检索结果-内蒙古大学图书馆

Explicit data-driven models for prediction of pressure fluctuations occur during turbulent flows on sloping channels

引用

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2020年第5期34卷 691-707页

作者： Samadi, Mehrshad Sarkardeh, Hamed Jabbari, Ebrahim IUST Sch Civil Engn POB 16765-163 Tehran Iran Hakim Sabzevari Univ Dept Civil Engn POB 961797648 Sabzevar Iran

Pressure fluctuations are among the favorite topics for hydraulic engineers due to their critical role in the design and safe operation of hydraulic structures. In this study, three explicit data-driven models, including Classification and Regression Trees (CART), gene expression programming (GEP), and Multivariate Adaptive Regression Splines (MARS), are used. These methods are applied to predict the pressure fluctuation intensity oC 0 PTHORN, which occurs beneath a hydraulic jump on sloping channels using reliable experimental observations. The main features of the suggested data-driven models are their capability to provide practical formulas for prediction of C0 P based on the dimensionless form of the effective parameters. The application of these models leads to the extraction of knowledge from data in the form of predictive formulas. In addition, to evaluate the accuracy and performance of the proposed models, some statistical indices and several validation criteria are employed. The obtained results confirm that MARS predicts C 0 P better than CART and GEP. Based on the effective independent variables, MARS presents a simple predictive formula for the computation of C 0 P. GEP-based equations use mathematical trigonometric functions that cause a more complicated and time-consuming process to predict C0 P. However, the application of simple rules generated by CART enables hydraulic engineers to predict C0 P, easily and quickly.

关键词： Multivariate adaptive regression splines gene expression programming Decision trees Pressure fluctuation Hydraulic structures Turbulent flow

来源：评论

学校读者我要写书评

暂无评论

Exterior RC joints subjected to monotonic and cyclic loading

引用

ENGINEERING COMPUTATIONS 2020年第7期37卷 2319-2336页

作者： Murad, Yasmin Abdel-Jabar, Haneen Diab, Amjad Abu Hajar, Husam Univ Jordan Dept Civil Engn Amman Jordan

Purpose The purpose of this study is to develop two empirical models that predict the shear strength of exterior beam-column joints exposed to monotonic and cyclic loading using gene expression programming (GEP). Design/methodology/approach The GEP model developed for the monotonic loading case is trained and validated using 81 data test points and that for cyclic loading case is trained and validated using 159 data test points that collected from different 9 and 39 experimental programs, respectively. The parameters that are selected to develop the cyclic GEP model are concrete compressive strength, joint aspect ratio, column axial load and joint transverse reinforcement. The monotonic GEP model is developed using concrete compressive strength, column depth, joint width and column axial load. Findings GEP models are proposed in this paper to predict the joint shear strength of beam-column joints under cyclic and monotonic loading. The predicted results obtained using the GEP models are compared to those calculated using the ACI-352 code formulations. A sensitivity analysis is also performed to further validate the GEP models. Originality/value The proposed GEP models provide an accurate prediction for joint shear strength of beam-column joints under cyclic and monotonic loading that is more fitting to the experimental database than the ACI-352 predictions where the GEP models have higher R-2 value than the code formulations.

关键词： Cyclic loading gene expression programming ACI Joint shear strength Monotonic loading

来源：评论

学校读者我要写书评

暂无评论

Deep neural network-based predictive modeling of road accidents

引用

NEURAL COMPUTING & APPLICATIONS 2020年第16期32卷 12417-12426页

作者： Singh, Gyanendra Pal, Mahesh Yadav, Yogender Singla, Tushar DeenbandhuChhotu Ram Univ Sci & Technol Civil Engn Dept Sonepat Haryana India Natl Inst Technol Dept Civil Engn Kurukshetra Haryana India

This work proposes to use deep neural networks (DNN) model for prediction of road accidents. DNN consists of two or more hidden layers with large number of nodes. Accident data of non-urban sections of eight highways were collected from official records, and dataset consists of a total of 2680 accidents. The data of 16 explanatory variables related to road geometry, traffic and road environment were collected from official records as well as through field studies. Out of a total of 222 data points of accident frequency, 148 were used for training and remaining 74 to test the models. To compare the performance of DNN-based modeling approach, gene expression programming (GEP) and random effect negative binomial (RENB) models were used. A correlation coefficient value of 0.945 (root mean square error = 5.908) was achieved by DNN in comparison with 0.914 (RMSE = 7.474) by GEP, and 0.891 (RMSE = 8.862) by RENB with the test dataset, indicating an improved performance by DNN in prediction of road accidents. In comparison with DNN, though lower value of correlation coefficient was achieved by GEP model, it quantified the effects of various variables on accident frequency and provided a ranked list of variables based upon their importance.

