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Predicting Standard Penetration Test N-value from Cone Penetration Test Data Using gene expression programming

GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2024年第7期42卷 5587-5613页

作者： Alam, Mehtab Chen, Jianfeng Umar, Muhammad Ullah, Faheem Shahkar, Muhammad Tongji Univ Coll Civil Engn Dept Geotech Engn Shanghai 200092 Peoples R China Ghulam Ishaq Khan Inst Engn Sci & Technol Fac Engn Sci Topi 23460 Khyber Pakhtunk Pakistan Univ Tokyo Inst Ind Sci Tokyo Japan Natl Univ Comp & Emerging Sci Dept Civil Engn Lahore 54770 Punjab Pakistan Chinese Acad Sci Inst Mt Hazards & Environm Key Lab Mt Hazards & Earth Surface Proc Chengdu 610041 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Standard Penetration Test (SPT) and the Cone Penetration Test (CPT) are employed in-situ to evaluate soil parameters. In geotechnical engineering practice, engineers often conduct in-situ tests either SPT or CPT to delineate soil profile and evaluate soil parameters for bearing capacity analysis. Most of the geotechnical parameters are correlated with SPT instead and widely employed. Since numerous soil parameters are correlated with SPT N-values, it is very beneficial to establish a correlation between CPT data and SPT N-values. To predict the SPT-N value from CPT data across various soil types such as silty sand, sandy silt, silty clay, and lean clay, this study has developed an empirical model using gene expression programming (GEP). Also a comprehensive GEP model encompassing all soil types has been proposed. The input parameter used in the GEP models are CPT tip resistance (q(c)), CPT-Sleeve friction (q(f)), and effective overburden pressure (sigma(v)'). The effectiveness of the models is evaluated through the implementation of statistical tests, employing a comprehensive index OBJ, and performing parametric analysis. Moreover, to test the reliability of the proposed GEP models, CPT-SPT data pairs that were not utilized in the model generation were employed. The results of the proposed models testing indicated that the models either under-predicts the targeted value by 3-9% or over-predicts by 3-12%. The OBJ values indicate that silty clay has the highest value of 4.985, making it the weakest model, while the all-soil model achieved the lowest value of 1.656, thus being considered the most effective model. The results indicated that the suggested models are precise and exhibit a strong potential for generalization and prediction.

关键词： Standard penetration test Cone penetration test Machine learning gene expression programming

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Prediction of optical properties of rare-earth doped phosphate glasses using gene expression programming

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SCIENTIFIC REPORTS 2024年第1期14卷 1-19页

作者： Ahmadi, Fahimeh El-Mallawany, Raouf Papanikolaou, Stefanos Asteris, Panagiotis G. Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Menoufia Univ Fac Sci Phys Dept Shibin Al Kawm Egypt Natl Ctr Nucl Res NOMATEN Ctr Excellence Ul A Soltana 7 PL-05400 Otwock Poland MIT Dept Nucl Sci & Engn 77 Massachusetts Ave Cambridge MA 02139 USA Computat Mech Lab Sch Pedag & Technol Educ Athens Greece

The progression of optical materials and their associated applications necessitates a profound comprehension of their optical characteristics, with the Judd-Ofelt (JO) theory commonly employed for this purpose. However, the computation of JO parameters (Omega 2, Omega 4, Omega 6) entails wide experimental and theoretical endeavors, rendering traditional calculations often impractical. To address these challenges, the correlations between JO parameters and the bulk matrix composition within a series of Rare-Earth ions doped sulfophosphate glass systems were explored in this research. In this regard, a novel soft computing technique named genetic expression programming (GEP) was employed to derive formulations for JO parameters and bulk matrix composition. The predictor variables integrated into the formulations consist of JO parameters. This investigation demonstrates the potential of GEP as a practical tool for defining functions and classifying important factors to predict JO parameters. Thus, precise characterization of such materials becomes crucial with minimal or no reliance on experimental work.

