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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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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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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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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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Forecasting Strength of CFRP Confined Concrete Using multi expression programming

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MATERIALS 2021年第23期14卷 7134-7134页

作者： Ilyas, Israr Zafar, Adeel Javed, Muhammad Faisal Farooq, Furqan Aslam, Fahid Musarat, Muhammad Ali Vatin, Nikolai Ivanovich Natl Univ Sci & Technol NUST Dept Struct Engn Mil Coll Engn MCE Islamabad 44000 Pakistan COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Abbottabad 22060 Pakistan Cracow Univ Technol Fac Civil Engn 24 Warszawska Str PL-31155 Krakow Poland Prince Sattam bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj 11942 Saudi Arabia Dept Civil & Environm Engn Bandar Seri Iskandar 32610 Perak Malaysia Peter Great St Petersburg Polytech Univ St Petersburg 195291 Russia

This study provides the application of a machine learning-based algorithm approach names "multi expression programming" (MEP) to forecast the compressive strength of carbon fiber-reinforced polymer (CFRP) confined concrete. The suggested computational multiphysics model is based on previously reported experimental results. However, critical parameters comprise both the geometrical and mechanical properties, including the height and diameter of the specimen, the modulus of elasticity of CFRP, unconfined strength of concrete, and CFRP overall layer thickness. A detailed statistical analysis is done to evaluate the model performance. Then the validation of the soft computational model is made by drawing a comparison with experimental results and other external validation criteria. Moreover, the results and predictions of the presented soft computing model are verified by incorporating a parametric analysis, and the reliability of the model is compared with available models in the literature by an experimental versus theoretical comparison. Based on the findings, the valuation and performance of the proposed model is assessed with other strength models provided in the literature using the collated database. Thus the proposed model outperformed other existing models in term of accuracy and predictability. Both parametric and statistical analysis demonstrate that the proposed model is well trained to efficiently forecast strength of CFRP wrapped structural members. The presented study will promote its utilization in rehabilitation and retrofitting and contribute towards sustainable construction material.

关键词： multiphysics model multi expression programming carbon fiber-reinforced polymer parametric analysis prediction

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A novel framework for developing a machine learning-based forecasting model using multi-stage sensitivity analysis to predict the energy consumption of PCM-integrated building

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APPLIED ENERGY 2024年 376卷

作者： Nazir, Kashif Memon, Shazim Ali Saurbayeva, Assemgul Nazarbayev Univ Sch Engn & Digital Sci Dept Civil & Environm Engn Astana Kazakhstan

Accurate machine learning (ML) predictions for the early stages of the building design are crucial to construct energy-efficient buildings utilizing limited resources. Several studies have employed ML methods for energy consumption (EC) prediction without considering the utmost crucial PCM-integrated building design parameters. In addition, reducing the dataset by considering only the most significant building design parameters before applying the ML-based method would be beneficial for reducing the computational power and memory usage of the system as well as utilizing less time in the modelling process. To this end, this research presents a novel framework to establish the most robust and reliable ML-based prediction model with less complexity, considering only the most influential PCM-integrated building design parameters. These parameters were identified for future scenarios of hot semi-arid (BSh) climate zones using multi-stage sensitivity analysis. Afterward, a reduced EC database based on the most significant building's early-design-stage parameters (EDSPs) was utilized to formulate several multi-expression programming (MEP) and support vector machines (SVM)-based forecasting models, considering the variations in their hyperparameter values. Formulated prediction models have shown less time utilization through the training and testing phases for the EC evaluations of selected PCM-integrated building compared to the physical-modelling process. Several statistical parameters were used to test and validate the performance of the formulated prediction models. The acquired model evaluation and validation results demonstrated that the MEP-based prediction model (MEP15) exhibited the highest level of reliability and accuracy, showing an R-2 value of >95% for both the training and testing phases. The model's interpretability showed that, throughout the parametric analysis, the developed prediction model adhered to the system's physical boundary constraints. Also, the

关键词： Phase change material integrated building Early-design-stage parameters Energy consumption multi expression programming Support vector machines multi-stage sensitivity analysis

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Evaluating the dynamic response and phase angle behavior of SBS-modified asphalt mixtures for enhanced pavement performance

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SCIENTIFIC REPORTS 2024年第1期14卷 1-26页

作者： Khan, Inamullah Bilal, Muhammad Khaliq, Wasim Khan, Nasir Khahro, Shabir Hussain Memon, Zubair Ahmed Malik, Mohammad Abdul Natl Univ Sci & Technol NUST Natl Inst Transportat NIT Islamabad Pakistan Univ Teknol PETRONAS Dept Civil & Environm Engn Seri Iskandar 32610 Malaysia Prince Sultan Univ Coll Engn Riyadh 11586 Saudi Arabia

