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New technology for controlling large deformation in soft rock mining roadways based on deep and shallow dense drilling and its engineering application 认领 引用
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作者 SUN Xiaoming LI Zhihu +2 位作者 ZHANG Yong SHI Fukun MIAO Chengyu 《Journal of Mountain Science》 SCIE CSCD 2026年第6期2948-2965,共18页
This study investigates the control of large deformation in extraction roadways,a critical issue for safe coal mining.While conventional dense drilling offers moderate effectiveness,it provides limited capability for ... This study investigates the control of large deformation in extraction roadways,a critical issue for safe coal mining.While conventional dense drilling offers moderate effectiveness,it provides limited capability for targeted treatment of roof strata at varying depths.To overcome this limitation,an innovative pressure relief technique employing both shallow and deep dense drilling is proposed and applied to the mining roadway of the New Shanghai No.1 Coal Mine,China.Through a synergistic pressure relief mechanism,the method effectively reduces the magnitude and propagation range of front abutment stress ahead of the mining face.Key design parameters—including borehole length,spacing,and angle—were optimized through an integrated approach combining theoretical calculations,numerical simulations,and field validation.Field measurements demonstrate that after implementing this technique,the average stable axial force in roof anchor cables decreased from 173.3 kN to 141.0 kN,representing a reduction of 18.6%.The average support resistance of hydraulic supports#210 and#212 dropped from 34.82 MPa and 29.15 MPa to 22.16 MPa and 14.88 MPa,respectively,corresponding to reductions exceeding 35%.Total cumulative convergence was reduced by more than 40%.These findings confirm the substantial deformation control performance of the proposed method,offering a practical reference for managing large deformations in tunnels under similar geological conditions. 展开更多
关键词 Mining roadway Deep and shallow dense drilling Pressure relief mechanism Large deformation control Engineering application
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Multiphysics Collaborative Detection Technology and Equipment Development in Deep Earth Engineering:Challenges,Advances,and Future Trends 认领 引用
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作者 Jia-kang Song Yue-ming Yuan +3 位作者 Xin-yu Liu Zhao Ma Jian-ning Wang Zong-wei Yan 《Applied Geophysics》 SCIE CSCD 2026年第2期814-842,873,共29页
As a major national strategic scientific challenge,deep geological engineering faces numerous technical hurdles,particularly in the detection and identification of adverse geological formations under complex stratigra... As a major national strategic scientific challenge,deep geological engineering faces numerous technical hurdles,particularly in the detection and identification of adverse geological formations under complex stratigraphic conditions.With the advancement of next-generation information technologies like big data and artifi cial intelligence,multi-fi eld collaborative detection technology has emerged as a pivotal direction for deep geological exploration.By integrating real-time multi-field data from drilling and heterogeneous multi-source information,this technology signifi cantly enhances detection accuracy and effi ciency,providing critical support for the safe and efficient advancement of deep geological projects.Currently,China still faces challenges in geological anomaly identification,including limited intelligent detection methods and insuffi cient data fusion capabilities,which constrain the deep implementation of deep geological engineering.Building on domestic and international research progress,this paper systematically analyzes the challenges and development trends in deep geological detection,focusing on three core scientifi c issues:the mechanism of real-time multi-fi eld collaborative detection in complex geological formations,the development of integrated detection equipment,and multi-source heterogeneous data fusion with 3D tomographic imaging technology.Future trends indicate that multi-fi eld collaborative detection mechanisms are evolving from single-parameter characterization to multi-field coupling and intelligent decision-making,while detection equipment is increasingly moving toward integration and intelligence.Overcoming these key technologies will drive systematic breakthroughs in China's deep geological exploration,achieving comprehensive progress from methodologies to equipment development. 展开更多
关键词 deep geotechnical engineering unfavorable geology multiphysics intelligent exploration exploration equipment data fusion
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Multi-scale characterization of karst media,negative-pressure suffusion mechanism and collapse risk assessment for urban metro engineering:A case study of Guiyang metro line 3,China 认领 引用
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作者 Lei Zhang Xiang-quan Li +4 位作者 Jian-fei Ma Zhen-yuan Zhang Chang-chang Fu Chun-chao Zhang Ce Zhang 《Journal of Groundwater Science and Engineering》 2026年第3期307-322,共16页
