Piles are long, slender structural elements used to transfer the loads from the superstructure through weak strata onto stiffer soils or rocks. For driven piles, the impact of the piling hammer induces compression and...Piles are long, slender structural elements used to transfer the loads from the superstructure through weak strata onto stiffer soils or rocks. For driven piles, the impact of the piling hammer induces compression and tension stresses in the piles. Hence, an important design consideration is to check that the strength of the pile is sufficient to resist the stresses caused by the impact of the pile hammer. Due to its complexity, pile drivability lacks a precise analytical solution with regard to the phenomena involved.In situations where measured data or numerical hypothetical results are available, neural networks stand out in mapping the nonlinear interactions and relationships between the system’s predictors and dependent responses. In addition, unlike most computational tools, no mathematical relationship assumption between the dependent and independent variables has to be made. Nevertheless, neural networks have been criticized for their long trial-and-error training process since the optimal configuration is not known a priori. This paper investigates the use of a fairly simple nonparametric regression algorithm known as multivariate adaptive regression splines（MARS）, as an alternative to neural networks, to approximate the relationship between the inputs and dependent response, and to mathematically interpret the relationship between the various parameters. In this paper, the Back propagation neural network（BPNN） and MARS models are developed for assessing pile drivability in relation to the prediction of the Maximum compressive stresses（MCS）, Maximum tensile stresses（MTS）, and Blow per foot（BPF）. A database of more than four thousand piles is utilized for model development and comparative performance between BPNN and MARS predictions.展开更多
This study aims to extend the multivariate adaptive regression splines(MARS)-Monte Carlo simulation(MCS) method for reliability analysis of slopes in spatially variable soils. This approach is used to explore the infl...This study aims to extend the multivariate adaptive regression splines(MARS)-Monte Carlo simulation(MCS) method for reliability analysis of slopes in spatially variable soils. This approach is used to explore the influences of the multiscale spatial variability of soil properties on the probability of failure(P_f) of the slopes. In the proposed approach, the relationship between the factor of safety and the soil strength parameters characterized with spatial variability is approximated by the MARS, with the aid of Karhunen-Loeve expansion. MCS is subsequently performed on the established MARS model to evaluate Pf.Finally, a nominally homogeneous cohesive-frictional slope and a heterogeneous cohesive slope, which are both characterized with different spatial variabilities, are utilized to illustrate the proposed approach.Results showed that the proposed approach can estimate the P_f of the slopes efficiently in spatially variable soils with sufficient accuracy. Moreover, the approach is relatively robust to the influence of different statistics of soil properties, thereby making it an effective and practical tool for addressing slope reliability problems concerning time-consuming deterministic stability models with low levels of P_f.Furthermore, disregarding the multiscale spatial variability of soil properties can overestimate or underestimate the P_f. Although the difference is small in general, the multiscale spatial variability of the soil properties must still be considered in the reliability analysis of heterogeneous slopes, especially for those highly related to cost effective and accurate designs.展开更多
The assessment of in situ permeability of rock mass is challenging for large-scale projects such as reservoirs created by dams,where water tightness issues are of prime importance.The in situ permeability is strongly ...The assessment of in situ permeability of rock mass is challenging for large-scale projects such as reservoirs created by dams,where water tightness issues are of prime importance.The in situ permeability is strongly related to the frequency and distribution of discontinuities in the rock mass and quantified by rock quality designation(RQD).This paper analyzes the data of hydraulic conductivity and discontinuities sampled at different depths during the borehole investigations in the limestone and sandstone formations for the construction of hydraulic structures in Oman.Cores recovered from boreholes provide RQD data,and in situ Lugeon tests elucidate the permeability.A modern technique of multivariate adaptive regression splines(MARS)assisted in correlating permeability and RQD along with the depth.In situ permeability shows a declining trend with increasing RQD,and the depth of investigation is within 50 m.This type of relationship can be developed based on detailed initial investigations at the site where the hydraulic conductivity of discontinuous rocks is required to be delineated.The relationship can approximate the permeability by only measuring the RQD in later investigations on the same site,thus saving the time and cost of the site investigations.The applicability of the relationship developed in this study to another location requires a lithological similarity of the rock mass that can be verified through preliminary investigation at the site.展开更多
In high mountainous areas, the development and distribution of alpine permafrost is greatly affected by macro- and mi- cro-topographic factors. The effects of latitude, altitude, slope, and aspect on the distribution ...In high mountainous areas, the development and distribution of alpine permafrost is greatly affected by macro- and mi- cro-topographic factors. The effects of latitude, altitude, slope, and aspect on the distribution of permafrost were studied to under- stand the dislribution patterns of permafrost in Wenquan on the Qinghai-Tibet Plateau. Cluster and correlation analysis were per- formed based on 30 m Global Digital Elevation Model （GDEM） data and field data obtained using geophysical exploration and borehole drilling methods. A Multivariate Adaptive Regression Spline model （MARS） was developed to simulate permafrost spa- tial distribution over the studied area. A validation was followed by comparing to 201 geophysical exploration sites, as well as by comparing to two other models, i.e., a binary logistic regression model and the Mean Annual Ground Temperature model （IVlAGT）. The MARS model provides a better simulation than the other two models. Besides the control effect of elevation on permafrost distribution, the MARS model also takes into account the impact of direct solar radiation on permafrost distribution.展开更多
This paper makes an approach to the approximate optimum in structural design,which combines the global response surface（GRS） based multivariate adaptive regression splines（MARS） with Move-Limit strategy（MLS）.MAR...This paper makes an approach to the approximate optimum in structural design,which combines the global response surface（GRS） based multivariate adaptive regression splines（MARS） with Move-Limit strategy（MLS）.MARS is an adaptive regression process,which fits in with the multidimensional problems.It adopts a modified recursive partitioning strategy to simplify high-dimensional problems into smaller highly accurate models.MLS for moving and resizing the search sub-regions is employed in the space of design variables.The quality of the approximation functions and the convergence history of the optimization process are reflected in MLS.The disadvantages of the conventional response surface method（RSM） have been avoided,specifically,highly nonlinear high-dimensional problems.The GRS/MARS with MLS is applied to a high-dimensional test function and an engineering problem to demonstrate its feasibility and convergence,and compared with quadratic response surface（QRS） models in terms of computational efficiency and accuracy.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
