With the growing demand for sustainable development in the mining industry,cemented paste backfill(CPB)materials,primarily composed of tailings,play a crucial role in mine backfilling and underground support systems.T...With the growing demand for sustainable development in the mining industry,cemented paste backfill(CPB)materials,primarily composed of tailings,play a crucial role in mine backfilling and underground support systems.To enhance the mechanical properties of CPB materials,fiber reinforcement technology has gradually gained attention,though challenges remain in predicting its performance.This study develops a hybrid model based on the adaptive equilibrium optimizer(adap-EO)-enhanced XGBoost method for accurately predicting the uniaxial compressive strength of fiber-reinforced CPB.Through systematic comparison with various other machine learning methods,results demonstrate that the proposed hybridmodel exhibits excellent predictive performance on the test set,achieving a coefficient of determination(R^(2))of 0.9675,root mean square error(RMSE)of 0.6084,and mean absolute error(MAE)of 0.4620.Input importance analysis reveals that cement-tailings ratio,curing time,and concentration are the three most critical factors affectingmaterial strength,with cement-tailings ratio showing a positive correlation with strength,concentrations above 70% significantly improvingmaterial strength,and curing periods beyond 28 days being essential for strength development.Fiber parameters contribute secondarily but notably to material strength,with fiber length exhibiting an optimal range of approximately 12 mm.This study not only provides a high-precision strength prediction model but also reveals the inherent correlations between various parameters and material performance,offering scientific basis for mixture optimization and engineering applications of fiber-reinforced CPB materials.展开更多
Real-time identification of rock strength and cuttability based on monitoring while cutting during excavation is essential for key procedures such as the precise adjustment of excavation parameters and the in-situ mod...Real-time identification of rock strength and cuttability based on monitoring while cutting during excavation is essential for key procedures such as the precise adjustment of excavation parameters and the in-situ modification of hard rocks.This study proposes an in-telligent approach for predicting rock strength and cuttability.A database comprising 132 data sets is established,containing cutting para-meters(such as cutting depth and pick angle),cutting responses(such as specific energy and instantaneous cutting rate),and rock mech-anical parameters collected from conical pick-cutting experiments.These parameters serve as input features for predicting the uniaxial compressive strength and tensile strength of rocks using regression fitting and machine learning methodologies.In addition,rock cuttabil-ity is classified using a combination of the analytic hierarchy process and fuzzy comprehensive evaluation method,and subsequently iden-tified through machine learning approaches.Various models are compared to determine the optimal predictive and classification models.The results indicate that the optimal model for uniaxial compressive strength and tensile strength prediction is the genetic algorithm-optimized backpropagation neural network model,and the optimal model for rock cuttability classification is the radial basis neural network model.展开更多
Rock strength evaluation is critical in oil and gas exploration,but traditional methods,such as empirical formulas,laboratory tests,and numerical simulations,often struggle with accuracy,generalizability,and alignment...Rock strength evaluation is critical in oil and gas exploration,but traditional methods,such as empirical formulas,laboratory tests,and numerical simulations,often struggle with accuracy,generalizability,and alignment with field conditions.This study proposes the use of Random Forest and Transformer algorithms to predict rock strength from Elemental Capture Spectroscopy(ECS)logs.By utilizing the dry weight of minerals as input,the model predicts key mechanical properties,including Young's modulus,Poisson's ratio,bulk modulus,shear modulus,and uniaxial compressive strength.The findings demonstrate that mineral compositions,such as clay,quartz-feldspar-mica,carbonate,anhydrite,and pyrite,significantly influence rock strength.Specifically,clay content impacts Young's modulus,bulk modulus,and shear modulus,while quartz-feldspar-mica affects Poisson's ratio,and anhydrite is the primary factor influencing compressive strength.Positive correlations were observed between rock strength and the dry weight of anhydrite and carbonate minerals,while negative correlations emerged with clay,pyrite,and quartz-feldspar-mica.The Random Forest model outperformed the Transformer model in terms of predictive accuracy and computational efficiency.Its training time is only one three hundredth of the latter and its prediction time is just one tenth of the later,making it highly suitable for welllogging interpretation.Although the Transformer model was less computationally efficient,it exhibited strengths in predicting subsurface strength parameters,particularly in capturing spatial variations and forecasting these parameters across different spatial locations.This study introduces a novel AI-driven approach to rock strength evaluation,bridging the gap between mineral composition and mechanical properties,with significant implications for resource extraction and reservoir management.展开更多
