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Prediction of UCS of fine-grained soil based on machine learning part 1: multivariable regression analysis, gaussian process regression, and gene expression programming

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2023年第2期6卷 199-222页

作者： Khatti, Jitendra Grover, Kamaldeep Singh Rajasthan Tech Univ Dept Civil Engn Kota Rajasthan India

The present research introduces the best architecture approach and model for predicting the unconfined compressive strength (UCS) of cohesive virgin soil by comparing the multivariable regression analysis (MRA), gaussian process regression (GPR), and gene expression programming (GEP) approaches. The present research reveals the effect of the quality & quantity of the training database and the impact of the multicollinearity on the performance and overfitting of the MRA, GPR, and GEP models. The performance of the soft computing models has been measured by root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), weighted mean absolute percentage error (WMAPE), coefficient of determination (R-2), correlation coefficient (r), variance accounted for (VAF), Nash-Sutcliffe efficiency (NS), normalized mean bias error (NMBE), Legate McCabe's Index (LMI), root mean square error to observations' standard deviation ratio (RSR), a20-index, index of agreement (IOA), and index of scatter (IOS) statistical tools. The performance comparison of MRA, GPR, and GEP shows that GPR model MD11 has predicted UCS of soil with high performance (R = 0.9959, VAF = 99.18, NS = 0.9909, LMI = 0.1026, RSR = 0.0952, a20-index = 100.00, IOA = 0.9487 & IOS = 0.0531) and the least prediction error (RMSE = 2.4482 N/cm(2), MAE = 1.8840 N/cm(2), MAPE = 5.0849 N/cm(2), WMAPE = 0.0408 N/cm(2), NMBE = 0.1299 N/cm(2)). In the validation, model MD11 has achieved RMSE = 3.4849 N/cm(2), MAE = 3.1845 N/cm(2), R = 0.9040, R-2 = 0.8172, confidence interval of +/- 5.0% by predicting UCS of lab-tested twelve soil samples, which is acceptable. This study shows that the GPR approach predicts better UCS in the presence of multicollinearity and using a small database. The sensitivity analysis illustrates that the UCS prediction of cohesive virgin soil is very highly influenced by fine content, dry unit weight, porosity, void ratio, degree of saturation, and specific gravity of soi

关键词： Machine learning Regression analysis Gaussian process regression gene expression programming Unconfined compressive strength

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Forecasting Caspian Sea level changes using satellite altimetry data (June 1992-December 2013) based on evolutionary support vector regression algorithms and gene expression programming

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GLOBAL AND PLANETARY CHANGE 2014年 121卷 53-63页

作者： Imani, Moslem You, Rey-Jer Kuo, Chung-Yen Natl Cheng Kung Univ Dept Geomat Tainan 701 Taiwan

Sea level forecasting at various time intervals is of great importance in water supply management. Evolutionary artificial intelligence (AI) approaches have been accepted as an appropriate tool for modeling complex nonlinear phenomena in water bodies. In the study, we investigated the ability of two AI techniques: support vector machine (SVM), which is mathematically well-founded and provides new insights into function approximation, and gene expression programming (GEP), which is used to forecast Caspian Sea level anomalies using satellite altimetry observations from June 1992 to December 2013. SVM demonstrates the best performance in predicting Caspian Sea level anomalies, given the minimum root mean square error (RMSE = 0.035) and maximum coefficient of determination (R-2 = 0.96) during the prediction periods. A comparison between the proposed AI approaches and the cascade correlation neural network (CCNN) model also shows the superiority of the GEP and SVM models over the CCNN. (C) 2014 Elsevier B.V. All rights reserved.

关键词： support vector machine gene expression programming Caspian Sea level satellite altimetry

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Utilization of Support Vector Models and gene expression programming for Soil Strength Modeling

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020年第5期45卷 4301-4319页

作者： Tenpe, Ashwini R. Patel, Anjan Visvesvaraya Natl Inst Technol Nagpur 440010 Maharashtra India

The subgrade strength of roads and highways is based on the California bearing ratio (CBR) value. In this investigation, attempts have been made to overcome the limited boundary condition approach by using advanced methods, support vector machine (SVM) and gene expression programming (GEP) for prediction of CBR value. A large and wide range of datasets of different types of soils have been utilized in the analysis. The grain size distribution, Atterberg's limits and compaction characteristics of soils have been used as the input variables. Best models with different variables were developed by using GEP and the same were used for SVM analysis. The advantage of SVM over others is that it works on the principle of statistical risk minimization. A comparative study of SVM and GEP models indicates that the SVM has better predictability than GEP. Further, it was found that the five-input variable (including gravel content, sand content, plasticity index, maximum dry density and optimum moisture content) model is the best one to predict the CBR value. The detailed statistical analysis including Pearson coefficient correlation (R) and Error analysis have also been carried out. Based upon the statistical analysis, overfitting ratio of SVM was found to be 0.630 against the value of 1.02 in GEP analysis. Further, sensitivity analysis was carried out and it was found that the CBR value is highly dependent on gravel and sand contents. On the other hand, plastic limit plays an insignificant role in determining the CBR value of soils.

