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A new model for prediction of soil thermal conductivity

INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2021年 129卷

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

This study presents a gene expression programming (GEP) method for determining soil thermal conductivity (k). A large and reliable database containing 444 thermal conductivity measurements was collected and employed to develop the GEP model. Four governing parameters (i.e., porosity, saturation degree, quartz content and temperature) were selected as inputs based on a Pearson correlation analysis. Through the determination of the optimal parameter setting of GEP, a predictive equation was first proposed. Then, the proposed GEP model was evaluated and compared with several previous models. The results indicate the proposed GEP model has higher prediction precision than the other models. In addition, a monotonicity analysis was performed to validate the rationality of the proposed GEP model. According to the monotonicity analysis results, the soil thermal conductivity increases with the increase of saturation degree, quartz content and temperature, and it decreases with the increasing of the porosity. Finally, a parametric analysis was conducted to investigate the significance of the inputs. The results show that the effect rank of these four input variables on soil thermal conductivity is saturation degree > porosity > temperature > quartz content, which can provide a reference for similar projects.

关键词： Thermal conductivity Unsaturated soil gene expression programming Heat transfer

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Prediction of residual compressive strength of fly ash based concrete exposed to high temperature using GEP

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COMPUTERS AND CONCRETE 2023年第2期31卷 111-121页

作者： Tung, Tran M. Le, Duc-Hien Babalola, Olusola E. Ton Duc Thang Univ Sustainable Developments Civil Engn Res Grp Fac Civil Engn Ho Chi Minh City Vietnam

The influence of material composition such as aggregate types, addition of supplementary cementitious materials as well as exposed temperature levels have significant impacts on concrete residual mechanical strength properties when exposed to elevated temperature. This study is based on data obtained from literature for fly ash blended concrete produced with natural and recycled concrete aggregates to efficiently develop prediction models for estimating its residual compressive strength after exposure to high temperatures. To achieve this, an extensive database that contains different mix proportions of fly ash blended concrete was gathered from published articles. The specific design variables considered were percentage replacement level of Recycled Concrete Aggregate (RCA) in the mix, fly ash content (FA), Water to Binder Ratio (W/B), and exposed Temperature level. Thereafter, a simplified mathematical equation for the prediction of concrete's residual compressive strength using gene expression programming (GEP) was developed. The relative importance of each variable on the model outputs was also determined through global sensitivity analysis. The GEP model performance was validated using different statistical fitness formulas including R-2, MSE, RMSE, RAE, and MAE in which high R-2 values above 0.9 are obtained in both the training and validation phase. The low measured errors (e.g., mean square error and mean absolute error are in the range of 0.0160 - 0.0327 and 0.0912 - 0.1281 MPa, respectively) in the developed model also indicate high efficiency and accuracy of the model in predicting the residual compressive strength of fly ash blended concrete exposed to elevated temperatures.

关键词： elevated temperature fly ash gene expression programming recycled aggregates residual compressive strength

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Optimizing compressive strength prediction models for rice husk ash concrete with evolutionary machine intelligence techniques

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CASE STUDIES IN CONSTRUCTION MATERIALS 2023年 18卷

作者： Amin, Muhammad Nasir Ahmad, Waqas Khan, Kaffayatullah Deifalla, Ahmed Farouk King Faisal Univ Coll Engn Dept Civil & Environm Engn Al Hasa 31982 Saudi Arabia COMSATS Univ Islamabad Dept Civil Engn Abbottabad 22060 Pakistan Future Univ Egypt Dept Struct Engn & Construct Management New Cairo 11835 Egypt

