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multi expression programming and interpretable machine learning for determining compressive strength of coral sand aggregate concrete

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MATERIALS TODAY COMMUNICATIONS 2025年 45卷

作者： Ehsan, Kamran Mohamed, Abdinor Hussein Bin Inqiad, Waleed Javed, Muhammad Abdullah Iqbal, Imtiaz Tongji Univ Coll Civil Engn Shanghai 200092 Peoples R China Aston Univ Coll Engn & Phys Sci Aston St Birmingham B4 7ET England Natl Univ Sci & Technol NUST Mil Coll Engn MCE Dept Struct Engn Islamabad 44000 Pakistan

The extensive utilization of natural aggregates in the construction industry can be reduced by replacing them with natural and locally available materials like coral sand aggregate. However, accurately determining the compressive strength (CS) of coral sand aggregate concrete (CSAC) is challenging due to its complex and nonlinear behavior. Consequently, traditional techniques are ineffective. Also, there are very few reliable prediction models available in the literature for determining CS of CSAC. Thus, this study aimed to use machine learning (ML) algorithms like multi expression programming (MEP), AdaBoost, and Bagging Regressor (BR) using dataset already available in the literature for predicting CS of CSAC. The dataset collected featured crucial input parameters like immersion period, confining pressure, size of coral aggregate etc. and a single output i.e., CS. The predictive models were validated by using residual assessment, k-fold cross validation, and external validation checks etc. which revealed that BR exhibited the highest accuracy having testing R2 value of 0.996. However, MEP provided an empirical equation as an output while BR failed to do so. In addition, explanatory techniques like shapely additive analysis (SHAP) and individual conditional expectation (ICE) analysis were used on the BR model to investigate the significance of input features as well as their relationship with the predicted outcome. Finally, a graphical user interface has been developed to help effectively implement the predictive models developed in this study in the industry.

关键词： Coral sand aggregate Compressive strength prediction Explainable machine learning Marine Environment multi expression programming

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multi expression programming: a new approach to formulation of soil classification

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ENGINEERING WITH COMPUTERS 2010年第2期26卷 111-118页

作者： Alavi, Amir Hossein Gandomi, Amir Hossein Sahab, Mohammad Ghasem Gandomi, Mostafa Natl Elites Fdn Highest Prestige Sci & Profess Natl Fdn Tehran Iran Iran Univ Sci & Technol Coll Civil Engn Tehran Iran Tafresh Univ Coll Civil Engn Tafresh Iran Univ Tehran Coll Engn Sch Civil Engn Tehran Iran

This paper presents an alternative approach to formulation of soil classification by means of a promising variant of genetic programming (GP), namely multi expression programming (MEP). Properties of soil, namely plastic limit, liquid limit, color of soil, percentages of gravel, sand, and fine-grained particles are used as input variables to predict the classification of soils. The models are developed using a reliable database obtained from the previously published literature. The results demonstrate that the MEP-based formulas are able to predict the target values to high degree of accuracy. The MEP-based formulation results are found to be more accurate compared with numerical and analytical results obtained by other researchers.

关键词： multi expression programming Soil classification Formulation

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Formulation of secant and reloading soil deformation moduli using multi expression programming

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ENGINEERING COMPUTATIONS 2012年第1-2期29卷 173-197页

作者： Alavi, Amir Hossein Mollahasani, Ali Gandomi, Amir Hossein Bazaz, Jafar Boluori Islamic Azad Univ Mashhad Branch Mashhad Iran Univ Bologna Dept Civil Environm & Mat Engn DICAM Bologna Italy Islamic Azad Univ Cent Tehran Branch Tehran Iran Ferdowsi Univ Mashhad Dept Civil Engn Mashhad Iran

Purpose - The purpose of this paper is to develop new constitutive models to predict the soil deformation moduli using multi expression programming (MEP). The soil deformation parameters formulated are secant (Es) and reloading (Er) moduli. Design/methodology/approach - MEP is a new branch of classical genetic programming. The models obtained using this method are developed upon a series of plate load tests conducted on different soil types. The best models are selected after developing and controlling several models with different combinations of the influencing parameters. The validation of the models is verified using several statistical criteria. For more verification, sensitivity and parametric analyses are carried out. Findings T- he results indicate that the proposed models give precise estimations of the soil deformation moduli. The Es prediction model provides considerably better results than the model developed for Er. The Es formulation outperforms several empirical models found in the literature. The validation phases confirm the efficiency of the models for their general application to the soil moduli estimation. In general, the derived models are suitable for fine-grained soils. Originality/value - These equations may be used by designers to check the general validity of the laboratory and field test results or to control the solutions developed by more in-depth deterministic analyses.

