检索结果-内蒙古大学图书馆

Data-driven evolutionary programming for evaluating the mechanical properties of concrete containing plastic waste

CASE STUDIES IN CONSTRUCTION MATERIALS 2024年 21卷

作者： Asif, Usama Javed, Muhammad Faisal Alsekait, Deema Mohammed Aslam, Fahid Elminaam, Diaa Salama Abd Nazarbayev Univ Dept Civil Engn Astana Kazakhstan COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Abbottabad 22060 Pakistan Ghulam Ishaq Khan Inst Engn Sci & Technol Dept Civil Engn Topi Pakistan Western Caspian Univ Baku Azerbaijan Princess Nourah Bint Abdulrahman Univ Appl Coll Dept Comp Sci & Informat Technol POB 84428 Riyadh 11671 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj 11942 Saudi Arabia Middle East Univ MEU Res Unit Amman 11831 Jordan Jadara Univ Jadara Res Ctr Irbid 21110 Jordan

Plastic waste (PW) has emerged as a global environmental concern due to its detrimental impact on ecosystems and human health. Traditional concrete heavily relies on natural aggregates like sand, gravel, and crushed stone, whose extraction leads to environmental degradation, including habitat destruction and resource depletion. Recently, the use of PW in concrete has gained attention as a sustainable alternative to these conventional aggregates. By incorporating PW as a partial replacement for natural aggregates, the construction industry can reduce its reliance on finite resources while also addressing the issue of PW. However, despite its potential environmental benefits, the incorporation of PW into concrete has primarily been explored through experimental studies, which are often time-consuming and resource-intensive. Therefore, this study aims to optimize the utilization of waste plastic in concrete through machine learning (ML) techniques, specifically multi-expression programming (MEP) and Gene expression programming (GEP). A comprehensive literature review was conducted to compile a database for evaluating the compressive strength (CS) and tensile strength (TS) of PW concrete. The most influential parameters, such as plastic (P), gravel (G), water (W), cement (C), sand (S), and age (A), were considered as inputs in the models' development. The models developed were thoroughly evaluated using multiple statistical measures. Additionally, sensitivity analysis was conducted to discern and highlight influential factors that have a significant impact on the predicted outcomes. The findings indicate that both MEP (CS_R-2 = 0.88, and TS_R-2 = 0.89) and GEP (CS_R-2 = 0.87, and TS_R-2 = 0.88) models performed well, with MEP demonstrating slightly superior performance. Sensitivity analysis highlights the significant influence of cement (25.63 % and 24.53 %) and plastic (22.4 % and 23.44 %) on concrete strength properties. Furthermore, the equations provided by GEP and

关键词： Plastic waste concrete Compressive strength Gene expression programming multi-expression programming Tensile strength Sustainable development

来源：评论

学校读者我要写书评

暂无评论

Analytical and innovative modeling investigations on the performance of nanoparticle-modified self-compacting mortars

引用

Revue Française de Génie Civil 2025年第5期29卷

作者： Rabar H. Faraj Hemn Unis Ahmed Serwan Khwrshid Rafiq Nadhim Hamah Sor a Civil Engineering Department University of Halabja Halabja Kurdistan Region Iraq b Civil Engineering Department Komar University of Science and Technology Sulaimani Kurdistan Region Iraqc Civil Engineering Department College of Engineering University of Sulaimani Sulaimani Kurdistan Region Iraq c Civil Engineering Department College of Engineering University of Sulaimani Sulaimani Kurdistan Region Iraq d Civil Engineering Department University of Garmian Kalar Kurdistan Region Iraq

