Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human safety.These sudden and violent failures of rock masses are characterized by the rapid ...Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human safety.These sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock,leading to severe seismic events and structural damage.Therefore,the development of reliable prediction models for rock bursts is paramount to mitigating these hazards.This study aims to propose a tree-based model—a Light Gradient Boosting Machine(LightGBM)—to predict the intensity of rock bursts in underground engineering.322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset,which serves to train the LightGBMmodel.Two population-basedmetaheuristic algorithms are used to optimize the hyperparameters of the LightGBM model.Finally,the sensitivity analysis is used to identify the predominant factors that may incur the occurrence of rock bursts.The results show that the population-based metaheuristic algorithms have a good ability to search out the optimal hyperparameters of the LightGBM model.The developed LightGBM model yields promising performance in predicting the intensity of rock bursts,with which accuracy on training and testing sets are 0.972 and 0.944,respectively.The sensitivity analysis discloses that the risk of occurring rock burst is significantly sensitive to three factors:uniaxial compressive strength(σc),stress concentration factor(SCF),and elastic strain energy index(Wet).Moreover,this study clarifies the particular impact of these three factors on the intensity of rock bursts through the partial dependence plot.展开更多
Located downstream the Kupang Catchment in Indonesia,Pekalongan faces significant land subsidence issues,leading to severe coastal flooding.This study aimed to assess the impact of climate change on future flow regime...Located downstream the Kupang Catchment in Indonesia,Pekalongan faces significant land subsidence issues,leading to severe coastal flooding.This study aimed to assess the impact of climate change on future flow regimes and hydrological extremes to inform long-term water resources management strategies for the Kupang Catchment.Utilizing precipitation and air temperature data from general circulation models in the Coupled Model Intercomparison Project 6(CMIP6)and employing bias correction techniques,the Soil and Water Assessment Tool(SWAT)hydrological model was employed to analyze climate-induced changes in hydrological fluxes,specifically streamflow.Results indicated a consistent increase in monthly streamflow during the wet season,with a substantial rise of 22.8%,alongside a slight decrease of 18.0%during the dry season.Moreover,both the frequency and severity of extremely low and high flows were projected to intensify by approximately 50%and 70%,respectively,for a 20-year return period,suggesting heightened flood and drought risks in the future.The observed declining trend in low flow,by up to 11%,indicated the potential for long-term groundwater depletion exacerbating the threat of land subsidence and coastal flooding,especially in areas with inadequate surface water management policies and infrastructure.展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered so...Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.展开更多
The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to u...The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.展开更多
This study investigates the innovative reuse of sewage sludge with eco-friendly alkaline solutes to improve clayey soil without conventional cementitious binders.The unconfined compressive strength(UCS)was the main cr...This study investigates the innovative reuse of sewage sludge with eco-friendly alkaline solutes to improve clayey soil without conventional cementitious binders.The unconfined compressive strength(UCS)was the main criterion to assess the quality and effectiveness of the proposed solutions,as this test was performed to measure the strength of the stabilized clay by varying binders’dosages and curing times.Moreover,the direct shear test(DST)was used to investigate the Mohr-Coulomb parameters of the treated soil.Microstructure observations of the natural and treated soil were conducted using scanning electron microscope(SEM),energy-dispersive spectroscopy(EDS),and FTIR.Furthermore,toxicity characteristic leaching procedure(TCLP)tests were performed on the treated soil to investigate the leachability of metals.According to the results,using 2.5%of sewage sludge activated by NaOH and Na_(2)SiO_(3)increases the UCS values from 176 kPa to 1.46 MPa after 7 d and 56 d of curing,respectively.The results of the DST indicate that sewage sludge as a precursor increases cohesion and enhances frictional resistance,thereby improving the Mohr-Coulomb parameters of the stabilized soil.The SEM micrographs show that alkali-activated sewage sludge increases the integrity and reduces the cavity volumes in the stabilized soil.Moreover,TCLP tests revealed that the solubility of metals in the treated soil alkaliactivated by sewage sludge significantly decreased.This study suggests that using sewage sludge can replace cement and lime in ground improvement,improve the circular economy,and reduce the carbon footprint of construction projects.展开更多
The growing importance of maintaining and extending the functional lifespan of reinforced concrete structures has resulted in an increased emphasis on non-destructive testing techniques as essential tools for evaluati...The growing importance of maintaining and extending the functional lifespan of reinforced concrete structures has resulted in an increased emphasis on non-destructive testing techniques as essential tools for evaluating structural conditions.Non-destructive testing procedures offer a notable benefit in assessing the uniformity,homogeneity,ability to withstand compression,durability,and degree of corrosion in reinforcing bars within reinforced concrete structures.This study aimed to evaluate the existing condition of partially constructed residential buildings in Rewari district,located in the state of Haryana.The reinforced concrete structure of the building had been completed eight years ago,however,the project was abruptly stopped.Prior to recommencing the construction,it is important to assess the present state of the structure in order to evaluate the deterioration in Reinforced Cement Concrete(RCC).The building’s state was evaluated by visually inspecting the building,conducting on-site examinations,and analyzing samples in a laboratory.The findings emphasize the assessment of the robustness and durability of concrete to ascertain the degree of deterioration and degradation in the structure.The study incorporates visual inspection,and non-destructive evaluation utilizing different instruments to evaluate the corrosion condition of reinforcing bars.In addition,selected RCC columns,beams,and slabs undergo chemical testing.It has been observed that the strength results and chemical results were within permissible limits.展开更多
