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Performance prediction of tunnel boring machine through developing a gene expression programming equation

ENGINEERING WITH COMPUTERS 2018年第1期34卷 129-141页

作者： Armaghani, Danial Jahed Faradonbeh, Roohollah Shirani Momeni, Ehsan Fahimifar, Ahmad Tahir, M. M. Amirkabir Univ Technol Dept Civil & Environm Engn Tehran 15914 Iran Tarbiat Modares Univ Dept Min Tehran 14115143 Iran Univ Tehran Sch Civil Engn Tehran 141556619 Iran Lorestan Univ Fac Engn Khorramabad *** Iran Univ Teknol Malaysia UTM Construct Res Ctr ISIIC Fac Civil Engn Johor Baharu 81310 Johor Malaysia

The prediction of tunnel boring machine (TBM) performance in a specified rock mass condition is crucial for any mechanical tunneling project. TBM performance prediction in accurate may reduce the risks related to high capital costs and scheduling for tunneling. This paper presents a new model/equation based on gene expression programming (GEP) to estimate performance of TBM by means of the penetration rate (PR). To achieve the aim of the study, the Pahang-Selangor Raw Water Transfer tunnel in Malaysia was investigated and the data related to field observations and laboratory tests were used in modelling of PR of TBM. A database (1286 datasets in total) comprising 7 model inputs related to rock (mass and material) properties and machine characteristics and 1 output (PR) was prepared to use in GEP modelling. To evaluate capability of the developed GEP equation, a multiple regression (MR) model was also proposed. A comparison between the obtained results has been done using several performance indices and the best equations of GEP and MR were selected. System results for the developed GEP equation based on coefficient of determination (R-2) were obtained as 0.855 and 0.829 for training and testing datasets, respectively, while these values were achieved as 0.795 and 0.789 for the developed MR equation. Concluding remark is that the GEP equation is superior in comparison with the MR equation and it can be introduced as a new equation in the field of TBM performance prediction.

关键词： Tunnel boring machine Penetration rate gene expression programming Multiple regression

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The recovery prediction of Zn and Pb from cerussite leaching using the fluoroboric acid by gene expression programming

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SEPARATION SCIENCE AND TECHNOLOGY 2021年第1期56卷 194-202页

作者： Sobouti, Arash Rezai, Bahram Rayati, Mohammad Taghi Hoseinian, Fatemeh Sadat Amirkabir Univ Technol Dept Min & Met Engn Tehran Iran Univ Kashan Dept Chem Engn Kashan Iran

Predicting the recoveries of lead and zinc during the leaching process is necessary to evaluate the effect of various parameters on the process efficiency which is possible using appropriate modeling and simulation. Firstly, the dissolution of lead and zinc using fluoroboric acid from cerussite concentrate by hydrometallurgical method was evaluated in order to reduce the disadvantages of the pyrometallurgical methods. Then, new predictive models based on gene expression programming (GEP) were developed to predict the recoveries of lead and zinc. The results indicate the sufficiency of the GEP models in predicting the recoveries of lead and zinc during the process.

关键词： Cerussite concentrate Hydrometallurgy method Fluoroboric acid Prediction gene expression programming

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Artificial intelligence-based gene expression programming (GEP) model for assessing sprayed seal performance

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ROAD MATERIALS AND PAVEMENT DESIGN 2023年第8期24卷 1977-1994页

作者： Tamanna, Afifa Shamsaei, Ezzatollah Urquhart, Robert Nguyen, Hoan D. Sagoe-Crentsil, Kwesi Duan, Wenhui Monash Univ Dept Civil Engn Clayton Vic Australia Australian Rd Res Board ARRB Port Melbourne Vic Australia

This research predicts residual solvent (alpha), which is a key component of the performance assessment for a sprayed/chip seal. In this study, conventional equations for alpha were assessed that showed prediction inefficiency (R-2 value as low as 0.82) under different experimental conditions. Accordingly, gene expression programming (GEP), an emerging branch in artificial intelligence, was utilised to resolve these difficulties by developing empirical models for alpha. The data required for model development was obtained from extensive laboratory tests conducted on bitumen-solvent binder films in this research. Model evaluation results showed an excellent degree of correspondence between predictions and experimental results (R-2 = 0.94). This is the first study to model a key component of sprayed seal performance using GEP. The model is recommended for pre-design purposes or as a tool to determine residual solvent in a sprayed seal when laboratory testing is not feasible, thereby saving time and expenditure.

