In this review paper,the applications of biomineralization in environmental geotechnics are analyzed.Three environmental geotechnics scenarios,namely heavy metal contamination immobilization and removal,waste and CO_(...In this review paper,the applications of biomineralization in environmental geotechnics are analyzed.Three environmental geotechnics scenarios,namely heavy metal contamination immobilization and removal,waste and CO_(2)containment,and recycled use of industrial byproducts,are discussed and evaluated regarding current trends and prospects.The biomineralization process,specifically the Microbially Induced Carbonate Precipitation(MICP)technology,is an effective solution for immobilizing heavy metals through co-precipitation with calcium carbonate,with successful results in cleaning up contaminated soils.The nature of biomineralization enhances earth material strength and decreases permeability,making it suitable for waste and CO_(2)containment.Additionally,using industrial byproducts in MICP technology can improve the physical,mechanical,and hydraulic properties of earth materials,making it a potential solution for efficient waste utilization.In conclusion,the applications of biomineralization in environmental geotechnics hold great promise for solving various environmental problems.However,further research is needed to better understand the control and consistency of biomineralization processes,the durability of biominerals,the scale of applications,and environmental concerns.展开更多
High level of industrialization of northern regions of Russia cause significant pollution problems in the soil. The new approach based on fuzzy modeling for reception and processing of the geocryological information i...High level of industrialization of northern regions of Russia cause significant pollution problems in the soil. The new approach based on fuzzy modeling for reception and processing of the geocryological information is offered. The technique and methodology of presentation of variables in a logic and linguistic way in a combination to elements of the experimental planning theory is developed. Comparison of the calculated data on model has shown its high adequacy of the experimental data of the various authors. The forecast of geoecological processes in cryolitozone on the basis of the described method will allow increasing reliability of the accepted design decisions.展开更多
As urbanization progresses,the demand for high-rise buildings and underground spaces is growing,and the need for firm geotechnical construction materials,efficient excavation methods,accurate testing instruments,and i...As urbanization progresses,the demand for high-rise buildings and underground spaces is growing,and the need for firm geotechnical construction materials,efficient excavation methods,accurate testing instruments,and innovative geotechnical engineering theories and technologies is increasing.By investigating the phenomena of strengthening and toughening in nature,hydrophobic and ice-phobic,friction anisotropy and drilling as well as excavation,etc,researchers have found that organisms have distinctive external morphology and organization.By imitating the external morphology,structural characteristics or movement mechanism of organisms,novel ideas,new principles,and innovative theories can be provided for the innovation and sustainable development of geotechnical engineering.This paper mainly expounds on the bio-inspired application in geotechnical engineering from three perspectives:geo-materials,geotechnical components,and drilling&excavation equipment,and lists typical application cases.In conclusion,this paper presents a summary and prospects of bio-inspired geotechnical engineering,offering fundamental insights for future research.展开更多
The purpose of this paper(presented online as a keynote lecture at the 25th Annual Indonesian Geotechnical Conference on 10 Nov 2021)is to broadly conceptualize the agenda for data-centric geotechnics,an emerging fiel...The purpose of this paper(presented online as a keynote lecture at the 25th Annual Indonesian Geotechnical Conference on 10 Nov 2021)is to broadly conceptualize the agenda for data-centric geotechnics,an emerging field that attempts to prepare geotechnical engineering for digital transformation.The agenda must include(1)development of methods that make sense of all real-world data(not selective input data for a physical model),(2)offering insights of significant value to critical real-world decisions for current or future practice(not decisions for an ideal world or decisions of minor concern to geotechnical engineers),and(3)sensitivity to the physical context of geotechnics(not abstract data-driven analysis connected to geotechnics in a peripheral way,i.e.,engagement with the knowledge and experience base should be substantial).These three elements are termed“data centricity”,“fit for(and transform)practice”,and“geotechnical context”in the agenda.Given that a knowledge of the site is central to any geotechnical engineering project,datadriven site characterization(DDSC)must constitute one key application domain in data-centric geotechnics,although other infrastructure lifecycle phases such as project conceptualization,design,construction,operation,and decommission/reuse would benefit from data-informed decision support as well.One part of DDSC that addresses numerical soil data in a site investigation report and soil property databases is pursued under Project DeepGeo.In principle,the source of data can also go beyond site investigation,and the type of data can go beyond numbers,such as categorical data,text,audios,images,videos,and expert opinion.The purpose of Project DeepGeo is to produce a 3D stratigraphic map of the subsurface volume below a full-scale project site and to estimate relevant engineering properties at each spatial point based on actual site investigation data and other relevant Big Indirect Data(BID).Uncertainty quantification is necessary,as current real-world data is insufficient,incomplete,and/or not directly relevant to construct a deterministic map.The value of a deterministic map for decision support is debatable.The computational cost to do this for a 3D true scale subsurface volume must be reasonable.Ultimately,geotechnical structures need to be a part of a completely smart infrastructure that fits the circular economy and need to focus on delivering service to end-users and the community from project conceptualization to decommission/reuse with full integration to smart city and smart society.Although current geotechnical practice has been very successful in taking“calculated risk”informed by limited data,imperfect theories,prototype testing,observations,among others and exercising judicious caution and engineering judgment,there is no clear pathway forward to leverage on big data and digital technologies such as machine learning,BIM,and digital twin to meet more challenging needs such as sustainability and resilience engineering.展开更多
Rockfalls are among the frequent hazards in underground mines worldwide,requiring effective methods for detecting unstable rock blocks to ensure miners’and equipment’s safety.This study proposes a novel approach for...Rockfalls are among the frequent hazards in underground mines worldwide,requiring effective methods for detecting unstable rock blocks to ensure miners’and equipment’s safety.This study proposes a novel approach for identifying potential rockfall zones using infrared thermal imaging and image segmentation techniques.Infrared images of rock blocks were captured at the Draa Sfar deep underground mine in Morocco using the FLUKE TI401 PRO thermal camera.Two segmentation methods were applied to locate the potential unstable areas:the classical thresholding and the K-means clustering model.The results show that while thresholding allows a binary distinction between stable and unstable areas,K-means clustering is more accurate,especially when using multiple clusters to show different risk levels.The close match between the clustering masks of unstable blocks and their corresponding visible light images further validated this.The findings confirm that thermal image segmentation can serve as an alternative method for predicting rockfalls and monitoring geotechnical issues in underground mines.Underground operators worldwide can apply this approach to monitor rock mass stability.However,further research is recommended to enhance these results,particularly through deep learning-based segmentation and object detection models.展开更多
