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Prediction of interfaces of geological formations using the Multivariate adaptive regression spline method

Underground Space 2021年第3期6卷 252-266页

作者： Xiaohui Qi Hao Wang Xiaohua Pan Jian Chu Kiefer Chiam School of Civil and Environmental Engineering Nanyang Technological University639798Singapore Building and Construction Authority 200 Braddell Road579700Singapore

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.

关键词： Geological interface Rockhead Multivariate adaptive regression spline Spatial prediction

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Exploring the potential of artificial intelligence techniques in prediction of the removal efficiency of vortex tube silt ejector

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International Journal of Sediment Research 2023年第4期38卷 615-627页

作者： Sanjeev Kumar Chandra Shekhar Prasad Ojha Nand Kumar Tiwari Subodh Ranjan Department of Civil Engineering Indian Institute of Technology Department of Civil Engineering National Institute of Technology

A vortex tube silt ejector is a curative hydraulic structure used to remove sediment deposits from canals and is recognized as one of the most efficient substitutes for physically removing canal sediment. The spatially varied flow in the channel and the rotational flow behavior in the tube make the silt removal process complex. It is even harder to accurately predict the silt removal efficiency by traditional models accurately. However, artificial intelligence(AI) and machine learning approaches have emerged as robust substitutes for studying complex processes. Therefore, this research makes use of AI approaches; support vector machine(SVM), random forest(RF), random tree(RT), and Multivariate adaptive regression spline(MARS) to compute the vortex tube silt ejection efficiency using the laboratory data sets. The outcomes of the artificial intelligence(AI)-based techniques also were compared with traditional models. It was found that the RT model(root mean square error, RMSE = 2.165, Nash Sutcliffe efficiency, NSE = 0.98) outperforms the other applied approaches which had relatively more significant result errors. The sensitivity analysis of the process depicts the extraction ratio as the key parameter in the computation of vortex tube silt ejector removal efficiency. The findings of the AI-based approaches discussed in the current study might be helpful for hydraulic engineers as well as researchers in the assessment of the removal efficiency of vortex tube silt ejectors.

关键词： Vortex tube silt ejector Support vector machine Random forest Random tree Multivariate adaptive regression spline

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Experimental investigation and prediction of free fall jet scouring using machine learning models

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International Journal of Sediment Research 2023年第3期38卷 405-420页

作者： Farzin Salmasi Parveen Sihag John Abraham Meysam Nouri Department of Water Engineering Faculty of Agriculture University of Tabriz Department of Civil Engineering Chandigarh University School of Engineering University of St.Thomas Minnesota Department of Water Engineering Faculty of Agriculture Urmia University Department of Civil Engineering Saeb University

The current study deals with the depth of scour at the location of impact between a free fall jet and a riverbed. The current study is based on extensive laboratory experiments that were designed to mimic full-scale behavior. The literature review shows that relations among hydraulic parameters for predicting the depth of scour are complex; therefore, six artificial intelligence techniques are used in the current study to capture these complex relation. A total of 120 observations are used for the analysis. Results from the experiments show that with increasing downstream water depth(h), the impinging jet causes increasingly turbulent currents and large vortices that increase the scouring of the riverbed. Increasing discharge per unit width(q) enhances the relative scour depth(D/H) while increasing the average diameter of the riverbed materials(d) decreases D/H, where D is maximum scour depth and H is the height of the falling jet. With increasing(particle Froude number Fr), the relative scour depth increases.In the current study the prediction accuracy of Gene Expression Programming(GEP), Multivariate adaptive regression spline(MARS), M5P Tree, Random Forest(RF), Random Tree(RT), and Reduces Error Pruning Tree(REP Tree) techniques are evaluated using the relative scour depth(D/(H-h)). The performance evaluation indices and graphical methods suggest that the GEP based model is more accurate than other prediction methods for the relative scour depth with a coefficient of determination(R2) equal to 0.8330 and 0.8270, a mean absolute error(MAE) equal to 0.1125 and 0.0902, root mean square error(RMSE) values of 0.1463 and 0.1116, and Willmott’s Index(WI) equal to 0.8998 and 0.9014, for the training and testing stages.

关键词： Free jet Scour Gene Expression Programming (GEP) Random Forest (RF) Multivariate adaptive regression spline (MARS)

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Soft computing approach for prediction of surface settlement induced by earth pressure balance shield tunneling

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Underground Space 2021年第4期6卷 353-363页

作者： W.G.Zhang H.R.Li C.Z.Wu Y.Q.Li Z.Q.Liu H.L.Liu Key Laboratory of New Technology for Construction of Cities in Mountain Area Chongqing UniversityChongqing 400045China National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas Chongqing UniversityChongqing 400045China School of Civil Engineering Chongqing UniversityChongqing 400045China Norwegian Geotechnical Institute(NGI) Sognsveien 720855 OsloNorway

Estimating surface settlement induced by excavation construction is an indispensable task in tunneling,particularly for earth pressure balance(EPB)shield machines.In this study,predictive models for assessing surface settlement caused by EPB tunneling were established based on extreme gradient boosting(XGBoost),artificial neural network,support vector machine,and Multivariate adaptive regression spline.Datasets from three tunnel construction projects in Singapore were used,with main input parameters of cover depth,advance rate,earth pressure,mean standard penetration test(SPT)value above crown level,mean tunnel SPT value,mean moisture content,mean soil elastic modulus,and grout pressure.The performances of these soft computing models were evaluated by comparing predicted deformation with measured values.Results demonstrate the acceptable accuracy of the model in predicting ground settlement,while XGBoost demonstrates a slightly higher accuracy.In addition,the ensemble method of XGBoost is more computationally efficient and can be used as a reliable alternative in solving multivariate nonlinear geo-engineering problems.

关键词： EPB Surface settlement Soft computing XGBoost Multivariate adaptive regression spline

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Data-driven intelligent modeling framework for the steam cracking process

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Chinese Journal of Chemical Engineering 2023年第9期61卷 237-247页

作者： Qiming Zhao Kexin Bi Tong Qiu Department of Chemical Engineering Tsinghua UniversityBeijing 100084China Beijing Key Laboratory of Industrial Big Data Systems and Applications Tsinghua UniversityBeijing 100084China School of Chemical Engineering Sichuan UniversitySichuan 610065China Department of Bioprocess Engineering Institute of BiotechnologyTechnische Universität BerlinBerlin 10623Germany

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.

关键词： Mathematical modeling Data-driven modeling Process systems Steam cracking Clustering Multivariate adaptive regression spline

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