The transition of a material from macro- to Nano-scale brings about significant changes in electron conductivity, optical absorption, mechanical properties, chemical reactivity, and surface morphology. These changes present opportunities for creating innovative composite mixtures. As there is a growing need for improved infrastructure, it becomes crucial to develop new, high-performance materials. To enhance the performance of concrete mixtures, various methods have been explored, including the utilization of nanoparticles (NPs). Incorporating NPs aims to improve the fresh and mechanical properties of self-compacting concrete (SCC) while also enhancing the permeability and absorption capacity of the composite by introducing extremely fine particles to fill micro-pores and voids. Numerous initiatives have been implemented to explore the mechanical characteristics of SCC. Typically, compressive strength (CS) serves as a crucial mechanical parameter for assessing concrete quality. Conventional methods for determining SCC’s CS are costly, time-intensive, and restrictive due to the intricate interplay of various mixing proportions and curing processes. Thus, this investigation employs machine learning techniques, including artificial neural network (ANN), multi-expression programming (MEP), full quadratic (FQ), and linear regression (LR), to predict self-compacting mortar’s CS. Approximately 292 CS values from the literature were extracted and analyzed to facilitate model development. Six influential variables were used as input parameters and one as an output during the modeling process. Four statistical metrics gauged model performance, and sensitivity analysis was conducted. Results indicate that the ANN model outperformed other models in predicting self-compacting mortar’s CS. Meanwhile, the water-to-binder ratio, nanoparticle dosage, and concrete age significantly influence self-compacting mortar’s CS.

关键词： Self-compacting mortar nanoparticles modeling artificial neural network multi-expression programming

来源：评论

学校读者我要写书评

暂无评论

An evolutionary computational approach for formulation of compression index of fine-grained soils

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2014年 33卷 58-68页

作者： Mohammadzadeh, Danial S. Bazaz, Jafar Bolouri Alavi, Amir H. Ferdowsi Univ Mashhad Dept Civil Engn Mashhad Iran Michigan State Univ Dept Civil & Environm Engn E Lansing MI 48824 USA

This study presents a robust evolutionary computational technique, called multi-expression programming (MEP), to derive a highly nonlinear model for the prediction of compression index of fine-grained soils. The proposed model relates the soil compression index to its liquid limit, plastic limit and void ratio. The experimental database used for developing the models was established upon 108 consolidation tests conducted on different soils sampled from different construction sites in Iran. The generalization capability of the model was verified via several statistical criteria. The parametric and sensitivity analyses were performed and discussed. The results indicate that the MEP approach accurately characterizes the soil compression index leading to a very good prediction performance. The correlation coefficients between the experimental and predicted soil compression index values are equal to 0.935 and 0.901 for the calibration and testing data sets, respectively. The developed model has a significantly better performance than the existing empirical equations for the soil compression index. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Evolutionary computation multi-expression programming Soil compression index Soil engineering properties Prediction

来源：评论

学校读者我要写书评

暂无评论

Toward sustainability: Integrating experimental study and data-driven modeling for eco-friendly paver blocks containing plastic waste

引用

REVIEWS ON ADVANCED MATERIALS SCIENCE 2024年第1期63卷

作者： Asif, Usama Javed, Muhammad Faisal Alsekait, Deema Mohammed Abdelminaam, Diaa Salama Alabduljabbar, Hisham Nazarbayev Univ Sch Engn & Digital Sci Dept Civil & Environm Engn Nur Sultan 010000 Kazakhstan Princess Nourah Bint Abdulrahman Univ Appl Coll Dept Comp Sci & Informat Technol POB 84428 Riyadh 11671 Saudi Arabia Ghulam Ishaq Khan Inst Engn Sci & Technol Dept Civil Engn Topi Pakistan Western Caspian Univ Baku Azerbaijan Middle East Univ MEU Res Unit Amman 11831 Jordan Jadara Univ Jadara Res Ctr Irbid 21110 Jordan Benha Univ Fac Comp & Artificial Intelligence Informat Syst Dept Banha 13511 Egypt Prince Sattam Bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj 11942 Saudi Arabia