In order to develop optimal multi-regime traffic stream models, a new method that integrates cluster analysis and B-spline regression is presented. First, for identifying the proper number of regimes, the K-means and ...In order to develop optimal multi-regime traffic stream models, a new method that integrates cluster analysis and B-spline regression is presented. First, for identifying the proper number of regimes, the K-means and the fuzzy c-means methods are applied in cluster analysis to actual traffic data, which suggests that dividing the traffic flow into two or three clusters can best reflect intrinsic patterns of traffic flows. Such information is then taken as guidance in spline regression, thus significantly reducing the computational burden of estimating spline models. Spline regression is used to estimate the locations of knots and the coefficients of the model so that the global error can be minimized. Model analysis results demonstrate that the proposed spline models have better fitting and generalization capability than the conventional models. In addition, the new method is more flexible in terms of data fitting and can provide smoother traffic stream models.展开更多
Tunnel heading stability in two dimensions(2D)has been extensively investigated by numerous scholars in the past decade.One significant limitation of 2D analysis is the absence of actual tunnel geometry modeling with ...Tunnel heading stability in two dimensions(2D)has been extensively investigated by numerous scholars in the past decade.One significant limitation of 2D analysis is the absence of actual tunnel geometry modeling with a considerable degree of idealization.Nevertheless,it is possible to study the stability of tunnels in three dimensions(3D)with a rectangular shape using finite element limit analysis(FELA)and a nonlinear programming technique.This paper employs 3D FELA to generate rigorous solutions for stability numbers,failure mechanisms,and safety factors for rectangular-shaped tunnels.To further explore the usefulness of the produced results,multivariate adaptive regression spline(MARS)is used for machine learning of big dataset and development of design equations for practical design applications.The study should be of great benefit to tunnel design practices using the developed equations provided in the paper.展开更多
Machine learning methods have advantages in predicting excavation-induced lateral wall displacements.Due to lack of sufficient field data,training data for prediction models were often derived from the results of nume...Machine learning methods have advantages in predicting excavation-induced lateral wall displacements.Due to lack of sufficient field data,training data for prediction models were often derived from the results of numerical simulations,leading to poor prediction accuracy.Based on a specific quantity of data,a multivariate adaptive regression splines method(MARS)was introduced to predict lateral wall deflections caused by deep excavations in thick water-rich sands.Sensitivity of lateral wall deflections to affecting factors was analyzed.It is disclosed that dewatering mode has the most significant influence on lateral wall deflections,while the soil cohesion has the least influence.Using crossvalidation analysis,weights were introduced to modify the MARS method to optimize the prediction model.Comparison of the predicted and measured deflections shows that the prediction based on the modified multivariate adaptive regression splines method(MMARS)is more accurate than that based on the traditional MARS method.The prediction model established in this paper can help engineers make predictions for wall displacement,and the proposed methodology can also serve as a reference for researchers to develop prediction models.展开更多
This study presents a hybrid framework to predict stability solutions of buried structures under active trapdoor conditions in natural clays with anisotropy and heterogeneity by combining physics-based and data-driven...This study presents a hybrid framework to predict stability solutions of buried structures under active trapdoor conditions in natural clays with anisotropy and heterogeneity by combining physics-based and data-driven modeling.Finite-element limit analysis(FELA)with a newly developed anisotropic undrained shear(AUS)failure criterion is used to identify the underlying active failure mechanisms as well as to develop a numerical(physics-based)database of stability numbers for both planar and circular trapdoors.Practical considerations are given for natural clays to three linearly increasing shear strengths in compression,extension,and direct simple shear in the AUS material model.The obtained numerical solutions are compared and validated with published solutions in the literature.A multivariate adaptive regression splines(MARS)algorithm is further utilized to learn the numerical solutions to act as fast FELA data-driven surrogates for stability evaluation.The current MARS-based modeling provides both relative importance index and accurate design equations that can be used with confidence by practitioners.展开更多
Solar radiation is an important parameter in the fields of computer modeling,engineering technology and energy development.This paper evaluated the ability of three machine learning models,i.e.,Extreme Gradient Boosti...Solar radiation is an important parameter in the fields of computer modeling,engineering technology and energy development.This paper evaluated the ability of three machine learning models,i.e.,Extreme Gradient Boosting(XGBoost),Support Vector Machine(SVM)and Multivariate Adaptive Regression Splines(MARS),to estimate the daily diffuse solar radiation(Rd).The regular meteorological data of 1966-2015 at five stations in China were taken as the input parameters(including mean average temperature(Ta),theoretical sunshine duration(N),actual sunshine duration(n),daily average air relative humidity(RH),and extra-terrestrial solar radiation(Ra)).And their estimation accuracies were subjected to comparative analysis.The three models were first trained using meteorological data from 1966 to 2000.Then,the 2001-2015 data was used to test the trained machine learning model.The results show that the XGBoost had better accuracy than the other two models in coefficient of determination(R2),root mean square error(RMSE),mean bias error(MBE)and normalized root mean square error(NRMSE).The MARS performed better in the training phase than the testing phase,but became less accurate in the testing phase,with the R2 value falling by 2.7-16.9%on average.By contrast,the R2 values of SVM and XGBoost increased by 2.9-12.2%and 1.9-14.3%,respectively.Despite trailing slightly behind the SVM at the Beijing station,the XGBoost showed good performance at the rest of the stations in the two phases.In the training phase,the accuracy growth is small but observable.In addition,the XGBoost had a slightly lower RMSE than the SVM,a signal of its edge in stability.Therefore,the three machine learning models can estimate the daily Rd based on local inputs and the XGBoost stands out for its excellent performance and stability.展开更多