关键词： Phosphate glass Rare-earth ions Optical properties Judd-Ofelt parameters gene expression programming

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gene expression programming based mathematical modeling for leak detection of water distribution networks

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MEASUREMENT 2022年 188卷 110611-110611页

作者： Tijani, I. A. Zayed, Tarek Hong Kong Polytech Univ Dept Bldg & Real Estate Hung Hom Kowloon Hong Kong Peoples R China

Globally, the loss of potable water through buried and service water distribution networks (WDNs) is one of the persistent challenges confronted by water utilities. Most of the WDNs inspection methods are ad-hoc and typically provide the current status of the pipes. Despite the significant research efforts, a reliable prediction method of the water leak of buried and service water distribution pipes is rare. To overcome this challenge, gene expression programming (GEP) models - that uses extracted features of acoustic signals collected from noise loggers attached to pipeline valves at the chamber - are developed and validated in the current study. To develop these models, extensive fieldwork hitherto unavailable was undertaken in this study to record the flow-induced acoustic signal of WDNs. The GEP models are developed using a step function - that returns a binary output - which is leveraged to exhibit the proposed detection and prediction methods using the acoustic signal collected from the buried WDNs. The models only consider the highly correlated features with the leakage. The models are found to be able to predict leakages on metal and nonmetal pipes with about 95% accuracy. These models were developed to reduce the time and cost of deployment of equipment for leakage detection of pipes. The proposed method presented in this study highlights the prospect and advantage of using the GEP in infrastructural management given the amazing capability of the machine intelligence technique to recognize multi-dimensional circumstances with ease, high prediction accuracy, and potential for unceasing improvement.

关键词： Acoustic signals gene expression programming Prediction model Water distribution networks Water leak detection

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gene expression programming Coupled with Unsupervised Learning: A Two-Stage Learning Process in Multi-Scale, Short-Term Water Demand Forecasts

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WATER 2018年第2期10卷 142页

作者： Shabani, Sina Candelieri, Antonio Archetti, Francesco Naser, Gholamreza Univ British Columbia Sch Engn Kelowna BC V1V 1V7 Canada Univ Milano Bicocca Dept Comp Sci Viale Sarca 336 I-20126 Milan Italy

This article proposes a new general approach in short-term water demand forecasting based on a two-stage learning process that couples time-series clustering with gene expression programming (GEP). The approach was tested on the real life water demand data of the city of Milan, in Italy. Moreover, multi-scale modeling using a series of head-time was deployed to investigate the optimum temporal resolution under study. Multi-scale modeling was performed based on rearranging hourly based patterns of water demand into 3, 6, 12, and 24 h lead times. Results showed that GEP should receive more attention among the emerging nonlinear modelling techniques if coupled with unsupervised learning algorithms in detailed spherical k-means.

关键词： short-term water demand forecasting multi-scale modeling gene expression programming clustering average mutual information

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Tea Classification by Near Infrared Spectroscopy with Projection Discriminant Analysis and gene expression programming

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ANALYTICAL LETTERS 2015年第18期48卷 2833-2842页

作者： Shi, Weimin Liu, Yang Kong, Wei Shen, Qi Zhengzhou Univ Coll Chem & Mol Engn Zhengzhou 450052 Peoples R China Henan Med Tech Inst Dept Pharmaceut Preparat Engn Kaifeng Peoples R China Zhengzhou Univ Editorial Board Journal Zhengzhou 450052 Peoples R China

Traditional gene expression programming for classification is designed for binary decisions. Herein, projection discriminant analysis for direct multiclass categorization using gene expression programming is described. gene expression programming was first employed to examine new synthetic variables that were built as nonlinear combinations of the original features. The data were projected on planes spanned by these new synthetic variables and the nearest centroid was employed to classify new samples. A new objective function was formulated to determine optimum synthetic variables. Direct multiclass categorization using a gene expression programming algorithm was used to classify six tea varieties analyzed by near infrared spectroscopy. Compared with traditional gene expression programming, principal component analysis, and linear discriminant analysis, direct multiclass categorization with gene expression programming algorithm was more efficient. Visual inspection of high dimensional data by this approach also facilitated classification and comprehension of data.

