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An optimized equation based on the gene expression programming method for estimating tunnel construction costs considering a variety of variables and indexes

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APPLIED SOFT COMPUTING 2023年 147卷

作者： Mahmoodzadeh, Arsalan Nejati, Hamid Reza Tarbiat Modares Univ Sch Engn Rock Mech Div Tehran Iran

Accurate cost estimation in tunneling is key to the project's success. Such information is critical for the early conceptual and design phases when key choices must be made. Numerous variables influence the cost of tunnel construction, and limited information is available at the early stages of design when the possible use of tunnels is being studied. Therefore, there is a limited number of models at engineers' disposal to develop a proper cost estimate for tunneling projects. This study aimed to offer a model for estimating the construction cost of drilling and blasting tunnels in the preliminary stage of a project. For this purpose, an optimized gene expression programming (GEP) method was used based on the study of 900 data points obtained from ten drilling and blasting tunnels, which were randomly split into the training (800 data points) and testing (100 data points) datasets. With the experience of previously constructed tunnels, eleven parameters were considered the most effective for the tunnel's construction cost. The best fit on predictions generated an equation for the optimized GEP model. Finally, by comparing the equation's outputs with the actual costs and its behavior with practice, its potential ability to estimate the construction cost of drilling and blasting tunnels was approved. Moreover, the Graphical User Interface (GUI) of the GEP model was created as a practical tool for estimating the construction cost of tunnels. This model can reduce tunnel uncertainties and give ML development in tunnel planning.

关键词： Soft computing gene expression programming Tunnel construction costs Drilling and blasting tunnels Graphical user interface

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A differential evolutionary chromosomal gene expression programming technique for electronic nose applications

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APPLIED SOFT COMPUTING 2023年 136卷

作者： Ari, Davut Alagoz, Baris Baykant Bitlis Eren Univ Dept Comp Engn Bitlis Turkiye Inonu Univ Dept Comp Engn Malatya Turkiye

The intelligent system applications require automated data-driven modeling tools. The performance consistency of modeling tools is very essential to reduce the need for human intervention. Classical gene expression programmings (GEPs) employ predefined genetic rules for the node-based evolution of expression trees in the absence of optimal numerical values of constant terminals, and these shortcomings can limit the search efficiency of expression trees. To alleviate negative impacts of these limitations on the data-driven GEP modeling performance, a Differential Evolutionary Chromosomal GEP (DEC-GEP) algorithm is suggested. The DEC-GEP utilizes the Differential Evolution (DE) algorithm for the optimization of a complete genotype of expression trees. For this purpose, a modifier gene container, which stores numerical values of constant terminals, is appended to the frame of GEP chromosome, and this modified chromosome structure enables simultaneous optimization of expression tree genotypes together with numerical values of constant terminals. Besides, the DEC-GEP algorithm can benefit from exploration and exploitation capabilities of the DE algorithm for more efficient evolution of GEP expression trees. To investigate consistency of the DEC-GEP algorithm in a data-driven modeling application, an experimental study was conducted for soft calibration of the low-cost, solid-state sensor array measurements, and results indicated that the DEC-GEP could yield dependable CO concentration estimation models for electronic nose applications.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Air quality electronic nose gene expression programming Differential evolution Sensor calibration

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Optimization of Thermal Decomposition Conditions of Bone to Achieve the Highest Percentage of Crystalline Phase in Bone Char using gene expression programming and Artificial Neural Network

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INTERNATIONAL JOURNAL OF ENGINEERING 2021年第1期34卷 184-194页

作者： Fatahi, F. Khayati, G. R. Shahid Bahonar Univ Kerman Dept Mat Sci & Engn Kerman Iran

Bone char (BC) is one of the most common adsorbent with extensive applications in the removal of pollutions. The adsorption capability of BC is proportional to the crystalline index, i.e, the atomic ratio of Ca/P. This study is an attempt to model the crystalline index of BC that by thermal decomposition of natural bone using artificial neural network (ANN) and genetic expression programming (GEP). In this regard, 100 various experimental data used to construct the ANN and GEP models, separately. Through the data collection step, heating rate, the type of precursor, calcination temperature, and residence time selected as the inputs for the preset output as Ca/P ratio. The results reveal that the minimum amount of Ca/P ratio are at the heating rate 10 degrees C/min, HNO3 1.6 M as activation agent, calcination temperature 1000 degrees C, and residence time 2 h. R squared indices is used to compare the performance of extracted models. Finally, the best ANN uses to investigate the effect of each practical variable by sensitivity analysis and revealed that the residence time is the most effective parameter on the crystalline index while acid activation is of secondary importance.

