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Prediction of lightweight aggregate concrete compressive strength using ultrasonic pulse velocity test through gene expression programming

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SCIENTIA IRANICA 2016年第6期23卷 2506-2513页

作者： Hadianfard, M. A. Jafari, S. Shiraz Univ Technol Dept Civil & Environm Engn Modarres BlvdPOB 71555313 Shiraz Iran

The use of lightweight structural concrete to reduce the weight of earthquake resistant buildings is very useful, and utilizing non-destructive tests for determining the concrete strength of these structures is essential. Ultrasonic pulse velocity test is one of the main methods of non-destructive testing in the assessment of compressive strength of concrete in the service area. The aim of this study is estimation of the compressive strength of lightweight aggregate concrete by offering suitable mathematical formulations. For this purpose, many samples of three different types of lightweight aggregate concrete are made and tested by concrete breaking machine and ultrasonic pulse velocity instrument. Then, some relationships are presented to predict the compressive strength of the concrete by using ultrasonic test results. Two different methods of fitting exponential function and gene expression programming (GEP) are utilized to find the proper relationships. The results show that the exponential function has high accuracy in estimating the compressive strength of lightweight aggregate concrete made with natural pumices. But, it does not have good results for concrete made of expanded clay. However, the GEP has high accuracy for all the materials, and the experimental results are perfectly compatible with the test results. (C) 2016 Sharif University of Technology. All rights reserved.

关键词： Non-destructive test Ultrasonic pulse velocity gene expression programming Light weight aggregate concrete Prediction of concrete compressive strength

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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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Predicting Breaking Wave Conditions Using gene expression programming

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COASTAL ENGINEERING JOURNAL 2017年第3期59卷 1750017-1750017页

作者： Robertson, Bryson Gharabaghi, Bahram Power, Hannah E. Univ Victoria POB 1700 STN CSC Victoria BC V8W 3P6 Canada Univ Guelph Water Resources Engn 50 Stone Rd East Guelph ON N1G 2W1 Canada Univ Newcastle Univ Dr Callaghan NSW 2308 Australia

The forces and loading resulting from shallow water breaking waves are one of the most important drivers in coastal engineering design and morphological change. The importance of accurately and precisely predicting breaking wave conditions cannot be overstated. Using a novel dataset of laboratory and field scale breaking wave conditions, this study assesses the performance of widely applied empirical relationships for breaking waves and uses newly available artificial neural networks and gene expression programming (GEP) numerical methods to develop an accurate and easily applied predictor of breaking conditions for coastal engineers and planners. A novel GEP model is developed and shown to: provide excellent predictive ability at all scales, greatly improve prediction compared with previous works at laboratory scale, and clearly identify the relevant importance of seafloor slope and the water depth to wavelength ratio.

关键词： Breaking waves gene expression programming breaker index coastal processes ocean engineering

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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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A decision model based on gene expression programming for discretionary lane-changing move

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TRANSPORTATION PLANNING AND TECHNOLOGY 2024年第7期47卷 1133-1155页

作者： Bagdatli, Muhammed Emin Cihangir Choghtay, Raz Mohammad Nigde Omer Halisdemir Univ Engn Fac Dept Civil Engn Nigde Turkiye

This study focuses on modeling Discretionary Lane-Changing (DLC), which accounts for the majority of lane-change moves in traffic flows. A binary decision model for lane-changing moves was improved with the method of gene expression programming (GEP). The decision to prefer GEP is due to its high performance in a variety of engineering solutions in the literature. The GEP model was trained with Next generation SIMulation (NGSIM) trajectory data gathered at the I-80 Freeway in Emeryville, California, and then tested with data gathered at the U.S. Highway 101 in LA, California. The test results indicate that the model made decisions of "change lane" with 92.98% accuracy, and "do not change lane" with 99.65% accuracy. A sensitivity analysis was also conducted to discover potential limits of the performance of the GEP model. The performance of this model was compared with other high-performance decision models developed with the NGSIM's DLC data in the literature and with TransModeler's gap acceptance model. This comparison indicates that the GEP model is the most successful decision model for discretionary lane-changing moves. The GEP model has a high potential to be applied in DLC decision support systems in (semi-) automated vehicles, as well as traffic simulation software.

关键词： Discretionary lane-changing decision support gene expression programming lane change NGSIM

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Prediction of unsaturated soils effective stress parameter using gene expression programming

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SCIENTIA IRANICA 2013年第5期20卷 1433-1444页

作者： Johari, A. Nakhaee, M. Habibagahi, G. Shiraz Univ Technol Dept Civil & Environm Engn Shiraz Iran Shiraz Univ Dept Civil & Environm Engn Shiraz Iran

Unsaturated soil shear strength can be determined using effective stress relation that depends on the effective stress parameter. Several models have been developed in the past few years to estimate this parameter. In this research, the gene expression programming (GEP) is used as an Artificial Intelligence (Al) method for developing a model to predict the effective stress parameter, using efficient parameters. The principal advantage of the GEP approach is its ability to generate powerful prediction equations without any prior assumption on the possible form of the functional relationship. The input terminal set consists of net confining pressure, suction, Soil Water Characteristic Curve (SWCC) fitting parameter, bubbling pressure, residual and saturated volumetric water content. The output terminal set has one member, which is the effective stress parameter. An experimental database obtained from the literature is employed to develop the model. Comparison of the model prediction with the actual data, as well as other investigators, indicates a very good performance and ability of model. Sensitivity and parametric analyses are conducted to verify the results. It is also shown that soil suction is the most influential parameter in the effective stress parameter of unsaturated soils. (C) 2013 Sharif University of Technology. All rights reserved.