Many researchers have focused on the behavior of fiber-reinforced concrete(FRC)in the construction of various defensive structures to resist against impact forces resulting from explosions and projectiles.However,the ...Many researchers have focused on the behavior of fiber-reinforced concrete(FRC)in the construction of various defensive structures to resist against impact forces resulting from explosions and projectiles.However,the lack of sufficient research regarding the resistance of functionally graded fiber-reinforced concrete against projectile impacts has resulted in a limited understanding of the performance of this concrete type,which is necessary for the design and construction of structures requiring great resistance against external threats.Here,the performance of functionally graded fiber-reinforced concrete against projectile impacts was investigated experimentally using a(two-stage light)gas gun and a drop weight testing machine.For this objective,12 mix designs,with which 35 cylindrical specimens and 30 slab specimens were made,were prepared,and the main variables were the magnetite aggregate vol%(55%)replacing natural coarse aggregate,steel fiber vol%,and steel fiber type(3D and 5D).The fibers were added at six vol%of 0%,0.5%,0.75%,1%,1.25%,and 1.5%in 10 specimen series(three identical specimens per each series)with dimensions of 40×40×7.5 cm and functional grading(three layers),and the manufactured specimens were subjected to the drop weight impact and projectile penetration tests by the drop weight testing machine and gas gun,respectively,to assess their performance.Parameters under study included the compressive strength,destruction level,and penetration depth.The experimental results demonstrate that using the magnetite aggregate instead of the natural coarse aggregate elevated the compressive strength of the concrete by 61%.In the tests by the drop weight machine,it was observed that by increasing the total vol%of the fibers,especially by increasing the fiber content in the outer layers(impact surface),the cracking resistance and energy absorption increased by around 100%.Note that the fiber geometry had little effect on the energy absorption in the drop weight test.Investigating the optimum specimens showed that using 3D steel fibers at a total fiber content of 1 vol%,consisting of a layered grading of 1.5 vol%,0 vol%,and 1.5 vol%,improved the penetration depth by 76%and lowered the destruction level by 85%.In addition,incorporating the 5D steel fibers at a total fiber content of 1 vol%,consisting of the layered fiber contents of 1.5%,0%,and 1.5%,improved the projectile penetration depth by 50%and lowered the damage level by 61%compared with the case of using the 3D fibers.展开更多
Soil liquefaction,a seismic-induced phenomenon,is of significant concern in geotechnical engineering due to its potential to cause severe structural damage and ground instability during earthquakes.This study explores...Soil liquefaction,a seismic-induced phenomenon,is of significant concern in geotechnical engineering due to its potential to cause severe structural damage and ground instability during earthquakes.This study explores the prediction of the Liquefaction Severity Index(LSI)by integrating extensive borehole investigation data with seismic records from the Kahramanmara?(M_(w)7.8)and Hatay(M_(w)6.4)earthquakes that occurred in 2023.Nine machine learning models,Random Forest(RF),M5P,REPTree,IBk,Random Tree(RT),Gaussian Processes(GP),SMOreg,Locally Weighted Learning(LWL),and Linear Regression(LR),were employed with 10-fold cross-validation to ensure reliable predictions.Twelve geotechnical and seismic parameters,groundwater level,earthquake magnitude,peak ground acceleration,V_(s30),dominant frequency,dominant period,longitudinal wave velocity,dynamic modulus of elasticity,dynamic shear modulus,modulus of incompressibility,standard penetration test(SPT)values,and cyclic stress ratio(CSR)values,were utilized as inputs.The analysis results were evaluated with respect to RMSE,MAE,R2,RAE,P/M,error category limits,Taylor diagram,and relative importance of input parameters.Among the models,Random Forest outperformed with an R2 of 0.94,MAE of 2.35,with minimal prediction errors,followed by M5P and REPTree.Error analysis indicated that 80%of Random Forest and REPTree predictions fell within±7,while M5P showed slightly higher variability.Model-based feature ranking demonstrated that Cyclic Stress Ratio(CSR),Ground Water Level(GWL),and Standard Penetration Test(SPT)value emerged as dominant predictors.These findings highlight the study’s contribution to developing a reliable,datadriven framework for LSI prediction,offering a robust basis for improving site-specific liquefaction risk assessment and informed geotechnical decisionmaking in future seismic events.展开更多
Concrete is one of the most important elements in building construction.However,concrete used in construction is susceptible to damage due to corrosion.The influence of corrosive substances causes changes in the reinf...Concrete is one of the most important elements in building construction.However,concrete used in construction is susceptible to damage due to corrosion.The influence of corrosive substances causes changes in the reinforcing steel and affects the strength of the structure.The repair method is one approach to overcome this problem.This research aims to determine the effect of grouting and jacketing repairs on corroded concrete.The concrete used has dimensions of 15 cm×15 cm×60 cm with planned corrosion variations of 50%,60%,and 70%.The test objects were tested using the Non-Destructive Testing(NDT)method using Ultrasonic Pulse Velocity(UPV).The test results show that the average speed of normal concrete is 5070 m/s,while the lowest average speed is 3070 m/s on the 70%planned corrosion test object.The test object was then given a load of 1600 kgf.At this stage,there is a decrease in speed and wave shape with the lowest average speed obtained at 2753 m/s.The repair method is an effort to restore concrete performance by using grouting and jacketing.Grouting is done by injecting mortar material into it.Jacketing involves adding thickness to the existing concrete layer with additional layers of concrete.After improvements were made,there was an improvement in the UPV test,with a peak speed value of 4910 m/s.Repairing concrete by filling cracks can improve concrete continuity and reduce waveform distortion,thereby increasing wave propagation speed.展开更多
Professional and trade skills are required for handling the construction related projects;Construction industries of the present day however lack useful information concerning different practices,patterns and trends i...Professional and trade skills are required for handling the construction related projects;Construction industries of the present day however lack useful information concerning different practices,patterns and trends involved in risk management.Considering this,the present study focuses on the aforementioned variables of risk management by quantitative analysis specifically in the domain of construction industry.This study has used IBM’s SPSS(Statistical Package for Social Sciences)version 25.0 to analyze the results.This study is an initiative to assess the impact of risk management in the construction sector of Jordan.It will assist the construction sector for exploring the limitations with respect to integrate effective risk management.A sense of competition will be developed through a comparison of risk factors of construction projects among the project stakeholders such as contractors should enhance their risk management practices.展开更多
This research addresses the growing demand for high-performance protective materials against high-velocity projectile impacts.The performance of multi-layered steel fiber-reinforced mortar(SFRM)panels with varying thi...This research addresses the growing demand for high-performance protective materials against high-velocity projectile impacts.The performance of multi-layered steel fiber-reinforced mortar(SFRM)panels with varying thicknesses and air gaps,was experimentally investigated under single and repeated impacts of 7.62×51 mm bullets fired from a distance of 50 m.The impact events were recorded using a high-speed camera at 40000 fps.Panel performance was assessed in terms of failure modes,kinetic energy absorption,spalling diameter,and percentage of back-face damage area,and weight loss.Results showed that panel configuration significantly influenced performance.Panel P10,with 70 mm SFRM thickness and 20 mm air gaps,provided the highest resistance,dissipating 5223 J of kinetic energy and preventing back-face damage.In contrast,P7,which absorbed 4476 J,presented a back damage area percentage of 8.93%after three impacts.Weight loss analysis further confirmed durability improvements,with P10 showing only 1.53%cumulative loss compared to 3.26%in P7.The inclusion of wider air gaps enhanced energy dissipation and reduced damage.Comparison between single and repeated impacts demonstrated the sustained resistance of high-performance panels,with P10 maintaining minimal degradation across three consecutive impacts.These findings highlight the potential of multi-layer SFRM panels to enhance ballistic resistance,making them suitable for military,security,and civilian protective applications requiring long-term durability.展开更多