关键词： Sprayed seal chip seal residual solvent gene expression programming performance prediction performance evaluation

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Peak flood estimation using gene expression programming

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JOURNAL OF HYDROLOGY 2015年第Part3期531卷 1122-1128页

作者： Zorn, Conrad R. Shamseldin, Asaad Y. Univ Auckland Dept Civil & Environm Engn Auckland Mail Ctr Auckland 1 New Zealand

As a case study for the Auckland Region of New Zealand, this paper investigates the potential use of gene-expression programming (GEP) in predicting specific return period events in comparison to the established and widely used Regional Flood Estimation (RFE) method. Initially calibrated to 14 gauged sites, the GEP derived model was further validated to 10 and 100 year flood events with a relative errors of 29% and 18%, respectively. This is compared to the RFE method providing 48% and 44% errors for the same flood events. While the effectiveness of GEP in predicting specific return period events is made apparent, it is argued that the derived equations should be used in conjunction with those existing methodologies rather than as a replacement. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Flood estimation Ungauged catchment gene expression programming

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A prediction model for strength and strain of CFRP-confined concrete cylinders using gene expression programming

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COMPUTERS AND CONCRETE 2022年第6期30卷 377-391页

作者： Alacali, Sema Yildiz Tech Univ Dept Civil Engn TR-34220 Istanbul Turkey

The use of carbon fiber-reinforced polymers (CFRP) has widely increased due to its enhancement in the ultimate strength and ductility of the reinforced concrete (RC) structures. This study presents a prediction model for the axial compressive strength and strain of normal-strength concrete cylinders confined with CFRP. Besides, soft computing approaches have been extensively used to model in many areas of civil engineering applications. Therefore, the genetic expression programming (GEP) models to predict axial compressive strength and strain of CFRP-confined concrete specimens were used in this study. For this purpose, the parameters of 283 CFRP-confined concrete specimens collected from 38 experimental studies in the literature were taken into account as input variables to predict GEP based models. Then, the results of GEP models were statistically compared with those of models proposed by various researchers. The values of R2 for strength and strain of CFRP-confined concrete were obtained as 0.897 and 0.713, respectively. The results of the comparison reveal that the proposed GEP-based models for CFRP-confined concrete have the best efficiency among the existing models and provide the best performance.

关键词： carbon fiber-reinforced polymers confined concrete gene expression programming strain strength

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New predictive models via gene expression programming and multiple nonlinear regression for SFRC beams

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JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T 2020年第6期9卷 14294-14306页

作者： Sharifi, Yasser Moghbeli, Adel Vali E Asr Univ Rafsanjan Dept Civil Engn Rafsanjan Iran

A soft computing based study aimed to predict the shear capacity of steel fiber RC (SFRC) beams is qualified and new formulae based on gene expression programming (GEP) and nonlinear multiple regression (NMR) are suggested. To extend the proposed formulae, extensive experimental data collected from existing studies in the literature were considered. The data utilized in the GEP and NMR models are ordered in a format of six input such as concrete compressive strength, effective depth, shear span to the effective depth ratio, the ratio of the longitudinal reinforcement, length of fiber to the diameter of itself, and fiber content by volume. The accuracy of the developed formulae is verified versus the experimental data and the rates of efficiency and performance are compared with those provided by suggested equations presented by some of the researchers. The developed models that investigated in present study demonstrated that the GEP and NMR methodologies displayed strong potential for estimating the shear capacity of SFRC beams. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Steel fiber reinforced concrete Shear strength Strength prediction gene expression programming Multiple nonlinear regression