This research intended to increase the understanding of using modern machine intelligence techniques, including multi-expression programming (MEP) and gene expression programming (GEP), for the compressive strength (CS) prediction of rice husk ash (RHA) concrete. In addition, SHapley Additive exExplanations (SHAP) analysis was made to study the impact and interaction of raw materials on the CS of RHA concrete. A comprehensive database of 192 points with six inputs (cement, specimen age, RHA, superplasticizer, water, and fine aggregate) was used for developing prediction models. This research determined that both GEP and MEP models for the CS prediction of RHA concrete yielded reliable results, which were in close agreement with the real CS. Comparing the performance of both GEP and MEP models, it was noted that MEP, with an R2 of 0.89, outperformed the GEP model having an R2 of 0.83. Additionally, SHAP analysis indicated that specimen age was the most vital measure, followed by cement, which positively correlated with CS of RHA. The overall effect of RHA was found to be more positive, suggesting RHA utilization in the optimal range of 75-100 kg/m3 in the RHA concrete mix. The use of prediction models and SHAP analysis will help the building industry assess material properties and raw material effects faster and more economical.

关键词： Rice husk ash Sustainable concrete gene expression programming Multi -expression programming SHapley Additive exExplanations

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GEP-based classifier for mining imbalanced data

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 164卷

作者： Jedrzejowicz, Joanna Jedrzejowicz, Piotr Univ Gdansk Inst Informat Fac Math Phys & Informat PL-80308 Gdansk Poland Gdynia Maritime Univ Dept Informat Syst Morska 83 PL-81225 Gdynia Poland

The paper proposes an incremental gene expression programming classifier for mining imbalanced datasets. Imbalanced datasets are commonly encountered in real-life applications. There exist numerous algorithms, techniques, and tools which are proposed as suitable for dealing with imbalanced class distribution. Yet, none of them seems to be able to outperform all others in all possible applications. We believe that our approach can extend the available range of learners that have proven good performance in mining imbalanced data and imbalanced streams. The idea is to adapt the GEP classifier to requirements of the imbalanced data environment with reuse of the minority class instances, and application of the incremental learning paradigm. The paper offers an overview of the related work and a detailed description of the proposed incremental learner. An extensive computational experiment, based on data from the KEEL dataset repository, proves that in numerous cases the approach is competitive to other state-of-the-art learners.

关键词： Imbalanced classification Incremental learning gene expression programming

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Study on Dynamic Modulus Prediction Model of In-Service Asphalt Pavement

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BUILDINGS 2024年第8期14卷

作者： Wang, Duanyi Luo, Chuanxi Li, Jian He, Jun South China Univ Technol Sch Civil Engn & Transportat Guangzhou 510000 Peoples R China Xiaoning Inst Roadway Engn Guangzhou 510000 Peoples R China

The dynamic modulus of in-service asphalt pavements serves as a critical parameter for the computation of residual life and the design of overlays. However, its acquisition is currently limited to laboratory dynamic modulus testing using a limited number of core samples, necessitating a reassessment of its representativeness. To facilitate the prediction of dynamic modulus design parameters through Falling Weight Deflectometer (FWD) back-calculated modulus data, an integrated approach encompassing FWD testing, modulus back-calculation, core sample dynamic modulus testing, and asphalt DSR testing was employed to concurrently acquire dynamic modulus at identical locations under varying temperatures and frequencies. Dynamic modulus prediction models for in-service asphalt pavements were developed utilizing fundamental model deduction and gene expression programming (GEP) techniques. The findings indicate that GEP exhibits superior efficacy in the development of dynamic modulus prediction models. The dynamic modulus prediction model developed can enhance both the precision and representativeness of asphalt pavement's dynamic modulus design parameters, as well as refine the accuracy of residual life estimations for in-service asphalt pavements. Concurrently, the modulus derived from FWD back-calculation can be transmuted into the dynamic modulus adhering to a uniform standard criterion, facilitating the identification of problematic segments within the asphalt structural layer. This is of paramount importance for the maintenance or reconstruction of in-service asphalt pavements.