Structures constructed on collapsible soil are prone to failure under flooding.Agro-waste like rice husk ash(RHA)and its geopolymer(LGR),consisting of lime(L),RHA,water glass(Na2SiO3),and caustic soda(NaOH),present a ...Structures constructed on collapsible soil are prone to failure under flooding.Agro-waste like rice husk ash(RHA)and its geopolymer(LGR),consisting of lime(L),RHA,water glass(Na2SiO3),and caustic soda(NaOH),present a potential solution to address this issue.RHA and LGR were mixed up to 16%to improve the collapsible soil.Samples were remolded at optimal water content and maximum dry density for strength and collapsible potential tests.Unconfined compressive strength,deformation modulus,and soaked California bearing ratio exhibit exponential improvement with the inclusion of LGR.Additionally,for comparison of microstructural characteristics,analyses involving energy-dispersive X-ray spectroscopy(EDAX)and scanning electron microscope(SEM)were conducted on both virgin and treated specimens.LGR resulted in the emergence of new peaks of sodium silicates and calcium silicates,as indicated by EDAX.The formation of H-C-A-S gel and H-N-A-S gel observed in SEM suggests the development of bonds among soil particles attributed to geopolymerization.SEM reveals the transformation of the inherent collapsible soil from a dispersed and silt-dominated structure to a reticulated structure devoid of micro-pores following the incorporation of LGR.A numerical model was constructed to forecast the performance of both virgin and stabilized collapsible soils under pre-and post-flooding conditions.The outcomes indicate an enhancement in the soil's bearing capacity upon stabilization with 12%LGR.The implementation of 12%LGR significantly resulted in a lower embodied energy-tostrength ratio,emissions-to-strength ratio,and relatively lower cost-to-strength ratio compared to the soil treated with 16%cement kiln dust(CKD).展开更多
This paper aims to evaluate the stochastic response of steel columns subjected to blast loads using the modified single degree of freedom(MSDOF)method,which assessed towards the conventional single degree of freedom(S...This paper aims to evaluate the stochastic response of steel columns subjected to blast loads using the modified single degree of freedom(MSDOF)method,which assessed towards the conventional single degree of freedom(SDOF)and the experimentally validated Finite Element(FE)methods(LSDYNA).For this purpose,special atten-tion is given to calculating the response of H-shaped steel columns under blast.The damage amount is determined based on the support rotation criterion,which is expressed as a function of their maximum lateral mid-span dis-placement.To account for uncertainties in input parameters and obtain the failure probability,the Monte Carlo simulation(MCS)method is employed,complemented by the Latin Hypercube Sampling(LHS)method to reduce the number of simulations.A parametric analysis is hence performed to examine the effect of several input pa-rameters(including both deterministic and probabilistic parameters)on the probability of column damage as a function of support rotation.First,the MSDOF method confirms its higher accuracy in estimating the probability of column damage due to blast,compared to the conventional SDOF.The collected results also show that un-certainties of several input parameters have significant effects on the column behavior.In particular,geometric parameters(including cross-sectional characteristics,boundary conditions and column length)have major effect on the corresponding column response,in the same way of input blast load parameters and material properties.展开更多
Global climate change is the most serious challenge that modern society faces.Soil-biochar carbon sequestration is a promising natural solution for capturing carbon.This study monitored the CO_(2) emissions of five bi...Global climate change is the most serious challenge that modern society faces.Soil-biochar carbon sequestration is a promising natural solution for capturing carbon.This study monitored the CO_(2) emissions of five biochar incubated Malaysian Tropical soils(MT-Soil).The recalcitrance index of palm kernel shell biochar(PKS)was higher than that of wood chip biochar(WCB),bamboo biochar(BB),coconut shell biochar(CHB)and rice husk biochar(RHB),and was different from the observed CO_(2) emission characteristics(WCB>CHB>RHB>BB>PKS).Thus,the carbon sequestration potential of biochar could not be evaluated solely by the recalcitrance index.This CO_(2) emission is linked not only to the total organic carbon(TOC)and total carbon(TC)of the biochar but also associated with mobile matter(MM),water holding capacity(WHC),available phosphorus(AP),exchangeable potassium(AK),and nitrogen content.The multiple linear regression analysis(MLRA)shows that the weights of these factors on CO_(2) emissions are as follows:TC>pH>MM>WHC>AP>AK.The results show that in addition to biochar stability,pore structure and available phosphorus release also affect carbon dynamics through indirect effects on microbial activity.This means that to minimize CO_(2) emissions during application of biochar,it is necessary to use soil that is rich in phosphorus and biochar that has undeveloped pore structure and high stable carbon.Finally,this study provides valuable theoretical underpinnings biochar application in MT-Soil.展开更多
The nine typical Shanghai soils are usually silty clay or clay,which appears inconsistent with their low clay content in the relevant publications.The literature review shows that the documented clay content of Shangh...The nine typical Shanghai soils are usually silty clay or clay,which appears inconsistent with their low clay content in the relevant publications.The literature review shows that the documented clay content of Shanghai soils ranges from 0%to 30.8%by weight.This inconsistency may stem from two factors:(1)the Shanghai soil classification system relies solely on the plasticity index for soil naming;and(2)the conventional steel sieving method cannot separate the clay from the fine soils(clay and silt mixtures).This paper aims to accurately determine the clay content in Shanghai soils.It uses nylon cloth sieves with apertures ranging from 0.063 mm to 0.0008 mm and completely separates the clay particles from the fine soils.The nine typical Shanghai soils are tested and sieved into distinct subgroups of clay,silt,sand,and gravel particles.Results demonstrate clay content ranges from 18.99%to 79.33%,substantially higher than literature values and consistent with their names of either silty clay or clay.Macro,micro,and scanning electron microscope(SEM)images reveal effective separation of clay,silt,sand,and gravel particles.The clay exhibits cohesive properties,while the silt,sand,and gravel comprise clean,non-cohesive individual particles.The clay and silt fractions are confirmed to be within their respective sieving limits by SEM-based particle size measurements.Additionally,Atterberg limits testing highlights the high plasticity of the clay particles and the non-plastic nature of the silt particles.展开更多