关键词： gene expression programming Deep neural network Back-propagation neural network Random effect negative binomial model Accident prediction model Road safety

来源：评论

学校读者我要写书评

暂无评论

Shape Optimization of Marine Propeller Based On P-System Diffusion Algorithm and Approximate Model Technology

Shape Optimization of Marine Propeller Based On P-System Dif...

引用

2021 International Conference on Mechanical, Aerospace and Automotive Engineering, CMAAE 2021

作者： Wu, JunJun Zheng, Long Chen, ShunHuai Wang, Hao Wuhan Rules and Research Institute Ccs Hubei Wuhan430033 China Wuhan University of Technology Hubei Wuhan430063 China

ISBN: (纸本)9781450385206

Based on a novel optimization algorithm called Diffusion Algorithm Based on P Systems (DAPS) and a new type of approximate called gene expression programming (GEP), we present an optimization method of marine propeller. In this work, the hydrodynamic performance of the marine propeller under different geometrical and physical conditions are evaluated by GEP, the final model is found to be of reasonable accuracy between observed and GEP-predicted values. On the basis of obtaining an approximate model with good response, we use the DAPS algorithm to optimize the shape of the propeller, the profile shape of the propeller is considered as an optimization variable. Diffusion Algorithm Based on P Systems (DAPS) is a novel optimization algorithm, this algorithm utilizes four different search strategies and a P system to explore the search space and find the global minima, through the benchmark functions test, DAPS is highly competitive and can provide better solution. Through calculation verification, it can be seen that the final GEP model has a good prediction of the propeller hydrodynamic performance, and the proposed optimization method can improve the open water efficiency of the propeller. © 2021 ACM.

关键词： Diffusion Algorithm Based on P Systems gene expression programming Optimization Propeller

来源：评论

学校读者我要写书评

暂无评论

Experimental & computational study on fly ash and kaolin based synthetic lightweight aggregate

引用

COMPUTERS AND CONCRETE 2020年第4期26卷 327-342页

作者： Ipek, Suleyman Mermerdas, Kasim Bingol Univ Dept Architecture Bingol Turkey Harran Univ Dept Civil Engn Sanliurfa Turkey

The objective of this study is to manufacture environmentally-friendly synthetic lightweight aggregates that may be used in the structural lightweight concrete production. The cold-bonding pelletization process has been used in the agglomeration of the pozzolanic materials to achieve these synthetic lightweight aggregates. In this context, it was aimed to recycle the waste fly ash by employing it in the manufacturing process as the major cementitious component. According to the well-known facts reported in the literature, it is stated that the main disadvantage of the synthetic lightweight aggregate produced by applying the cold-bonding pelletization technique to the pozzolanic materials is that it has a lower strength in comparison with the natural aggregate. Therefore, in this study, the metakaolin made of high purity kaolin and calcined kaolin obtained from impure kaolin have been employed at particular contents in the synthetic lightweight aggregate manufacturing as a cementitious material to enhance the particle crushing strength. Additionally, to propose a curing condition for practical attempts, different curing conditions were designated and their influences on the characteristics of the synthetic lightweight aggregates were investigated. Three substantial features of the aggregates, specific gravity, water absorption capacity, and particle crushing strength, were measured at the end of 28-day adopted curing conditions. Observed that the incorporation of thermally treated kaolin significantly influenced the crushing strength and water absorption of the aggregates. The statistical evaluation indicated that the investigated properties of the synthetic lightweight aggregate were affected by the thermally treated kaolin content more than the kaoline type and curing regime Utilizing the thermally treated kaolin in the synthetic aggregate manufacturing lead to a more than 40% increase in the crushing strength of the pellets in all curing regimes. Moreover, two num