关键词： in-service asphalt pavement dynamic modulus prediction model modulus back-calculation gene expression programming

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Predicting post-fire mechanical properties of grade 8.8 and 10.9 steel bolts

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JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH 2019年 162卷

作者： Ketabdari, Hesamoddin Daryan, Amir Saedi Hassani, Nemat Shahid Beheshti Univ Fac Civil Water & Environm Engn Tehran Iran

Structural fire safety is one of the primary considerations in the design of high-rise buildings where steel is often a popular material for structural members selection. Therefore, predicting post-fire mechanical properties of steel bolts as a crucial element in steel structures is highly valuable. In this paper, the behavior of the High-Strength Steel Bolts (HSSB), after exposing to fire, is investigated and practical equations for the mechanical properties, including the ultimate strength, the yield strength, and the modulus of elasticity, are proposed as well. Accordingly, Grade 8.8 and Grade 10.9 steel bolts are employed in a variety of sizes, from M6 to M24, experiencing six different target temperatures. After natural cooling, a tensile test is applied to all the bolts, the corresponding stress-strain curves are derived, and finally all required data for each specimen are obtained by means of these curves. Results from these curves indicate that at 400 degrees C or fewer temperatures, more than 80% of the mechanical properties are recovered. Between 400 degrees C and 500 degrees C, the features began to reduce, however, above 500 degrees C, a sudden drop was noticeable. Besides, by using both the obtained data and the gene expression programming (GEP) as a branch of genetic Algorithm, equations for mechanical properties of HSSB are derived. The validation results indicate that the relative error of GEP-based models is less than 10%. All in all, the minimum error in the GEP-based models demonstrates favorable equations for post-fire mechanical properties of HSSB and also better predictions than the traditional models. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Steel bolts Mechanical properties Post-fire gene expression programming Practical equation

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Computational AI Models for Investigating the Radiation Shielding Potential of High-Density Concrete

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MATERIALS 2022年第13期15卷 4573页

作者： Amin, Muhammad Nasir Ahmad, Izaz Iqbal, Mudassir Abbas, Asim Khan, Kaffayatullah Faraz, Muhammad Iftikhar Alabdullah, Anas Abdulalim Ullah, Shahid King Faisal Univ Coll Engn Dept Civil & Environm Engn Al Hasa 31982 Saudi Arabia Univ Engn & Technol Peshawar Dept Civil Engn Peshawar 25120 Pakistan Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn State Key Lab Ocean Engn Shanghai Key Lab Digital Maintenance Bldg & Infra Shanghai 200240 Peoples R China King Faisal Univ Coll Engn Dept Mech Engn Al Hasa 31982 Saudi Arabia

Concrete is an economical and efficient material for attenuating radiation. The potential of concrete in attenuating radiation is attributed to its density, which in turn depends on the mix design of concrete. This paper presents the findings of a study conducted to evaluate the radiation attenuation with varying water-cement ratio (w/c), thickness, density, and compressive strength of concrete. Three different types of concrete, i.e., normal concrete, barite, and magnetite containing concrete, were prepared to investigate this study. The radiation attenuation was calculated by studying the dose absorbed by the concrete and the linear attenuation coefficient. Additionally, artificial neural network (ANN) and gene expression programming (GEP) models were developed for predicting the radiation shielding capacity of concrete. A correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE) were calculated as 0.999, 1.474 mGy, 2.154 mGy and 0.994, 5.07 mGy, 5.772 mGy for the training and validation sets of the ANN model, respectively. Similarly, for the GEP model, these values were recorded as 0.981, 13.17 mGy, and 20.20 mGy for the training set, whereas the validation data yielded R = 0.985, MAE = 12.2 mGy, and RMSE = 14.96 mGy. The statistical evaluation reflects that the developed models manifested close agreement between experimental and predicted results. In comparison, the ANN model surpassed the accuracy of the GEP models, yielding the highest R and the lowest MAE and RMSE. The parametric and sensitivity analysis revealed the thickness and density of concrete as the most influential parameters in contributing towards radiation shielding. The mathematical equation derived from the GEP models signifies its importance such that the equation can be easily used for future prediction of radiation shielding of high-density concrete.