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Development of a Predictive Equation for Modelling the Infiltration Process Using gene expression programming

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WATER RESOURCES MANAGEMENT 2021年第6期35卷 1871-1888页

作者： Rasool, Tabasum Dar, A. Q. Wani, M. A. Natl Inst Technol Srinagar Dept Civil Engn Srinagar 190006 Jammu & Kashmir India HMAARI Res & Extens Ladakh 194101 J&k India

In this study, the soft computing technique of gene expression programming (GEP) has been employed to generate a predictive equation of infiltration rate (f(p)). Infiltration experiments were conducted at 124 different sites and soil samples were collected to assess various soil properties throughout the Himalayan lake catchment. Parameters determined from observed data using nonlinear-Levenberg Marquardt algorithm were substituted in Horton, Kostiakov and Philip infiltration models and f(p) were predicted. Using soil data generated by laboratory investigation of soil samples, the GEP model was developed. Training and testing of the GEP model was performed using 70% and 30% of data respectively. Performance of GEP developed functional relationship was evaluated by comparing predictions from it and aforementioned infiltration models with field observed f(p), and by applying overall performance index (OPI) computed using Coefficient of Determination (R-2), Nash-Sutcliffe Efficiency (E-NS), Willmott's Index of Agreement (W), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). expression developed using GEP indicated feasibility of developed equation with E-NS,E- R-2, W, RMSE and MAE of 0.84, 0.84, 0.96, 1.9, and 0.8, respectively for training data-set and 0.84, 0.85, 0.95, 1.2, and 0.95, respectively for testing data-set. Comparative analysis revealed that though with a slightly higher OPI value (0.7-0.8), the performance of conventional models is better compared to the GEP model (0.66) but the GEP model having satisfactory performance may be used for f(p) prediction particularly in absence of observed data.

关键词： Infiltration rate gene expression programming Levenberg Marquardt algorithm Soil physical properties Kostiakov infiltration model Philip infiltration model

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An efficient model for auxiliary diagnosis of hepatocellular carcinoma based on gene expression programming

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2018年第10期56卷 1771-1779页

作者： Zhang, Li Chen, Jiasheng Gao, Chunming Liu, Chuanmiao Xu, Kuihua Bengbu Med Coll Affiliated Hosp 1 Dept Infect Dis 287 Changhuai Rd Bengbu Anhui Peoples R China

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. The early diagnosis of HCC is greatly helpful to achieve long-term disease-free survival. However, HCC is usually difficult to be diagnosed at an early stage. The aim of this study was to create the prediction model to diagnose HCC based on gene expression programming (GEP). GEP is an evolutionary algorithm and a domain-independent problem-solving technique. Clinical data show that six serum biomarkers, including gamma-glutamyl transferase, C-reaction protein, carcinoembryonic antigen, alpha-fetoprotein, carbohydrate antigen 153, and carbohydrate antigen 199, are related to HCC characteristics. In this study, the prediction of HCC was made based on these six biomarkers (195 HCC patients and 215 non-HCC controls) by setting up optimal joint models with GEP. The GEP model discriminated 353 out of 410 subjects, representing a determination coefficient of 86.28% (283/328) and 85.37% (70/82) for training and test sets, respectively. Compared to the results from the support vector machine, the artificial neural network, and the multilayer perceptron, GEP showed a better outcome. The results suggested that GEP modeling was a promising and excellent tool in diagnosis of hepatocellular carcinoma, and it could be widely used in HCC auxiliary diagnosis.