Clay deposits typically exhibit significant degrees of heterogeneity and anisotropy in their strength and stiffness properties.Such non-monotonic responses can significantly impact the stability analysis and design of...Clay deposits typically exhibit significant degrees of heterogeneity and anisotropy in their strength and stiffness properties.Such non-monotonic responses can significantly impact the stability analysis and design of overlying shallow foundations.In this study,the undrained bearing capacity of shallow foundations resting on inhomogeneous and anisotropic clay layers subjected to oblique-eccentric combined loading is investigated through a comprehensive series of finite element limit analysis(FELA)based on the well-established lower-bound theorem and second-order cone programming(SOCP).The heterogeneity of normally consolidated(NC)clays is simulated by adopting a well-known general model of undrained shear strength increasing linearly with depth.In contrast,for overconsolidated(OC)clays,the variation of undrained shear strength with depth is considered to follow a bilinear trend.Furthermore,the inherent anisotropy is accounted for by adopting different values of undrained shear strength along different directions within the soil medium,employing an iterative-based algorithm.The results of numerical simulations are utilized to investigate the influences of natural soil heterogeneity and inherent anisotropy on the ultimate bearing capacity,failure envelope,and failure mechanism of shallow foundations subjected to the various combinations of vertical-horizontal(V-H)and vertical-moment(V-M)loads.展开更多
Ultrahigh-performance concrete(UHPC)is a groundbreaking kind of concrete that distinguishes itself from conventional concrete through its unique material properties.Understanding and managing the time-dependent charac...Ultrahigh-performance concrete(UHPC)is a groundbreaking kind of concrete that distinguishes itself from conventional concrete through its unique material properties.Understanding and managing the time-dependent characteristics of these materials is essential for their effective use in various construction applications.This study presents an experimental evaluation of the compressive and bending properties of the UHPC incorporating polypropylene,steel,and glass fibers.Based on ACI-211 guidelines,the UHPC mix was designed by using three types of aggregates:limestone,andesite,and quartzite,along with 5%fiber content(at varying percentages of 0,5%,10%,15%,and 20%)relative to the cementitious materials,and three different water-to-cement(w/c)ratios(0.24,0.3,and 0.4)were used.In this research,the compressive and flexural strength tests were conducted.The results show that increasing the values of the fibers significantly enhances the compressive strength of the studied samples.Furthermore,the utilization of fibers markedly improves the bending strength of the samples,demonstrating a strong correlation with the yield resistance of the material.Also,findings show that using steel fibers increases the compressive and bending strength of the tested samples more than polypropylene and glass fibers.For instance,in UHPC samples with 0.4 w/c,the average compressive strength values are 82.2 MPa,70.3 MPa,and 67.1 MPa for steel,polypropylene,and glass fibers,respectively.Also,in the flexural strength test,the modulus of rupture is obtained as an average of 6.24 MPa,5.24 MPa and 4.83 MPa for UHPC samples with steel,polypropylene and glass fibers,respectively.展开更多
Aceh in Indonesia is rich inmarine resources and abundant fishery products such as oyster.Traditionally,fishermen only harvest oysters and discard the shells,which can cause pollution and environmental contamination.W...Aceh in Indonesia is rich inmarine resources and abundant fishery products such as oyster.Traditionally,fishermen only harvest oysters and discard the shells,which can cause pollution and environmental contamination.Waste Oyster Shells(WOS)contain a high percentage of calcium carbonate(CaCO_(3))that experiences thermal decomposition at high temperature,following the reaction CaCO_(3)→CaO+CO_(2)(ΔT=825℃).At temperature>900℃,dead-burned lime is formed,which severely influences CaO reactivity.However,the optimum temperature for producing high CaO content is still uncertain.Therefore,this study aimed to determine the optimum calcination temperature to produce high CaO content,assess initial setting time of WOS paste,and identify the best compressive strength of paste.For the experiment,WOS was used as a partial cement replacement(with a size of 0.075 mm)in paste at a proportion of 5%and calcined at temperature of 700℃,800℃,900℃,and 1000℃.The specimens used were an ebonite ring(dimensions:70 mm bottom diameter,60 mm top diameter,and 40 mm height)and a cube(dimensions:5 cm×5 cm×5 cm).The experiment was conducted following the ASTM(American Society for Testing andMaterials)standards and optimumcompressive strength values were analyzed using ANOVA(Analysis of Variance)and Response Surface Methodology(RSM)through the Design Expert software.The results showed that WOS calcined at 1000℃ increased CaO content by approximately 57.40%.Furthermore,the initial setting time test of 5%WOS paste at 1000℃ showed a more uniform binding performance compared to conventional cement paste,with an initial setting time of 75 min and a penetration depth of 15 mm.In line with the analysis,optimum compressive strength of 71.028 MPa with a desirability value of 0.986 was achieved at 5%cement replacement and calcination temperature of 786.44℃.展开更多
The precise identification of quartz minerals is crucial in mineralogy and geology due to their widespread occurrence and industrial significance.Traditional methods of quartz identification in thin sections are labor...The precise identification of quartz minerals is crucial in mineralogy and geology due to their widespread occurrence and industrial significance.Traditional methods of quartz identification in thin sections are labor-intensive and require significant expertise,often complicated by the coexistence of other minerals.This study presents a novel approach leveraging deep learning techniques combined with hyperspectral imaging to automate the identification process of quartz minerals.The utilizied four advanced deep learning models—PSPNet,U-Net,FPN,and LinkNet—has significant advancements in efficiency and accuracy.Among these models,PSPNet exhibited superior performance,achieving the highest intersection over union(IoU)scores and demonstrating exceptional reliability in segmenting quartz minerals,even in complex scenarios.The study involved a comprehensive dataset of 120 thin sections,encompassing 2470 hyperspectral images prepared from 20 rock samples.Expert-reviewed masks were used for model training,ensuring robust segmentation results.This automated approach not only expedites the recognition process but also enhances reliability,providing a valuable tool for geologists and advancing the field of mineralogical analysis.展开更多
文摘Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human safety.These sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock,leading to severe seismic events and structural damage.Therefore,the development of reliable prediction models for rock bursts is paramount to mitigating these hazards.This study aims to propose a tree-based model—a Light Gradient Boosting Machine(LightGBM)—to predict the intensity of rock bursts in underground engineering.322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset,which serves to train the LightGBMmodel.Two population-basedmetaheuristic algorithms are used to optimize the hyperparameters of the LightGBM model.Finally,the sensitivity analysis is used to identify the predominant factors that may incur the occurrence of rock bursts.The results show that the population-based metaheuristic algorithms have a good ability to search out the optimal hyperparameters of the LightGBM model.The developed LightGBM model yields promising performance in predicting the intensity of rock bursts,with which accuracy on training and testing sets are 0.972 and 0.944,respectively.The sensitivity analysis discloses that the risk of occurring rock burst is significantly sensitive to three factors:uniaxial compressive strength(σc),stress concentration factor(SCF),and elastic strain energy index(Wet).Moreover,this study clarifies the particular impact of these three factors on the intensity of rock bursts through the partial dependence plot.