关键词： synthetic lightweight aggregate calcined kaolin metakaolin fly ash recycling cold-bonding pelletization gene expression programming artificial neural networks

来源：评论

学校读者我要写书评

暂无评论

Experimental investigations and GEP modelling of compressive strength of ferrosialate based geopolymer mortars

引用

CONSTRUCTION AND BUILDING MATERIALS 2020年第0期236卷 117602-000页

作者： Yeddula, Bharath Simha Reddy Karthiyaini, S. Vellore Inst Technol Sch Mech & Bldg Sci Chennai Campus Chennai Tamil Nadu India

In the present scenario where there are no standard procedure/codal provisions for the mix design of ferrosialate geopolymers, this study proposes a new and efficient way to predict the compressive strength of these mortar specimens by generating prediction equations. This work also studies the influence of various parameters on the strength gain of ferrosialate geopolymers and compares that with conventional fly ash/sialate based geopolymers. Red mud (RM) and fly ash are used as a precursor for geopolymerization, and gene expression programming (GEP) is used for predictive analytics. Ferrosialate mortar samples displayed 112.4% higher compressive strength than sialate mortar samples with denser microstructure and least unreacted phases in the binder. Ideal RM replacement in the binder is determined as 30% and 35% for the oven and ambient curing conditions, respectively. If RM is used as raw feed for ferrosialate geopolymers, the optimum molarity of NaOH solution is found to be 8 M. Oven curing type has higher possibility of strength gain than ambient curing type, and increased curing time leads to a higher possibility of strength gain in ferrosialate geopolymers. The average r(2) values for GEP I and GEP II models are 0.88 and 0.92, respectively. Errors of training and validation sets also suggest that a good fit is obtained between observed and predicted values. This indicates that both the models performed well in predicting the compressive strength. Though this model is generated for ferrosialate geopolymer, a similar procedure can be adapted to any geopolymer for the strength prediction. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Ferrosialate Red mud gene expression programming Geopolymer Sialate Mortar

来源：评论

学校读者我要写书评

暂无评论

Establishing and analyzing the design space in the development of direct compression formulations by gene expression programming

引用

INTERNATIONAL JOURNAL OF PHARMACEUTICS 2012年第1-2期434卷 35-42页

作者： Landin, Mariana Rowe, Raymond C. York, Peter Univ Santiago de Compostela Dept Farm & Tecnol Farmaceut Fac Farm Santiago De Compostela 15782 Spain Intelligensys Ltd Springboard Business Ctr Stokesley TS9 5JZ N Yorkshire England Univ Bradford Inst Pharmaceut Innovat Bradford BD7 1DP W Yorkshire England

Purpose: In this paper we have evaluated the gene expression programming (GEP) methodology for modeling the effect of different variables (continuous and nominal) and their interactions on the properties of direct compression formulations. Methods: The effect of four variables was studied;variety of diluents, type and percentage of drug and maximum compression force, on the mechanical and drug release properties of direct compression tablets. The generated database (36 formulations) was used for mathematical and GEP modeling. Results: GEP has been shown to have a high accuracy in prediction for four out five outputs studied including friability which had no replicate measurements. Compared to the traditional statistical treatment GEP is less time consuming and gives equations which are extremely helpful in understanding the interactions of the different variables and for establishing the design space in the development of direct compression formulations. Conclusions: GEP allows similar conclusions than traditional statistical treatment. The helpfulness of this methodology in establishing the design space has been demonstrated. The knowledge derived from GEP can easily be increased by including additional information or new inputs, such as additional drugs or combinations of excipients in the data set. (c) 2012 Elsevier B.V. All rights reserved.