Multi-scale characterization of karst media is a fundamental prerequisite for accurate stability evaluation and collapse risk assessment in karst terrains,especially for the safety control of urban metro engineering.T... Multi-scale characterization of karst media is a fundamental prerequisite for accurate stability evaluation and collapse risk assessment in karst terrains,especially for the safety control of urban metro engineering.Taking the Huaxi South Parking Lot of Guiyang metro line 3 as a case study,this paper proposes an integrated framework for karst collapse risk assessment by coupling multi-scale geological characterization,hydrodynamic-mechanical coupling simulation,and spatial multi-factor analysis.A comprehensive dataset,including 339 borehole records,core CT scanning results,long-term hydrogeological monitoring data,and laboratory test results,was collected to conduct multi-scale characterization of karst media across macro,meso and micro scales,reveal the vertical zonation of karst structures,clarify the hydrodynamic triggering mechanism of karst collapse,determine the critical instability threshold,and reproduce the entire evolution process of collapse.The results show that negative-pressure suffusion induced by rapid groundwater level decline,with a critical pressure difference of≤-190 kPa,is the dominant trigger of karst collapse in the study area.The lowest stratum stability and highest collapse risk occur in the strata with an overburden thickness of 2—5 m and a karst cavity diameter of>3 m.The high-risk zones account for 2.3%of the total study area,and are mainly distributed in the southern part,while the overall site remains stable under normal hydrodynamic conditions.This study can provide theoretical support and technical reference for karst collapse risk prevention and control in urban metro engineering. 展开更多
关键词 Karst collapse Media characterization Negative-pressure suffusion Critical collapse threshold Risk assessment
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Hydromechanical behaviour and stability of reservoir landslides in the Danjiangkou Reservoir area under rainfall and water level fluctuation 认领 引用
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作者 SHENG Yifan SHI Zijian +1 位作者 XU Guangli ZHANG Bin 《Journal of Mountain Science》 SCIE CSCD 2026年第7期3207-3227,共21页
Expansive soil landslides in reservoiraffected mountain slopes represent complex geohazards governed by coupled hydro-mechanical processes.However,the progressive deformation mechanisms under concurrent rainfall infil... Expansive soil landslides in reservoiraffected mountain slopes represent complex geohazards governed by coupled hydro-mechanical processes.However,the progressive deformation mechanisms under concurrent rainfall infiltration and reservoir water level(RWL)fluctuations—particularly the quantitative contribution of swelling pressure to failure progression—remain inadequately constrained.This study addresses this knowledge gap through an integrated investigation of the actively deforming Weijiapo landslide(Danjiangkou Reservoir,China),where synergistic rainfall-RWL interactions drive instability.Field monitoring data(GPS displacements,groundwater levels,precipitation,RWL)were synthesized with laboratory experiments and Geo Studio-based numerical modeling to develop a conceptual disaster cascade framework characterizing the rainfall-RWL-expansive soil coupling.A modified residual thrust stability model explicitly incorporating swelling pressure dynamics was formulated.Results demonstrate that rainfall primarily governs shallow fissure propagation and pore pressure response,while RWL fluctuations control deep-seated toe destabilization and saturation-induced shear failure.Critically,swelling pressure integration reduces the factor of safety by 18-32% under flood-level drawdown conditions.By establishing the hydro-mechanical linkage between external triggers and internal expansive responses,the expansion process was systematically delineated.A fitted time-history deformation function exhibits strong alignment with laboratory-observed shear strength attenuation(R2=0.92).These findings provide mechanistic insights for defining early-warning thresholds and predicting failure evolution in reservoir-affected mountainous terrain.This work substantiates the critical need to integrate expansive soil mechanics into geohazard assessment frameworks to enhance predictive accuracy and mitigation design in reservoir slopes. 展开更多
关键词 Expansive soil landslide Rainfall Reservoir water level Ring shear test Early warning
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Development of the water-conducting fracture zone in longwall top-coal caving mining with super-large mining height and weak cemented overburden 认领 引用
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作者 Cun Zhang Juhao Cheng +4 位作者 Jun He Yongqi Lian Qingsheng Bai Zhiwen Da Yi Wang 《International Journal of Coal Science & Technology》 SCIE EI CAS CSCD 2026年第2期406-426,共21页
This study investigates the height evolution of the water-conducting fracture zone(WCFZ)under super-high mining conditions in the 1101 longwall face of Zhundong No.2 Mine.Based on 28 measured datasets,a multivariate n... This study investigates the height evolution of the water-conducting fracture zone(WCFZ)under super-high mining conditions in the 1101 longwall face of Zhundong No.2 Mine.Based on 28 measured datasets,a multivariate nonlinear regression model is proposed,outperforming traditional empirical formulas in accuracy.Numerical simulations reveal four developmental stages of the WCFZ:initial acceleration(0–180 m),deceleration(180–270 m),renewed acceleration(270–420 m),and stabilization(≥420 m).Morphologically,the WCFZ transforms from arch-shaped to trapezoidal and ultimately to a flattened arch.Three-dimensional simulations show synchronized evolution between the plastic zone and the WCFZ.Field validation is achieved through microseismic monitoring and borehole leakage data.A critical mining height of 16 m is identified,beyond which WCFZ growth shifts from linear(11.38 m/unit)to nonlinear(19.45 m/unit),causing destabilization of the beam-arch structure and promoting vertical fractures.Fracture patterns vary by lithology:weakly consolidated strata form mesh-like networks,while cemented layers exhibit stepwise,slip-induced fractures.This study offers an accurate prediction model and insights into WCFZ mechanics for improved mining safety and groundwater protection. 展开更多