基金supported by the funding Riset Unggulan Daerah 2022 of the Bureau of Development Planning and Research in Central Java Province(BAPPEDA Provinsi Jawa Tengah).
文摘Located downstream the Kupang Catchment in Indonesia,Pekalongan faces significant land subsidence issues,leading to severe coastal flooding.This study aimed to assess the impact of climate change on future flow regimes and hydrological extremes to inform long-term water resources management strategies for the Kupang Catchment.Utilizing precipitation and air temperature data from general circulation models in the Coupled Model Intercomparison Project 6(CMIP6)and employing bias correction techniques,the Soil and Water Assessment Tool(SWAT)hydrological model was employed to analyze climate-induced changes in hydrological fluxes,specifically streamflow.Results indicated a consistent increase in monthly streamflow during the wet season,with a substantial rise of 22.8%,alongside a slight decrease of 18.0%during the dry season.Moreover,both the frequency and severity of extremely low and high flows were projected to intensify by approximately 50%and 70%,respectively,for a 20-year return period,suggesting heightened flood and drought risks in the future.The observed declining trend in low flow,by up to 11%,indicated the potential for long-term groundwater depletion exacerbating the threat of land subsidence and coastal flooding,especially in areas with inadequate surface water management policies and infrastructure.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
文摘Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.
文摘The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.
文摘This study investigates the innovative reuse of sewage sludge with eco-friendly alkaline solutes to improve clayey soil without conventional cementitious binders.The unconfined compressive strength(UCS)was the main criterion to assess the quality and effectiveness of the proposed solutions,as this test was performed to measure the strength of the stabilized clay by varying binders’dosages and curing times.Moreover,the direct shear test(DST)was used to investigate the Mohr-Coulomb parameters of the treated soil.Microstructure observations of the natural and treated soil were conducted using scanning electron microscope(SEM),energy-dispersive spectroscopy(EDS),and FTIR.Furthermore,toxicity characteristic leaching procedure(TCLP)tests were performed on the treated soil to investigate the leachability of metals.According to the results,using 2.5%of sewage sludge activated by NaOH and Na_(2)SiO_(3)increases the UCS values from 176 kPa to 1.46 MPa after 7 d and 56 d of curing,respectively.The results of the DST indicate that sewage sludge as a precursor increases cohesion and enhances frictional resistance,thereby improving the Mohr-Coulomb parameters of the stabilized soil.The SEM micrographs show that alkali-activated sewage sludge increases the integrity and reduces the cavity volumes in the stabilized soil.Moreover,TCLP tests revealed that the solubility of metals in the treated soil alkaliactivated by sewage sludge significantly decreased.This study suggests that using sewage sludge can replace cement and lime in ground improvement,improve the circular economy,and reduce the carbon footprint of construction projects.
文摘The growing importance of maintaining and extending the functional lifespan of reinforced concrete structures has resulted in an increased emphasis on non-destructive testing techniques as essential tools for evaluating structural conditions.Non-destructive testing procedures offer a notable benefit in assessing the uniformity,homogeneity,ability to withstand compression,durability,and degree of corrosion in reinforcing bars within reinforced concrete structures.This study aimed to evaluate the existing condition of partially constructed residential buildings in Rewari district,located in the state of Haryana.The reinforced concrete structure of the building had been completed eight years ago,however,the project was abruptly stopped.Prior to recommencing the construction,it is important to assess the present state of the structure in order to evaluate the deterioration in Reinforced Cement Concrete(RCC).The building’s state was evaluated by visually inspecting the building,conducting on-site examinations,and analyzing samples in a laboratory.The findings emphasize the assessment of the robustness and durability of concrete to ascertain the degree of deterioration and degradation in the structure.The study incorporates visual inspection,and non-destructive evaluation utilizing different instruments to evaluate the corrosion condition of reinforcing bars.In addition,selected RCC columns,beams,and slabs undergo chemical testing.It has been observed that the strength results and chemical results were within permissible limits.