关键词： Prediction California bearing ratio Subgrade Support vector machine gene expression programming

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Prediction of Pile Setup for Driven Pipe Piles in Fine-Grained Soils Using gene expression programming

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GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2023年第6期41卷 3605-3624页

作者： Alzahrani, Saeed Bilgin, Omer Imam Abdulrahman Bin Faisal Univ Coll Engn Dept Civil & Construct Engn Dammam 31451 Saudi Arabia Univ Dayton Dept Civil Engn 300 Coll Pk Dayton OH 45469 USA

This paper presents the development of a novel model for predicting setup of closed-ended steel pipe (CEP) piles driven in predominantly fine-grained soils using gene expression programming (GEP). The proposed GEP model incorporates both pile and soil properties and is based on dynamic pile load test data obtained from 109 piles installed at 59 different sites. Multiple variable regression analyses conducted on the compiled dataset showed that the most influential parameters in predicting the time-dependent resistance of driven piles were the resistance mobilized at the end of pile installation, time elapsed after installation, pile shaft surface area, and average silt content along the pile length. The data were divided into a training set for model calibration and a validation set for model verification. Sensitivity analysis was performed to assess the model's robustness. A comparison of the new GEP model with existing pile setup models in the literature was carried out using the collected data, which demonstrated that the proposed GEP model significantly outperformed the tested models. Additionally, data from 22 additional CEP piles were compiled from the literature for model verification purposes. The results showed that the proposed GEP model can predict the total pile resistance with good accuracy.

关键词： Driven piles Pile setup Pile resistance Dynamic load test CAPWAP Fine-grained soils gene expression programming Soil properties Pile properties

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Application of gene expression programming for proton-proton interactions at large hadrons collider

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INDIAN JOURNAL OF PHYSICS 2013年第6期87卷 593-599页

作者： Radi, A. Ain Shams Univ Fac Sci Dept Phys Cairo 11566 Egypt British Univ Egypt Ctr Theoret Phys El Sherouk City 11837 Egypt

This paper describes how to use gene expression programming (GEP) as an evolutionary computational optimization approach. GEP, as a machine learning technique is usually used for modeling physical phenomena by discovering a new function. In case of modeling the p-p interactions at large hadrons collider (LHC) experiments, GEP is used to simulate and predict the number of charged particles multiplicity and total cross-section, sigma (T) , as a function of total center-of-mass energy, . Considering the discovered function for (), the general trend of the predicted values shows good agreement at LHC [predicted values are 31.3251, 32.8638 and 35.3520 at = 8 TeV, = 10 TeV and = 14 TeV respectively]. The discovered function, trained on experimental data of particle data group shows a good match as compared with the other models. The predicted values of cross section at = 8, 10 and 14 TeV are found to be 101.0417, 105.0690 and 111.3407 mb respectively. Moreover, those predicted values are in good agreement with those reported by Block, Cudell and Nakamura.

关键词： Proton-proton interaction Multiplicity distribution Modeling Machine learning gene expression programming

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gene expression programming Model for Tribological Behavior of Novel SiC-ZrO2-Al Hybrid Composites

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MATERIALS 2022年第23期15卷 8593-8593页

作者： Abbasi, Hossein Zeraati, Malihe Moghaddam, Reza Fallah Chauhan, Narendra Pal Singh Sargazi, Ghasem Di Lorenzo, Ritamaria Islamic Azad Univ Dept Comp Engn Tangestan Branch Ahram 75541 Iran Shahid Bahonar Univ Kerman Fac Engn Dept Met & Mat Sci Kerman 14111 Iran Univ Garmsar Fac Engn Garmsar *** Iran Bhupal Nobles Univ Fac Sci Dept Chem Udaipur 313002 Rajasthan India Bam Univ Med Sci Noncommunicable Dis Res Ctr Bam *** Iran Univ Napoli Federico II Fac Med & Chirurg I-80138 Naples Italy

In order to improve product format quality and material flexibility, variety of application, and cost-effectiveness, SiC, ZrO2, and Al hybrid composites were manufactured in the research utilizing the powder metallurgy (PM) technique. A model was created to predict the tribological behavior of SiC-ZrO2-Al hybrid composites using statistical data analysis and gene expression programming (GEP) based on artificial intelligence. For the purpose of examining the impact of zirconia concentration, sliding distance, and applied stress on the wear behavior of hybrid composites, a comprehensive factor design of experiments was used. The developed GEP model was sufficiently robust to achieve extremely high accuracy in the prediction of the determine coefficient (R-2), the root mean square error (RMSE), and the root relative square error (RRSE). The maximum state of the RMSE was 0.4357 for the GEP-1 (w1) model and the lowest state was 0.7591 for the GEP-4 (w1) model, while the maximum state of the RRSE was 0.4357 for the GEP-1 (w1) model and the minimum state was 0.3115 for the GEP-3 model (w1).