关键词 Super-large mining height Weakly cemented overburden Water-conducting fracture zone(WCFZ) WCFZ morphology
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Mechanical response and brittle-ductile transition of porous sandstone under true-triaxial loading 认领 引用
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作者 Junjie Xiao Xing Li +2 位作者 Jiacun Liu Dongping Liu Kaiwen Xia 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第5期3394-3406,共13页
This study investigates the influence of mean stress and Lode angle on the mechanical behavior of porous sandstone.Sandstone specimens were tested using a newly developed true-triaxial loading apparatus under five con... This study investigates the influence of mean stress and Lode angle on the mechanical behavior of porous sandstone.Sandstone specimens were tested using a newly developed true-triaxial loading apparatus under five constant Lode angle conditions and seven different mean stresses,covering a transition from brittle to ductile regimes.Based on the experimental results,three types of stress-strain responses were identified,transitioning progressively from Type 1,through Type 2 to Type 3 as the mean stress increases.Type 1 response represents typical brittle behavior,characterized by prominent shear fractures.Type 2 response corresponds to the brittle-ductile transition behavior,exhibiting non-penetrating shear fractures.Type 3 response is associated with ductile behavior,characterized by no visible shear fractures.The deviatoric stress initially increases and then decreases with increasing mean stress,forming a cap surface in the meridian plane.A generalized failure criterion is subsequently developed,capable of accurately characterizing this strength response.Furthermore,the brittle-ductile transition behavior is found to be significantly dependent on the Lode angle.Finally,the brittle-ductile transition boundary is described,incorporating the dependence of Lode angle. 展开更多
关键词 True-triaxial loading Lode angle Brittle-ductile transition Porous sandstone Failure surface
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Deep learning-enhanced prediction of small intestinal bleeding points using long short-term memory networks 认领 引用
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作者 Hsin-Yu Kuo Kun-Hua Lee +6 位作者 Chu-Kuang Chou Arvind Mukundan Riya Karmakar Tsung-Hsien Chen Thong-Lin Wang Ping-Hung Liu Hsiang-Chen Wang 《World Journal of Gastroenterology》 SCIE CAS 2026年第15期88-101,共14页
BACKGROUND Small intestinal bleeding(SIB)remains a significant challenge in the diagnosis of obscure gastrointestinal bleeding.While capsule endoscopy(CE)is the gold standard for visualization,manual interpretation of... BACKGROUND Small intestinal bleeding(SIB)remains a significant challenge in the diagnosis of obscure gastrointestinal bleeding.While capsule endoscopy(CE)is the gold standard for visualization,manual interpretation of the extensive video footage is labor-intensive and subject to inter-observer variability.Although convolutional neural networks(CNNs)have improved lesion detection,standard models often fail to account for temporal continuity,limiting their ability to accurately predict the specific location of bleeding points within the small bowel.AIM To develop and validate a deep learning framework integrating CNNs with long short-term memory(LSTM)networks to enhance the automated detection and precise localization of SIB.METHODS This study employed two datasets for automated bleeding detection:One from Cheng Kung University,consisting of white light imaging images from 100 patients obtained via PillCamTMSB 3 CE,and the Kvasir-Capsule Image dataset,which includes 47238 labeled images across 14 pathological categories.Nineteen continuous picture sequences were recovered,comprising 3806 bleeding photos and 3275 non-bleeding images.RESULTS Data augmentation was implemented,utilizing CNNs for feature extraction,succeeded by long short-term memory networks for prediction.The CNN model attained an accuracy of 98.6%for 10 categories and 96.7%for 2 categories.Findings demonstrate that CNN-LSTM models exhibit superior performance with expanded category sets.CONCLUSION These findings underscore the capability of deep learning models to enhance the accuracy and efficiency of CEbased gastrointestinal bleeding diagnosis,hence facilitating improved clinical decision-making. 展开更多
关键词 Capsule endoscopy Small intestinal bleeding Convolutional neural networks Long short-term memory networks Temporal modeling
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Study on the regulation of copper in the degradation resistance,antibacterial,and osteogenic properties of micro-arc oxidation coatings on magnesium alloy 认领 引用
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作者 CHEN Jun-xiu ZHUO Yan-na +7 位作者 KOLAWOLE Sharafadeen Kunle SIDDIQUI Muhammad Ali SHAN Xian-feng XU Yu LIU Ya ZHU Xiang-ying WU Chang-jun SU Xu-ping 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第5期1966-1986,共21页