文摘Many researchers have focused on the behavior of fiber-reinforced concrete(FRC)in the construction of various defensive structures to resist against impact forces resulting from explosions and projectiles.However,the lack of sufficient research regarding the resistance of functionally graded fiber-reinforced concrete against projectile impacts has resulted in a limited understanding of the performance of this concrete type,which is necessary for the design and construction of structures requiring great resistance against external threats.Here,the performance of functionally graded fiber-reinforced concrete against projectile impacts was investigated experimentally using a(two-stage light)gas gun and a drop weight testing machine.For this objective,12 mix designs,with which 35 cylindrical specimens and 30 slab specimens were made,were prepared,and the main variables were the magnetite aggregate vol%(55%)replacing natural coarse aggregate,steel fiber vol%,and steel fiber type(3D and 5D).The fibers were added at six vol%of 0%,0.5%,0.75%,1%,1.25%,and 1.5%in 10 specimen series(three identical specimens per each series)with dimensions of 40×40×7.5 cm and functional grading(three layers),and the manufactured specimens were subjected to the drop weight impact and projectile penetration tests by the drop weight testing machine and gas gun,respectively,to assess their performance.Parameters under study included the compressive strength,destruction level,and penetration depth.The experimental results demonstrate that using the magnetite aggregate instead of the natural coarse aggregate elevated the compressive strength of the concrete by 61%.In the tests by the drop weight machine,it was observed that by increasing the total vol%of the fibers,especially by increasing the fiber content in the outer layers(impact surface),the cracking resistance and energy absorption increased by around 100%.Note that the fiber geometry had little effect on the energy absorption in the drop weight test.Investigating the optimum specimens showed that using 3D steel fibers at a total fiber content of 1 vol%,consisting of a layered grading of 1.5 vol%,0 vol%,and 1.5 vol%,improved the penetration depth by 76%and lowered the destruction level by 85%.In addition,incorporating the 5D steel fibers at a total fiber content of 1 vol%,consisting of the layered fiber contents of 1.5%,0%,and 1.5%,improved the projectile penetration depth by 50%and lowered the damage level by 61%compared with the case of using the 3D fibers.
基金supported by Osmaniye Korkut Ata University Scientific Research Projects Unit(Project No:OKüBAP-2024-PT1-015)。
文摘Soil liquefaction,a seismic-induced phenomenon,is of significant concern in geotechnical engineering due to its potential to cause severe structural damage and ground instability during earthquakes.This study explores the prediction of the Liquefaction Severity Index(LSI)by integrating extensive borehole investigation data with seismic records from the Kahramanmara?(M_(w)7.8)and Hatay(M_(w)6.4)earthquakes that occurred in 2023.Nine machine learning models,Random Forest(RF),M5P,REPTree,IBk,Random Tree(RT),Gaussian Processes(GP),SMOreg,Locally Weighted Learning(LWL),and Linear Regression(LR),were employed with 10-fold cross-validation to ensure reliable predictions.Twelve geotechnical and seismic parameters,groundwater level,earthquake magnitude,peak ground acceleration,V_(s30),dominant frequency,dominant period,longitudinal wave velocity,dynamic modulus of elasticity,dynamic shear modulus,modulus of incompressibility,standard penetration test(SPT)values,and cyclic stress ratio(CSR)values,were utilized as inputs.The analysis results were evaluated with respect to RMSE,MAE,R2,RAE,P/M,error category limits,Taylor diagram,and relative importance of input parameters.Among the models,Random Forest outperformed with an R2 of 0.94,MAE of 2.35,with minimal prediction errors,followed by M5P and REPTree.Error analysis indicated that 80%of Random Forest and REPTree predictions fell within±7,while M5P showed slightly higher variability.Model-based feature ranking demonstrated that Cyclic Stress Ratio(CSR),Ground Water Level(GWL),and Standard Penetration Test(SPT)value emerged as dominant predictors.These findings highlight the study’s contribution to developing a reliable,datadriven framework for LSI prediction,offering a robust basis for improving site-specific liquefaction risk assessment and informed geotechnical decisionmaking in future seismic events.
基金supported by the Ministry of Education,Culture,Research,and Technology(Indonesia),Grant number 107/E5/PG.02.00.PL/2024,AZ.
文摘Concrete is one of the most important elements in building construction.However,concrete used in construction is susceptible to damage due to corrosion.The influence of corrosive substances causes changes in the reinforcing steel and affects the strength of the structure.The repair method is one approach to overcome this problem.This research aims to determine the effect of grouting and jacketing repairs on corroded concrete.The concrete used has dimensions of 15 cm×15 cm×60 cm with planned corrosion variations of 50%,60%,and 70%.The test objects were tested using the Non-Destructive Testing(NDT)method using Ultrasonic Pulse Velocity(UPV).The test results show that the average speed of normal concrete is 5070 m/s,while the lowest average speed is 3070 m/s on the 70%planned corrosion test object.The test object was then given a load of 1600 kgf.At this stage,there is a decrease in speed and wave shape with the lowest average speed obtained at 2753 m/s.The repair method is an effort to restore concrete performance by using grouting and jacketing.Grouting is done by injecting mortar material into it.Jacketing involves adding thickness to the existing concrete layer with additional layers of concrete.After improvements were made,there was an improvement in the UPV test,with a peak speed value of 4910 m/s.Repairing concrete by filling cracks can improve concrete continuity and reduce waveform distortion,thereby increasing wave propagation speed.
文摘Professional and trade skills are required for handling the construction related projects;Construction industries of the present day however lack useful information concerning different practices,patterns and trends involved in risk management.Considering this,the present study focuses on the aforementioned variables of risk management by quantitative analysis specifically in the domain of construction industry.This study has used IBM’s SPSS(Statistical Package for Social Sciences)version 25.0 to analyze the results.This study is an initiative to assess the impact of risk management in the construction sector of Jordan.It will assist the construction sector for exploring the limitations with respect to integrate effective risk management.A sense of competition will be developed through a comparison of risk factors of construction projects among the project stakeholders such as contractors should enhance their risk management practices.