On June 3,Scopus,a database owned by Elsevier,released CiteScore 2024 metrics for academic journal evaluation.Both the Chinese and English editions of Petroleum Exploration and Development achieved record highs in the...On June 3,Scopus,a database owned by Elsevier,released CiteScore 2024 metrics for academic journal evaluation.Both the Chinese and English editions of Petroleum Exploration and Development achieved record highs in the past year,ranking among the top in various categories.The CiteScore of the Chinese edition increased to 9.9 in 2024 from 8.4 in 2023,ranking 13th out of 330 journals in the Earth and Planetary Sciences:Geology category and 2lst out of 239 journals in the Earth and Planetary Sciences:Geotechnical Engineering and Engineering Geology category.展开更多
Despite the extensive use of distributed fiber optic sensing(DFOS)in monitoring underground structures,its potential in detecting structural anomalies,such as cracks and cavities,is still not fully understood.To contr...Despite the extensive use of distributed fiber optic sensing(DFOS)in monitoring underground structures,its potential in detecting structural anomalies,such as cracks and cavities,is still not fully understood.To contribute to the identification of defects in underground structures,this study conducted a four-point bending test of a reinforced concrete(RC)beam and uniaxial loading tests of an RC specimen with local cavities.The experimental results revealed the disparity in DFOS strain spike profiles between these two structural anomalies.The effectiveness of DFOS in the quantification of crack opening displacement(COD)was also demonstrated,even in cases where perfect bonding was not achievable between the cable and structures.In addition,DFOS strain spikes observed in two diaphragm wall panels of a twin circular shaft were also reported.The most probable cause of those spikes was identified as the mechanical behavior associated with local concrete contamination.With the utilization of the strain profiles obtained from laboratory tests and field monitoring,three types of multi-classifiers,based on support vector machine(SVM),random forest(RF),and backpropagation neural network(BP),were employed to classify strain profiles,including crack-induced spikes,non-crack-induced spikes,and non-spike strain profiles.Among these classifiers,the SVM-based classifier exhibited superior performance in terms of accuracy and model robustness.This finding suggests that the SVM-based classifier holds promise as a potential solution for the automatic detection and classification of defects in underground structures during long-term monitoring.展开更多
The rapid expansion of urban development has led to the extensive construction of civil infrastructures.However,these urban development zones frequently face potential geohazards,primarily due to the lack of detailed ...The rapid expansion of urban development has led to the extensive construction of civil infrastructures.However,these urban development zones frequently face potential geohazards,primarily due to the lack of detailed site investigations and long-term monitoring of subsurface geological conditions.Understanding the temporal and spatial distributions of underground multi-field information is vital for successful engineering construction and effective utilization of urban underground space.In this study,a fiber optic nerve system(FONS)was utilized in the Tianfu New Area,Sichuan Province,China,to obtain comprehensive subsurface multi-physical information,including geological deformation,temperature,and surface hydrological data.The FONS incorporates three advanced fiber optic sensing techniques,i.e.fiber Bragg grating(FBG),Brillouin optical time domain reflectometry(BOTDR),and Raman optical time domain reflectometry(ROTDR).Fully-and quasi-distributed strain/temperature sensing cables have been installed in nine monitoring boreholes,covering various geological features such as plains,terraces,and areas within active fault zones.The field monitoring results confirm the feasibility of employing FONS for geological investigations within urban development zones,offering a valuable reference for future applications of this cost-effective technology in geohazard mitigation.展开更多
The rise of deep learning has brought about transformative advancements in both scientific research and engineering applications.The 2024 Nobel Prizes,particularly in Physics and Chemistry,highlighted the revolutionar...The rise of deep learning has brought about transformative advancements in both scientific research and engineering applications.The 2024 Nobel Prizes,particularly in Physics and Chemistry,highlighted the revolutionary impact of deep learning,with AlphaFold’s breakthrough in protein structure prediction exemplifying its potential.This review explores the historical evolution of deep learning,from its foundational theories in neural networks and connectionism to its modern applications in various fields.Focus is given to its use in geotechnical engineering,particularly in geological disaster prediction,tunnel safety monitoring,and structural design optimization.The integration of deep learning models such as Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),and Transformers has enabled significant progress in analyzing complex,unstructured data,offering innovative solutions to longstanding engineering challenges.The review also examines the opportunities and challenges faced by the field,advocating for interdisciplinary collaboration and open data sharing to further unlock deep learning’s potential in advancing both scientific and engineering disciplines.As deep learning continues to evolve,it promises to drive further innovation,shaping the future of engineering practices and scientific discovery.展开更多
Frost heave and thaw settlement in cold regions pose a significant threat to engineering construction.Optical frequency domain reflectometry(OFDR)based on Rayleigh scattering can be applied to monitor ground deformati...Frost heave and thaw settlement in cold regions pose a significant threat to engineering construction.Optical frequency domain reflectometry(OFDR)based on Rayleigh scattering can be applied to monitor ground deformation in frozen soil areas,where the interface behavior of soil-embedded fiber optic sensors governs the monitoring accuracy.In this paper,a series of pullout tests were conducted on fiber optic(FO)cables embedded in the frozen soil to investigate the cable‒soil interface behavior.An experimental study was performed on interaction effects,particularly focused on the water content of unfrozen soil,freezing duration,and differential distribution of water content in frozen soil.The highresolution axial strains of FO cables were obtained using a sensing interrogator,and were used to calculate the interface shear stress.The interfacial mechanical response was analytically modeled using the ideal elasto‒plastic and softening constitutive models.Three freezing periods,correlating with the phase change process between ice and water,were analyzed.The results shows that the freezing effect can amplify the peak shear stress at the cable-soil interface by eight times.A criterion for the interface coupling states was proposed by normalizing the pullout force‒displacement information.Additionally,the applicability of existing theoretical models was discussed by comparing the results of theoretical back‒calculations with experimental measurements.This study provides new insights into the progressive interfacial failure behavior between strain sensing cable and frozen soil,which can be used to assist the interpretation of FO monitoring results of frozen soil deformation.展开更多