Magnesium alloys,among the most promising biomaterials for orthopedic applications,face challenges with post-implantation infection.Copper offers potent antibacterial activity while exhibiting low biotoxicity at appro... Magnesium alloys,among the most promising biomaterials for orthopedic applications,face challenges with post-implantation infection.Copper offers potent antibacterial activity while exhibiting low biotoxicity at appropriate concentrations.This study investigated the incorporation of copper oxide nanoparticles into micro-arc oxidation(MAO)electrolytes to develop a coating combining enhanced antibacterial performance with improved corrosion resistance for Mg alloys.We systematically examined the influence of Cu content on the microstructure,corrosion resistance,antibacterial efficacy,cytotoxicity,and osteogenic properties of the coated Mg alloy samples.Electrochemical tests demonstrated that MAO coatings incorporating 1 g/L and 3 g/L CuO significantly enhanced corrosion resistance,and the corrosion rates were reduced to 0.16 mm/a and 0.38 mm/a,respectively.In immersion tests,the lowest corrosion rate of 0.31 mm/a was recorded for the 1 g/L CuO coating,which represents a 40%reduction compared to the 0 g/L CuO coating.However,further increases in CuO concentration degraded the coating's protective properties.Antibacterial assays revealed excellent efficacy against both Staphylococcus aureus(S.aureus)and Escherichia coli(E.coli)for coatings containing≥3 g/L CuO.In vivo animal testing indicated that the 3 g/L CuO MAO coating promoted optimal osteogenesis,with substantial new bone formation observed after 4 weeks in vivo.Based on the comprehensive in vitro and in vivo results,the MAO coating modified with 3 g/L CuO exhibited the greatest potential for orthopedic implant applications,offering a balanced combination of corrosion resistance,antibacterial activity,biocompatibility,and osteogenic capability. 展开更多
关键词 magnesium alloy CuO corrosion resistance antibacterial property osteogenic property
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Will the typical soums in the Selenge River Basin of Mongolia become more overgrazed in the future? 认领 引用
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作者 XU Zengrang WANG Juanle +1 位作者 DAVAASUREN Davaadorj OCHIR Altansukh 《Journal of Geographical Sciences》 SCIE CSCD 2026年第1期236-254,共19页
The Selenge River Basin(SRB)in Mongolia has faced ecosystem degradation because of climate change and overloading.The dynamics of the pastoral system and the extent of overload under future scenarios have not been doc... The Selenge River Basin(SRB)in Mongolia has faced ecosystem degradation because of climate change and overloading.The dynamics of the pastoral system and the extent of overload under future scenarios have not been documented.This study aims to answer the following questions:Will the typical soums in the SRB become more overgrazed in the future?What optimal strategy should be implemented?Multisource data were integrated and utilized to model the pastoral system of typical soums using a system dynamics approach.Future scenarios under three SSP-RCPs were projected using the model.The conclusions are as follows:(1)From upstream to downstream,rational scenarios for pastoral system transferred from SSP1-RCP2.6 to SSP2-RCP4.5,which reflect improved productivity at the expense of ecosystem stability.(2)Compared with that during the historical period of 2000-2020,the projected carrying capacity of the soums decreases by 15.2%-37.3%,whereas the number of livestock continues to increase.Consequently,the stocking rate is expected to increase from 0.32-1.16 during 2000-2020 to 1.26-2.02 during 2021-2050,indicating that rangeland will become more overloaded.(3)A livestock reduction strategy based on future livestock stock and grassland carrying capacity scenarios was proposed to maintain a dynamic forage-livestock equilibrium.It is suggested that reducing livestock is a practical option for harmonizing grassland conservation with livestock husbandry development. 展开更多
关键词 livestock husbandry grassland conservation carrying capacity system dynamics SSP-RCP scenarios Selenge River Basin
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Integration of Large Language Models(LLMs)and Static Analysis for Improving the Efficacy of Security Vulnerability Detection in Source Code 认领 引用 被引量：1
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作者 José Armando Santas Ciavatta Juan Ramón Bermejo Higuera +3 位作者 Javier Bermejo Higuera Juan Antonio Sicilia Montalvo Tomás Sureda Riera Jesús Pérez Melero 《Computers, Materials & Continua》 SCIE EI 2026年第3期351-390,共40页
As artificial Intelligence(AI)continues to expand exponentially,particularly with the emergence of generative pre-trained transformers(GPT)based on a transformer’s architecture,which has revolutionized data processin... As artificial Intelligence(AI)continues to expand exponentially,particularly with the emergence of generative pre-trained transformers(GPT)based on a transformer’s architecture,which has revolutionized data processing and enabled significant improvements in various applications.This document seeks to investigate the security vulnerabilities detection in the source code using a range of large language models(LLM).Our primary objective is to evaluate the effectiveness of Static Application Security Testing(SAST)by applying various techniques such as prompt persona,structure outputs and zero-shot.To the selection of the LLMs(CodeLlama 7B,DeepSeek coder 7B,Gemini 1.5 Flash,Gemini 2.0 Flash,Mistral 7b Instruct,Phi 38b Mini 128K instruct,Qwen 2.5 coder,StartCoder 27B)with comparison and combination with Find Security Bugs.The evaluation method will involve using a selected dataset containing vulnerabilities,and the results to provide insights for different scenarios according to the software criticality(Business critical,non-critical,minimum effort,best effort)In detail,the main objectives of this study are to investigate if large language models outperform or exceed the capabilities of traditional static analysis tools,if the combining LLMs with Static Application Security Testing(SAST)tools lead to an improvement and the possibility that local machine learning models on a normal computer produce reliable results.Summarizing the most important conclusions of the research,it can be said that while it is true that the results have improved depending on the size of the LLM for business-critical software,the best results have been obtained by SAST analysis.This differs in“NonCritical,”“Best Effort,”and“Minimum Effort”scenarios,where the combination of LLM(Gemini)+SAST has obtained better results. 展开更多