关键词： Soils Deformation Modelling Substructures Soil deformation moduli multi expression programming Plate load test Soil physical properties Prediction

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Formulation of uplift capacity of suction caissons using multi expression programming

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KSCE JOURNAL OF CIVIL ENGINEERING 2011年第2期15卷 363-373页

作者： Gandomi, Amir Hossein Alavi, Amir Hossein Yun, Gun Jin Univ Akron Dept Civil Engn Akron OH 44325 USA Tafresh Univ Dept Civil Engineeing Tafresh 19395 Markazi Iran Iran Univ Sci & Technol Sch Civil Engn Tehran 16846 Iran

Suction caissons have increasingly been used as foundations and anchors for deepwater offshore structures in the last decade. The increased use of suction caissons defines a serious need to develop more authentic methods for simulating their behavior. Reliable assessment of uplift capacity of caissons in cohesive soils is a critical issue facing design engineers. This paper proposes a new approach for the formulation of the uplift capacity of suction caissons using a promising variant of Genetic programming (GP), namely multi expression programming (MEP). The proposed model is developed based on experimental results obtained from the literature. The derived MEP-based formula takes into account the effect of aspect ratio of caisson, shear strength of clayey soil, point of application and angle of inclination of loading, soil permeability and loading rate. A subsequent parametric analysis is carried out and the trends of the results are confirmed via previous studies. The results indicate that the MEP formulation can predict the uplift capacity of suction caissons with an acceptable level of accuracy. The proposed formula provides a prediction performance better than or comparable with the models found in the literature. The MEP-based simplified formulation is particularly valuable for providing an analysis tool accessible to practicing engineers.

关键词： suction caissons uplift capacity multi expression programming formulation

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Treatment prediction of sugar industry wastewater in moving-bed biofilm reactor using multi expression programming

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DESALINATION AND WATER TREATMENT 2020年第0期191卷 82-92页

作者： Talebidalouei, M. Mirbagheri, Seyed Ahmad Nakhaei, Pouria KN Toosi Univ Technol Fac Civil Engn Tehran Iran Amirkabir Univ Technol Fac Civil Engn Tehran Iran

Minimizing water consumption and optimizing wastewater treatment of the sugar industry is one of the most water consuming industries that have significant importance. In this research, the advanced treatment of sugar factory wastewater in a three-step process was carried out using a combined process integrating a moving-bed biofilm reactor (MBBR) and membrane separation processes. The integrated system yields a high-quality effluent by resulting 99.25%, 98%, and 99.2% removal for chemical oxygen demand (COD), nitrate, and total suspended solids, respectively. Determining the level of wastewater treatment requires laboratory equipment with sophisticated measuring devices which is time-consuming and costly. Hence, equations for predicting the removal rate of COD and nitrate are derived from the data obtained from the treatment of sugar wastewater with the integrated system. The equations provide a quick and easy initial estimation for researchers. In this regard, wastewater with COD of 2,000 mg/L and nitrate of 55 mg/L were synthesized. The treatment is performed for five filling ratios (FR) of 40%, 45%, 50%, 55%, and 60% of MBBR with the Kaldnes k2, and four hydraulic retention times (HRT) of 6, 8, 10, and 12 h. Artificial intelligence called multi expression programming (MEP) was used to develop models for predicting the COD and nitrate. The input variables are FR and HRT, and the output variable is the final removal level of organic matters. Excellent correlation between the MEP-based models and the experimental results was achieved which indicates that COD and nitrate models are capable of effectively estimating the amount of COD and nitrate removal. Parametric sensitivity analysis was used to determine the impact of input parameter changes on the output parameter.

关键词： Wastewater Sugar Moving-bed biofilm reactor Membrane separation multi expression programming

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Improving multi expression programming Using Reuse-Based Evaluation

Improving Multi Expression Programming Using Reuse-Based Eva...

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6th International Symposium on Intelligence Computation and Applications (ISICA 2012)

作者： Deng, Wei He, Pei Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410114 Hunan Peoples R China

ISBN: (纸本)9783642342882

multi expression programming is a linear genetic programming that dynamically determines its output from a series of genes of the chromosome. It works on a fixed-length individual, but gives rise to the complexity of the decoding process and fitness computations. To solve this problem, we proposed an improved algorithm that can speed up individual assessments through reuse analysis of evaluations. The experimental result shows that the present approach performs quite well on the considered problems.

关键词： multi expression programming Linear Genetic programming Fitness Computations Reuse Analysis of Evaluations

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Inference of Differential Equation Models by multi expression programming for Gene Regulatory Networks

Inference of Differential Equation Models by Multi Expressio...