Accurate and interpretable prediction of shield tunnel-induced settlement poses a significant challenge due to the complex interplay of various influencing factors.This paper proposes a novel interpretable hybrid mode...Accurate and interpretable prediction of shield tunnel-induced settlement poses a significant challenge due to the complex interplay of various influencing factors.This paper proposes a novel interpretable hybrid model that combines complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),slime mold algorithm(SMA),and least squares support vector machine(LSSVM)to enhance prediction accuracy and model transparency.The CEEMDAN method,optimized by SMA,decomposes settlement data into intrinsic mode functions(IMFs)and residuals,thereby reducing data noise.The LSSVM,also optimized by SMA,is then applied to predict each IMF and residual.The final settlement prediction is derived from the aggregation of these results.The model was rigorously validated using the Changsha(China)and Singapore Metro projects,demonstrating superior performance to traditional machine learning models.The evaluation metrics,including root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2),underscore the model's effectiveness.The model achieved the lowest error rates and highest accuracy across these metrics.Notably,Shapley additive explanations(SHAP)provided insights into the model's decision-making process,identifying shield stoppage and moisture content as the most influential factors in settlement prediction.This study contributes to the advancement of the methodological framework for predicting tunnel settlement.It addresses the discrepancy between prediction accuracy and interpretability,providing a robust tool for practical engineering applications.展开更多
Plant root systems,a crucial component of biogeotechnics,have been recognized as a promising and sustainable strategy to address novel challenges in geotechnical engineering,i.e.,climate change(Ng et al.,2022).Root-so...Plant root systems,a crucial component of biogeotechnics,have been recognized as a promising and sustainable strategy to address novel challenges in geotechnical engineering,i.e.,climate change(Ng et al.,2022).Root-soil composite and root-reinforced slopes have re-ceived widespread attention in recent decades,due to the ability of root to regulate soil properties through mechanical reinforcement and hy-draulic transpiration(Li&Duan,2023;Ni et al.,2024).Fig.1 provides a co-occurrence network plot of plant root-based soil reinforcement strategies published over the last decade,where three clusters are identified with different colors.On the left of the network map,clusters in red and blue are primarily driven by geotechnical investigations of vegetated slopes(i.e.,plant root reinforced slopes)and root-soil com-posite/root-permeated soils,as denoted by the terms like"model","test","slope","strength"and"vegetation",while the green cluster on the right side demonstrates botany-related domains,for instance,"plant growth",Indeed,the reinforcement of vegetated soil strength is com-plex and varies significantly with an abundance of factors,both me-chanically and hydraulically.Particularly,the impact of root mor-phology and architecture cannot be negligible,including keywords"root area ratio"root distribution""root morphology"root diame-ter"root density"in Fig.1 with the root size and root depth ranking foremost.展开更多
Numerical challenges,incorporating non-uniqueness,non-convexity,undefined gradients,and high curvature,of the positive level sets of yield function are encountered in stress integration when utilizing the return-mappi...Numerical challenges,incorporating non-uniqueness,non-convexity,undefined gradients,and high curvature,of the positive level sets of yield function are encountered in stress integration when utilizing the return-mapping algorithm family.These phenomena are illustrated by an assessment of four typical yield functions:modified spatially mobilized plane criterion,Lade criterion,Bigoni-Piccolroaz criterion,and micromechanics-based upscaled Drucker-Prager criterion.One remedy to these issues,named the"Hop-to-Hug"(H2H)algorithm,is proposed via a convexification enhancement upon the classical cutting-plane algorithm(CPA).The improved robustness of the H2H algorithm is demonstrated through a series of integration tests in one single material point.Furthermore,a constitutive model is implemented with the H2H algorithm into the Abaqus/Standard finite-element platform.Element-level and structure-level analyses are carried out to validate the effectiveness of the H2H algorithm in convergence.All validation analyses manifest that the proposed H2H algorithm can offer enhanced stability over the classical CPA method while maintaining the ease of implementation,in which evaluations of the second-order derivatives of yield function and plastic potential function are circumvented.展开更多
Geotechnical parameters derived from an intrusive cone penetration test(CPT)are used to asses mechanical properties to inform the design phase of infrastructure projects.However,local,in situ 1D measurements can fail ...Geotechnical parameters derived from an intrusive cone penetration test(CPT)are used to asses mechanical properties to inform the design phase of infrastructure projects.However,local,in situ 1D measurements can fail to capture 3D subsurface variations,which could mean less than optimal design decisions for foundation engineering.By coupling the localised measurements from CPTs with more global 3D measurements derived from geophysical methods,a higher fidelity 3D overview of the subsurface can be obtained.Machine Learning(ML)may offer an effective means to capture all types of geophysical information associated with CPT data at a site scale to build a 2D or 3D ground model.In this paper,we present an ML approach to build a 3D ground model of cone resistance and sleeve friction by combining several CPT measurements with Multichannel Analysis of Surface Waves(MASW)and Electrical Resistivity Tomography(ERT)data on a land site characterisation project in the United Arab Emirates(UAE).To avoid a potential overfitting problem inherent to the use of machine learning and a lack of data at certain locations,we explore the possibility of using a prior Geo-Statistical(GS)approach that attempts to constrain the overfitting process by“artificially”increasing the amount of input data.A sensitivity study is also performed on input features used to train the ML algorithm to better define the optimal combination of input features for the prediction.Our results showed that ERT data were not useful in capturing 3D variations of geotechnical properties compared to Vs due to the geographical location of the site(200 m east from the Oman Gulf)and the possible effect of saline water intrusion.Additionally,we demonstrate that the use of a prior GS phase could be a promising and interesting means to make the prediction of ground properties more robust,especially for this specific case study described in this paper.Looking ahead,better representation of the subsurface can lead to a number of benefits for stakeholders involved in developing assets.Better ground/geotechnical models mean better site calibration of design methods and fewer design assumptions for reliability-based design,creating an opportunity for value engineering in the form of lighter construction without compromising safety,shorter construction timelines,and reduced resource requirements.展开更多
Thermo-poro-mechanical responses along sliding zone/surface have been extensively studied.However,it has not been recognized that the potential contribution of other crucial engineering geological interfaces beyond th...Thermo-poro-mechanical responses along sliding zone/surface have been extensively studied.However,it has not been recognized that the potential contribution of other crucial engineering geological interfaces beyond the slip surface to progressive failure.Here,we aim to investigate the subsurface multiphysics of reservoir landslides under two extreme hydrologic conditions(i.e.wet and dry),particularly within sliding masses.Based on ultra-weak fiber Bragg grating(UWFBG)technology,we employ specialpurpose fiber optic sensing cables that can be implanted into boreholes as“nerves of the Earth”to collect data on soil temperature,water content,pore water pressure,and strain.The Xinpu landslide in the middle reach of the Three Gorges Reservoir Area in China was selected as a case study to establish a paradigm for in situ thermo-hydro-poro-mechanical monitoring.These UWFBG-based sensing cables were vertically buried in a 31 m-deep borehole at the foot of the landslide,with a resolution of 1 m except for the pressure sensor.We reported field measurements covering the period 2021 and 2022 and produced the spatiotemporal profiles throughout the borehole.Results show that wet years are more likely to motivate landslide motions than dry years.The annual thermally active layer of the landslide has a critical depth of roughly 9 m and might move downward in warmer years.The dynamic groundwater table is located at depths of 9e15 m,where the peaked strain undergoes a periodical response of leap and withdrawal to annual hydrometeorological cycles.These interface behaviors may support the interpretation of the contribution of reservoir regulation to slope stability,allowing us to correlate them to local damage events and potential global destabilization.This paper also offers a natural framework for interpreting thermo-hydro-poro-mechanical signatures from creeping reservoir bank slopes,which may form the basis for a landslide monitoring and early warning system.展开更多