In the present scenario,computational modeling has gained much importance for the prediction of the properties of concrete.This paper depicts that how computational intelligence can be applied for the prediction of co...In the present scenario,computational modeling has gained much importance for the prediction of the properties of concrete.This paper depicts that how computational intelligence can be applied for the prediction of compressive strength of Self Compacting Concrete(SCC).Three models,namely,Extreme Learning Machine(ELM),Adaptive Neuro Fuzzy Inference System(ANFIS)and Multi Adaptive Regression Spline(MARS)have been employed in the present study for the prediction of compressive strength of self compacting concrete.The contents of cement(c),sand(s),coarse aggregate(a),fly ash(f),water/powder(w/p)ratio and superplasticizer(sp)dosage have been taken as inputs and 28 days compressive strength(fck)as output for ELM,ANFIS and MARS models.A relatively large set of data including 80 normalized data available in the literature has been taken for the study.A comparison is made between the results obtained from all the above-mentioned models and the model which provides best fit is established.The experimental results demonstrate that proposed models are robust for determination of compressive strength of self-compacting concrete.展开更多
The design and construction of underground structures are significantly affected by the distribution of geological formations.Prediction of the geological interfaces using limited data has been a difficult task.A mult...The design and construction of underground structures are significantly affected by the distribution of geological formations.Prediction of the geological interfaces using limited data has been a difficult task.A multivariate adaptive regression spline(MARS)method capable of modeling nonlinearities automatically was used in this study to spatially predict the elevations of geological interfaces.Borehole data from two sites in Singapore were used to evaluate the capability of the MARS method for predicting geological interfaces.By comparing the predicted values with the borehole data,it is shown that the MARS method has a mean of root mean square error of 4.4 m for the predicted elevations of the Kallang Formation–Old Alluvium interface.In addition,the MARS method is able to produce reasonable prediction intervals in the sense that the percentage of testing data covered by 95% prediction intervals was close to the associated confidence level,95%.More importantly,the prediction interval evaluated by the MARS method had a non-constant width that appropriately reflected the data density and geological complexity.展开更多
Steam cracking is the dominant technology for producing light olefins,which are believed to be the foundation of the chemical industry.Predictive models of the cracking process can boost production efficiency and prof...Steam cracking is the dominant technology for producing light olefins,which are believed to be the foundation of the chemical industry.Predictive models of the cracking process can boost production efficiency and profit margin.Rapid advancements in machine learning research have recently enabled data-driven solutions to usher in a new era of process modeling.Meanwhile,its practical application to steam cracking is still hindered by the trade-off between prediction accuracy and computational speed.This research presents a framework for data-driven intelligent modeling of the steam cracking process.Industrial data preparation and feature engineering techniques provide computational-ready datasets for the framework,and feedstock similarities are exploited using k-means clustering.We propose LArge-Residuals-Deletion Multivariate Adaptive Regression Spline(LARD-MARS),a modeling approach that explicitly generates output formulas and eliminates potentially outlying instances.The framework is validated further by the presentation of clustering results,the explanation of variable importance,and the testing and comparison of model performance.展开更多
In genetic studies of complex diseases, particularly mental illnesses, and behavior disorders, two distinct characteristics have emerged in some data sets. First, genetic data sets are collected with a large number of...In genetic studies of complex diseases, particularly mental illnesses, and behavior disorders, two distinct characteristics have emerged in some data sets. First, genetic data sets are collected with a large number of phenotypes that are potentially related to the complex disease under study. Second, each phenotype is collected from the same subject repeatedly over time. In this study, we present a nonparametric regression approach to study multivariate and time-repeated phenotypes together by using the technique of the multivariate adaptive regression splines for analysis of longitudinal data （MASAL）, which makes it possible to identify genes, gene-gene and gene-environment, including time, interactions associated with the phenotypes of interest. Furthermore, we propose a permutation test to assess the associations between the phenotypes and selected markers. Through simulation, we demonstrate that our proposed approach has advantages over the existing methods that examine each longitudinal phenotype separately or analyze the summarized values of phenotypes by compressing them into one-time-point phenotypes. Application of the proposed method to the Framingham Heart Study illustrates that the use of multivariate longitudinal phenotypes enhanced the significance of the association test.展开更多
We propose a method for spatial downscaling of Landsat 8-derived LST maps from 100(30 m)resolution down to 2–4 m with the use of the Multiple Adaptive Regression Splines(MARS)models coupled with very high resolution ...We propose a method for spatial downscaling of Landsat 8-derived LST maps from 100(30 m)resolution down to 2–4 m with the use of the Multiple Adaptive Regression Splines(MARS)models coupled with very high resolution auxiliary data derived from hyperspectral aerial imagery and large-scale topographic maps.We applied the method to four Landsat 8 scenes,two collected in summer and two in winter,for three British towns collectively representing a variety of urban form.We used several spectral indices as well as fractional coverage of water and paved surfaces as LST predictors,and applied a novel method for the correction of temporal mismatch between spectral indices derived from aerial and satellite imagery captured at different dates,allowing for the application of the downscaling method for multiple dates without the need for repeating the aerial survey.Our results suggest that the method performed well for the summer dates,achieving RMSE of 1.40–1.83 K prior to and 0.76–1.21 K after correction for residuals.We conclude that the MARS models,by addressing the non-linear relationship of LST at coarse and fine spatial resolutions,can be successfully applied to produce high resolution LST maps suitable for studies of urban thermal environment at local scales.展开更多