关键词 AI+SAST secure code LLM benchmarking LLM vulnerability detection
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Optimization of Truss Structures Using Nature-Inspired Algorithms with Frequency and Stress Constraints 认领 引用 被引量：1
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作者 Sanjog Chhetri Sapkota Liborio Cavaleri +3 位作者 Ajaya Khatri Siddhi Pandey Satish Paudel Panagiotis G.Asteris 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期436-464,共29页
Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises stru... Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints.Two new algorithms,the Red Kite Optimization Algorithm(ROA)and Secretary Bird Optimization Algorithm(SBOA),are utilized on five benchmark trusses with 10,18,37,72,and 200-bar trusses.Both algorithms are evaluated against benchmarks in the literature.The results indicate that SBOA always reaches a lighter optimal.Designs with reducing structural weight ranging from 0.02%to 0.15%compared to ROA,and up to 6%–8%as compared to conventional algorithms.In addition,SBOA can achieve 15%–20%faster convergence speed and 10%–18%reduction in computational time with a smaller standard deviation over independent runs,which demonstrates its robustness and reliability.It is indicated that the adaptive exploration mechanism of SBOA,especially its Levy flight–based search strategy,can obviously improve optimization performance for low-and high-dimensional trusses.The research has implications in the context of promoting bio-inspired optimization techniques by demonstrating the viability of SBOA,a reliable model for large-scale structural design that provides significant enhancements in performance and convergence behavior. 展开更多
关键词 Optimization truss structures nature-inspired algorithms meta-heuristic algorithms red kite opti-mization algorithm secretary bird optimization algorithm
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Climate and human drivers of surface water changes in the Zavkhan River-Khyargas Lake Basin,western Mongolia 认领 引用 被引量：1
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作者 BATSUREN Dorjsuren VALERY A.Zemtsov +4 位作者 ERDENEBAYAR Bavuu SANDELGER Dorligjav YAN Denghua GAO Hongkai ALTANBOLD Enkhbold 《Journal of Geographical Sciences》 SCIE CSCD 2026年第1期255-280,共26页
This study investigates climate-and human-induced hydrological changes in the Zavkhan River-Khyargas Lake Basin,a highly sensitive arid and semi-arid region of Central Asia.Using Mann-Kendall,innovative trend analysis... This study investigates climate-and human-induced hydrological changes in the Zavkhan River-Khyargas Lake Basin,a highly sensitive arid and semi-arid region of Central Asia.Using Mann-Kendall,innovative trend analysis,and Sen's slope estimation methods,historical climate trends(1980-2100)were analyzed,while land cover changes represented human impacts.Future projections were simulated using the MIROC model with Shared Socioeconomic Pathways(SSPs)and the Tank model.Results show that during the past 40 years,air temperature significantly increased(Z=3.93***),while precipitation(Z=-1.54*)and river flow(Z=-1.73*)both declined.The Khyargas Lake water level dropped markedly(Z=-5.57***).Land cover analysis reveals expanded cropland and impervious areas due to human activity.Under the SSP1.26 scenario,which assumes minimal climate change,air temperature is projected to rise by 2.0℃,precipitation by 21.8 mm,and river discharge by 1.61 m3/s between 2000 and 2100.These findings indicate that both global warming and intensified land use have substantially altered hydrological and climatic processes in the basin,highlighting the vulnerability of western Mongolia's water resources to combined climatic and anthropogenic influence. 展开更多
关键词 arid zone semi-arid zone climate change land cover trend analysis western Mongolia
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Probabilistic assessment of high-rise buildings with fluid viscous dampers under hurricane wind loads 认领 引用
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作者 Iván F.HUERGO Josue X.ROCHA +2 位作者 Octavio F.URIBE Miguel Angel MENDOZA-LUGO Oswaldo MORALES-NÁPOLES 《ENGINEERING Structure and Civil Engineering》 SCIE EI CAS CSCD 2026年第3期580-604,共25页
Current wind design codes incorporate turbulence through gust factors and rely on historical wind data,including tropical cyclones.While generally conservative,standard code wind profiles and spectra do not fully repr... Current wind design codes incorporate turbulence through gust factors and rely on historical wind data,including tropical cyclones.While generally conservative,standard code wind profiles and spectra do not fully reproduce the vertical distribution and dynamic characteristics of hurricane winds,particularly in the supergradient region near the eyewall,and can sometimes underestimate tail risks,low-probability,high-impact events,as observed during Hurricane Otis in Acapulco(2023).This study probabilistically evaluates wind-induced vibrations in high-rise buildings with different lateral resisting systems equipped with fluid viscous dampers(FVDs),under non-tropical storm and tropical cyclone conditions.Along-wind loads were modeled in the time domain as stationary,multidimensional stochastic processes and analyzed using one million Monte Carlo simulations and Incremental Dynamic Analysis on the DelftBlue supercomputer.Statistical distributions of responses,bivariate dependence via copulas,and fragility curves were obtained.Results show that wind type,structural deformation mode,and damper properties significantly affect response distributions,correlation structures,and failure probabilities.FVDs effectively reduce structural dynamic response,improving serviceability,while increased shear stiffness further reduces fragility.Modeling hurricane winds as nontropical storms can overestimate damper effectiveness.These findings provide insights for refining wind codes and designing high-rise buildings that remain safe and functional under extreme events. 展开更多