关键词： Hepatocellular carcinoma gene expression programming Auxiliary diagnosis Serum biomarkers

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Predictive modeling of the compressive strength of bacteria-incorporated geopolymer concrete using a gene expression programming approach

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COMPUTERS AND CONCRETE 2021年第4期27卷 319-332页

作者： Mansouri, Iman Ostovari, Mobin Awoyera, Paul O. Hu, Jong Wan Birjand Univ Technol Dept Civil Engn Birjand Iran Covenant Univ Dept Civil Engn Ota Nigeria Incheon Natl Univ Dept Civil & Environm Engn Incheon 22012 South Korea Incheon Natl Univ Incheon Disaster Prevent Res Ctr Incheon 22012 South Korea

The performance of gene expression programming (GEP) in predicting the compressive strength of bacteria-incorporated geopolymer concrete (GPC) was examined in this study. Ground-granulated blast-furnace slag (GGBS), new bacterial strains, fly ash (FA), silica fume (SF), metakaolin (MK), and manufactured sand were used as ingredients in the concrete mixture. For the geopolymer preparation, an 8 M sodium hydroxide (NaOH) solution was used, and the ambient curing temperature (28 degrees C) was maintained for all mixtures. The ratio of sodium silicate (Na2SiO3) to NaOH was 2.33, and the ratio of alkaline liquid to binder was 0.35. Based on experimental data collected from the literature, an evolutionary-based algorithm (GEP) was proposed to develop new predictive models for estimating the compressive strength of GPC containing bacteria. Data were classified into training and testing sets to obtain a closed-form solution using GEP. Independent variables for the model were the constituent materials of GPC, such as FA, MK, SF, and Bacillus bacteria. A total of six GEP formulations were developed for predicting the compressive strength of bacteria-incorporated GPC obtained at 1, 3, 7, 28, 56, and 90 days of curing. 80% and 20% of the data were used for training and testing the models, respectively. R-2 values in the range of 0.9747 and 0.9950 (including train and test dataset) were obtained for the concrete samples, which showed that GEP can be used to predict the compressive strength of GPC containing bacteria with minimal error. Moreover, the GEP models were in good agreement with the experimental datasets and were robust and reliable. The models developed could serve as a tool for concrete constructors using geopolymers within the framework of this research.

关键词： green concrete geopolymer concrete soft computing gene expression programming analytical modeling

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Estimation of the Darcy-Weisbach friction factor for ungauged streams using gene expression programming and Extreme Learning Machines

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JOURNAL OF HYDROLOGY 2019年 568卷 311-321页

作者： Milukow, Heather Anne Binns, Andrew D. Adamowski, Jan Bonakdari, Hossein Gharabaghi, Bahram Univ Guelph Sch Engn Guelph ON N1G 2W1 Canada McGill Univ Dept Bioresource Engn Ste Anne De Bellevue PQ H9X 3V9 Canada

The most uncertain parameter in determining the stage-discharge relationship of an ungauged stream is the friction factor, a parameter for which a gauged stream can be back-calculated using hydrometric survey data that has been compiled over a wide range of flow conditions. The existing friction factor models for ungauged streams that do not require hydrometric survey data as input can have very large prediction errors. In this study, 415 streamflow events collected from 52 rivers across three continents were assembled to allow for the use of a suitable global dataset size/range to develop a robust model for the prediction of the friction factor in ungauged streams using two machine learning algorithms (gene expression Programing (GEP) and Extreme Learning Machine (ELM)). The new GEP and ELM models outperformed existing models with significantly improved R-squared values of 0.76 and 0.78, respectively. The combination of the two input parameters (relative smoothness and the friction slope) allowed the new models to more accurately predict the friction factor for a wider range of streams and flow conditions. The forecasting uncertainty of the developed GEP and ELM models were compared with past models and found to be the least uncertain, with Width Uncertainty Band (WUB) values of +/- 0.0536 and +/- 0.0504, respectively. For the first time in this field of research, partial derivative sensitivity analysis was applied to the trends of the friction factor estimation by GEP and ELM. It was found that GEP and ELM models are extremely sensitive to friction slope in comparison with relative smoothness. The GEP and ELM model results for predicting the Darcy-Weisbach friction factor were compared with different existing relationships, and the superior performance of the proposed models was illustrated.

关键词： Roughness coefficient Darcy-Weisbach friction factor Extreme Learning Machine gene expression programming Ungauged streams

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