Plastic waste (PW) poses a significant threat as a hazardous material, while the production of cement raises environmental concerns. It is imperative to urgently address and reduce both PW and cement usage in concrete products. Recently, several experimental studies have been performed to incorporate PW into paver blocks (PBs) as a substitute for cement. However, the experimental testing is not enough to optimize the use of waste plastic in pavers due to resource and time limitations. This study proposes an innovative approach, integrating experimental testing with machine learning to optimize PW ratios in PBs efficiently. Initially, experimental investigations are performed to examine the compressive strength (CS) of plastic sand paver blocks (PSPBs). Varied mix proportions of plastic and sand with different sizes of sand are employed. Moreover, to enhance the CS and meet the minimum requirements of ASTM C902-15 for light traffic, basalt fibers, a sustainable industrial material, are also utilized in the manufacturing process of environmentally friendly PSPB. The highest CS of 17.26 MPa is achieved by using the finest-size sand particles with a plastic-to-sand ratio of 30:70. Additionally, the inclusion of 0.5% basalt fiber, measuring 4 mm in length, yields further enhancement in outcome by significantly improving CS by 25.4% (21.65 MPa). Following that, an extensive experimental record is established, and multi-expression programming (MEP) is used to forecast the CS of PSPB. The model's projected results are confirmed by using various statistical procedures and external validation methods. Furthermore, comprehensive parametric and sensitivity studies are conducted to assess the effectiveness of the MEP-based proposed models. The sensitivity analysis demonstrates that the size of the sand particles and the fiber content are the primary factors contributing to more than 50% of the CS in PSPB. The parametric analysis confirmed the model's accuracy by demonstrating a

关键词： compressive strength plastic waste basalt fiber multi-expression programming paver blocks graphical user interface

来源：评论

学校读者我要写书评

暂无评论

Innovative modeling techniques including MEP, ANN and FQ to forecast the compressive strength of geopolymer concrete modified with nanoparticles

引用

NEURAL COMPUTING & APPLICATIONS 2023年第17期35卷 12453-12479页

作者： Ahmed, Hemn Unis Mohammed, Ahmed S. S. Faraj, Rabar H. H. Abdalla, Aso A. A. Qaidi, Shaker M. A. Sor, Nadhim Hamah Mohammed, Azad A. A. Univ Sulaimani Coll Engn Civil Engn Dept Sulaimaniyah Kurdistan Regio Iraq Komar Univ Sci & Technol Civil Engn Dept Sulaimani Kurdistan Regio Iraq Univ Halabja Civil Engn Dept Halabja Kurdistan Regio Iraq Univ Duhok Coll Engn Civil Engn Dept Duhok Kurdistan Regio Iraq Univ Garmian Civil Engn Dept Kalar Kurdistan Regio Iraq Nawroz Univ Coll Engn Dept Civil Engn Duhok 42001 Iraq

The use of nano-materials to improve the engineering properties of different types of concrete composites including geopolymer concrete (GPC) has recently gained popularity. Numerous programs have been executed to investigate the mechanical properties of GPC. In general, compressive strength (CS) is an essential mechanical indicator for judging the quality of concrete. Traditional test methods for determining the CS of GPC are expensive, time-consuming and limiting due to the complicated interplay of a wide variety of mixing proportions and curing regimes. Therefore, in this study, artificial neural network (ANN), multi-expression programming, full quadratic, linear regression and M5P-tree machine learning techniques were used to predict the CS of GPC. In this instance, around 207 tested CS values were extracted from the literature and studied to promote the models. During the process of modeling, eleven effective variables were utilized as input model parameters, and one variable was utilized as an output. Four statistical indicators were used to judge how well the models worked, and the sensitivity analysis was carried out. According to the results, the ANN model calculated the CS of GPC with greater precision than the other models. On the other hand, the ratio of alkaline solution to the binder, molarity, NaOH content, curing temperature and concrete age have substantial effects on the CS of GPC.