Anti-slide piles are one of the most important reinforcement structures against landslides,and evalu-ating the working conditions is of great significance for landslide mitigation.The widely adopted analytical methods...Anti-slide piles are one of the most important reinforcement structures against landslides,and evalu-ating the working conditions is of great significance for landslide mitigation.The widely adopted analytical methods of pile internal forces include cantilever beam method and elastic foundation beam method.However,due to many assumptions involved in calculation,the analytical models cannot be fully applicable to complex site situations,e.g.landslides with multi-sliding surfaces and pile-soil interface separation as discussed herein.In view of this,the combination of distributed fiber optic sensing(DFOS)and strain-internal force conversion methods was proposed to evaluate the working conditions of an anti-sliding pile in a typical retrogressive landslide in the Three Gorges reservoir area,China.Brillouin optical time domain reflectometry(BOTDR)was utilized to monitor the strain distri-bution along the pile.Next,by analyzing the relative deformation between the pile and its adjacent inclinometer,the pile-soil interface separation was profiled.Finally,the internal forces of the anti-slide pile were derived based on the strain-internal force conversion method.According to the ratio of calculated internal forces to the design values,the working conditions of the anti-slide pile could be evaluated.The results demonstrated that the proposed method could reveal the deformation pattern of the anti-slide pile system,and can quantitatively evaluate its working conditions.展开更多
The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering applications.This study addresses the importance of estimating the friction angle and presents the develop...The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering applications.This study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJCSA-MLPnet.First of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal variances.This technique is expected to improve the accuracy of friction angle prediction models.The friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,i.e.FPA-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,i.e.YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJ-CSA-MLPnet.Among these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering scenarios.This improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.展开更多
Determining homogeneous domains statistically is helpful for engineering geological modeling and rock mass stability evaluation.In this text,a technique that can integrate lithology,geotechnical and structural informa...Determining homogeneous domains statistically is helpful for engineering geological modeling and rock mass stability evaluation.In this text,a technique that can integrate lithology,geotechnical and structural information is proposed to delineate homogeneous domains.This technique is then applied to a high and steep slope along a road.First,geological and geotechnical domains were described based on lithology,faults,and shear zones.Next,topological manifolds were used to eliminate the incompatibility between orientations and other parameters(i.e.trace length and roughness)so that the data concerning various properties of each discontinuity can be matched and characterized in the same Euclidean space.Thus,the influence of implicit combined effect in between parameter sequences on the homogeneous domains could be considered.Deep learning technique was employed to quantify abstract features of the characterization images of discontinuity properties,and to assess the similarity of rock mass structures.The results show that the technique can effectively distinguish structural variations and outperform conventional methods.It can handle multisource engineering geological information and multiple discontinuity parameters.This technique can also minimize the interference of human factors and delineate homogeneous domains based on orientations or multi-parameter with arbitrary distributions to satisfy different engineering requirements.展开更多
The comprehension of sediment grain size parameters and the corresponding sedimentary environment holds paramount importance in elucidating the engineering geological attributes of the subaqueous seabed.This study del...The comprehension of sediment grain size parameters and the corresponding sedimentary environment holds paramount importance in elucidating the engineering geological attributes of the subaqueous seabed.This study delineated the sedimentary environment zoning in the northern sea area of Qingdao through cluster analysis of grain size parameters derived from 123 surface sediment samples.The study analyzed the correlation between sediment geotechnical indices and grain size parameters across diverse sedimentary environments.A correlation equation was established for samples exhibiting a strong correlation.The study found four distinct sedimentary environments in the study area:coastal,transitional,shallow sea,and residual.Within the same sedimentary environment,the average grain size and sorting coefficient exhibit significant correlations with geotechnical indices such as water content,density,shear strength,plastic limit,liquid limit,and plastic index.However,notable disparities in the correlation between grain size parameters and geotechnical indices emerge across different sedimentary environments.展开更多
To investigate the stability and interaction mechanism of the slope-pile-footing system under surcharge effects,the finite difference method(FDM)was adopted to analyze the response laws of the stability of the reinfor...To investigate the stability and interaction mechanism of the slope-pile-footing system under surcharge effects,the finite difference method(FDM)was adopted to analyze the response laws of the stability of the reinforced slope,evolution of the critical slip surface,stress characteristic of retaining structures,deformation and failure modes of the slope foundation and building footing under surcharge parameters,including the surcharge intensity,the surcharge position,and the surcharge width.The results show that surcharge parameters significantly affect the stability and the deformation characteristics of the slope-pile-footing system.Specifically speaking,with the increasing surcharge intensity and the decreasing surcharge position and width,the deformation and failure mode of the system will gradually evolve in a direction that is harmful to its stability.The interaction mechanism of the slope-pile-footing system is further clarified as the load transfer of the building footing,the generation of the additional stress in the slope foundation,and the adjustment of pile bending moment due to the stress redistribution.Correspondingly,the safety of anti-slide piles will determine the stability of the slope foundation and building footing.These findings are expected to provide guidance for the comprehensive development and utilization of filled slopes after reinforcement.展开更多
基金supported by the Natural Science Foundation of China(42007246)and the Fundamental Research Funds for the Central Universities.