The aim of the current work is to compare susceptibility maps of landslides produced using machine learning techniques i.e.multilayer perception neural nets(MLP),kernel logistic regression(KLR),random forest(RF),and m...The aim of the current work is to compare susceptibility maps of landslides produced using machine learning techniques i.e.multilayer perception neural nets(MLP),kernel logistic regression(KLR),random forest(RF),and multivariate adaptive regression splines(MARS);novel ensemble approaches i.e.MLP-Bagging,KLR-Bagging,RFBagging and MARS-Bagging in the Kurseong-Himalayan region.For the ensemble models the RF,KLR,MLP and MARS were used as base classifiers,and Bagging was used as meta classifier.Another objective of the current work is to introduce and evaluate the effectiveness of the novel KLR-Bagging and MARS-Bagging ensembles in susceptibility to landslide.Compiling 303 landslide locations to calibrate and test the models,an inventory map was created.Eighteen LCFs were chosen using the Relief-F and multi-collinearity tests for mapping the landslide susceptibility.Applying receiver operating characteristic(ROC),precision,accuracy,incorrectly categorized proportion,mean-absolute-error(MAE),and root-mean-square-error(RMSE),the LSMs were subsequently verified.The different validation results showed RF-Bagging(AUC training 88.69%&testing 92.28%)with ensemble Meta classifier gives better performance than the MLP,KLR,RF,MARS,MLP-Bagging,KLR-Bagging,and MARSBagging based LSMs.RF model showed that the slope,altitude,rainfall,and geomorphology played the most vital role in landslide occurrence comparing the other LCFs.These results will help to reduce the losses caused by the landslides in the Kurseong and in other areas where geo-environmental and geological conditions more or less similar.展开更多
To achieve fine segmentation of complex natural images, people often resort to an interactive segmentation paradigm, since fully automatic methods often fail to obtain a result consistent with the ground truth. Howeve...To achieve fine segmentation of complex natural images, people often resort to an interactive segmentation paradigm, since fully automatic methods often fail to obtain a result consistent with the ground truth. However, when the foreground and background share some similar areas in color, the fine segmentation result of conventional interactive methods usually relies on the increase o f manual labels. This paper presents a novel interactive image segmentation method via a regression-based ensemble model with semi-supervised learning. The task is formulated as a non-linear problem integrating two complementary spline regressors and strengthening the robustness of each regressor via semi-supervised learning. First, two spline regressors with a complementary nature are constructed based on multivariate adaptive regression splines （MARS） and smooth thin plate spline regression （TPSR）. Then, a regressor boosting method based on a clustering hypothesis and semi-supervised learning is proposed to assist the training of MARS and TPSR by using the region segmentation information contained in unlabeled pixels. Next, a support vector regression （SVR） based decision fusion model is adopted to integrate the results of MARS and TPSR. Finally, the GraphCut is introduced and combined with the SVR ensemble results to achieve image segmentation. Extensive experimental results on benchmark datasets of BSDS500 and Pascal VOC have demonstrated the effectiveness of our method, and the com- parison with experiment results has validated that the proposed method is comparable with the state-of-the-art methods for in- teractive natural image segmentation.展开更多
AIM:To assess the relationship between dietary inflammatory index(DII)and prevalence of glaucoma among individuals aged 40y and above in the United States.METHODS:Participants were drawn from 2 cycles of the National ...AIM:To assess the relationship between dietary inflammatory index(DII)and prevalence of glaucoma among individuals aged 40y and above in the United States.METHODS:Participants were drawn from 2 cycles of the National Health and Nutrition Examination Survey(NHANES,2005-2008)for a cross-sectional study.DII was calculated from 24-hour dietary recall questionnaire conducted by experienced researchers and data analyzed in R according to the NHANES user guide,“Stratified Multi-stage Probability Sampling”.The relationship between DII and glaucoma was evaluated by multi-factor logistic regression analysis and the existence of a non-linear association examined by restricted cubic spline(RCS)analysis.RESULTS:A total of 5359 subjects were included and the cross-sectional analysis weighted to represent the US population of 109 million.DII was elevated in glaucoma patients(P<0.001)and smoking and alcohol use contributed to significant differences(P<0.001).DII correlated negatively with Healthy Eating Index(HEI)-2015(Spearman rank correlation coefficient,r=-0.49).RCS analysis showed a linear relationship between DII and glaucoma risk(P of non-linear relationship=0.575).CONCLUSION:An increased DII is strongly associated with high risk of glaucoma and diet-induced inflammation should be controlled to delay glaucoma progression.展开更多
This paper is concerned with the estimating problem of seemingly unrelated(SU)nonparametric additive regression models.A polynomial spline based two-stage efficient approach is proposed to estimate the nonparametric c...This paper is concerned with the estimating problem of seemingly unrelated(SU)nonparametric additive regression models.A polynomial spline based two-stage efficient approach is proposed to estimate the nonparametric components,which takes both of the additive structure and correlation between equations into account.The asymptotic normality of the derived estimators are established.The authors also show they own some advantages,including they are asymptotically more efficient than those based on only the individual regression equation and have an oracle property,which is the asymptotic distribution of each additive component is the same as it would be if the other components were known with certainty.Some simulation studies are conducted to illustrate the finite sample performance of the proposed procedure.Applying the proposed procedure to a real data set is also made.展开更多
文摘Piles are long, slender structural elements used to transfer the loads from the superstructure through weak strata onto stiffer soils or rocks. For driven piles, the impact of the piling hammer induces compression and tension stresses in the piles. Hence, an important design consideration is to check that the strength of the pile is sufficient to resist the stresses caused by the impact of the pile hammer. Due to its complexity, pile drivability lacks a precise analytical solution with regard to the phenomena involved.In situations where measured data or numerical hypothetical results are available, neural networks stand out in mapping the nonlinear interactions and relationships between the system’s predictors and dependent responses. In addition, unlike most computational tools, no mathematical relationship assumption between the dependent and independent variables has to be made. Nevertheless, neural networks have been criticized for their long trial-and-error training process since the optimal configuration is not known a priori. This paper investigates the use of a fairly simple nonparametric regression algorithm known as multivariate adaptive regression splines（MARS）, as an alternative to neural networks, to approximate the relationship between the inputs and dependent response, and to mathematically interpret the relationship between the various parameters. In this paper, the Back propagation neural network（BPNN） and MARS models are developed for assessing pile drivability in relation to the prediction of the Maximum compressive stresses（MCS）, Maximum tensile stresses（MTS）, and Blow per foot（BPF）. A database of more than four thousand piles is utilized for model development and comparative performance between BPNN and MARS predictions.
