xining Center for Integrated Natural Resources Survey China Geological SurveyXining 810000QinghaiChina

An increasing number of geological hazards along high-speed railways on the Qinghai‒Tibetan Plateau have occurred and have resulted in a profound influence on old infrastructure,which has attracted increasing *** landslide event that occurred on September 15,2022,in Jiujiawan village,Xining city,Qinghai province,is a typical *** on field investigations and remote sensing interpretations,a comprehensive analysis was conducted on the ***,the potential secondary failure of the current Jiujiawan landslide was assessed using Fast Lagrangian Analysis of Continua in Three Dimensions(FLAC3D).Based on the application of the small baseline subset-interferometric synthetic aperture radar(SBAS-InSAR)technique to SAR images from February 24,2017 to September 14,2022,a significant westward horizontal deformation was found to have been formed prior to the occurrence of the *** maximum annual average deformation rate in the line of sight(LOS)direction reached-45 mm/yr,with a maximum cumulative deformation of-178 *** value was consistent with the continual increase in annual precipitation(2.51 mm/yr)prior to the occurrence of the *** accumulated precipitation before the landslide was 279.8 mm,accounting for 54.2%of the total annual precipitation,with a particularly notable surge in monthly precipitation observed during August(250.3 mm).Additionally,the occurrence of a seismic event with a magnitude of Ms 6.9 in Menyuan County,80 km away from Xining,could be a potential triggering factor to the landslide,as evidenced by an abrupt subsidence alteration observed prior to and following the *** maximum subsidence in the line of sight(LOS)direction exceeded 11 mm,exhibiting a highly consistent spatial distribution with the occurrence range of *** results suggest that the Jiujiawan landslide was likely induced by earthquake events in the early stage and heavy rainfall in the later *** FLAC3D numerical

关键词： Landslide Development characteristic Driver Mudstone Xining Basin

Spatial-temporal shifting patterns and in situ conservation of spotted seal(Phoca largha)populations in the Yellow Sea ecoregion

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Integrative Zoology 2024年第2期19卷 307-318页

作者： Hongfei ZHUANG Fei SHAO Chao ZHANG Wancai XIA Shouqiang WANG Fangyuan QU Zongling WANG Zhichuang LU Linlin ZHAO Zhaohui ZHANG First institute of Oceanography Ministry of Natural ResourcesQingdaoChina Department of Natural Resources of shandong province Shandong Forestry Protection and Development Service CenterJinanChina National Park(Protected Areas)Development Center National Forestry and Grassland AdministrationBeijingChina College of Life Science China West Normal UniversityNanchongChina Liaoning Ocean and Fisheries Science Research institute DalianChina

Understanding the habitat shifting pattern is a prerequisite for implementing in situ conservation of migratory *** seals(Phoca largha)inhabiting the Yellow Sea ecoregion(YSE)comprise a small population with independent genes and represent a charismatic flagship species in this ***,this population has declined by 80%since the 1940s,and increased support from the countries around the YSE is urgently needed to address the potential local extinction risk.A time-series niche model and life-history weighted systematic conservation planning were designed on the basis of a satellite beacon tracking survey(2010–2020)of the YSE *** results showed clustering and spreading shifting patterns during the breeding and migratory seasons,*** closed-loop migration route formed in the YSE indicated that this population might be geographically isolated from populations in other breeding areas around the *** conservation priority area(CPA),with an area of 19632 km^(2)(3.58%of the total YSE area),was the most effective response to the potential in situ ***,nearly 80%of the CPA was exposed outside the existing marine protected areas(MPAs).Future establishment of MPAs in China should strategically consider the conservation gap identified herein,and it is recommended for Korea’s closed fishing season to be spatially set in the western Korean Peninsula from May to *** study also exemplified that the lack of temporal information would lead to the dislocation of niche modeling for migratory species represented by spotted *** should be paid to protecting small and migratory populations in marine biodiversity conservation planning.