关键词： Multiclass classification Near infrared spectroscopy gene expression programming

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sEMG-Based Drawing Trace Reconstruction: A Novel Hybrid Algorithm Fusing gene expression programming into Kalman Filter

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SENSORS 2018年第10期18卷 3296-3296页

作者： Yang, Zhongliang Wen, Yangliang Chen, Yumiao Donghua Univ Coll Mech Engn Shanghai 201620 Peoples R China East China Univ Sci & Technol Sch Art Design & Media Shanghai 200237 Peoples R China

How to reconstruct drawing and handwriting traces from surface electromyography (sEMG) signals accurately has attracted a number of researchers recently. An effective algorithm is crucial to reliable reconstruction. Previously, nonlinear regression methods have been utilized successfully to some extent. In the quest to improve the accuracy of transient myoelectric signal decoding, a novel hybrid algorithm KF-GEP fusing gene expression programming (GEP) into Kalman Filter (KF) framework is proposed for sEMG-based drawing trace reconstruction. In this work, the KF-GEP was applied to reconstruct fourteen drawn shapes and ten numeric characters from sEMG signals across five participants. Then the reconstruction performance of KF-GEP, KF and GEP were compared. The experimental results show that the KF-GEP algorithm performs best because it combines the advantages of KF and GEP. The findings add to the literature on the muscle-computer interface and can be introduced to many practical fields.

关键词： drawing trace electromyography gene expression programming Kalman Filter muscle-computer interface

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Study of Human Dopamine Sulfotransferases Based on gene expression programming

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CHEMICAL BIOLOGY & DRUG DESIGN 2011年第3期78卷 370-377页

作者： Si, Hongzong Zhao, Jiangang Cui, Lianhua Lian, Ning Feng, Hanlin Duan, Yun-Bo Hu, Zhide Qingdao Univ Inst Computat Sci & Engn Lab New Fibrous Mat & Modern Text Growing Base State Key Lab Qingdao 266071 Shandong Peoples R China Gansu Prov Ctr Dis Control & Prevent Lanzhou 730020 Gansu Peoples R China Qingdao Univ Sch Publ Hlth Qingdao 266071 Peoples R China Jiangsu Teachers Univ Technol Sch Chem & Chem Engn Changzhou 213001 Jiangsu Peoples R China Shenzhen Neptunus Bioengn Shenzhen 518057 Peoples R China Lanzhou Univ Dept Chem Lanzhou 730000 Peoples R China

A quantitative model is developed to predict the Km of 47 human dopamine sulfotransferases by gene expression programming. Each kind of compound is represented by several calculated structural descriptors of moment of inertia A, average electrophilic reactivity index for a C atom, relative number of triple bonds, RNCG relative negative charge, HA-dependent HDSA-1, and HBCA H-bonding charged surface area. Eight fitness functions of the gene expression programming method are used to find the best nonlinear model. The best quantitative model with squared standard error and square of correlation coefficient are 0.096 and 0.91 for training data set, and 0.102 and 0.88 for test set, respectively. It is shown that the gene expression programming-predicted results with fitness function are in good agreement with experimental ones.