The increasing demand for sustainable construction practices has led to growing interest in recycled aggregate concrete(RAC)as an eco-friendly alternative to conventional concrete.However,predicting its compressive st...The increasing demand for sustainable construction practices has led to growing interest in recycled aggregate concrete(RAC)as an eco-friendly alternative to conventional concrete.However,predicting its compressive strength remains a challenge due to the variability in recycled materials and mix design parameters.This study presents a robust machine learning framework for predicting the compressive strength of recycled aggregate concrete using feedforward neural networks(FFNN),Random Forest(RF),and XGBoost.A literature-derived dataset of 502 samples was enriched via interpolation-based data augmentation and modeled using five distinct optimization techniques within MATLAB’s Neural Net Fitting module:Bayesian Regularization,Levenberg-Marquardt,and three conjugate gradient variants—Powell/Beale Restarts,Fletcher-Powell,and Polak-Ribiere.Hyperparameter tuning,dropout regularization,and early stopping were employed to enhance generalization.Comparative analysis revealed that FFNN outperformed RF and XGBoost,achieving an R2 of 0.9669.To ensure interpretability,accumulated local effects(ALE)along with partial dependence plots(PDP)were utilized.This revealed trends consistent with the pre-existent domain knowledge.This allows estimation of strength from the properties of the mix without extensive lab testing,permitting designers to track the performance and sustainability trends in concrete mix designs while promoting responsible construction and demolition waste utilization.展开更多
Machine learning(ML)models are widely used for predicting undrained shear strength(USS),but interpretability has been a limitation in various studies.Therefore,this study introduced shapley additive explanations(SHAP)...Machine learning(ML)models are widely used for predicting undrained shear strength(USS),but interpretability has been a limitation in various studies.Therefore,this study introduced shapley additive explanations(SHAP)to clarify the contribution of each input feature in USS prediction.Three ML models,artificial neural network(ANN),extreme gradient boosting(XGBoost),and random forest(RF),were employed,with accuracy evaluated using mean squared error,mean absolute error,and coefficient of determination(R^(2)).The RF achieved the highest performance with an R^(2) of 0.82.SHAP analysis identified pre-consolidation stress as a key contributor to USS prediction.SHAP dependence plots reveal that the ANN captures smoother,linear feature-output relationships,while the RF handles complex,non-linear interactions more effectively.This suggests a non-linear relationship between USS and input features,with RF outperforming ANN.These findings highlight SHAP’s role in enhancing interpretability and promoting transparency and reliability in ML predictions for geotechnical applications.展开更多
This work aims to evaluate the feasibility of the fabrication of nanostructured Cu/Al/Ag multi-layered composites by accumulative roll bonding(ARB),and to analyze the tensile properties and electrical conductivity of ...This work aims to evaluate the feasibility of the fabrication of nanostructured Cu/Al/Ag multi-layered composites by accumulative roll bonding(ARB),and to analyze the tensile properties and electrical conductivity of the produced composites.A theoretical model using strengthening mechanisms and some structural parameters extracted from X-ray diffraction is also developed to predict the tensile strength of the composites.It was found that by progression of ARB,the experimental and calculated tensile strengths are enhanced,reach a maximum of about 450 and 510 MPa at the fifth cycle of ARB,respectively and then are reduced.The electrical conductivity decreased slightly by increasing the number of ARB cycles at initial ARB cycles,but the decrease was intensified at the final ARB cycles.In conclusion,the merit of ARB to fabricate this type of multi-layered nanocomposites and the accuracy of the developed model to predict tensile strength were realized.展开更多
In order to improve the prediction accuracy of compressive strength of concrete,103 groups of concrete data were collected as the samples.We selected seven kinds of ingredients from the concrete samples, using Grid-SV...In order to improve the prediction accuracy of compressive strength of concrete,103 groups of concrete data were collected as the samples.We selected seven kinds of ingredients from the concrete samples, using Grid-SVM, PSO-SVM, and GA-SVM models to establish the prediction model of cubic meter compressive strength of concrete.The experimental results show that SVM model based on Grid optimization algorithm,SVM model based on Particle swarm optimization algorithm,SVM model based on Genetic optimization algorithm mean square error respectively are 0.001, 0.489 8, and 0.304 2, correlation coefficients are 0.994 8, 0.994 6, and 0.993 0. It is shown that cubic meter compressive strength prediction method based on Grid-SVM model is the best optimization algorithm.展开更多