关键词： Artificial Neural Networks Bone Char gene expression programming Modeling Pyrolysis Conditions

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GPR: A Python implementation of an extremely simple classifier based on fuzzy logic and gene expression programming

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SOFTWAREX 2023年 22卷

作者： Czmil, Anna Kluska, Jacek Czmil, Sylwester Rzeszow Univ Technol Fac Elect & Comp Engn Powstancow Warszawy 12 PL-35959 Rzeszow Poland

In this work, we present a Python-based implementation of an extremely simple classifier (GPR), which combines gene expression programming (GEP) features and the algebraic representation of the 'if-then' fuzzy rules theory of the Takagi-Sugeno fuzzy inference system. generated fuzzy metarules are highly interpretable and suitable for many applications. We provide an open-source Python implementation of the GPR algorithm to enable the use of the algorithm without any commercial software tools and open access to the research community. We also added enhancements to improve the readability and interpretability of the rules.(c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Fuzzy rule-based classifier gene expression programming Interpretability

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Investigation of motion characteristics of catastrophic landslide using material point method and gene expression programming

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INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES 2023年第1期170卷

作者： Zhang, Ruiliang Xue, Xinhua Deng, Chubing Sichuan Univ Coll Water Resource & Hydropower State Key Lab Hydraul & Mt River Engn Chengdu 610065 Peoples R China

This study presents two approaches (i.e., the material point method (MPM) and gene expression programming (GEP)) for prediction of landslide runout. The MPM was modified based on a strain-softening constitutive model. A collapse test and physical model test were conducted to verify the suitability of the modified MPM. The sensitivity analysis was performed to investigate the importance of the softening parameters on the motion characteristics and final accumulation state. In addition, a predictive equation for prediction of the maximum horizontal distance was proposed based on GEP, with the coefficient of determination of 0.8825. The effects of the maximum vertical distance, landslide volume and landslide posture on the maximum horizontal distance were quantitatively analyzed. Moreover, the GEP model was verified by comparison with other machine learning approaches. Finally, taking the Jiweishan landslide as an example, the simulation and prediction results obtained from the MPM and GEP approaches were compared. The results show that the two are both within a reasonable range, with the errors of 14.1% and 10.6%, respectively.

关键词： Catastrophic landslide Motion characteristics Material point method Landslide runout process Runout distance prediction gene expression programming

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A set of transient correlations for fast and unprotected loss of flow accident in VVER-1000 reactor using single-heated channel approach and gene expression programming

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ANNALS OF NUCLEAR ENERGY 2023年第1期183卷

作者： Sadeghi, Khashayar Ghazaie, Seyed Hadi Sokolova, Ekaterina Cammi, Antonio Arab, Hamid Reza Usta, Selma Politecn Milan DOE Nucl Engn Div Via Masa 34 I-20156 Milan Italy Peter Great St Petersburg Polytech Univ Dept Nucl & Heat Power Engn Peter Great St St Petersburg 195251 Russia Shahid Beheshti Univ Fac Nucl Engn POB *** Tehran Iran