基金supported by The Hong Kong Polytechnic University through the project RU3Ythe Research Grant Council through the project PolyU 5128/13E+1 种基金National Natural Science Foundation of China(Grant No.51778313)Cooperative Innovation Center of Engineering Construction and Safety in Shangdong Blue Economic Zone
文摘This study aims to extend the multivariate adaptive regression splines(MARS)-Monte Carlo simulation(MCS) method for reliability analysis of slopes in spatially variable soils. This approach is used to explore the influences of the multiscale spatial variability of soil properties on the probability of failure(P_f) of the slopes. In the proposed approach, the relationship between the factor of safety and the soil strength parameters characterized with spatial variability is approximated by the MARS, with the aid of Karhunen-Loeve expansion. MCS is subsequently performed on the established MARS model to evaluate Pf.Finally, a nominally homogeneous cohesive-frictional slope and a heterogeneous cohesive slope, which are both characterized with different spatial variabilities, are utilized to illustrate the proposed approach.Results showed that the proposed approach can estimate the P_f of the slopes efficiently in spatially variable soils with sufficient accuracy. Moreover, the approach is relatively robust to the influence of different statistics of soil properties, thereby making it an effective and practical tool for addressing slope reliability problems concerning time-consuming deterministic stability models with low levels of P_f.Furthermore, disregarding the multiscale spatial variability of soil properties can overestimate or underestimate the P_f. Although the difference is small in general, the multiscale spatial variability of the soil properties must still be considered in the reliability analysis of heterogeneous slopes, especially for those highly related to cost effective and accurate designs.
基金indebted to the Sohar University and the University of Buraimi, Oman, to support this study
文摘The assessment of in situ permeability of rock mass is challenging for large-scale projects such as reservoirs created by dams,where water tightness issues are of prime importance.The in situ permeability is strongly related to the frequency and distribution of discontinuities in the rock mass and quantified by rock quality designation(RQD).This paper analyzes the data of hydraulic conductivity and discontinuities sampled at different depths during the borehole investigations in the limestone and sandstone formations for the construction of hydraulic structures in Oman.Cores recovered from boreholes provide RQD data,and in situ Lugeon tests elucidate the permeability.A modern technique of multivariate adaptive regression splines(MARS)assisted in correlating permeability and RQD along with the depth.In situ permeability shows a declining trend with increasing RQD,and the depth of investigation is within 50 m.This type of relationship can be developed based on detailed initial investigations at the site where the hydraulic conductivity of discontinuous rocks is required to be delineated.The relationship can approximate the permeability by only measuring the RQD in later investigations on the same site,thus saving the time and cost of the site investigations.The applicability of the relationship developed in this study to another location requires a lithological similarity of the rock mass that can be verified through preliminary investigation at the site.
基金supported financially by the Special Basic Research Program of China(Grant No.2008FY110200)partially by Open Programme of State Key Laboratory(No.SKLFSE201009)
文摘In high mountainous areas, the development and distribution of alpine permafrost is greatly affected by macro- and mi- cro-topographic factors. The effects of latitude, altitude, slope, and aspect on the distribution of permafrost were studied to under- stand the dislribution patterns of permafrost in Wenquan on the Qinghai-Tibet Plateau. Cluster and correlation analysis were per- formed based on 30 m Global Digital Elevation Model （GDEM） data and field data obtained using geophysical exploration and borehole drilling methods. A Multivariate Adaptive Regression Spline model （MARS） was developed to simulate permafrost spa- tial distribution over the studied area. A validation was followed by comparing to 201 geophysical exploration sites, as well as by comparing to two other models, i.e., a binary logistic regression model and the Mean Annual Ground Temperature model （IVlAGT）. The MARS model provides a better simulation than the other two models. Besides the control effect of elevation on permafrost distribution, the MARS model also takes into account the impact of direct solar radiation on permafrost distribution.
基金Project supported by the National Natural Science Foundation of China (Grant No.50775084)the National Hightechnology Research and Development Program of China (Grant No.2006AA04Z121)
文摘This paper makes an approach to the approximate optimum in structural design,which combines the global response surface（GRS） based multivariate adaptive regression splines（MARS） with Move-Limit strategy（MLS）.MARS is an adaptive regression process,which fits in with the multidimensional problems.It adopts a modified recursive partitioning strategy to simplify high-dimensional problems into smaller highly accurate models.MLS for moving and resizing the search sub-regions is employed in the space of design variables.The quality of the approximation functions and the convergence history of the optimization process are reflected in MLS.The disadvantages of the conventional response surface method（RSM） have been avoided,specifically,highly nonlinear high-dimensional problems.The GRS/MARS with MLS is applied to a high-dimensional test function and an engineering problem to demonstrate its feasibility and convergence,and compared with quadratic response surface（QRS） models in terms of computational efficiency and accuracy.
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.