The rule of infant age concrete strength development under low temperature and complex affecting factors is researched. An efficient and reliable mathematical forecast model is set up to predict the infant age concret...The rule of infant age concrete strength development under low temperature and complex affecting factors is researched. An efficient and reliable mathematical forecast model is set up to predict the infant age concrete antifreeze critical strength under low temperature at construction site. On the basis of the revision of concrete equivalent coefficient under complex influencing factors, least-squares curve-fitting method is applied to approximate the concrete strength under standard curing and the forecast formula of concrete compressive strength could be obtained under natural temperature condition by various effects. When the amounts of double-doped are 10% fly ashes and 4% silica fumes as coment replacement, the antifreeze critical strength changes form 3.5-4.1MPa under different low temperature curing. The equivalent coefficient correction formula of concrete under low temperature affected by various factors could be obtained. The obtained equivalent coefficient is suitable for calculating the strength which is between 10% to 40% of standard strength and the curing temperature from 5-20 ℃. The forecast value of concrete antifreeze critical strength under low temperature could be achieved by combining the concrete antifreeze critical strength value with the compressive strength forecast of infant age concrete under low temperature. Then the theory for construction quality control under low temperature is provided.展开更多
The effects of Si content on the microstructure and yield strength of Al-(1.44-12.40)Si-0.7 Mg(wt.%)alloy sheets under the T4 condition were systematically studied via laser scanning confocal microscopy(LSCM),DSC,TEM ...The effects of Si content on the microstructure and yield strength of Al-(1.44-12.40)Si-0.7 Mg(wt.%)alloy sheets under the T4 condition were systematically studied via laser scanning confocal microscopy(LSCM),DSC,TEM and tensile tests.The results show that the recrystallization grain of the alloy sheets becomes more refined with an increase in Si content.When the Si content increases from 1.44 to 12.4 wt.%,the grain size of the alloy sheets decreases from approximately 47 to 10μm.Further,with an increase in Si content,the volume fraction of the GP zones in the matrix increases slightly.Based on the existing model,a yield strength model for alloy sheets was proposed.The predicted results are in good agreement with the actual experimental results and reveal the strengthening mechanisms of the Al-(1.44-12.40)Si-0.7 Mg alloy sheets under the T4 condition and how they are influenced by the Si content.展开更多
Landslides are common natural hazards occurring in most parts of the world and have considerable adverse economic effects. Residual shear strength of clay is one of the most important factors in the determination of s...Landslides are common natural hazards occurring in most parts of the world and have considerable adverse economic effects. Residual shear strength of clay is one of the most important factors in the determination of stability of slopes or landslides. This effect is more pronounced in sensitive clays which show large changes in shear strength from peak to residual states. This study analyses the prediction of the residual strength of clay based on a new prediction model, functional networks（FN） using data available in the literature. The performance of FN was compared with support vector machine（SVM） and artificial neural network（ANN） based on statistical parameters like correlation coefficient（R）, Nash–Sutcliff coefficient of efficiency（E）, absolute average error（AAE）, maximum average error（MAE） and root mean square error（RMSE）. Based on R and E parameters, FN is found to be a better prediction tool than ANN for the given data. However, the R and E values for FN are less than SVM. A prediction equation is presented that can be used by practicing geotechnical engineers. A sensitivity analysis is carried out to ascertain the importance of various inputs in the prediction of the output.展开更多
Traditional laboratory tests for measuring rock uniaxial compressive strength(UCS)are tedious and timeconsuming.There is a pressing need for more effective methods to determine rock UCS,especially in deep mining envir...Traditional laboratory tests for measuring rock uniaxial compressive strength(UCS)are tedious and timeconsuming.There is a pressing need for more effective methods to determine rock UCS,especially in deep mining environments under high in-situ stress.Thus,this study aims to develop an advanced model for predicting the UCS of rockmaterial in deepmining environments by combining three boosting-basedmachine learning methods with four optimization algorithms.For this purpose,the Lead-Zinc mine in Southwest China is considered as the case study.Rock density,P-wave velocity,and point load strength index are used as input variables,and UCS is regarded as the output.Subsequently,twelve hybrid predictive models are obtained.Root mean square error(RMSE),mean absolute error(MAE),coefficient of determination(R2),and the proportion of the mean absolute percentage error less than 20%(A-20)are selected as the evaluation metrics.Experimental results showed that the hybridmodel consisting of the extreme gradient boostingmethod and the artificial bee colony algorithm(XGBoost-ABC)achieved satisfactory results on the training dataset and exhibited the best generalization performance on the testing dataset.The values of R2,A-20,RMSE,and MAE on the training dataset are 0.98,1.0,3.11 MPa,and 2.23MPa,respectively.The highest values of R2 and A-20(0.93 and 0.96),and the smallest RMSE and MAE values of 4.78 MPa and 3.76MPa,are observed on the testing dataset.The proposed hybrid model can be considered a reliable and effective method for predicting rock UCS in deep mines.展开更多