关键词： Geopolymer concrete Modeling Compressive strength Artificial neural network multi-expression programming Sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

Compressive strength of waste-derived cementitious composites using machine learning

引用

REVIEWS ON ADVANCED MATERIALS SCIENCE 2024年第1期63卷

作者： Tian, Qiong Lu, Yijun Zhou, Ji Song, Shutong Yang, Liming Cheng, Tao Huang, Jiandong Hunan Univ Sci & Engn Coll Civil & Environm Engn Yongzhou 425199 Peoples R China Hubei Polytech Univ Sch Civil Engn Huangshi 435003 Peoples R China Guangzhou Univ Sch Civil Engn Guangzhou Peoples R China China Construct Railway Investment Rail Traff Cons Guangzhou Peoples R China

Marble cement (MC) is a new binding material for concrete, and the strength assessment of the resulting materials is the subject of this investigation. MC was tested in combination with rice husk ash (RHA) and fly ash (FA) to uncover its full potential. Machine learning (ML) algorithms can help with the formulation of better MC-based concrete. ML models that could predict the compressive strength (CS) of MC-based concrete that contained FA and RHA were built. Gene expression programming (GEP) and multi-expression programming (MEP) were used to build these models. Additionally, models were evaluated by calculating R 2 values, carrying out statistical tests, creating Taylor's diagram, and comparing theoretical and experimental readings. When comparing the MEP and GEP models, MEP yielded a slightly better-fitted model and better prediction performance (R 2 = 0.96, mean absolute error = 0.646, root mean square error = 0.900, and Nash-Sutcliffe efficiency = 0.960). According to the sensitivity analysis, the prediction of CS was most affected by curing age and MC content, then by FA and RHA contents. Incorporating waste materials such as marble powder, RHA, and FA into building materials can help reduce environmental impacts and encourage sustainable development.

关键词： marble cement gene expression programming multi-expression programming

来源：评论

学校读者我要写书评

暂无评论

New Ground-Motion Prediction Equations Using multi expression Programing

引用

JOURNAL OF EARTHQUAKE ENGINEERING 2011年第4期15卷 511-536页

作者： Alavi, Amir Hossein Gandomi, Amir Hossein Modaresnezhad, Minoo Mousavi, Mehdi Tafresh Univ Coll Civil Engn Tafresh Iran Iran Univ Sci & Technol Sch Civil Engn Tehran Iran Ferdowsi Univ Mashhad Dept Civil Engn Mashhad Iran Arak Univ Dept Civil Engn Arak Iran

High-precision attenuation models were derived to estimate peak ground acceleration (PGA), velocity (PGV), and displacement (PGD) using a new variant of genetic programming, namely multi expression programming (MEP). The models were established based on an extensive database of ground-motion recordings released by Pacific Earthquake Engineering Research Center (PEER). For more validity verification, the models were employed to predict the ground-motion parameters of the Iranian plateau earthquakes. The results indicate that the MEP attenuation models are capable of effectively estimating the peak ground-motion parameters. The proposed models are able to reach a prediction performance comparable with the attenuation relationships found in the literature.

关键词： Time-Domain Ground-Motion Parameters Attenuation Relationship multi-expression programming Nonlinear Modeling

来源：评论

学校读者我要写书评

暂无评论

Investigating the feasibility of genetic algorithms in predicting the properties of eco-friendly alkali-based concrete

引用

CONSTRUCTION AND BUILDING MATERIALS 2023年 409卷

作者： Jin, Conghe Qian, Yongjiu Khan, Suleman Ayub Ahmad, Waqas Althoey, Fadi Alotaibi, Badr Saad Abuhussain, Mohammed Awad Southwest Jiaotong Univ Sch Civil Engn Chengdu 610031 Peoples R China COMSATS Univ Islamabad Dept Civil Engn Abbottabad 22060 Pakistan Najran Univ Coll Engn Dept Civil Engn Najran Saudi Arabia Najran Univ Coll Engn Architectural Engn Dept Najran Saudi Arabia Najran Univ Sci & Engn Res Ctr Najran Saudi Arabia