基金The US National Science Foundation (No.BCS-0527508)
文摘In order to develop optimal multi-regime traffic stream models, a new method that integrates cluster analysis and B-spline regression is presented. First, for identifying the proper number of regimes, the K-means and the fuzzy c-means methods are applied in cluster analysis to actual traffic data, which suggests that dividing the traffic flow into two or three clusters can best reflect intrinsic patterns of traffic flows. Such information is then taken as guidance in spline regression, thus significantly reducing the computational burden of estimating spline models. Spline regression is used to estimate the locations of knots and the coefficients of the model so that the global error can be minimized. Model analysis results demonstrate that the proposed spline models have better fitting and generalization capability than the conventional models. In addition, the new method is more flexible in terms of data fitting and can provide smoother traffic stream models.
基金supported by the Thailand Science Research and Innovation Fundamental Fund fiscal year 2023The fifth author (V.Kamchoom)acknowledges the financial support from the National Science,Research and Innovation Fund (NSRF)at King Mongkut's Institute of Technology Ladkrabang (KMITL),Thailand (Grant No.FRB66065/0258-RE-KRIS/FF66/53)+1 种基金the Climate Change and Climate Variability Research in Monsoon Asia (CMON3)from the National Research Council of Thailand (NRCT) (Grant No.N10A650844)the National Natural Science Foundation of China (NSFC).
文摘Tunnel heading stability in two dimensions(2D)has been extensively investigated by numerous scholars in the past decade.One significant limitation of 2D analysis is the absence of actual tunnel geometry modeling with a considerable degree of idealization.Nevertheless,it is possible to study the stability of tunnels in three dimensions(3D)with a rectangular shape using finite element limit analysis(FELA)and a nonlinear programming technique.This paper employs 3D FELA to generate rigorous solutions for stability numbers,failure mechanisms,and safety factors for rectangular-shaped tunnels.To further explore the usefulness of the produced results,multivariate adaptive regression spline(MARS)is used for machine learning of big dataset and development of design equations for practical design applications.The study should be of great benefit to tunnel design practices using the developed equations provided in the paper.
基金Financial support from the National Natural Science Foundation of China(Grant No.42177179)is gratefully acknowledged.
文摘Machine learning methods have advantages in predicting excavation-induced lateral wall displacements.Due to lack of sufficient field data,training data for prediction models were often derived from the results of numerical simulations,leading to poor prediction accuracy.Based on a specific quantity of data,a multivariate adaptive regression splines method(MARS)was introduced to predict lateral wall deflections caused by deep excavations in thick water-rich sands.Sensitivity of lateral wall deflections to affecting factors was analyzed.It is disclosed that dewatering mode has the most significant influence on lateral wall deflections,while the soil cohesion has the least influence.Using crossvalidation analysis,weights were introduced to modify the MARS method to optimize the prediction model.Comparison of the predicted and measured deflections shows that the prediction based on the modified multivariate adaptive regression splines method(MMARS)is more accurate than that based on the traditional MARS method.The prediction model established in this paper can help engineers make predictions for wall displacement,and the proposed methodology can also serve as a reference for researchers to develop prediction models.
基金the funding support provided by National Natural Science Foundation of China(Grant No.42177121)Thammasat University Research Unit in Structural and Foundation Engineering.
文摘This study presents a hybrid framework to predict stability solutions of buried structures under active trapdoor conditions in natural clays with anisotropy and heterogeneity by combining physics-based and data-driven modeling.Finite-element limit analysis(FELA)with a newly developed anisotropic undrained shear(AUS)failure criterion is used to identify the underlying active failure mechanisms as well as to develop a numerical(physics-based)database of stability numbers for both planar and circular trapdoors.Practical considerations are given for natural clays to three linearly increasing shear strengths in compression,extension,and direct simple shear in the AUS material model.The obtained numerical solutions are compared and validated with published solutions in the literature.A multivariate adaptive regression splines(MARS)algorithm is further utilized to learn the numerical solutions to act as fast FELA data-driven surrogates for stability evaluation.The current MARS-based modeling provides both relative importance index and accurate design equations that can be used with confidence by practitioners.
基金supported by National Natural Science Foundation of China(51769010,51979133,51469010 and 51109102).
文摘Solar radiation is an important parameter in the fields of computer modeling,engineering technology and energy development.This paper evaluated the ability of three machine learning models,i.e.,Extreme Gradient Boosting(XGBoost),Support Vector Machine(SVM)and Multivariate Adaptive Regression Splines(MARS),to estimate the daily diffuse solar radiation(Rd).The regular meteorological data of 1966-2015 at five stations in China were taken as the input parameters(including mean average temperature(Ta),theoretical sunshine duration(N),actual sunshine duration(n),daily average air relative humidity(RH),and extra-terrestrial solar radiation(Ra)).And their estimation accuracies were subjected to comparative analysis.The three models were first trained using meteorological data from 1966 to 2000.Then,the 2001-2015 data was used to test the trained machine learning model.The results show that the XGBoost had better accuracy than the other two models in coefficient of determination(R2),root mean square error(RMSE),mean bias error(MBE)and normalized root mean square error(NRMSE).The MARS performed better in the training phase than the testing phase,but became less accurate in the testing phase,with the R2 value falling by 2.7-16.9%on average.By contrast,the R2 values of SVM and XGBoost increased by 2.9-12.2%and 1.9-14.3%,respectively.Despite trailing slightly behind the SVM at the Beijing station,the XGBoost showed good performance at the rest of the stations in the two phases.In the training phase,the accuracy growth is small but observable.In addition,the XGBoost had a slightly lower RMSE than the SVM,a signal of its edge in stability.Therefore,the three machine learning models can estimate the daily Rd based on local inputs and the XGBoost stands out for its excellent performance and stability.