This article describes GIS-based models successfully developed for predicting the coverage of Cityphone cellular network,visualizing the predicted signal strength,and analyzing the field strength coverage.In order to ...This article describes GIS-based models successfully developed for predicting the coverage of Cityphone cellular network,visualizing the predicted signal strength,and analyzing the field strength coverage.In order to predict the signal coverage strength of communication network more accurately,the spatial and nonspatial databases of a mobile cellular network are combined by GIS and produce the necessary parameters.A GIS model named COST-231-Walfisch–Ikegami model(WIM)is developed for signal coverage prediction in Ho Chi Minh City.Radio-line-of-sight and nonradio-lineof-sight conditions can be determined by this model.In addition,in case of nonradio-line-of-sight conditions,average building height,building separation,building width,incident radio path,and road orientation with respect to the direct radio path were obtained using GIS.Road orientation loss,multiscreen diffraction loss,and shadowing gain were predicted more accurate by this model.The scale of maps in the experiment was 1:2000 and the average of floor height was 3 m because there were no exact building height measurements.Statistical results show that the path loss predicted by the COST 231 WIM overcame the real path loss of each cell station.And this method can be used for signal coverage prediction of mobile cellular network in urban areas.Compared to the current situation with the Ho Chi Minh City Posts and Telecommunications system,this model can be effectively applied to improve the Cityphone mobile network quality as well as capability.Developed GIS models can help designers in predicting cell station coverage using real spatial maps that make the results more reliable.This research can help network operators improve the network quality and capability with the best investment efficiently.展开更多
The bursting strength is an essential quality parameter of knit fabric. The fabric structure, weight, types of fibers, and fiber blend proportion influence the bursting strength parameter. The tenacity of polyester fi...The bursting strength is an essential quality parameter of knit fabric. The fabric structure, weight, types of fibers, and fiber blend proportion influence the bursting strength parameter. The tenacity of polyester fiber is better than cotton and spandex. The study focused on predicting knit fabric bursting strength test value using different fibers (cotton, polyester, and spandex) with varying percentages of the blend ratio. This study used fifteen categories of blended fabrics. The Pearson Correlation and the hypothetical ANOVA regression analysis were conducted to do the statistical significance test. The experimental result reveals that the bursting strength test result increased with the increased percentage of polyester and suggested a suitable regression equation. The dominance of the polyester fiber was observed throughout the experiment, i.e., the higher the polyester blend proportion, the higher the bursting strength value. The inclusion of polyester in blends can reduce the cost of fabric. The developed prediction model or equation can help the fabric manufacturer make appropriate decisions regarding getting the expected bursting strength. The researcher hopes that the findings from this study will motivate new researchers, advanced researchers, and the textile manufacturing industry.展开更多
Welding defects influence the desired properties of welded joints giving fabrication experts a common problem of not being able to produce weld structures with optimal strength and quality. In this study, the fuz...Welding defects influence the desired properties of welded joints giving fabrication experts a common problem of not being able to produce weld structures with optimal strength and quality. In this study, the fuzzy logic system was employed to predict welding tensile strength. 30 sets of welding experiments were conducted and tensile strength data was collected which were converted from crisp variables into fuzzy sets. The result showed that the fuzzy logic tool is a highly effective tool for predicting tensile strength present in TIG mild steel weld having a coefficient of determination value of 99%.展开更多