关键词： Unsaturated soil Effective stress parameter gene expression programming Soil suction

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Improved Shear Strength Prediction Model of Steel Fiber Reinforced Concrete Beams by Adopting gene expression programming

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MATERIALS 2022年第11期15卷 3758页

作者： Tariq, Moiz Khan, Azam Ullah, Asad Shayanfar, Javad Niaz, Momina Natl Univ Sci & Technol NUST Sch Civil & Environm Engn NUST Inst Civil Engn NICE Sect H-12 Islamabad 44000 Pakistan Univ Minho Dept Civil Engn Azurem Guimaraes Portugal Univ Engn & Technol Dept Civil Engn Peshawar 25130 Pakistan

In this study, an artificial intelligence tool called gene expression programming (GEP) has been successfully applied to develop an empirical model that can predict the shear strength of steel fiber reinforced concrete beams. The proposed genetic model incorporates all the influencing parameters such as the geometric properties of the beam, the concrete compressive strength, the shear span-to-depth ratio, and the mechanical and material properties of steel fiber. Existing empirical models ignore the tensile strength of steel fibers, which exercise a strong influence on the crack propagation of concrete matrix, thereby affecting the beam shear strength. To overcome this limitation, an improved and robust empirical model is proposed herein that incorporates the fiber tensile strength along with the other influencing factors. For this purpose, an extensive experimental database subjected to four-point loading is constructed comprising results of 488 tests drawn from the literature. The data are divided based on different shapes (hooked or straight fiber) and the tensile strength of steel fiber. The empirical model is developed using this experimental database and statistically compared with previously established empirical equations. This comparison indicates that the proposed model shows significant improvement in predicting the shear strength of steel fiber reinforced concrete beams, thus substantiating the important role of fiber tensile strength.

关键词： gene expression programming reinforced concrete steel fiber reinforced concrete shear strength

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geneTIC REPRESENTATION AND geneTIC NEUTRALITY IN gene expression programming

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Advances in Complex Systems 2002年第4期5卷 389-408页

作者： CÂNDIDA FERREIRA Gepsoft 37 The Ridings Bristol BS13 8NU UK

The neutral theory of molecular evolution states that the accumulation of neutral mutations in the genome is fundamental for evolution to occur. The genetic representation of gene expression programming, an artificial genotype/phenotype system, not only allows the existence of non-coding regions in the genome where neutral mutations can accumulate but also allows the controlled manipulation of both the number and the extent of these non-coding regions. Therefore, gene expression programming is an ideal artificial system where the neutral theory of evolution can be tested in order to gain some insights into the workings of artificial evolutionary systems. The results presented in this work show beyond any doubt that the existence of neutral regions in the genome is fundamental for evolution to occur efficiently.

关键词： genetic neutrality gene expression programming evolutionary computation

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Fatigue Life Prediction Model of FRP-Concrete Interface Based on gene expression programming

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MATERIALS 2024年第3期17卷 690页

作者： Zhang, Zhimei Huo, Yinglong Shanghai Univ Sch Mech & Engn Sci Dept Civil Engn Shanghai 200444 Peoples R China

Under fatigue loading, the interfacial fatigue life of fiber-reinforced polymer(FRP)-concrete is an important index for the analysis of the fatigue performance of reinforced concrete beams strengthened with FRP materials and the evaluation of the reinforcement effect. To solve the problems of the inconsistent and limited accuracy of existing fatigue life prediction models, gene expression programming (GEP) was used to study the interfacial fatigue life of FRP-concrete. Firstly, 219 sets of interfacial fatigue test data were collected, which included two kinds of reinforcement methods, namely, externally bonded (EB) reinforcement and near-surface-mounted (NSM) reinforcement;secondly, Pearson correlation analysis was used to determine the key factors affecting the fatigue life, and then GEP was used to explore the influence of different input forms on the prediction accuracy of the model. Fatigue life calculation formulas applicable to the two kinds of reinforcement methods, i.e., EB and NSM, were established, and a specific calculation formula was established. The model was subjected to parameter sensitivity analysis and variable importance analysis and was found to reflect the intrinsic relationship between the fatigue life and various factors. Finally, the GEP model was compared with the models proposed by other researchers. Five statistical indices, such as the coefficient of determination and the average absolute error, were selected to assess the model, and the results show that the GEP model has higher prediction accuracy than other models, with a coefficient of determination of 0.819, and indicators such as the average absolute error are also lower than those of the rest of the models.

关键词： gene expression programming FRP-concrete interface fatigue life prediction model

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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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