文摘In the present scenario,computational modeling has gained much importance for the prediction of the properties of concrete.This paper depicts that how computational intelligence can be applied for the prediction of compressive strength of Self Compacting Concrete(SCC).Three models,namely,Extreme Learning Machine(ELM),Adaptive Neuro Fuzzy Inference System(ANFIS)and Multi Adaptive Regression Spline(MARS)have been employed in the present study for the prediction of compressive strength of self compacting concrete.The contents of cement(c),sand(s),coarse aggregate(a),fly ash(f),water/powder(w/p)ratio and superplasticizer(sp)dosage have been taken as inputs and 28 days compressive strength(fck)as output for ELM,ANFIS and MARS models.A relatively large set of data including 80 normalized data available in the literature has been taken for the study.A comparison is made between the results obtained from all the above-mentioned models and the model which provides best fit is established.The experimental results demonstrate that proposed models are robust for determination of compressive strength of self-compacting concrete.
基金supported by the Singapore Ministry of National Development and the National Research Foundation,Prime Minister’s Office under the Land and Liveability National Innovation Challenge(L2 NIC)Research Programme(Award No.L2NICCFP2-2015-1).
文摘The design and construction of underground structures are significantly affected by the distribution of geological formations.Prediction of the geological interfaces using limited data has been a difficult task.A multivariate adaptive regression spline(MARS)method capable of modeling nonlinearities automatically was used in this study to spatially predict the elevations of geological interfaces.Borehole data from two sites in Singapore were used to evaluate the capability of the MARS method for predicting geological interfaces.By comparing the predicted values with the borehole data,it is shown that the MARS method has a mean of root mean square error of 4.4 m for the predicted elevations of the Kallang Formation–Old Alluvium interface.In addition,the MARS method is able to produce reasonable prediction intervals in the sense that the percentage of testing data covered by 95% prediction intervals was close to the associated confidence level,95%.More importantly,the prediction interval evaluated by the MARS method had a non-constant width that appropriately reflected the data density and geological complexity.
基金supported by the National Key Research and Development Program of China(2021 YFB 4000500,2021 YFB 4000501,and 2021 YFB 4000502)。
文摘Steam cracking is the dominant technology for producing light olefins,which are believed to be the foundation of the chemical industry.Predictive models of the cracking process can boost production efficiency and profit margin.Rapid advancements in machine learning research have recently enabled data-driven solutions to usher in a new era of process modeling.Meanwhile,its practical application to steam cracking is still hindered by the trade-off between prediction accuracy and computational speed.This research presents a framework for data-driven intelligent modeling of the steam cracking process.Industrial data preparation and feature engineering techniques provide computational-ready datasets for the framework,and feedstock similarities are exploited using k-means clustering.We propose LArge-Residuals-Deletion Multivariate Adaptive Regression Spline(LARD-MARS),a modeling approach that explicitly generates output formulas and eliminates potentially outlying instances.The framework is validated further by the presentation of clustering results,the explanation of variable importance,and the testing and comparison of model performance.
基金The authors thank two anonymous referees for their constructive comments and suggestions. This work was supported by grant R01 DA016750-09 from the National Institute on Drug Abuse. Zhu＇s work was also supported by the National Natural Science Foundation of China （Grant No. 11001044）, the Yhndamental Research ~nds for the Central Universities （11CXPY007, 10JCXK001）, the Natural Science Foundation of Jilin Province （Grant No. 201215007）, the Scientific Research Foundation for Returned Scholars, MOE of China, and the Program for Changjiang Scholars and Innovative Research Team in University. The Framingham Heart Study project is conducted and supported by the National Heart, Lung, and Blood Institute （NHLBI） in collaboration with Boston University （N01 HC25195）. The Framingham data used for the analyses described in this manuscript were obtained through dbGaP （phs000128.v3.p3）.
文摘In genetic studies of complex diseases, particularly mental illnesses, and behavior disorders, two distinct characteristics have emerged in some data sets. First, genetic data sets are collected with a large number of phenotypes that are potentially related to the complex disease under study. Second, each phenotype is collected from the same subject repeatedly over time. In this study, we present a nonparametric regression approach to study multivariate and time-repeated phenotypes together by using the technique of the multivariate adaptive regression splines for analysis of longitudinal data （MASAL）, which makes it possible to identify genes, gene-gene and gene-environment, including time, interactions associated with the phenotypes of interest. Furthermore, we propose a permutation test to assess the associations between the phenotypes and selected markers. Through simulation, we demonstrate that our proposed approach has advantages over the existing methods that examine each longitudinal phenotype separately or analyze the summarized values of phenotypes by compressing them into one-time-point phenotypes. Application of the proposed method to the Framingham Heart Study illustrates that the use of multivariate longitudinal phenotypes enhanced the significance of the association test.
基金This research(Grant Number NE/J015067/1)the Fragments,Functions and Flows in Urban Ecosystem Services(F3UES)project as part of the larger Biodiversity and Ecosystem Service Sustainability(BESS)framework.BESS is a six-year programme(2011-2017)funded+1 种基金the UK Natural Environment Research Council(NERC)the Biotechnology and Biological Sciences Research Council(BBSRC)as part of the UK’s Living with Environmental Change(LWEC)programme.
文摘We propose a method for spatial downscaling of Landsat 8-derived LST maps from 100(30 m)resolution down to 2–4 m with the use of the Multiple Adaptive Regression Splines(MARS)models coupled with very high resolution auxiliary data derived from hyperspectral aerial imagery and large-scale topographic maps.We applied the method to four Landsat 8 scenes,two collected in summer and two in winter,for three British towns collectively representing a variety of urban form.We used several spectral indices as well as fractional coverage of water and paved surfaces as LST predictors,and applied a novel method for the correction of temporal mismatch between spectral indices derived from aerial and satellite imagery captured at different dates,allowing for the application of the downscaling method for multiple dates without the need for repeating the aerial survey.Our results suggest that the method performed well for the summer dates,achieving RMSE of 1.40–1.83 K prior to and 0.76–1.21 K after correction for residuals.We conclude that the MARS models,by addressing the non-linear relationship of LST at coarse and fine spatial resolutions,can be successfully applied to produce high resolution LST maps suitable for studies of urban thermal environment at local scales.