关键词 bivariate analysis fluid viscous dampers fragility curves high-rise buildings hurricane wind loads Monte Carlo simulation
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Research on rock mechanics parameter prediction based on the frequency spectrum characteristics of vibration while drilling 认领 引用
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作者 Jia-kang Song Jian-ning Wang +4 位作者 Zhao Ma Yi-guo Xue Rui-rui Cai Fan-meng Kong Lei Gao 《Applied Geophysics》 SCIE CSCD 2026年第1期99-108,428,共10页
To enable real-time prediction of rock mechanical parameters during drilling,this study proposes a method based on vibration-while-drilling(VWD)spectral features.Three-component vibration signals were collected experi... To enable real-time prediction of rock mechanical parameters during drilling,this study proposes a method based on vibration-while-drilling(VWD)spectral features.Three-component vibration signals were collected experimentally,and their dominant frequency,low-frequency energy,and spectral centroid were extracted as predictors.A ridge regression model was developed to map these spectral features to rock mechanical parameters.Compared with conventional drilling parameters,the spectral descriptors capture lithology-dependent stiffness more effectively,with the low-frequency(0-20 Hz)energy showing a strong correlation with rock strength.Validation on tuspecimens achieved high accuracy(mean R2>0.80)and stable calibration between predicted and measured values.Bootstrap and permutation analyses conrmed the consistency and interpretability of feature contributions,while ridge-penalty scanning demonstrated strong resistance to overtting.The proposed approach provides an efcient and interpretable framework for realtime eld prediction of rock mechanical parameters and oers a foundation for multi-lithology and physicsinformed model extensions. 展开更多
关键词 VWD spectral features rock mechanical parameters ridge regression real-time prediction
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Data-driven and experimental synergy:Metaheuristicoptimized extreme gradient boosting modeling of self-compacting concrete compressive strength 认领 引用
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作者 Divi SAI VARDHAN Akhilendra SHARMA +1 位作者 Rahul BISWAS Akshay BURA 《ENGINEERING Structure and Civil Engineering》 SCIE EI CAS CSCD 2026年第3期555-579,共25页
This investigation develops a comprehensive framework that integrates machine learning modeling with experimental validation for predicting the compressive strength of self-compacting concrete(SCC).The predictive mode... This investigation develops a comprehensive framework that integrates machine learning modeling with experimental validation for predicting the compressive strength of self-compacting concrete(SCC).The predictive models were constructed using a consolidated data set from existing literature and validated through independent laboratory experiments on ternary blends incorporating fly ash and silica fume.Experimental results demonstrated that optimized combinations of 30%fly ash and 5%–7.5%silica fume produced superior fresh-state properties,including enhanced flowability and reduced segregation,while achieving substantial improvements in compressive,tensile,and flexural strengths across all curing ages.Advanced ensemble learning techniques were employed by coupling Extreme Gradient Boosting(XGBoost)with three metaheuristic optimization algorithms:Tuna Swarm Optimization(TSO),Coyote Optimization Algorithm,and Giant Trevally Optimization.The XGB-TSO model demonstrated superior predictive performance,achieving coefficient of determination R2=0.9690,root mean square error of 2.15 MPa,weighted mean absolute percentage error of 2.63%,and Nash-Sutcliffe efficiency of 0.9674.Model interpretability analysis using SHapley Additive exPlanations(SHAP)identified concrete age and cement content as the most influential parameters,providing transparent insights into strength development mechanisms.Taylor diagram analysis confirmed statistical robustness through high correlation coefficients and minimal centered root mean square error.A graphical user interface was developed to enable real-time strength prediction from standard mix parameters,facilitating practical implementation.Experimental validation using laboratory-produced SCC specimens demonstrated excellent agreement with model predictions,achieving validation R2=0.9698 and mean absolute error of 1.83 MPa.The strong correlation between predicted and measured values validates the framework’s reliability for engineering applications.This research advances concrete technology by providing a validated,interpretable,and deployable tool that combines data-driven modeling with experimental verification,enabling intelligent mix design and informed decision-making in sustainable concrete construction. 展开更多
关键词 SCC XGBoost metaheuristic optimization TSO SHAP analysis graphical user interface
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Using 2D electrical resistivity imaging and borehole data to estimate N₆₀-value of soils with k-means clustering for subsurface geomaterials categorization 认领 引用