Artificial intelligence methodologies along with human observations in the main control room of nuclear power plants can be applied for predictive analysis and accident detection in the early phase of accidents. This study set out with the aim of assessing the importance of accumulated information in the early phase of a fast and un-protected Loss of Flow Accident (LOFA) for exploring the behavior of significant operating parameters in the reactor. In this study, various fast and unprotected LOFA scenarios are numerically simulated using a single-hearted approach to generate a comprehensive dataset in a forward direction. The nondimensional transient dataset including inlet mass flux and outlet temperature of the core has been given to the gene expression programming (GEP) algorithm for developing a set of correlations at different moments after initiating the ac-cident. The Multi Time-Step Data (MTSD) and Single Time-Step Data (STSD) approaches have been used to extract the required correlations from the generated dataset. Mean Square Error (MSE) and coefficient of determination are used to measure the error of each approach. The most striking result to emerge from the computed results is that the MTSD approach based on trigonometric functions has exceptionally high prediction accuracy (MSE < 10-5). Overall, the obtained results show that the GEP algorithm can be used as a powerful tool for generating a set of transient correlations with high accuracy to identify the progress in a fast and unprotected LOFA.

关键词： Single-heated channel approach gene expression programming Nuclear accident detection Transient correlation in reactor

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Predicting fire resistance of SRC columns through gene expression programming

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JOURNAL OF STRUCTURAL FIRE ENGINEERING 2021年第2期12卷 125-140页

作者： Hassani, Meisam Safi, Mohammad Ardakani, Reza Rasti Daryan, Amir Saedi Shahid Beheshti Univ Fac Civil Water & Environm Engn Tehran Iran

Purpose This paper aims to predict the fire resistance of steel-reinforced concrete columns by application of the genetic algorithm. Design/methodology/approach In total, 11 effective parameters are considered including mechanical and geometrical properties of columns and loading values as input parameters and the duration of concrete resistance at elevated temperatures as the output parameter. Then, experimental data of several studies - with extensive ranges - are collected and divided into two categories. Findings Using the first set of the data along with the gene expression programming (GEP), the fire resistance predictive model of steel-reinforced concrete (SRC) composite columns is presented. By application of the second category, evaluation and validation of the proposed model are investigated as well, and the correspondent time-temperature diagrams are derived. Originality/value The relative error of 10% and the R coefficient of 0.9 for the predicted model are among the highlighted results of this validation. Based on the statistical errors, a fair agreement exists between the experimental data and predicted values, indicating the appropriate performance of the proposed GEP model for fire resistance prediction of SRC columns.

关键词： gene expression programming Predictive model Fire resistance Steel-reinforced concrete columns

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Prediction of Enhanced Soil-Anchored Geogrid Interactions in Direct Shear Mode Using gene expression programming

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GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2021年第2期39卷 957-972页

作者： Abdi, Mahmood Reza Nakhaei, Pouria Gonbad, Mahdi Safdari Seh KN Toosi Univ Technol Fac Civil Engn Tehran Iran Amirkabir Univ Technol Fac Civil Engn Tehran Iran

Design of reinforced soil structures is greatly influenced by soil-geosynthetic interactions at interface which is normally assessed by costly and time consuming laboratory tests. In present research, using the results of large-scale direct shear tests conducted on soil-anchored geogrid samples a model for predicting Enhanced Interaction Coefficient (EIC) is proposed enabling researchers/engineers easily, quickly and at no cost to estimate soil-geosynthetic interactions. In this regard well and poorly graded sands, anchors of three different size and anchorage lengths from the shear surface together with normal pressures of 12.5, 25 and 50 kPa were used. Artificial Intelligence (AI) called the gene expression programming (GEP) was adopted to develop the model. Input variables included coefficients of curvature and uniformity, normal pressure, effective grain size, anchor base and surface area, anchorage length and the output variable was EIC. Contributions of input variables were evaluated using sensitivity analysis. Excellent correlation between the GEP-based model and the experimental results were achieved showing that the proposed model is well capable of effectively estimating soil-anchored geogrid enhanced interaction coefficient. Sensitivity analysis for parameter importance shows that the most influential variables are normal pressure (sigma(n)) and anchorage length (L) and the least effective parameters are average particle size (D-50) and anchor base area (A(b)).