文摘In this review paper,the applications of biomineralization in environmental geotechnics are analyzed.Three environmental geotechnics scenarios,namely heavy metal contamination immobilization and removal,waste and CO_(2)containment,and recycled use of industrial byproducts,are discussed and evaluated regarding current trends and prospects.The biomineralization process,specifically the Microbially Induced Carbonate Precipitation(MICP)technology,is an effective solution for immobilizing heavy metals through co-precipitation with calcium carbonate,with successful results in cleaning up contaminated soils.The nature of biomineralization enhances earth material strength and decreases permeability,making it suitable for waste and CO_(2)containment.Additionally,using industrial byproducts in MICP technology can improve the physical,mechanical,and hydraulic properties of earth materials,making it a potential solution for efficient waste utilization.In conclusion,the applications of biomineralization in environmental geotechnics hold great promise for solving various environmental problems.However,further research is needed to better understand the control and consistency of biomineralization processes,the durability of biominerals,the scale of applications,and environmental concerns.
文摘High level of industrialization of northern regions of Russia cause significant pollution problems in the soil. The new approach based on fuzzy modeling for reception and processing of the geocryological information is offered. The technique and methodology of presentation of variables in a logic and linguistic way in a combination to elements of the experimental planning theory is developed. Comparison of the calculated data on model has shown its high adequacy of the experimental data of the various authors. The forecast of geoecological processes in cryolitozone on the basis of the described method will allow increasing reliability of the accepted design decisions.
基金supported by Natural Science Foundation of Chongqing(No.CSTB2022NSCQ-LZX0001)High-end Foreign Expert Introduction Program(No.G2022165004L).
文摘As urbanization progresses,the demand for high-rise buildings and underground spaces is growing,and the need for firm geotechnical construction materials,efficient excavation methods,accurate testing instruments,and innovative geotechnical engineering theories and technologies is increasing.By investigating the phenomena of strengthening and toughening in nature,hydrophobic and ice-phobic,friction anisotropy and drilling as well as excavation,etc,researchers have found that organisms have distinctive external morphology and organization.By imitating the external morphology,structural characteristics or movement mechanism of organisms,novel ideas,new principles,and innovative theories can be provided for the innovation and sustainable development of geotechnical engineering.This paper mainly expounds on the bio-inspired application in geotechnical engineering from three perspectives:geo-materials,geotechnical components,and drilling&excavation equipment,and lists typical application cases.In conclusion,this paper presents a summary and prospects of bio-inspired geotechnical engineering,offering fundamental insights for future research.
文摘The purpose of this paper(presented online as a keynote lecture at the 25th Annual Indonesian Geotechnical Conference on 10 Nov 2021)is to broadly conceptualize the agenda for data-centric geotechnics,an emerging field that attempts to prepare geotechnical engineering for digital transformation.The agenda must include(1)development of methods that make sense of all real-world data(not selective input data for a physical model),(2)offering insights of significant value to critical real-world decisions for current or future practice(not decisions for an ideal world or decisions of minor concern to geotechnical engineers),and(3)sensitivity to the physical context of geotechnics(not abstract data-driven analysis connected to geotechnics in a peripheral way,i.e.,engagement with the knowledge and experience base should be substantial).These three elements are termed“data centricity”,“fit for(and transform)practice”,and“geotechnical context”in the agenda.Given that a knowledge of the site is central to any geotechnical engineering project,datadriven site characterization(DDSC)must constitute one key application domain in data-centric geotechnics,although other infrastructure lifecycle phases such as project conceptualization,design,construction,operation,and decommission/reuse would benefit from data-informed decision support as well.One part of DDSC that addresses numerical soil data in a site investigation report and soil property databases is pursued under Project DeepGeo.In principle,the source of data can also go beyond site investigation,and the type of data can go beyond numbers,such as categorical data,text,audios,images,videos,and expert opinion.The purpose of Project DeepGeo is to produce a 3D stratigraphic map of the subsurface volume below a full-scale project site and to estimate relevant engineering properties at each spatial point based on actual site investigation data and other relevant Big Indirect Data(BID).Uncertainty quantification is necessary,as current real-world data is insufficient,incomplete,and/or not directly relevant to construct a deterministic map.The value of a deterministic map for decision support is debatable.The computational cost to do this for a 3D true scale subsurface volume must be reasonable.Ultimately,geotechnical structures need to be a part of a completely smart infrastructure that fits the circular economy and need to focus on delivering service to end-users and the community from project conceptualization to decommission/reuse with full integration to smart city and smart society.Although current geotechnical practice has been very successful in taking“calculated risk”informed by limited data,imperfect theories,prototype testing,observations,among others and exercising judicious caution and engineering judgment,there is no clear pathway forward to leverage on big data and digital technologies such as machine learning,BIM,and digital twin to meet more challenging needs such as sustainability and resilience engineering.
基金supported by the Moroccan Ministry of Higher Education,Scientific Research,and Innovationthe Moroccan Digital Development Agency(DDA)+2 种基金the National Center for Scientific and Technical Research of Morocco(CNRST)through the Al-Khawarizmi projectthe MANAGEM groupMASCIR supporting this project.
文摘Rockfalls are among the frequent hazards in underground mines worldwide,requiring effective methods for detecting unstable rock blocks to ensure miners’and equipment’s safety.This study proposes a novel approach for identifying potential rockfall zones using infrared thermal imaging and image segmentation techniques.Infrared images of rock blocks were captured at the Draa Sfar deep underground mine in Morocco using the FLUKE TI401 PRO thermal camera.Two segmentation methods were applied to locate the potential unstable areas:the classical thresholding and the K-means clustering model.The results show that while thresholding allows a binary distinction between stable and unstable areas,K-means clustering is more accurate,especially when using multiple clusters to show different risk levels.The close match between the clustering masks of unstable blocks and their corresponding visible light images further validated this.The findings confirm that thermal image segmentation can serve as an alternative method for predicting rockfalls and monitoring geotechnical issues in underground mines.Underground operators worldwide can apply this approach to monitor rock mass stability.However,further research is recommended to enhance these results,particularly through deep learning-based segmentation and object detection models.