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作者 Ratchadakorn Chumkhiao Sukit Yindeesuk +1 位作者 Kuo Chieh Chao Shinya Inazumi 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第4期3218-3233,共16页
Geotechnical information forms the cornerstone of geotechnical engineering,playing a vital role in the design,construction,protection,and mitigation of structures by providing critical insights into geomaterials and s... Geotechnical information forms the cornerstone of geotechnical engineering,playing a vital role in the design,construction,protection,and mitigation of structures by providing critical insights into geomaterials and subsurface characteristics.Traditionally,drilling has been the primary method for acquiring subsurface data.Nevertheless,in recent decades,non-intrusive methods such as electrical resistivity surveys,complemented by machine learning(ML)techniques,have emerged as effective tools for comprehensive geotechnical site investigations.This study aims to integrate two-dimensional(2D)electrical resistivity imaging(ERI)with the standard penetration test(SPT)to categorize geomaterials employing k-means clustering analysis(KMCA),facilitating a better understanding of subsurface characteristics.A power equation is developed by correlating soil resistivity with N₆₀-value along a specific 2D line,supported by borehole data.This equation is subsequently applied to derive the estimate of N₆₀-value in areas of the slope where SPT data could not be directly obtained.KMCA proves to be a robust and versatile tool,enabling the classification of geomaterials into three primary clusters corresponding to different competency levels based on SPT measurements.These clusters include low-competency materials(loose sand),moderate-competency materials(medium-stiff clay),and high-competency materials(stiff-hard clay).The application of the power equation successfully estimates the N₆₀-value in areas lacking borehole data,achieving a high level of accuracy with a coefficient of determination(R²)of 0.9467,a mean absolute error(MAE)of 3.94,and a root mean squared error(RMSE)of 5.21.Consequently,this approach demonstrates the potential of integrating geophysical surveys with ML techniques to enhance subsurface characterization and extend the reach of traditional geotechnical methods. 展开更多
关键词 Electrical resistivity imaging(ERI) Estimated N₆₀-value k-means clustering analysis(KMCA) Machine learning(ML) Standard penetration test(SPT) Subsurface characterization
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Finite element method for transient response of viscoelastic multidirectional FGP skew-nanoplate resting on visco-Pasternak foundation taking into account surface effect using nonlocal strain gradient theory 认领 引用
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作者 Thu Huong Nguyen Thi Van Ke Tran +1 位作者 Pham Hoang Tu Pham Hong Thao 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第4期110-135,共26页
The finite element approach is used for the first time to simulate and examine the free oscillation and transient response of a visco-elastic multi-directional functionally graded porous(MFGP)skew-nanoplate,taking int... The finite element approach is used for the first time to simulate and examine the free oscillation and transient response of a visco-elastic multi-directional functionally graded porous(MFGP)skew-nanoplate,taking into account surface effects using nonlocal strain gradient hypothesis.The mechanical characteristics of the materials vary in all three directions of length,width,and thickness of the plate according to the exponential law.Additionally,it has viscoelastic behavior according to the Kelvin-Voigt model.The novelty of this paper lies in the incorporation of the spatial variability of nonlocal and lengthscale factors as additional mechanical characteristics of the material.The overall equation of motion for the plate is derived by including the classical plate hypothesis and Hamilton’s principle.A quadrilateral plate element with four nodes and six degrees of freedom is created using a non-conforming C2-level Hermitian function.This function offers precise results and rapid convergence for various forms and boundary conditions(BCs)that low-order elements cannot accomplish.The Newmark-beta direct integration technique is used to calculate the transient responses of the visco-elastic MFGP skew-nanoplate under various BCs.Furthermore,a thorough assessment of the impacts of several factors such as residual surface stress,grading indices,elastic foundation stiffness,skew angle,other geometrical parameters,and BCs on the transient responses of the viscoelastic MFGP skew-nanoplate has been uncovered. 展开更多
关键词 Nonlocal strain gradient hypothesis Surface effect Finite element method Variable length scale Nonlocal coefficient
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Preliminary Fabrication and Characterization of Cross-Laminated PanelMade from Nipah Palm Petiole Fibres 认领 引用
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作者 Ros Syazmini Mohd Ghani Mohamad Saiful Sulaiman +2 位作者 Sofiyah Mohd Razali Madihan Yusof Ellisha Iling 《Journal of Renewable Materials》 EI CAS 2026年第4期167-185,共19页