关键词： Anchored Geogrid Interaction gene expression programming Sensitivity analysis

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Artificial intelligence-based model prediction of biodiesel-fueled engine performance and emission characteristics: A comparative evaluation of gene expression programming and artificial neural network

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HEAT TRANSFER 2021年第6期50卷 5563-5587页

作者： Sharma, Prabhakar GND Inst Technol Mech Engn Dept Delhi 110089 India

The prognostic capability of gene expression programming (GEP) and artificial neural network (ANN) are compared to estimate the engine performance and emission characteristics. A stationary diesel engine was powered with linseed oil biodiesel-mineral diesel blends. A total of 60 lab-based test-run were conducted by varying the engine input operating conditions, namely fuel injection parameters, diesel/biodiesel blending ratio, and engine load. The engine output data, namely brake thermal efficiency and brake-specific fuel consumption, were calculated, while emission data for oxides of nitrogen, carbon monoxide, and unburnt hydrocarbon, were recorded. The experimental data were used for predictive model development using artificial intelligence-based GEP and ANN techniques. The developed models were tested on statistical outcomes, such as the absolute fraction of variance (0.9698-0.997 for GEP and 0.9949-0.9998 for ANN), correlation coefficient (0.9848-0.998 for GEP and 0.9974-0.9998 for ANN), establishing these two models as an efficient machine identical tool. Also, Nash-Sutcliffe efficiency (0.937-0.9999 for GEP and 0.995-0.999 for ANN) and Kling-Gupta efficiency (0.834-0.9999 for GEP and 0.989-0.999 for ANN) elevate the prediction quality of developed models. The result showed that the ANN model was slightly more accurate than the GEP-based model for the same parametric range.

关键词： artificial intelligence artificial neural network biodiesel gene expression programming renewable fuel

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Artificial neural networks vs. gene expression programming for predicting emission & engine efficiency of SI operated on blends of gasoline-methanol-hydrogen fuel

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CASE STUDIES IN THERMAL ENGINEERING 2023年 49卷

作者： Zhu, Chao-zhe Samuel, Olusegun D. Elboughdiri, Noureddine Abbas, Mohamed Saleel, C. Ahamed Ganesan, Nataraj Enweremadu, Christopher C. Fayaz, H. Jining Med Univ Sch Med Engn Jining Shandong Peoples R China Fed Univ Petr Resources Dept Mech Engn PMB 1221 Effurun Delta Nigeria Univ Hail Coll Engn Chem Engn Dept POB 2440 Hail 81441 Saudi Arabia Univ Gabes Natl Sch Engineers Gabes Chem Engn Proc Dept Gabes 6029 Tunisia King Khalid Univ Coll Engn Elect Engn Dept Abha 61421 Saudi Arabia King Khalid Univ Coll Engn Dept Mech Engn POB 394 Abha 61421 Saudi Arabia Vellore Inst Technol Sch Mech Engn Vellore Tamil Nadu India Univ South Africa Dept Mech Engn Private Bag X6Sci Campus ZA-1709 Florida South Africa Ton Duc Thang Univ Fac Elect & Elect Engn Modeling Evolutionary Algorithms Simulat & Artifi Ho Chi Minh City Vietnam

This study is the first to employ Al techniques such as artificial neural networks (ANN) and gene expression programming (GEP) to predict the performance and emissions of a gasohol/hydrogenpowered SI engine. The ANN was adopted to correlate the engine variables viz. engine speed and gasohol/hydrogen mix vs. responses namely brake thermal efficiency (BTE), brake specific energy consumption (BSEC), carbon monoxide (CO), hydrocarbon (HC), oxides of nitrogen (NOx) and carbon dioxide (CO2). GEP model was further employed to predict BTE, BSEC, CO, HC, NOx and CO2. To examine the prediction efficacy of both AI techniques, a set of advanced statistical approaches was used. A set of advanced statistical techniques were employed to test the prediction efficiency of both AI techniques. It was revealed that ANN outperformed the GEP since the values for R in the case of ANN were 0.9864-0.9998 whereas the values for R in the case of GEP were 0.9864-0.9994. Similarly, in the instance of R2, ANN outperformed GEP. Furthermore, Kling Gupta efficiency was greater in the case of ANN (0.9684-0.9999) than in GEP (0.8912-0.9991). Both AI approaches, however, displayed great prognostic effectiveness in forecasting engine performance and emissions.

关键词： ANN gene expression programming Modelling Gasohol Hydrogen Spark ignition engine

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