文摘On June 3,Scopus,a database owned by Elsevier,released CiteScore 2024 metrics for academic journal evaluation.Both the Chinese and English editions of Petroleum Exploration and Development achieved record highs in the past year,ranking among the top in various categories.The CiteScore of the Chinese edition increased to 9.9 in 2024 from 8.4 in 2023,ranking 13th out of 330 journals in the Earth and Planetary Sciences:Geology category and 2lst out of 239 journals in the Earth and Planetary Sciences:Geotechnical Engineering and Engineering Geology category.
基金support from the Open Research Project Programme of the State Key Laboratory of Internet of Things for Smart City,University of Macao (Grant No.SKL-IoTSC (UM)-2021-2023/ORPF/A19/2022)the General Research Fund project from Research Grants Council of Hong Kong Special Administrative Region Government of China (Grant No.15214722)the Start-up Fund from The Hong Kong Polytechnic University (Grant No.BD88).
文摘Despite the extensive use of distributed fiber optic sensing(DFOS)in monitoring underground structures,its potential in detecting structural anomalies,such as cracks and cavities,is still not fully understood.To contribute to the identification of defects in underground structures,this study conducted a four-point bending test of a reinforced concrete(RC)beam and uniaxial loading tests of an RC specimen with local cavities.The experimental results revealed the disparity in DFOS strain spike profiles between these two structural anomalies.The effectiveness of DFOS in the quantification of crack opening displacement(COD)was also demonstrated,even in cases where perfect bonding was not achievable between the cable and structures.In addition,DFOS strain spikes observed in two diaphragm wall panels of a twin circular shaft were also reported.The most probable cause of those spikes was identified as the mechanical behavior associated with local concrete contamination.With the utilization of the strain profiles obtained from laboratory tests and field monitoring,three types of multi-classifiers,based on support vector machine(SVM),random forest(RF),and backpropagation neural network(BP),were employed to classify strain profiles,including crack-induced spikes,non-crack-induced spikes,and non-spike strain profiles.Among these classifiers,the SVM-based classifier exhibited superior performance in terms of accuracy and model robustness.This finding suggests that the SVM-based classifier holds promise as a potential solution for the automatic detection and classification of defects in underground structures during long-term monitoring.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.42225702 and 42077235).
文摘The rapid expansion of urban development has led to the extensive construction of civil infrastructures.However,these urban development zones frequently face potential geohazards,primarily due to the lack of detailed site investigations and long-term monitoring of subsurface geological conditions.Understanding the temporal and spatial distributions of underground multi-field information is vital for successful engineering construction and effective utilization of urban underground space.In this study,a fiber optic nerve system(FONS)was utilized in the Tianfu New Area,Sichuan Province,China,to obtain comprehensive subsurface multi-physical information,including geological deformation,temperature,and surface hydrological data.The FONS incorporates three advanced fiber optic sensing techniques,i.e.fiber Bragg grating(FBG),Brillouin optical time domain reflectometry(BOTDR),and Raman optical time domain reflectometry(ROTDR).Fully-and quasi-distributed strain/temperature sensing cables have been installed in nine monitoring boreholes,covering various geological features such as plains,terraces,and areas within active fault zones.The field monitoring results confirm the feasibility of employing FONS for geological investigations within urban development zones,offering a valuable reference for future applications of this cost-effective technology in geohazard mitigation.
基金support provided by the Hebei Province Full-Time Recruitment of National High-Level Innovative Talents Research Project(Grant No.2023HBQZYCSB004).
文摘The rise of deep learning has brought about transformative advancements in both scientific research and engineering applications.The 2024 Nobel Prizes,particularly in Physics and Chemistry,highlighted the revolutionary impact of deep learning,with AlphaFold’s breakthrough in protein structure prediction exemplifying its potential.This review explores the historical evolution of deep learning,from its foundational theories in neural networks and connectionism to its modern applications in various fields.Focus is given to its use in geotechnical engineering,particularly in geological disaster prediction,tunnel safety monitoring,and structural design optimization.The integration of deep learning models such as Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),and Transformers has enabled significant progress in analyzing complex,unstructured data,offering innovative solutions to longstanding engineering challenges.The review also examines the opportunities and challenges faced by the field,advocating for interdisciplinary collaboration and open data sharing to further unlock deep learning’s potential in advancing both scientific and engineering disciplines.As deep learning continues to evolve,it promises to drive further innovation,shaping the future of engineering practices and scientific discovery.
基金the National Key Research and Development Program of China(Grant No.2023YFF1303501)the National Science Fund for Distinguished Young Scholars of China(Grant No.42225702)the Open Fund of State Key Laboratory of Frozen Soil Engineering(Grant No.SKLFSE201814).
文摘Frost heave and thaw settlement in cold regions pose a significant threat to engineering construction.Optical frequency domain reflectometry(OFDR)based on Rayleigh scattering can be applied to monitor ground deformation in frozen soil areas,where the interface behavior of soil-embedded fiber optic sensors governs the monitoring accuracy.In this paper,a series of pullout tests were conducted on fiber optic(FO)cables embedded in the frozen soil to investigate the cable‒soil interface behavior.An experimental study was performed on interaction effects,particularly focused on the water content of unfrozen soil,freezing duration,and differential distribution of water content in frozen soil.The highresolution axial strains of FO cables were obtained using a sensing interrogator,and were used to calculate the interface shear stress.The interfacial mechanical response was analytically modeled using the ideal elasto‒plastic and softening constitutive models.Three freezing periods,correlating with the phase change process between ice and water,were analyzed.The results shows that the freezing effect can amplify the peak shear stress at the cable-soil interface by eight times.A criterion for the interface coupling states was proposed by normalizing the pullout force‒displacement information.Additionally,the applicability of existing theoretical models was discussed by comparing the results of theoretical back‒calculations with experimental measurements.This study provides new insights into the progressive interfacial failure behavior between strain sensing cable and frozen soil,which can be used to assist the interpretation of FO monitoring results of frozen soil deformation.