Accurate prediction of compressive strength of concrete is one of the key issues in the concrete industry. In this paper, a prediction method of fly ash-slag concrete compressive strength based on multiple adaptive re...Accurate prediction of compressive strength of concrete is one of the key issues in the concrete industry. In this paper, a prediction method of fly ash-slag concrete compressive strength based on multiple adaptive regression splines (MARS) is proposed, and the model analysis process is determined by analyzing the principle of this algorithm. Based on the Concrete Compressive Strength dataset of UCI, the MARS model for compressive strength prediction was constructed with cement content, blast furnace slag powder content, fly ash content, water content, reducing agent content, coarse aggregate content, fine aggregate content and age as independent variables. The prediction results of artificial neural network (BP), random forest (RF), support vector machine (SVM), extreme learning machine (ELM), and multiple nonlinear regression (MnLR) were compared and analyzed, and the prediction accuracy and model stability of MARS and RF models had obvious advantages, and the comprehensive performance of MARS model was slightly better than that of RF model. Finally, the explicit expression of the MARS model for compressive strength is given, which provides an effective method to achieve the prediction of compressive strength of fly ash-slag concrete.展开更多
为探究混凝土在碳化、冻融循环及硫酸盐侵蚀作用下的抗冻性作用机理,建立适用于混凝土相对动弹性模量的预测模型,提出改进的随机森林(Improved Random Forest,IRF)算法。以水胶比、粉煤灰掺量、聚丙烯纤维掺量、Na_(2)SO_(4)溶液质量分...为探究混凝土在碳化、冻融循环及硫酸盐侵蚀作用下的抗冻性作用机理,建立适用于混凝土相对动弹性模量的预测模型,提出改进的随机森林(Improved Random Forest,IRF)算法。以水胶比、粉煤灰掺量、聚丙烯纤维掺量、Na_(2)SO_(4)溶液质量分数和抗压强度为输入变量,混凝土相对动弹性模量为输出变量,分别采用BP、SVR、RF及IRF算法建立预测模型,并基于决定系数R^(2)、均方根误差RMSE及平均绝对误差MAE对各模型的预测性能进行对比,结果表明,IRF算法在混凝土相对动弹性模量预测中表现最优,其对应的R^(2)、RMSE及MAE分别为0.965 6、0.026 2及0.022 8。研究发现,抗压强度与混凝土相对动弹性模量呈正相关关系,而水胶比、粉煤灰掺量、聚丙烯纤维掺量及Na_(2)SO_(4)溶液质量分数均与相对动弹性模量呈负相关;当水胶比为0.45、粉煤灰掺量为30%且聚丙烯纤维掺量为1%时,混凝土配合比达到最优。此外,IRF模型通过合理的参数调优,在叶子节点选择上实现了最佳平衡,有效提升了模型的预测精度与泛化能力。展开更多
基金funded by the National Natural Science Foundation of China(Grant 42177164)the Distinguished Youth Science Foundation of Hunan Province of China(2022JJ10073)supported by China Scholarship Council with the grant number of 202006370006.
文摘With the growing demand for sustainable development in the mining industry,cemented paste backfill(CPB)materials,primarily composed of tailings,play a crucial role in mine backfilling and underground support systems.To enhance the mechanical properties of CPB materials,fiber reinforcement technology has gradually gained attention,though challenges remain in predicting its performance.This study develops a hybrid model based on the adaptive equilibrium optimizer(adap-EO)-enhanced XGBoost method for accurately predicting the uniaxial compressive strength of fiber-reinforced CPB.Through systematic comparison with various other machine learning methods,results demonstrate that the proposed hybridmodel exhibits excellent predictive performance on the test set,achieving a coefficient of determination(R^(2))of 0.9675,root mean square error(RMSE)of 0.6084,and mean absolute error(MAE)of 0.4620.Input importance analysis reveals that cement-tailings ratio,curing time,and concentration are the three most critical factors affectingmaterial strength,with cement-tailings ratio showing a positive correlation with strength,concentrations above 70% significantly improvingmaterial strength,and curing periods beyond 28 days being essential for strength development.Fiber parameters contribute secondarily but notably to material strength,with fiber length exhibiting an optimal range of approximately 12 mm.This study not only provides a high-precision strength prediction model but also reveals the inherent correlations between various parameters and material performance,offering scientific basis for mixture optimization and engineering applications of fiber-reinforced CPB materials.
基金supported by the National Natural Science Foundation of China(Nos.52174099 and 52474168)the Science and Technology Innovation Program of Hunan Province,China(No.2023RC3050)+1 种基金the Natural Science Foundation of Hunan,China(No.2024JJ4064)the Open Fund of the State Key Laboratory of Safety Technology of Metal Mines(No.kfkt2023-01).
文摘Real-time identification of rock strength and cuttability based on monitoring while cutting during excavation is essential for key procedures such as the precise adjustment of excavation parameters and the in-situ modification of hard rocks.This study proposes an in-telligent approach for predicting rock strength and cuttability.A database comprising 132 data sets is established,containing cutting para-meters(such as cutting depth and pick angle),cutting responses(such as specific energy and instantaneous cutting rate),and rock mech-anical parameters collected from conical pick-cutting experiments.These parameters serve as input features for predicting the uniaxial compressive strength and tensile strength of rocks using regression fitting and machine learning methodologies.In addition,rock cuttabil-ity is classified using a combination of the analytic hierarchy process and fuzzy comprehensive evaluation method,and subsequently iden-tified through machine learning approaches.Various models are compared to determine the optimal predictive and classification models.The results indicate that the optimal model for uniaxial compressive strength and tensile strength prediction is the genetic algorithm-optimized backpropagation neural network model,and the optimal model for rock cuttability classification is the radial basis neural network model.