关键词： gene expression programming heuristic method Km quantitative structure-activity relationship sulfotransferases

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Flexural strength prediction for concrete beams reinforced with FRP bars using gene expression programming

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STRUCTURES 2021年 33卷 3163-3172页

作者： Murad, Yasmin Tarawneh, Ahmad Arar, Fares Al-Zu'bi, Amr Al-Ghwairi, Abdullah Al-Jaafreh, Ahmad Tarawneh, Mohammad Univ Jordan Civil Engn Dept Amman 11942 Jordan Hashemite Univ Civil Engn Dept Zarqa Jordan

FRP bars have been recently used as an alternative to the traditional steel bars in construction, especially in harsh environmental regions. gene expression programming (GEP) is used in this research to develop a simplified model for predicting the flexural behavior of FRP reinforced concrete beams. An extensive and reliable database of 116 data points was collected from several experimental programs available in the literature to develop the GEP model. The model was established using six main parameters that predominantly control the flexural behaviour of the beams, which are beam width, concrete compressive strength, depth of the beam, FRP tensile reinforcement area, FRP modulus of elasticity, and FRP ultimate tensile strength. The accuracy of the model was then statistically evaluated, and a parametric study was conducted to check the sensitivity of the model to the input parameters. Finally, the performance of the GEP model was then compared to that obtained using the ACI-440-17 and the CSA S806-12 guidelines to further validate the model. The R-squared values of the three models are very high and are close to each other, indicating that the three models are accurate while the GEP model was the simplest one.

关键词： Flexural strength FRP bars Concrete beams gene expression programming

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Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming

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NEURAL COMPUTING & APPLICATIONS 2009年第8期18卷 1031-1041页

作者： Canakci, Hanifi Baykasoglu, Adil Gullu, Hamza Univ Gaziantep Dept Ind Engn Fac Engn TR-27310 Gaziantep Turkey Univ Gaziantep Dept Civil Engn TR-27310 Gaziantep Turkey

In this paper, two soft computing approaches, which are known as artificial neural networks and gene expression programming (GEP) are used in strength prediction of basalts which are collected from Gaziantep region in Turkey. The collected basalts samples are tested in the geotechnical engineering laboratory of the University of Gaziantep. The parameters, "ultrasound pulse velocity", "water absorption", "dry density", "saturated density", and "bulk density" which are experimentally determined based on the procedures given in ISRM (Rock characterisation testing and monitoring. Pergamon Press, Oxford, 1981) are used to predict "uniaxial compressive strength" and "tensile strength" of Gaziantep basalts. It is found out that neural networks are quite effective in comparison to GEP and classical regression analyses in predicting the strength of the basalts. The results obtained are also useful in characterizing the Gaziantep basalts for practical applications.

关键词： Artificial neural networks gene expression programming Basalt Tensile and compressive strength

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Modelling formulations using gene expression programming - A comparative analysis with artificial neural networks

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EUROPEAN JOURNAL OF PHARMACEUTICAL SCIENCES 2011年第3期44卷 366-374页

作者： Colbourn, E. A. Roskilly, S. J. Rowe, R. C. York, P. Intelligensys Ltd Springboard Business Ctr Stokesley TS9 5JZ Yorks England Formulating Knowledge Ltd Springboard Business Ctr Stokesley TS9 5JZ Yorks England Intelligensys Ltd Frodsham WA6 6XN Cheshire England Univ Bradford Inst Pharmaceut Innovat Bradford BD7 1DP W Yorkshire England

This study has investigated the utility and potential advantages of gene expression programming (GEP) - new development in evolutionary computing for modelling data and automatically generating equations that describe the cause-and-effect relationships in a system- to four types of pharmaceutical formulation and compared the models with those generated by neural networks, a technique now widely used in the formulation development. Both methods were capable of discovering subtle and non-linear relationships within the data, with no requirement from the user to specify the functional forms that should be used. Although the neural networks rapidly developed models with higher values for the ANOVA R-2 these were black box and provided little insight into the key relationships. However, GEP, although significantly slower at developing models, generated relatively simple equations describing the relationships that could be interpreted directly. The results indicate that GEP can be considered an effective and efficient modelling technique for formulation data. (C) 2011 Elsevier B.V. All rights reserved.

关键词： gene expression programming genetic programming Neural networks Modelling Formulation

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