基金support from the National Key Research and Development Program of China(Grant Nos.2023YFC3008300 and 2023YFC3008305)the National Natural Science Foundation of China(Grant Nos.42172296).
文摘Accurate and interpretable prediction of shield tunnel-induced settlement poses a significant challenge due to the complex interplay of various influencing factors.This paper proposes a novel interpretable hybrid model that combines complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),slime mold algorithm(SMA),and least squares support vector machine(LSSVM)to enhance prediction accuracy and model transparency.The CEEMDAN method,optimized by SMA,decomposes settlement data into intrinsic mode functions(IMFs)and residuals,thereby reducing data noise.The LSSVM,also optimized by SMA,is then applied to predict each IMF and residual.The final settlement prediction is derived from the aggregation of these results.The model was rigorously validated using the Changsha(China)and Singapore Metro projects,demonstrating superior performance to traditional machine learning models.The evaluation metrics,including root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2),underscore the model's effectiveness.The model achieved the lowest error rates and highest accuracy across these metrics.Notably,Shapley additive explanations(SHAP)provided insights into the model's decision-making process,identifying shield stoppage and moisture content as the most influential factors in settlement prediction.This study contributes to the advancement of the methodological framework for predicting tunnel settlement.It addresses the discrepancy between prediction accuracy and interpretability,providing a robust tool for practical engineering applications.
基金supported by Natural Science Foundation of Chongqing(No.CSTB2022NSCQ-LZX0001)High-end Foreign Expert Introduction program(No.G2022165004L)+1 种基金High-end Foreign Expert Introduction program(No.DL2021165001L)The fi-nancial supports are gratefully acknowledged.
文摘Plant root systems,a crucial component of biogeotechnics,have been recognized as a promising and sustainable strategy to address novel challenges in geotechnical engineering,i.e.,climate change(Ng et al.,2022).Root-soil composite and root-reinforced slopes have re-ceived widespread attention in recent decades,due to the ability of root to regulate soil properties through mechanical reinforcement and hy-draulic transpiration(Li&Duan,2023;Ni et al.,2024).Fig.1 provides a co-occurrence network plot of plant root-based soil reinforcement strategies published over the last decade,where three clusters are identified with different colors.On the left of the network map,clusters in red and blue are primarily driven by geotechnical investigations of vegetated slopes(i.e.,plant root reinforced slopes)and root-soil com-posite/root-permeated soils,as denoted by the terms like"model","test","slope","strength"and"vegetation",while the green cluster on the right side demonstrates botany-related domains,for instance,"plant growth",Indeed,the reinforcement of vegetated soil strength is com-plex and varies significantly with an abundance of factors,both me-chanically and hydraulically.Particularly,the impact of root mor-phology and architecture cannot be negligible,including keywords"root area ratio"root distribution""root morphology"root diame-ter"root density"in Fig.1 with the root size and root depth ranking foremost.
基金supported by the National Natural Science Foundation of China (Grant Nos.12372376 and U22A20596).
文摘Numerical challenges,incorporating non-uniqueness,non-convexity,undefined gradients,and high curvature,of the positive level sets of yield function are encountered in stress integration when utilizing the return-mapping algorithm family.These phenomena are illustrated by an assessment of four typical yield functions:modified spatially mobilized plane criterion,Lade criterion,Bigoni-Piccolroaz criterion,and micromechanics-based upscaled Drucker-Prager criterion.One remedy to these issues,named the"Hop-to-Hug"(H2H)algorithm,is proposed via a convexification enhancement upon the classical cutting-plane algorithm(CPA).The improved robustness of the H2H algorithm is demonstrated through a series of integration tests in one single material point.Furthermore,a constitutive model is implemented with the H2H algorithm into the Abaqus/Standard finite-element platform.Element-level and structure-level analyses are carried out to validate the effectiveness of the H2H algorithm in convergence.All validation analyses manifest that the proposed H2H algorithm can offer enhanced stability over the classical CPA method while maintaining the ease of implementation,in which evaluations of the second-order derivatives of yield function and plastic potential function are circumvented.
文摘Geotechnical parameters derived from an intrusive cone penetration test(CPT)are used to asses mechanical properties to inform the design phase of infrastructure projects.However,local,in situ 1D measurements can fail to capture 3D subsurface variations,which could mean less than optimal design decisions for foundation engineering.By coupling the localised measurements from CPTs with more global 3D measurements derived from geophysical methods,a higher fidelity 3D overview of the subsurface can be obtained.Machine Learning(ML)may offer an effective means to capture all types of geophysical information associated with CPT data at a site scale to build a 2D or 3D ground model.In this paper,we present an ML approach to build a 3D ground model of cone resistance and sleeve friction by combining several CPT measurements with Multichannel Analysis of Surface Waves(MASW)and Electrical Resistivity Tomography(ERT)data on a land site characterisation project in the United Arab Emirates(UAE).To avoid a potential overfitting problem inherent to the use of machine learning and a lack of data at certain locations,we explore the possibility of using a prior Geo-Statistical(GS)approach that attempts to constrain the overfitting process by“artificially”increasing the amount of input data.A sensitivity study is also performed on input features used to train the ML algorithm to better define the optimal combination of input features for the prediction.Our results showed that ERT data were not useful in capturing 3D variations of geotechnical properties compared to Vs due to the geographical location of the site(200 m east from the Oman Gulf)and the possible effect of saline water intrusion.Additionally,we demonstrate that the use of a prior GS phase could be a promising and interesting means to make the prediction of ground properties more robust,especially for this specific case study described in this paper.Looking ahead,better representation of the subsurface can lead to a number of benefits for stakeholders involved in developing assets.Better ground/geotechnical models mean better site calibration of design methods and fewer design assumptions for reliability-based design,creating an opportunity for value engineering in the form of lighter construction without compromising safety,shorter construction timelines,and reduced resource requirements.
基金We acknowledge the funding support from the National Science Fund for Distinguished Young Scholars of National Natural Science Foundation of China(Grant No.42225702)the National Natural Science Foundation of China(Grant No.42077235).