This study employed an AI-based strategy to determine the accurate parameters in alkali-activated concrete (AAC) mix design that contribute to optimal performance. The data mining approach was used to generate a dataset comprising seven crucial input parameters and three outputs. Gene expression programming (GEP) models and multi-expression programming (MEP) models were developed to predict the rheological characteristics (in terms of static/dynamic yield stress and plastic viscosity) of AAC. R-2 values, statistical checks, Taylor's diagram, and the difference between experimental and anticipated rheological parameters were used to assess the suitability of the created models. All prediction models developed using the MEP approach were found to be highly accurate (R-2 > 0.90), while all GEP models were found to be in the acceptable range of accuracy (R-2 near 0.90). Moreover, the greater accuracy of MEP models over GEP models was also confirmed by error evaluation by statistical tests. The empirical equations presented by the models might be useful for comprehending the mix design of AAC and the effect of each input parameter. Precursor content was found to have a significantly positive impact on ACC's rheological properties, as determined by the sensitivity analysis.

关键词： Alkali-activated materials Rheological properties multi-expression programming Gene-expression programming Prediction models

来源：评论

学校读者我要写书评

暂无评论

Development of prediction models for shear strength of SFRCB using a machine learning approach

引用

NEURAL COMPUTING & APPLICATIONS 2019年第7期31卷 2085-2094页

作者： Sarveghadi, Masoud Gandomi, Amir H. Bolandi, Flamed Alavi, Amir H. Islamic Azad Univ Dept Civil Engn Kashmar Branch Kashmar Iran Michigan State Univ BEACON Ctr Study Evolut Act E Lansing MI 48824 USA Islamic Azad Univ Dept Civil Engn Bandar Abbas Branch Bandar Abbas Iran Michigan State Univ Dept Civil & Environm Engn E Lansing MI 48824 USA

In this study, new design equations were derived for the assessment of shear resistance of steel fiber-reinforced concrete beams (SFRCB) utilizing multi-expression programming (MEP). The superiority of MEP over conventional statistical techniques is due to its ability in modeling of mechanical behavior without a need to pre-define the model structure. The MEP models were developed using a comprehensive database obtained through an extensive literature review. New criteria were checked to verify the validity of the models. A sensitivity analysis was carried out and discussed. The MEP models provide good estimations of the shear strength of SFRCB. The developed models significantly outperform several equations found in the literature.

关键词： SFRCB multi-expression programming Shear strength Prediction

来源：评论

学校读者我要写书评

暂无评论

A Robust Data-Mining Approach to Bankruptcy Prediction

引用

JOURNAL OF FORECASTING 2012年第6期31卷 504-523页

作者： Divsalar, Mehdi Roodsaz, Habib Vahdatinia, Farshad Norouzzadeh, Ghassem Behrooz, Amir Hossein Allameh Tabatabai Univ Fac Management & Accounting Tehran Iran Ferdowsi Univ Mashhad Dept Civil Engn Mashhad Iran Univ Tehran Fac Management Tehran Iran Allameh Tabatabai Univ Fac Management Business Sch ATUBS Tehran Iran

In this study, new variants of genetic programming (GP), namely gene expression programming (GEP) and multi-expression programming (MEP), are utilized to build models for bankruptcy prediction. Generalized relationships are obtained to classify samples of 136 bankrupt and non-bankrupt Iranian corporations based on their financial ratios. An important contribution of this paper is to identify the effective predictive financial ratios on the basis of an extensive bankruptcy prediction literature review and upon a sequential feature selection analysis. The predictive performance of the GEP and MEP forecasting methods is compared with the performance of traditional statistical methods and a generalized regression neural network. The proposed GEP and MEP models are effectively capable of classifying bankrupt and non-bankrupt firms and outperform the models developed using other methods. Copyright (c) 2011 John Wiley & Sons, Ltd.

关键词： bankruptcy prediction gene expression programming multi-expression programming sequential feature selection financial ratios

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：