关键词： gene expression programming tribological hybrid composite zirconia

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

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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

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Determination of Joint Roughness Coefficient of 2D Rock Joint Profile Based on Fractal Dimension by Using of the gene expression programming

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GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2020年第1期38卷 861-871页

作者： Wang, Min Wan, Wen Zhao, Yanlin Hunan Univ Sci Sch Mech Engn Technol Xiangtan Peoples R China Hunan Univ Sci Sch Resource Environm Safety Engn Technol Xiangtan Peoples R China

Rock joint roughness influence the shear behavior of rock joints, which have been extensively studied in last a few decades. Various parameters were recommended to quantify the joint roughness, in these parameters, the joint roughness coefficients (JRC value) were widely applied to depict the joint roughness quantitatively by many researchers. Through analysis the references, it is found that the empirical formula of the JRC value based on fractal dimension were commonly adopted, however, the empirical formulas were determined frequently by a limited number of data point, meanwhile, the type of empirical formula were chosen based on the researchers' subjectively. For obtaining a suitable and objective empirical formula of JRC values based on fractal dimension, a large number of experimental data were collected from the references, by using the gene expression programming method, an empirical formula was determined objectively. The obtained formula can reflect the relationship between the JRC values and fractal dimension extensively, the empirical formula is JRC 1/4 21:0810 similar to 0:0656 o ffiffiffiDp similar to 0:9969THORND

关键词： Rock joint profile Joint roughness coefficient Fractal dimension Empirical formula gene expression programming

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A New Algorithm for Detection of Animal and Plant Ion Concentration Based on gene expression programming

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2023年第1期17卷 1-11页

作者： Li, Kangshun Lin, Leqing Li, Jiaming Chen, Siwei Jalil, Hassan Dongguan City Coll Dongguan Peoples R China South China Agr Univ Coll Math & Informat Sci Guangzhou Peoples R China South China Agr Univ Guangzhou Peoples R China

In order to accurately predict the concentration detection data of ion sensors for animal and plant, this paper proposes a gene expression programming (GEP) based concentration detection method. The method includes collecting ion concentration data as well as voltage timing data;preprocessing all the collected data to obtain an initial sample set;constructing a prediction model of ion concentration, which is an explicit functional relationship between voltage and the concentration of a specific ion. The gene expression programming is used to train and evaluate the prediction model, and obtain a trained model. By comparing gene expression programming with other two modeling methods, it is found that the accuracy of the model established by gene expression programming has greater advantages than that established by polynomial fitting and neural network in processing animal and plant ion concentration data.

关键词： gene expression programming ion concentration prediction model

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A novel compressive strength estimation approach for 3D printed fiber-reinforced concrete: integrating machine learning and gene expression programming

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第5期7卷 4889-4910页

作者： Uddin, Md Nasir Ye, Junhong Haque, M. Aminul Yu, Kequan Li, Lingzhi Tongji Univ Coll Civil Engn Dept Disaster Mitigat Struct Shanghai Peoples R China Texas State Univ Ingram Sch Engn San Marcos TX 78666 USA Southwest Jiaotong Univ Inst Smart City & Intelligent Transportat Chengdu Peoples R China

3D concrete printing (3DCP) is crucial in the construction because of the low labor cost, eco-friendly behavior;however, getting a proper mixture is always a challenge. This study focuses on predicting the compressive strength (CS) of fiber-reinforced concrete produced with 3DCP using eight machine learning (ML) algorithms to get optimized mixture. The ML models were trained and tested using a comprehensive database on CS collected from literature considering the various fiber-reinforced cementitious composites, comprising over 299 mixtures with 11 features. The results show that the trained ML models could predict CS with R2 ranging from 0.927 to 0.990 and 0.914 to 0.988 for the training and testing dataset, respectively. Furthermore, supplementary experiments were conducted to create a new dataset to validate the predictive model's accuracy, with the extreme gradient boosting (XGB) and gene expression programming (GEP). Based on the GEP, a novel empirical equation was proposed and rigorously validated using experiments. The equation exhibits a high accuracy with the GEP algorithm (R2 = 0.89), providing real-world field applications that might be improve decision-making and mixture optimization, which contributes to advancements, efficiency, and innovative solutions in 3D printing practical domains.

关键词： Fiber-reinforced concrete 3D concrete printing Machine learning algorithm Hyperparameter optimization gene expression programming

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