文摘Thermo-poro-mechanical responses along sliding zone/surface have been extensively studied.However,it has not been recognized that the potential contribution of other crucial engineering geological interfaces beyond the slip surface to progressive failure.Here,we aim to investigate the subsurface multiphysics of reservoir landslides under two extreme hydrologic conditions(i.e.wet and dry),particularly within sliding masses.Based on ultra-weak fiber Bragg grating(UWFBG)technology,we employ specialpurpose fiber optic sensing cables that can be implanted into boreholes as“nerves of the Earth”to collect data on soil temperature,water content,pore water pressure,and strain.The Xinpu landslide in the middle reach of the Three Gorges Reservoir Area in China was selected as a case study to establish a paradigm for in situ thermo-hydro-poro-mechanical monitoring.These UWFBG-based sensing cables were vertically buried in a 31 m-deep borehole at the foot of the landslide,with a resolution of 1 m except for the pressure sensor.We reported field measurements covering the period 2021 and 2022 and produced the spatiotemporal profiles throughout the borehole.Results show that wet years are more likely to motivate landslide motions than dry years.The annual thermally active layer of the landslide has a critical depth of roughly 9 m and might move downward in warmer years.The dynamic groundwater table is located at depths of 9e15 m,where the peaked strain undergoes a periodical response of leap and withdrawal to annual hydrometeorological cycles.These interface behaviors may support the interpretation of the contribution of reservoir regulation to slope stability,allowing us to correlate them to local damage events and potential global destabilization.This paper also offers a natural framework for interpreting thermo-hydro-poro-mechanical signatures from creeping reservoir bank slopes,which may form the basis for a landslide monitoring and early warning system.
基金The authors gratefully acknowledge the financial support pro-vided by the Young Scientists Fund of the National Natural Science Foundation of China(Grant No.41907232)the National Science Fund for Distinguished Young Scholars of China(Grant No.42225702)the State Key Program of National Natural Science Foundation of China(Grant No.41230636).
文摘Anti-slide piles are one of the most important reinforcement structures against landslides,and evalu-ating the working conditions is of great significance for landslide mitigation.The widely adopted analytical methods of pile internal forces include cantilever beam method and elastic foundation beam method.However,due to many assumptions involved in calculation,the analytical models cannot be fully applicable to complex site situations,e.g.landslides with multi-sliding surfaces and pile-soil interface separation as discussed herein.In view of this,the combination of distributed fiber optic sensing(DFOS)and strain-internal force conversion methods was proposed to evaluate the working conditions of an anti-sliding pile in a typical retrogressive landslide in the Three Gorges reservoir area,China.Brillouin optical time domain reflectometry(BOTDR)was utilized to monitor the strain distri-bution along the pile.Next,by analyzing the relative deformation between the pile and its adjacent inclinometer,the pile-soil interface separation was profiled.Finally,the internal forces of the anti-slide pile were derived based on the strain-internal force conversion method.According to the ratio of calculated internal forces to the design values,the working conditions of the anti-slide pile could be evaluated.The results demonstrated that the proposed method could reveal the deformation pattern of the anti-slide pile system,and can quantitatively evaluate its working conditions.
文摘The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering applications.This study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJCSA-MLPnet.First of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal variances.This technique is expected to improve the accuracy of friction angle prediction models.The friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,i.e.FPA-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,i.e.YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJ-CSA-MLPnet.Among these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering scenarios.This improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.
基金the National Natural Science Foundation of China(Grant Nos.41941017 and U1702241).
文摘Determining homogeneous domains statistically is helpful for engineering geological modeling and rock mass stability evaluation.In this text,a technique that can integrate lithology,geotechnical and structural information is proposed to delineate homogeneous domains.This technique is then applied to a high and steep slope along a road.First,geological and geotechnical domains were described based on lithology,faults,and shear zones.Next,topological manifolds were used to eliminate the incompatibility between orientations and other parameters(i.e.trace length and roughness)so that the data concerning various properties of each discontinuity can be matched and characterized in the same Euclidean space.Thus,the influence of implicit combined effect in between parameter sequences on the homogeneous domains could be considered.Deep learning technique was employed to quantify abstract features of the characterization images of discontinuity properties,and to assess the similarity of rock mass structures.The results show that the technique can effectively distinguish structural variations and outperform conventional methods.It can handle multisource engineering geological information and multiple discontinuity parameters.This technique can also minimize the interference of human factors and delineate homogeneous domains based on orientations or multi-parameter with arbitrary distributions to satisfy different engineering requirements.
基金funded by the National Key R&D Program Project(No.2022YFC3103604).
文摘The comprehension of sediment grain size parameters and the corresponding sedimentary environment holds paramount importance in elucidating the engineering geological attributes of the subaqueous seabed.This study delineated the sedimentary environment zoning in the northern sea area of Qingdao through cluster analysis of grain size parameters derived from 123 surface sediment samples.The study analyzed the correlation between sediment geotechnical indices and grain size parameters across diverse sedimentary environments.A correlation equation was established for samples exhibiting a strong correlation.The study found four distinct sedimentary environments in the study area:coastal,transitional,shallow sea,and residual.Within the same sedimentary environment,the average grain size and sorting coefficient exhibit significant correlations with geotechnical indices such as water content,density,shear strength,plastic limit,liquid limit,and plastic index.However,notable disparities in the correlation between grain size parameters and geotechnical indices emerge across different sedimentary environments.
基金funded by the National Key Research and Development Program of China(Nos.2018YFC1505302 and 2019YFC1509701)the National Natural Science Foundation of China(Nos.41977249 and 42090052)。
文摘To investigate the stability and interaction mechanism of the slope-pile-footing system under surcharge effects,the finite difference method(FDM)was adopted to analyze the response laws of the stability of the reinforced slope,evolution of the critical slip surface,stress characteristic of retaining structures,deformation and failure modes of the slope foundation and building footing under surcharge parameters,including the surcharge intensity,the surcharge position,and the surcharge width.The results show that surcharge parameters significantly affect the stability and the deformation characteristics of the slope-pile-footing system.Specifically speaking,with the increasing surcharge intensity and the decreasing surcharge position and width,the deformation and failure mode of the system will gradually evolve in a direction that is harmful to its stability.The interaction mechanism of the slope-pile-footing system is further clarified as the load transfer of the building footing,the generation of the additional stress in the slope foundation,and the adjustment of pile bending moment due to the stress redistribution.Correspondingly,the safety of anti-slide piles will determine the stability of the slope foundation and building footing.These findings are expected to provide guidance for the comprehensive development and utilization of filled slopes after reinforcement.