基金funded by Thailand Research Fund under Research and Researchers for Industries (contract no. MSD62I0063)
文摘This research addresses the growing demand for high-performance protective materials against high-velocity projectile impacts.The performance of multi-layered steel fiber-reinforced mortar(SFRM)panels with varying thicknesses and air gaps,was experimentally investigated under single and repeated impacts of 7.62×51 mm bullets fired from a distance of 50 m.The impact events were recorded using a high-speed camera at 40000 fps.Panel performance was assessed in terms of failure modes,kinetic energy absorption,spalling diameter,and percentage of back-face damage area,and weight loss.Results showed that panel configuration significantly influenced performance.Panel P10,with 70 mm SFRM thickness and 20 mm air gaps,provided the highest resistance,dissipating 5223 J of kinetic energy and preventing back-face damage.In contrast,P7,which absorbed 4476 J,presented a back damage area percentage of 8.93%after three impacts.Weight loss analysis further confirmed durability improvements,with P10 showing only 1.53%cumulative loss compared to 3.26%in P7.The inclusion of wider air gaps enhanced energy dissipation and reduced damage.Comparison between single and repeated impacts demonstrated the sustained resistance of high-performance panels,with P10 maintaining minimal degradation across three consecutive impacts.These findings highlight the potential of multi-layer SFRM panels to enhance ballistic resistance,making them suitable for military,security,and civilian protective applications requiring long-term durability.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.DUT24GJ205)the Open Fund of Key Laboratory of Deep Earth Science and Engineering,Ministry of Education(Sichuan University)(Grant No.DESEYU202303).
文摘Structures constructed on collapsible soil are prone to failure under flooding.Agro-waste like rice husk ash(RHA)and its geopolymer(LGR),consisting of lime(L),RHA,water glass(Na2SiO3),and caustic soda(NaOH),present a potential solution to address this issue.RHA and LGR were mixed up to 16%to improve the collapsible soil.Samples were remolded at optimal water content and maximum dry density for strength and collapsible potential tests.Unconfined compressive strength,deformation modulus,and soaked California bearing ratio exhibit exponential improvement with the inclusion of LGR.Additionally,for comparison of microstructural characteristics,analyses involving energy-dispersive X-ray spectroscopy(EDAX)and scanning electron microscope(SEM)were conducted on both virgin and treated specimens.LGR resulted in the emergence of new peaks of sodium silicates and calcium silicates,as indicated by EDAX.The formation of H-C-A-S gel and H-N-A-S gel observed in SEM suggests the development of bonds among soil particles attributed to geopolymerization.SEM reveals the transformation of the inherent collapsible soil from a dispersed and silt-dominated structure to a reticulated structure devoid of micro-pores following the incorporation of LGR.A numerical model was constructed to forecast the performance of both virgin and stabilized collapsible soils under pre-and post-flooding conditions.The outcomes indicate an enhancement in the soil's bearing capacity upon stabilization with 12%LGR.The implementation of 12%LGR significantly resulted in a lower embodied energy-tostrength ratio,emissions-to-strength ratio,and relatively lower cost-to-strength ratio compared to the soil treated with 16%cement kiln dust(CKD).
文摘This paper aims to evaluate the stochastic response of steel columns subjected to blast loads using the modified single degree of freedom(MSDOF)method,which assessed towards the conventional single degree of freedom(SDOF)and the experimentally validated Finite Element(FE)methods(LSDYNA).For this purpose,special atten-tion is given to calculating the response of H-shaped steel columns under blast.The damage amount is determined based on the support rotation criterion,which is expressed as a function of their maximum lateral mid-span dis-placement.To account for uncertainties in input parameters and obtain the failure probability,the Monte Carlo simulation(MCS)method is employed,complemented by the Latin Hypercube Sampling(LHS)method to reduce the number of simulations.A parametric analysis is hence performed to examine the effect of several input pa-rameters(including both deterministic and probabilistic parameters)on the probability of column damage as a function of support rotation.First,the MSDOF method confirms its higher accuracy in estimating the probability of column damage due to blast,compared to the conventional SDOF.The collected results also show that un-certainties of several input parameters have significant effects on the column behavior.In particular,geometric parameters(including cross-sectional characteristics,boundary conditions and column length)have major effect on the corresponding column response,in the same way of input blast load parameters and material properties.
基金the support of the Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme(FRGS)(No.FRGS/1/2022/TK01/UM/02/2)the Young Innovative Talent Project-Guangdong Scientific Research Platform and Projects for the Higher-educational Institution&Education Science Planning Scheme(No.KY2022036401)+3 种基金University-level scientific research institution project(No.KY2023000401)Characteristic innovation project of colleges and universities in Guangdong Province(No.2021KTSCX191)Science and Technology developing Project of Dongguan City(No.20211800904572)the Instrument of Dongguan city college and Universiti Malaya for technical support。
文摘Global climate change is the most serious challenge that modern society faces.Soil-biochar carbon sequestration is a promising natural solution for capturing carbon.This study monitored the CO_(2) emissions of five biochar incubated Malaysian Tropical soils(MT-Soil).The recalcitrance index of palm kernel shell biochar(PKS)was higher than that of wood chip biochar(WCB),bamboo biochar(BB),coconut shell biochar(CHB)and rice husk biochar(RHB),and was different from the observed CO_(2) emission characteristics(WCB>CHB>RHB>BB>PKS).Thus,the carbon sequestration potential of biochar could not be evaluated solely by the recalcitrance index.This CO_(2) emission is linked not only to the total organic carbon(TOC)and total carbon(TC)of the biochar but also associated with mobile matter(MM),water holding capacity(WHC),available phosphorus(AP),exchangeable potassium(AK),and nitrogen content.The multiple linear regression analysis(MLRA)shows that the weights of these factors on CO_(2) emissions are as follows:TC>pH>MM>WHC>AP>AK.The results show that in addition to biochar stability,pore structure and available phosphorus release also affect carbon dynamics through indirect effects on microbial activity.This means that to minimize CO_(2) emissions during application of biochar,it is necessary to use soil that is rich in phosphorus and biochar that has undeveloped pore structure and high stable carbon.Finally,this study provides valuable theoretical underpinnings biochar application in MT-Soil.
基金supported by the Research Grant Council of the Hong Kong Special Administrative Region,China(Grant Nos.HKU 17207518 and R5037-18).