关键词： gene expression programming Quality by design Design space Direct compression tablets Response Surface Methodology

来源：评论

学校读者我要写书评

暂无评论

ACO-HCO: Heuristic Performance Tuning Scheme for the Hadoop MapReduce Architecture

引用

JOURNAL OF INTERNET TECHNOLOGY 2020年第4期21卷 1151-1159页

作者： Liu, Chiang-Lung Lo, Hsiang-Fu Lee, Wei-Tsong Natl Def Univ Dept Elect & Elect Engn Chung Cheng Inst Technol Taoyuan Taiwan Chung Shan Inst Sci & Technol Chem Syst Res Div Taoyuan Taiwan Tamkang Univ Dept Elect Engn New Taipei Taiwan

Hadoop MapReduce is a widely-used cloud computing technology for big data processing. However, the Hadoop configuration parameters settings can significantly change the execution performance. Manual adjustment of the Hadoop parameters will be a time consuming and difficult task. In this paper, we propose ACO-HCO, a Hadoop configuration tuning scheme for MapReduce applications. We use MapReduce applications job history records to generate specific job profiles. Based on these profiles, an objective function for execution time is constructed with gene expression programming algorithm by mining the correlation among the core Hadoop configuration parameters and input data size. Leveraging the objective function, an ACO-based configuration optimizer is able to heuristically search for the optimal configuration for a given application. Experimental results show that ACO-HCO enhances the performance of Hadoop significantly compared with the default configuration. Moreover, ACO-HCO performs better than heuristic approach and the cost-based model in Hadoop performance tuning.

关键词： Hadoop performance tuning Ant colony optimization algorithm gene expression programming

来源：评论

学校读者我要写书评

暂无评论

A rigorous model for prediction of viscosity of oil-based hybrid nanofluids

引用

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2020年 556卷 124827-124827页

作者： Jamei, Mehdi Ahmadianfar, Iman Shohadaye Hoveizeh Univ Technol Fac Engn Dasht E Azadegan Iran Behbahan Khatam Alanbia Univ Technol Dept Civil Engn Behbahan Iran

Oil-based hybrid nanofluids play an important role in heat transfer in cooling systems and lubrication. Therefore, various experimental investigations are conducted to estimate their viscosity. However, such measurements can be carried out on limited types of oil-based hybrid nanofluids and often are time consuming and expensive. The main objective of this paper is to develop a rigorous data-driven method based on an advanced genetic programming (GP) called multigene genetic programming (MGGP) to predict the viscosity of Newtonian oil-based hybrid nanofluids which has not previously been used in this area. A comparative analysis was performed using the gene expression programming (GEP), multi-variate linear regression (MLR) methods and various correlations. 679 experimental data points with different nanoparticles and oil-based fluids were collected from literature to develop the Artificial Intelligent (AI) models. The new approach showed superior performance in estimating of the relative viscosity of oil-based hybrid nanofluids in comparison with all correlations methods. Furthermore, the MGGP results for the test dataset (R=0.991, RMSE=0.05, PI=0.643) were more accurate than those obtained from the GEP (R=0.975, RMSE=0.083, PI=0.696) and MLR (R=0.912, RMSE = 0.153, PI=1), respectively. The sensitivity analysis was also performed demonstrating that the volume fraction (PIs=0.849, DV1=10.079%), temperature (PIs=0.463, DV2=9.966%) and nanoparticles size (PIs=0.420, DV3=6.092%) are the most significant factors in assessing relative viscosity, respectively. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Relative viscosity Oil-based hybrid nanofluids Artificial intelligence Multigene genetic programming gene expression programming

来源：评论

学校读者我要写书评

暂无评论

Water consumption forecasting using soft computing - a case study, Trinidad and Tobago

引用

WATER SUPPLY 2020年第8期20卷 3576-3584页

作者： Leon, Lee P. Chaplot, Barkha Solomon, Akil Univ West Indies Dept Civil & Environm Engn St Augustine Trinidad Tobago BRA Bihar Univ MJK Coll Bettiah Muzaffarpur India

Water scarcity is one of the world's fastest growing epidemics. Therefore, to combat it or mitigate the risks one must first understand how water is being consumed. This study focuses on the analysis of domestic water consumption with reference to how much of it is being consumed. Additionally, the study aims to propose an applicable and consistent method to forecast urban water consumption by using soft computing techniques. The investigation highlights the hourly, daily and monthly water consumption levels as well as the relationship between climate change and water demand using gene expression programming (GEP). The results of the study are relatively promising as it demonstrates that GEP can predict water consumption incorporating seasonal changes of wet and dry periods.

关键词： gene expression programming monthly rainfall Trinidad water consumption

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：