关键词： conservation priority area marine protected area migration seasonal management spotted seal

A local rainfall-triggered giant landslide occurred in a region along a high-speed railway on the Qinghai-Tibetan Plateau

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Journal of Mountain Science 2024年第09期 2939-2955页

作者： LI Qingpeng LIU Wenhui HE Renjie YING Chunye LIU Hairui DOU Zengning LIU Yabing YANG Sha SONG Xianteng School of Geological Engineering Qinghai University Key Lab of Cenozoic Resource and Environment in North Margin of the Tibetan Plateau Qinghai University College of Eco-Environmental Engineering Qinghai University Qinghai transportation planning and design institute Co Ltd Xining Center for Integrated Natural Resources Survey

An increasing number of geological hazards along high-speed railways on the Qinghai-Tibetan Plateau have occurred and have resulted in a profound influence on old infrastructure, which has attracted increasing attention. The landslide event that occurred on September 15, 2022, in Jiujiawan village, Xining city, Qinghai province, is a typical case. Based on field investigations and remote sensing interpretations, a comprehensive analysis was conducted on the landslide. Additionally, the potential secondary failure of the current Jiujiawan landslide was assessed using Fast Lagrangian Analysis of Continua in Three Dimensions（FLAC3D）. Based on the application of the small baseline subset-interferometric synthetic aperture radar（SBAS-In SAR） technique to SAR images from February 24, 2017 to September 14, 2022, a significant westward horizontal deformation was found to have been formed prior to the occurrence of the landslide. The maximum annual average deformation rate in the line of sight（LOS） direction reached-45 mm/yr, with a maximum cumulative deformation of-178 mm. This value was consistent with the continual increase in annual precipitation（2.51 mm/yr） prior to the occurrence of the landslide. The accumulated precipitation before the landslide was 279.8 mm, accounting for 54.2% of the total annual precipitation, with a particularly notable surge in monthly precipitation observed during August（250.3 mm）. Additionally, the occurrence of a seismic event with a magnitude of Ms 6.9 in Menyuan County, 80 km away from Xining, could be a potential triggering factor to the landslide, as evidenced by an abrupt subsidence alteration observed prior to and following the earthquake. The maximum subsidence in the line of sight（LOS） direction exceeded 11 mm, exhibiting a highly consistent spatial distribution with the occurrence range of landslides. These results suggest that the Jiujiawan landslide was likely induced by earthquake events in the early stage and heavy rainfall in t

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Characterization of alpine meadow surface crack and its correlation with root-soil properties

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Journal of Arid Land 2024年第6期16卷 834-851页

作者： WU Yuechen ZHU Haili ZHANG Yu ZHANG Hailong LIU Guosong LIU Yabin LI Guorong HU Xiasong School of Geological Engineering Qinghai UniversityXining 810016China Key Laboratory of the Cenozoic Resources and Environment on the North Rim of the Qinghai-Tibet Plateau Xining 810016China Qinghai Provincial transportation planning and design Research institute Co.Ltd. Xining 810002China

Quantifying surface cracks in alpine meadows is a prerequisite and a key aspect in the study of grassland crack *** characterization indices are crucial for the quantitative characterization of complex cracks,serving as vital factors in assessing the degree of cracking and the development *** far,research on evaluating the degree of grassland degradation through crack characterization indices is rare,especially the quantitative analysis of the development of surface cracks in alpine meadows is relatively ***,based on the phenomenon of surface cracking during the degradation of alpine meadows in some regions of the Qinghai-Tibet Plateau,we selected the alpine meadow in the Huangcheng Mongolian Township,Menyuan Hui Autonomous County,Qinghai province,China as the study area,used unmanned aerial vehicle(UAV)sensing technology to acquire low-altitude images of alpine meadow surface cracks at different degrees of degradation(light,medium,and heavy degradation),and analyzed the representative metrics characterizing the degree of crack development by interpreting the crack length,length density,branch angle,and burrow(rat hole)distribution density and combining them with in situ crack width and depth ***,the correlations between the crack characterization indices and the soil and root parameters of sample plots at different degrees of degradation in the study area were analyzed using the grey relation *** results revealed that with the increase of degradation,the physical and chemical properties of soil and the mechanical properties of root-soil composite changed significantly,the vegetation coverage reduced,and the root system aggregated in the surface layer of alpine *** the degree of degradation increased,the fracture morphology developed from"linear"to"dendritic",and eventually to a complex and irregular"polygonal"*** crack length,width,depth,and length density were identified as the crack char

关键词： alpine meadow grassland degradation grassland cracks crack characterization index crack morphology root length density grey relation analysis