基金funded by General Program of National Natural Science Foundation of China(No.52274016,52374016)the Foundation of State Key Laboratory of Petroleum Resources and Prospecting(PRE/DX-2402)。
文摘Rock strength evaluation is critical in oil and gas exploration,but traditional methods,such as empirical formulas,laboratory tests,and numerical simulations,often struggle with accuracy,generalizability,and alignment with field conditions.This study proposes the use of Random Forest and Transformer algorithms to predict rock strength from Elemental Capture Spectroscopy(ECS)logs.By utilizing the dry weight of minerals as input,the model predicts key mechanical properties,including Young's modulus,Poisson's ratio,bulk modulus,shear modulus,and uniaxial compressive strength.The findings demonstrate that mineral compositions,such as clay,quartz-feldspar-mica,carbonate,anhydrite,and pyrite,significantly influence rock strength.Specifically,clay content impacts Young's modulus,bulk modulus,and shear modulus,while quartz-feldspar-mica affects Poisson's ratio,and anhydrite is the primary factor influencing compressive strength.Positive correlations were observed between rock strength and the dry weight of anhydrite and carbonate minerals,while negative correlations emerged with clay,pyrite,and quartz-feldspar-mica.The Random Forest model outperformed the Transformer model in terms of predictive accuracy and computational efficiency.Its training time is only one three hundredth of the latter and its prediction time is just one tenth of the later,making it highly suitable for welllogging interpretation.Although the Transformer model was less computationally efficient,it exhibited strengths in predicting subsurface strength parameters,particularly in capturing spatial variations and forecasting these parameters across different spatial locations.This study introduces a novel AI-driven approach to rock strength evaluation,bridging the gap between mineral composition and mechanical properties,with significant implications for resource extraction and reservoir management.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2503)。
文摘The increasing demand for sustainable construction practices has led to growing interest in recycled aggregate concrete(RAC)as an eco-friendly alternative to conventional concrete.However,predicting its compressive strength remains a challenge due to the variability in recycled materials and mix design parameters.This study presents a robust machine learning framework for predicting the compressive strength of recycled aggregate concrete using feedforward neural networks(FFNN),Random Forest(RF),and XGBoost.A literature-derived dataset of 502 samples was enriched via interpolation-based data augmentation and modeled using five distinct optimization techniques within MATLAB’s Neural Net Fitting module:Bayesian Regularization,Levenberg-Marquardt,and three conjugate gradient variants—Powell/Beale Restarts,Fletcher-Powell,and Polak-Ribiere.Hyperparameter tuning,dropout regularization,and early stopping were employed to enhance generalization.Comparative analysis revealed that FFNN outperformed RF and XGBoost,achieving an R2 of 0.9669.To ensure interpretability,accumulated local effects(ALE)along with partial dependence plots(PDP)were utilized.This revealed trends consistent with the pre-existent domain knowledge.This allows estimation of strength from the properties of the mix without extensive lab testing,permitting designers to track the performance and sustainability trends in concrete mix designs while promoting responsible construction and demolition waste utilization.
基金Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for supporting this study
文摘Machine learning(ML)models are widely used for predicting undrained shear strength(USS),but interpretability has been a limitation in various studies.Therefore,this study introduced shapley additive explanations(SHAP)to clarify the contribution of each input feature in USS prediction.Three ML models,artificial neural network(ANN),extreme gradient boosting(XGBoost),and random forest(RF),were employed,with accuracy evaluated using mean squared error,mean absolute error,and coefficient of determination(R^(2)).The RF achieved the highest performance with an R^(2) of 0.82.SHAP analysis identified pre-consolidation stress as a key contributor to USS prediction.SHAP dependence plots reveal that the ANN captures smoother,linear feature-output relationships,while the RF handles complex,non-linear interactions more effectively.This suggests a non-linear relationship between USS and input features,with RF outperforming ANN.These findings highlight SHAP’s role in enhancing interpretability and promoting transparency and reliability in ML predictions for geotechnical applications.
文摘This work aims to evaluate the feasibility of the fabrication of nanostructured Cu/Al/Ag multi-layered composites by accumulative roll bonding(ARB),and to analyze the tensile properties and electrical conductivity of the produced composites.A theoretical model using strengthening mechanisms and some structural parameters extracted from X-ray diffraction is also developed to predict the tensile strength of the composites.It was found that by progression of ARB,the experimental and calculated tensile strengths are enhanced,reach a maximum of about 450 and 510 MPa at the fifth cycle of ARB,respectively and then are reduced.The electrical conductivity decreased slightly by increasing the number of ARB cycles at initial ARB cycles,but the decrease was intensified at the final ARB cycles.In conclusion,the merit of ARB to fabricate this type of multi-layered nanocomposites and the accuracy of the developed model to predict tensile strength were realized.