The increasing demand for sustainable construction materials has driven research into non-wood biomass for engineered composites.This study reports the preliminary fabrication and evaluation of cross-laminated panels(... The increasing demand for sustainable construction materials has driven research into non-wood biomass for engineered composites.This study reports the preliminary fabrication and evaluation of cross-laminated panels(CLPs)made from Nipah palm(Nypa fruticans)petioles bonded with a bio-epoxy resin adhesive.Panels were manufactured at three target densities(400,600,and 800 kg/m3)and evaluated for their physical,mechanical,and microstructural properties.Physical tests included moisture content,water absorption,and thickness swelling,while mechanical tests measured compressive and flexural strength in accordance with JIS A 5908:2022 and ASTM D1037 standards.The results showed that higher panel density reduced moisture content,swelling,and water absorption,thereby improving dimensional stability.Mechanical performance also increased significantly with density,reaching compressive strength of 25 MPa and flexural strength of 27.4 MPa at 800 kg/m3,values within the range of structuralgrade wood-based panels.Microstructural analysis confirmed enhanced adhesive penetration,reduced voids,and stronger fibre bonding at higher densities.These findings demonstrate the feasibility of Nipah palm petioles as a raw material for CLPs and highlight their potential as sustainable structural panels in construction and interior applications. 展开更多
关键词 Compressive strength flexural strength bio-based epoxy structural grade panel density
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Chronology and evolution process of the Zhaizicun ancient landslidedammed lake in the middle reaches of the Jinsha River,southwest China 认领 引用
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作者 LIANG Minqiang LV Tongyan CHEN Jian 《Journal of Mountain Science》 SCIE CSCD 2026年第5期1921-1940,共20页
Floods caused by landslide dam failure are destructive natural disasters that threaten downstream lives and property.The Jinsha River,located on the southeastern margin of the Tibetan Plateau,contains numerous large-s... Floods caused by landslide dam failure are destructive natural disasters that threaten downstream lives and property.The Jinsha River,located on the southeastern margin of the Tibetan Plateau,contains numerous large-scale paleo-landslides and riverblocking events,which provide important information for studying regional tectonic activity,paleoenvironmental changes,and landscape evolution.For these paleo-landslide-dammed lakes along the Jinsha River,their formation time,dynamic processes,and evolution of paleolake outburst have not been clearly clarified,requiring in-depth targeted research.This study focuses on the Zhaizicun paleo-dammed lake in the Taoyuan section in the middle reaches of the Jinsha River.The chronology of this giant paleolandslide-dammed lake is controversial,and its evolutionary process has not been fully reconstructed.Optically stimulated luminescence(OSL)dating,paleolake reconstruction and dam-breach flood simulations were employed to resolve these issues.The research results are as follows:(1)OSL ages suggests that this paleo-dammed lake formed around 59.9 ka.(2)Based on the DEM data,reconstruction analysis of this lake via ArcGIS shows its maximum surface area of about 1.38×10⁸m²and storage capacity of approximately 5.6×10⁹m³.(3)The dam breach occurred approximately 32.5 ka,using the DB-IWHR and HEC-RAS models,the peak flood discharge was estimated at around 109,702 m³/s.Within the simulated area(from Taoyuan to Panzhihua),the flood inundated a maximum area of 141.7 km²after 25 hours,with a maximum water depth of 89.5 m and a peak velocity of 24.1 m/s.These findings provide important insights into high-energy outburst floods and support the development of more effective disaster prevention and mitigation strategies in the region. 展开更多
关键词 Jinsha river Paleo-landslide dam OSL dating Numerical modeling Landslide evolution Outburst flood
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Machine Intelligence for Mental Health Diagnosis: A Systematic Review of Methods, Algorithms, and Key Challenges 认领 引用
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作者 Ravita Chahar Ashutosh Kumar Dubey 《Computers, Materials & Continua》 SCIE EI 2026年第1期67-131,共65页
Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),a... Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),and autism spectrum disorder(ASD)frequently arise from the complex interplay of demographic,biological,and socioeconomic factors,resulting in aggravated symptoms.This review investigates machine intelligence approaches for the early detection and prediction of mental health conditions.Methods:The preferred reporting items for systematic reviews and meta-analyses(PRISMA)framework was employed to conduct a systematic review and analysis covering the period 2018 to 2025.The potential impact of machine intelligence methods was assessed by considering various strategies,hybridization of algorithms,tools,techniques,and datasets,and their applicability.Results:Through a systematic review of studies concentrating on the prediction and evaluation of mental disorders using machine intelligence algorithms,advancements,limitations,and gaps in current methodologies were highlighted.The datasets and tools utilized in these investigations were examined,offering a detailed overview of the status of computational models in understanding and diagnosing mental health disorders.Recent research indicated considerable improvements in diagnostic accuracy and treatment effectiveness,particularly for depression and anxiety,which have shown the greatest methodological diversity and notable advancements in machine intelligence.Conclusions:Despite these improvements,challenges persist,including the need for more diverse datasets,ethical issues surrounding data privacy and algorithmic bias,and obstacles to integrating these technologies into clinical settings.This synthesis emphasizes the transformative potential of machine intelligence in enhancing mental healthcare. 展开更多
关键词 Mental health machine intelligence artificial intelligence deep learning mental disorders diagnostic precision
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