关键词： concrete water-cement ratio radiation shielding compressive strength artificial neural network gene expression programming

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Evaluating the mechanical strength prediction performances of fly ash-based MPC mortar with artificial intelligence approaches

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JOURNAL OF CLEANER PRODUCTION 2022年 355卷

作者： Haque, Aminul Chen, Bing Javed, Muhammad Faisal Jalal, Fazal E. Shanghai Jiao Tong Univ State Key Lab Ocean Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Dept Civil Engn Shanghai Key Lab Digital Maintenance Bldg & Infras Shanghai 20024 Peoples R China COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Abbottabad 22060 Pakistan

Introduction of Fly ash (FA) in the magnesium phosphate cement (MPC) mortars is considered as sustainable way to advance the microstructural characteristics and reduce the manufacturing cost of MPC products. However, artificial intelligence (AI) approaches are still need to forecast the strength properties of MPC compositions blended with FA and estimate the governing input elements for appropriate mix design with suitable contents. For this aims, the current research elected five AI models based on deep neural network (DNN), optimizable gaussian process regressor (OGPR) and gene expression programming (GEP) to judge the prediction accuracy of mechanical strength values of the MPC-FA compounds, where the literature data was collected for training the models. In addition, laboratory tests were conducted in this study for producing the data and validating the recommended AI methods. As is observed, DNN2 having 3 hidden layer and Bayesian optimization based Gaussian process regressor techniques presented prediction skills above 95% with errors below 5% at the training and validation phases. Moreover, sensitivity analysis of each input variable revealed that FA content has the prime impact on strength achievement of MPC-FA mixtures, which was corroborated by the correlation analysis between inputs and outputs of whole data points. Finally, forecasting the mechanical strength properties of FAbased MPC mortars using the DNN2 and OGPR methods might be applied in the practical field for reducing the workload, labor and material ingesting through optimizing the mix combinations.

关键词： Magnesium phosphate cement Mechanical strength Deep neural network gene expression programming Sensitivity analysis

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Effects of machining parameters and reinforcement content on thrust force during drilling of hybrid composites

MATERIALS TESTING

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MATERIALS TESTING 2022年第3期58卷 280-284页

作者： Aldas, Kemal Ozkul, Iskender Hayajneh, Mohammed T. Aksaray Univ Fac Engn Aksaray Turkey Aksaray Univ Dept Mech Engn Aksaray Turkey Jordan Univ Sci & Technol Dept Ind Engn Irbid Jordan

In this study, aluminum metal matrix composites were fabricated using the powder metallurgy technique with different ratios of alumina (Al2O3) and graphite as reinforcing elements. The obtained workpieces were then drilled using different machining parameters and the thrust force was measured using a force dynamometer. The thrust force data were modeled using a gene programming soft-computing technique and a function was obtained using the input parameters.

关键词： gene expression programming drilling thrust force metal matrix composites Al2O3 reinforcement

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gene expression Programing in Long Term Water Demand Forecasts Using Wavelet Decomposition

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Procedia Engineering 2017年 186卷 544-550页

作者： Peyman Yousefi Sina Shabani Hadi Mohammadi Gholamreza Naser Okanagan School of Engineering The University of British Columbia Canada

Increasing draught seasons and lack of access to potable water reserves have been the major risks threatening water authorities and governments over the recent years. Therefore, long term water forecasts are receiving much more attention nowadays. Unlike the conventional projection of historical water demand, researchers have tried to implement sophisticated mathematical models to predict demand of water. gene expression programming (GEP), as a relatively new forecasting technique, remains to be explored in this endeavor. The main purpose of this research was to assess the performance of GEP models using wavelet decomposition with 2 transfer functions ( db2 and haar ) and 3 levels. Results of this study showed GEP models can be highly sensitive to wavelet decomposition if all combinations of proper lag times are used as inputs feeding these models.

关键词： Water Demand Forecasting gene expression programming Wavelet Decomposition Lag Time

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