基金Funded by Natioanl Natural Science Foundation of Chin a(Nos.2012BAJ11B00,41301588,41471339,41571514)the Center for Materials Research and Analysis,Wuhan University of Technology
文摘In order to improve the prediction accuracy of compressive strength of concrete,103 groups of concrete data were collected as the samples.We selected seven kinds of ingredients from the concrete samples, using Grid-SVM, PSO-SVM, and GA-SVM models to establish the prediction model of cubic meter compressive strength of concrete.The experimental results show that SVM model based on Grid optimization algorithm,SVM model based on Particle swarm optimization algorithm,SVM model based on Genetic optimization algorithm mean square error respectively are 0.001, 0.489 8, and 0.304 2, correlation coefficients are 0.994 8, 0.994 6, and 0.993 0. It is shown that cubic meter compressive strength prediction method based on Grid-SVM model is the best optimization algorithm.
基金the New Century College Outstanding Person Foundation of Liaoning(No.R-04-02)
文摘The rule of infant age concrete strength development under low temperature and complex affecting factors is researched. An efficient and reliable mathematical forecast model is set up to predict the infant age concrete antifreeze critical strength under low temperature at construction site. On the basis of the revision of concrete equivalent coefficient under complex influencing factors, least-squares curve-fitting method is applied to approximate the concrete strength under standard curing and the forecast formula of concrete compressive strength could be obtained under natural temperature condition by various effects. When the amounts of double-doped are 10% fly ashes and 4% silica fumes as coment replacement, the antifreeze critical strength changes form 3.5-4.1MPa under different low temperature curing. The equivalent coefficient correction formula of concrete under low temperature affected by various factors could be obtained. The obtained equivalent coefficient is suitable for calculating the strength which is between 10% to 40% of standard strength and the curing temperature from 5-20 ℃. The forecast value of concrete antifreeze critical strength under low temperature could be achieved by combining the concrete antifreeze critical strength value with the compressive strength forecast of infant age concrete under low temperature. Then the theory for construction quality control under low temperature is provided.
基金Project(2016YFB0300801)supported by the National Key Research and Development Program of ChinaProject(51871043)supported by the National Natural Science Foundation of ChinaProject(N180212010)supported by the Fundamental Research Funds for the Central Universities of China。
文摘The effects of Si content on the microstructure and yield strength of Al-(1.44-12.40)Si-0.7 Mg(wt.%)alloy sheets under the T4 condition were systematically studied via laser scanning confocal microscopy(LSCM),DSC,TEM and tensile tests.The results show that the recrystallization grain of the alloy sheets becomes more refined with an increase in Si content.When the Si content increases from 1.44 to 12.4 wt.%,the grain size of the alloy sheets decreases from approximately 47 to 10μm.Further,with an increase in Si content,the volume fraction of the GP zones in the matrix increases slightly.Based on the existing model,a yield strength model for alloy sheets was proposed.The predicted results are in good agreement with the actual experimental results and reveal the strengthening mechanisms of the Al-(1.44-12.40)Si-0.7 Mg alloy sheets under the T4 condition and how they are influenced by the Si content.
文摘Landslides are common natural hazards occurring in most parts of the world and have considerable adverse economic effects. Residual shear strength of clay is one of the most important factors in the determination of stability of slopes or landslides. This effect is more pronounced in sensitive clays which show large changes in shear strength from peak to residual states. This study analyses the prediction of the residual strength of clay based on a new prediction model, functional networks（FN） using data available in the literature. The performance of FN was compared with support vector machine（SVM） and artificial neural network（ANN） based on statistical parameters like correlation coefficient（R）, Nash–Sutcliff coefficient of efficiency（E）, absolute average error（AAE）, maximum average error（MAE） and root mean square error（RMSE）. Based on R and E parameters, FN is found to be a better prediction tool than ANN for the given data. However, the R and E values for FN are less than SVM. A prediction equation is presented that can be used by practicing geotechnical engineers. A sensitivity analysis is carried out to ascertain the importance of various inputs in the prediction of the output.
基金supported by the National Natural Science Foundation of China(Grant No.52374153).