文摘The aim of the current work is to compare susceptibility maps of landslides produced using machine learning techniques i.e.multilayer perception neural nets(MLP),kernel logistic regression(KLR),random forest(RF),and multivariate adaptive regression splines(MARS);novel ensemble approaches i.e.MLP-Bagging,KLR-Bagging,RFBagging and MARS-Bagging in the Kurseong-Himalayan region.For the ensemble models the RF,KLR,MLP and MARS were used as base classifiers,and Bagging was used as meta classifier.Another objective of the current work is to introduce and evaluate the effectiveness of the novel KLR-Bagging and MARS-Bagging ensembles in susceptibility to landslide.Compiling 303 landslide locations to calibrate and test the models,an inventory map was created.Eighteen LCFs were chosen using the Relief-F and multi-collinearity tests for mapping the landslide susceptibility.Applying receiver operating characteristic(ROC),precision,accuracy,incorrectly categorized proportion,mean-absolute-error(MAE),and root-mean-square-error(RMSE),the LSMs were subsequently verified.The different validation results showed RF-Bagging(AUC training 88.69%&testing 92.28%)with ensemble Meta classifier gives better performance than the MLP,KLR,RF,MARS,MLP-Bagging,KLR-Bagging,and MARSBagging based LSMs.RF model showed that the slope,altitude,rainfall,and geomorphology played the most vital role in landslide occurrence comparing the other LCFs.These results will help to reduce the losses caused by the landslides in the Kurseong and in other areas where geo-environmental and geological conditions more or less similar.
基金the National Natural Science Foundation of China （Nos. 61071176, 61171192, and 61272337） and the Doctoral
文摘To achieve fine segmentation of complex natural images, people often resort to an interactive segmentation paradigm, since fully automatic methods often fail to obtain a result consistent with the ground truth. However, when the foreground and background share some similar areas in color, the fine segmentation result of conventional interactive methods usually relies on the increase o f manual labels. This paper presents a novel interactive image segmentation method via a regression-based ensemble model with semi-supervised learning. The task is formulated as a non-linear problem integrating two complementary spline regressors and strengthening the robustness of each regressor via semi-supervised learning. First, two spline regressors with a complementary nature are constructed based on multivariate adaptive regression splines （MARS） and smooth thin plate spline regression （TPSR）. Then, a regressor boosting method based on a clustering hypothesis and semi-supervised learning is proposed to assist the training of MARS and TPSR by using the region segmentation information contained in unlabeled pixels. Next, a support vector regression （SVR） based decision fusion model is adopted to integrate the results of MARS and TPSR. Finally, the GraphCut is introduced and combined with the SVR ensemble results to achieve image segmentation. Extensive experimental results on benchmark datasets of BSDS500 and Pascal VOC have demonstrated the effectiveness of our method, and the com- parison with experiment results has validated that the proposed method is comparable with the state-of-the-art methods for in- teractive natural image segmentation.
基金Supported by the National Natural Science Foundation of China(No.82074500)CACMS Innovation Fund(No.CI2021A02605)+1 种基金Administration of Traditional Chinese Medicine of Zhejiang Province(No.2024ZR029)Science and Technology Program of Wenzhou City(No.Y2023210).
文摘AIM:To assess the relationship between dietary inflammatory index(DII)and prevalence of glaucoma among individuals aged 40y and above in the United States.METHODS:Participants were drawn from 2 cycles of the National Health and Nutrition Examination Survey(NHANES,2005-2008)for a cross-sectional study.DII was calculated from 24-hour dietary recall questionnaire conducted by experienced researchers and data analyzed in R according to the NHANES user guide,“Stratified Multi-stage Probability Sampling”.The relationship between DII and glaucoma was evaluated by multi-factor logistic regression analysis and the existence of a non-linear association examined by restricted cubic spline(RCS)analysis.RESULTS:A total of 5359 subjects were included and the cross-sectional analysis weighted to represent the US population of 109 million.DII was elevated in glaucoma patients(P<0.001)and smoking and alcohol use contributed to significant differences(P<0.001).DII correlated negatively with Healthy Eating Index(HEI)-2015(Spearman rank correlation coefficient,r=-0.49).RCS analysis showed a linear relationship between DII and glaucoma risk(P of non-linear relationship=0.575).CONCLUSION:An increased DII is strongly associated with high risk of glaucoma and diet-induced inflammation should be controlled to delay glaucoma progression.
基金supported by National Natural Science Funds for Distinguished Young Scholar under Grant No.70825004National Natural Science Foundation of China under Grant Nos.10731010 and 10628104+3 种基金the National Basic Research Program under Grant No.2007CB814902Creative Research Groups of China under Grant No.10721101supported by leading Academic Discipline Program,211 Project for Shanghai University of Finance and Economics(the 3rd phase)and project number:B803supported by grants from the National Natural Science Foundation of China under Grant No.11071154
文摘This paper is concerned with the estimating problem of seemingly unrelated(SU)nonparametric additive regression models.A polynomial spline based two-stage efficient approach is proposed to estimate the nonparametric components,which takes both of the additive structure and correlation between equations into account.The asymptotic normality of the derived estimators are established.The authors also show they own some advantages,including they are asymptotically more efficient than those based on only the individual regression equation and have an oracle property,which is the asymptotic distribution of each additive component is the same as it would be if the other components were known with certainty.Some simulation studies are conducted to illustrate the finite sample performance of the proposed procedure.Applying the proposed procedure to a real data set is also made.