文摘The nine typical Shanghai soils are usually silty clay or clay,which appears inconsistent with their low clay content in the relevant publications.The literature review shows that the documented clay content of Shanghai soils ranges from 0%to 30.8%by weight.This inconsistency may stem from two factors:(1)the Shanghai soil classification system relies solely on the plasticity index for soil naming;and(2)the conventional steel sieving method cannot separate the clay from the fine soils(clay and silt mixtures).This paper aims to accurately determine the clay content in Shanghai soils.It uses nylon cloth sieves with apertures ranging from 0.063 mm to 0.0008 mm and completely separates the clay particles from the fine soils.The nine typical Shanghai soils are tested and sieved into distinct subgroups of clay,silt,sand,and gravel particles.Results demonstrate clay content ranges from 18.99%to 79.33%,substantially higher than literature values and consistent with their names of either silty clay or clay.Macro,micro,and scanning electron microscope(SEM)images reveal effective separation of clay,silt,sand,and gravel particles.The clay exhibits cohesive properties,while the silt,sand,and gravel comprise clean,non-cohesive individual particles.The clay and silt fractions are confirmed to be within their respective sieving limits by SEM-based particle size measurements.Additionally,Atterberg limits testing highlights the high plasticity of the clay particles and the non-plastic nature of the silt particles.
文摘Clay deposits typically exhibit significant degrees of heterogeneity and anisotropy in their strength and stiffness properties.Such non-monotonic responses can significantly impact the stability analysis and design of overlying shallow foundations.In this study,the undrained bearing capacity of shallow foundations resting on inhomogeneous and anisotropic clay layers subjected to oblique-eccentric combined loading is investigated through a comprehensive series of finite element limit analysis(FELA)based on the well-established lower-bound theorem and second-order cone programming(SOCP).The heterogeneity of normally consolidated(NC)clays is simulated by adopting a well-known general model of undrained shear strength increasing linearly with depth.In contrast,for overconsolidated(OC)clays,the variation of undrained shear strength with depth is considered to follow a bilinear trend.Furthermore,the inherent anisotropy is accounted for by adopting different values of undrained shear strength along different directions within the soil medium,employing an iterative-based algorithm.The results of numerical simulations are utilized to investigate the influences of natural soil heterogeneity and inherent anisotropy on the ultimate bearing capacity,failure envelope,and failure mechanism of shallow foundations subjected to the various combinations of vertical-horizontal(V-H)and vertical-moment(V-M)loads.
文摘Ultrahigh-performance concrete(UHPC)is a groundbreaking kind of concrete that distinguishes itself from conventional concrete through its unique material properties.Understanding and managing the time-dependent characteristics of these materials is essential for their effective use in various construction applications.This study presents an experimental evaluation of the compressive and bending properties of the UHPC incorporating polypropylene,steel,and glass fibers.Based on ACI-211 guidelines,the UHPC mix was designed by using three types of aggregates:limestone,andesite,and quartzite,along with 5%fiber content(at varying percentages of 0,5%,10%,15%,and 20%)relative to the cementitious materials,and three different water-to-cement(w/c)ratios(0.24,0.3,and 0.4)were used.In this research,the compressive and flexural strength tests were conducted.The results show that increasing the values of the fibers significantly enhances the compressive strength of the studied samples.Furthermore,the utilization of fibers markedly improves the bending strength of the samples,demonstrating a strong correlation with the yield resistance of the material.Also,findings show that using steel fibers increases the compressive and bending strength of the tested samples more than polypropylene and glass fibers.For instance,in UHPC samples with 0.4 w/c,the average compressive strength values are 82.2 MPa,70.3 MPa,and 67.1 MPa for steel,polypropylene,and glass fibers,respectively.Also,in the flexural strength test,the modulus of rupture is obtained as an average of 6.24 MPa,5.24 MPa and 4.83 MPa for UHPC samples with steel,polypropylene and glass fibers,respectively.
文摘Aceh in Indonesia is rich inmarine resources and abundant fishery products such as oyster.Traditionally,fishermen only harvest oysters and discard the shells,which can cause pollution and environmental contamination.Waste Oyster Shells(WOS)contain a high percentage of calcium carbonate(CaCO_(3))that experiences thermal decomposition at high temperature,following the reaction CaCO_(3)→CaO+CO_(2)(ΔT=825℃).At temperature>900℃,dead-burned lime is formed,which severely influences CaO reactivity.However,the optimum temperature for producing high CaO content is still uncertain.Therefore,this study aimed to determine the optimum calcination temperature to produce high CaO content,assess initial setting time of WOS paste,and identify the best compressive strength of paste.For the experiment,WOS was used as a partial cement replacement(with a size of 0.075 mm)in paste at a proportion of 5%and calcined at temperature of 700℃,800℃,900℃,and 1000℃.The specimens used were an ebonite ring(dimensions:70 mm bottom diameter,60 mm top diameter,and 40 mm height)and a cube(dimensions:5 cm×5 cm×5 cm).The experiment was conducted following the ASTM(American Society for Testing andMaterials)standards and optimumcompressive strength values were analyzed using ANOVA(Analysis of Variance)and Response Surface Methodology(RSM)through the Design Expert software.The results showed that WOS calcined at 1000℃ increased CaO content by approximately 57.40%.Furthermore,the initial setting time test of 5%WOS paste at 1000℃ showed a more uniform binding performance compared to conventional cement paste,with an initial setting time of 75 min and a penetration depth of 15 mm.In line with the analysis,optimum compressive strength of 71.028 MPa with a desirability value of 0.986 was achieved at 5%cement replacement and calcination temperature of 786.44℃.
文摘The precise identification of quartz minerals is crucial in mineralogy and geology due to their widespread occurrence and industrial significance.Traditional methods of quartz identification in thin sections are labor-intensive and require significant expertise,often complicated by the coexistence of other minerals.This study presents a novel approach leveraging deep learning techniques combined with hyperspectral imaging to automate the identification process of quartz minerals.The utilizied four advanced deep learning models—PSPNet,U-Net,FPN,and LinkNet—has significant advancements in efficiency and accuracy.Among these models,PSPNet exhibited superior performance,achieving the highest intersection over union(IoU)scores and demonstrating exceptional reliability in segmenting quartz minerals,even in complex scenarios.The study involved a comprehensive dataset of 120 thin sections,encompassing 2470 hyperspectral images prepared from 20 rock samples.Expert-reviewed masks were used for model training,ensuring robust segmentation results.This automated approach not only expedites the recognition process but also enhances reliability,providing a valuable tool for geologists and advancing the field of mineralogical analysis.