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5th International Conference on Intelligent Computing

作者： Yang, Bin Chen, Yuehui Meng, Qingfang Univ Jinan Computat Intelligence Lab Sch Informat Sci & Engn Jinan 250022 Peoples R China

ISBN: (纸本)9783642040191

This paper presents an evolutionary method for identifying the gene regulatory network from the observed time series data of gene expression using a system of ordinary differential equations (ODEs) as a model of network. The structure of ODE is inferred by the multi expression programming (MEP) and the ODE's parameters are optimized by using particle swarm optimization (PSO). The proposed method can acquire the best structure of the ODE only by a small population, and also by partitioning the search space of system of ODEs can be reduced significantly. The effectiveness and accuracy of the proposed method are demonstrated by using synthesis data from the artificial genetic networks.

关键词： Evolutionary method multi expression programming ordinary differential equations particle swarm optimization artificial genetic networks

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Design of Flexible Neural Trees using multi expression programming

Design of Flexible Neural Trees using Multi Expression Progr...

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20th Chinese Control and Decision Conference

作者： Chen, Yuehui Jia, Guangfeng Xiu, Liming Univ Jinan Sch Informat Sci & Engn Jinan 250022 Peoples R China

ISBN: (纸本)9781424417339

Automatic designing of both architecture and parameters of an artificial neural network is an important problem. This paper introduces a new approach for designing artificial neural networks using multi expression programming (MEP-NN). The approach employs the multi expression programming to evolve the architecture and the parameters encoded in the neural network simultaneously. Based on the predefined instruction sets, a MEP-NN model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The performance and effectiveness of the proposed method are evaluated using stock market forecasting problems and compared with the related methods.

关键词： Artificial Neural Network multi expression programming Feature Selection Stock Market Forecasting

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An evolutionary-based predictive soft computing model for the prediction of electricity consumption using multi expression programming

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JOURNAL OF CLEANER PRODUCTION 2021年 283卷 125287-125287页

作者： Fallahpour, Alireza Wong, Kuan Yew Rajoo, Srithar Tian, Guangdong Univ Teknol Malaysia Sch Mech Engn Skudai 81310 Malaysia Univ Teknol Malaysia UTM Ctr Low Carbon Transport Cooperat Imperial Coll London LoCARt Skudai 81310 Malaysia Shandong Univ Sch Mech Engn Jinan Peoples R China Shandong Univ Key Lab High Efficiency & Clean Mech Manufacture Jinan Peoples R China

Proper estimation of electricity consumption is one of the influential factors for sustainability and cleaner production in both developed and developing countries. Many studies have been conducted to present accurate prediction models for forecasting electricity demand. However, researchers are still working to develop models with higher accuracy. This study applies a newer branch of Genetic programming (GP) as a soft computing technique, known as multi expression programming (MEP) to predict the electricity consumption of China for the first time based on the data collected from 1991 to 2019. Specifically, a robust mathematical model was developed using MEP for this purpose. Different predictive techniques known as Gene expression programming (GEP) and Adaptive Neuro Fuzzy Inference System (ANFIS) were used to compare the accuracy of the model. Based on the results, the proposed MEP model is more powerful and accurate than both GEP and ANFIS. In addition, a sensitivity analysis was conducted to present the impact of each factor on the electricity consumption of China. It was shown that among the four independent factors (Population, Gross Domestic Product (GDP), Import, and Export), Population has the highest impact, followed by Export, Import and GDP, respectively. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Electricity consumption Energy demand Prediction Forecasting Soft computing multi expression programming

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Determining ultimate bearing capacity of shallow foundations by using multi expression programming (MEP)

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期115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 an artificial intelligence approach, namely multi expression programming (MEP), for determining ultimate bearing capacity of shallow foundations on cohesionless soils. Five governing parameters (i.e., internal friction angle, soil unit weight, the length to width ratio of foundation, foundation depth and foundation width) were used as input variables to develop the MEP model. Through the determination of the optimal parameter setting of MEP, a group of expressions were proposed. Then, the MEP model was compared with linear multiple regression, non-linear multiple regression and several previous models, and three statistical indices (i.e., coefficient of determination (R-2), root mean squared error (RMSE) and mean absolute error (MAE)) were employed to evaluate the prediction accuracy of these models. The results show that the proposed model has higher prediction precision than the other models, with higher R-2 value and lower RMSE and MAE values. Additionally, a monotonicity analysis was performed to verify the correct relationship between ultimate bearing capacity and various factors. From the monotonicity analysis, the ultimate bearing capacity increases with the increase of internal friction angle (Psi), soil unit weight (gamma), foundation width (B) and foundation depth (D), whereas it decreases with the increase of the length to width ratio of foundation (L/B). Then, a sensitivity analysis was performed. Through the sensitivity analysis, the effect rank of the five input parameters on ultimate bearing capacity is phi> B > D > gamma > L/B. Finally, a graphical user interface (GUI) of the MEP model is developed for practical application.

关键词： Ultimate bearing capacity Shallow foundations multi expression programming Cohesionless soil

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