文摘Traditional laboratory tests for measuring rock uniaxial compressive strength(UCS)are tedious and timeconsuming.There is a pressing need for more effective methods to determine rock UCS,especially in deep mining environments under high in-situ stress.Thus,this study aims to develop an advanced model for predicting the UCS of rockmaterial in deepmining environments by combining three boosting-basedmachine learning methods with four optimization algorithms.For this purpose,the Lead-Zinc mine in Southwest China is considered as the case study.Rock density,P-wave velocity,and point load strength index are used as input variables,and UCS is regarded as the output.Subsequently,twelve hybrid predictive models are obtained.Root mean square error(RMSE),mean absolute error(MAE),coefficient of determination(R2),and the proportion of the mean absolute percentage error less than 20%(A-20)are selected as the evaluation metrics.Experimental results showed that the hybridmodel consisting of the extreme gradient boostingmethod and the artificial bee colony algorithm(XGBoost-ABC)achieved satisfactory results on the training dataset and exhibited the best generalization performance on the testing dataset.The values of R2,A-20,RMSE,and MAE on the training dataset are 0.98,1.0,3.11 MPa,and 2.23MPa,respectively.The highest values of R2 and A-20(0.93 and 0.96),and the smallest RMSE and MAE values of 4.78 MPa and 3.76MPa,are observed on the testing dataset.The proposed hybrid model can be considered a reliable and effective method for predicting rock UCS in deep mines.
文摘This article describes GIS-based models successfully developed for predicting the coverage of Cityphone cellular network,visualizing the predicted signal strength,and analyzing the field strength coverage.In order to predict the signal coverage strength of communication network more accurately,the spatial and nonspatial databases of a mobile cellular network are combined by GIS and produce the necessary parameters.A GIS model named COST-231-Walfisch–Ikegami model(WIM)is developed for signal coverage prediction in Ho Chi Minh City.Radio-line-of-sight and nonradio-lineof-sight conditions can be determined by this model.In addition,in case of nonradio-line-of-sight conditions,average building height,building separation,building width,incident radio path,and road orientation with respect to the direct radio path were obtained using GIS.Road orientation loss,multiscreen diffraction loss,and shadowing gain were predicted more accurate by this model.The scale of maps in the experiment was 1:2000 and the average of floor height was 3 m because there were no exact building height measurements.Statistical results show that the path loss predicted by the COST 231 WIM overcame the real path loss of each cell station.And this method can be used for signal coverage prediction of mobile cellular network in urban areas.Compared to the current situation with the Ho Chi Minh City Posts and Telecommunications system,this model can be effectively applied to improve the Cityphone mobile network quality as well as capability.Developed GIS models can help designers in predicting cell station coverage using real spatial maps that make the results more reliable.This research can help network operators improve the network quality and capability with the best investment efficiently.
文摘The bursting strength is an essential quality parameter of knit fabric. The fabric structure, weight, types of fibers, and fiber blend proportion influence the bursting strength parameter. The tenacity of polyester fiber is better than cotton and spandex. The study focused on predicting knit fabric bursting strength test value using different fibers (cotton, polyester, and spandex) with varying percentages of the blend ratio. This study used fifteen categories of blended fabrics. The Pearson Correlation and the hypothetical ANOVA regression analysis were conducted to do the statistical significance test. The experimental result reveals that the bursting strength test result increased with the increased percentage of polyester and suggested a suitable regression equation. The dominance of the polyester fiber was observed throughout the experiment, i.e., the higher the polyester blend proportion, the higher the bursting strength value. The inclusion of polyester in blends can reduce the cost of fabric. The developed prediction model or equation can help the fabric manufacturer make appropriate decisions regarding getting the expected bursting strength. The researcher hopes that the findings from this study will motivate new researchers, advanced researchers, and the textile manufacturing industry.
文摘Welding defects influence the desired properties of welded joints giving fabrication experts a common problem of not being able to produce weld structures with optimal strength and quality. In this study, the fuzzy logic system was employed to predict welding tensile strength. 30 sets of welding experiments were conducted and tensile strength data was collected which were converted from crisp variables into fuzzy sets. The result showed that the fuzzy logic tool is a highly effective tool for predicting tensile strength present in TIG mild steel weld having a coefficient of determination value of 99%.
文摘Accurate prediction of compressive strength of concrete is one of the key issues in the concrete industry. In this paper, a prediction method of fly ash-slag concrete compressive strength based on multiple adaptive regression splines (MARS) is proposed, and the model analysis process is determined by analyzing the principle of this algorithm. Based on the Concrete Compressive Strength dataset of UCI, the MARS model for compressive strength prediction was constructed with cement content, blast furnace slag powder content, fly ash content, water content, reducing agent content, coarse aggregate content, fine aggregate content and age as independent variables. The prediction results of artificial neural network (BP), random forest (RF), support vector machine (SVM), extreme learning machine (ELM), and multiple nonlinear regression (MnLR) were compared and analyzed, and the prediction accuracy and model stability of MARS and RF models had obvious advantages, and the comprehensive performance of MARS model was slightly better than that of RF model. Finally, the explicit expression of the MARS model for compressive strength is given, which provides an effective method to achieve the prediction of compressive strength of fly ash-slag concrete.