Bitumen exhibits viscoelastic properties, showcasing both viscous and elastic behaviors, which are characterized by the phase angle and dynamic modulus. Issues like early fatigue fractures, rutting, and permanent deformations in bituminous asphalt pavements arise due to moisture susceptibility, high-temperature deformation, low-temperature cracking, and overloading. These distresses result in potholes, alligator cracks, and specific deformations that lead to early pavement failure, increasing rehabilitation and maintenance costs. To address these issues, this study examines the dynamic modulus and phase angle behavior of Styrene Butadiene Styrene (SBS) modified and unmodified asphalt mixtures. SBS was incorporated in various proportions, ranging from 2 to 7% by the weight of bitumen. The asphalt mixture performance tester (AMPT) was utilized to measure the dynamic modulus at temperatures of 4.4, 21.1, 37.8, and 54.4 degrees C, and frequencies of 0.1, 0.5, 1, 5, 10, and 25 Hz. The study found significant correlations between dynamic modulus, temperature, loading frequency, and SBS content. Additionally, multi expression programming (MEPX) and regression modeling were employed to estimate the dynamic modulus of SBS-modified HMA. Results indicated that increasing SBS content up to 7% decreased penetration and ductility values by up to 46% and 56%, respectively, while raising the softening point by 63% due to increased stiffness. The blend with 6% SBS by weight of bitumen exhibited superior performance compared to other mixtures. Phase angle initially increased with rising temperature, peaking at 37.8 degrees C at lower frequencies, and continued to increase at higher frequencies. Isothermal and isochronal plots showed that the 0% SBS mix had a higher phase angle due to increased bitumen content. Overall, the HMA mix with 6% SBS provided the best outcomes.

关键词： Styrene butadiene styrene (SBS) Dynamic modulus Phase angle multi expression programming Sensitivity analysis

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Predicting compressive strength of hollow concrete prisms using machine learning techniques and explainable artificial intelligence (XAI)

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HELIYON 2024年第17期10卷 e36841页

作者： Bin Inqiad, Waleed Dumitrascu, Elena Valentina Dobre, Robert Alexandru Khan, Naseer Muhammad Hammood, Abbas Hussein Henedy, Sadiq N. Khan, Rana Muhammad Asad Natl Univ Sci & Technol NUST Mil Coll Engn MCE Islamabad 44000 Pakistan Natl Univ Sci & Technol Politehn Bucharest Fac Automat Control & Comp Sci Comp Dept Bucharest Romania Natl Univ Sci & Technol Politehn Bucharest Elect Technol & Reliabil Dept Bucharest 061071 Romania Natl Univ Sci & Technol NUST Mil Coll Engn MCE Dept Sustainable Adv Geomech Engn Islamabad 44000 Pakistan Middle East Univ MEU Res Unit Amman 11831 Jordan Al Mamoon Univ Coll Dept Comp Tech Engn Baghdad Iraq Mazaya Univ Coll Dept Civil Engn Nasiriya 64001 Iraq Pak Austria Fachhochschule Inst Appl Sci & Technol Haripur Pakistan

The design of masonry structures requires accurate estimation of compressive strength (CS) of hollow concrete masonry prisms. Generally, the CS of masonry prisms is determined by destructive laboratory testing which results in time and resource wastage. Thus, this study aims to provide machine learning-based predictive models for CS of hollow concrete masonry blocks using different algorithms including multi expression programming (MEP), Random Forest Regression (RFR), and Extreme Gradient Boosting (XGB) etc. A dataset of 159 experimental results was collected from published literature for this purpose. The collected dataset consisted of four input parameters including strength of masonry units (f(b)), height-to-thickness ratio (h/t), strength of mortar (f(m)), and ratio of f(m)/f(b) and only one output parameter i.e., CS. Out of all the algorithms employed in current study, only MEP and GEP expressed their output in the form of an empirical equation. The accuracy of developed models was assessed using root mean squared error (RMSE), objective function (OF), and R(2 )etc. Among all algorithms assessed, XGB turned out to be the most accurate having R-2 = 0.99 and least OF value of 0.0063 followed by AdaBoost, RFR, and other algorithms. The developed XGB model was also used to conduct different explainable artificial intelligence (XAI) analysis including sensitivity and shapley analysis and the results showed that strength of masonry unit (f(b)) is the most significant variable in predicting CS. Thus, the ML-based predictive models presented in this study can be utilized practically for determining CS of hollow concrete masonry prisms without requiring expensive and time-consuming laboratory testing.

关键词： Concrete masonry prisms Machine learning multi expression programming Compressive strength Explainable artificial intelligence (XAI)

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Predicting 28-day compressive strength of fibre-reinforced self-compacting concrete (FR-SCC) using MEP and GEP

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SCIENTIFIC REPORTS 2024年第1期14卷 1-21页

作者： Inqiad, Waleed Bin Siddique, Muhammad Shahid Ali, Mujahid Najeh, Taoufik Natl Univ Sci & Technol NUST Mil Coll Engn MCE Islamabad 44000 Pakistan Silesian Univ Tech Fac Transport & Aviat Engn Dept Transport Syst Traff Engn & Logist Krasinskiego 8 St PL-40019 Katowice Poland Lulea Univ Technol Dept Civil Environm & Nat Resources Engn Operat & Maintenance Operat Maintenance & Acoust Lulea Sweden

The utilization of Self-compacting Concrete (SCC) has escalated worldwide due to its superior properties in comparison to normal concrete such as compaction without vibration, increased flowability and segregation resistance. Various other desirable properties like ductile behaviour, increased strain capacity and tensile strength etc. can be imparted to SCC by incorporation of fibres. Thus, this study presents a novel approach to predict 28-day compressive strength (C-S) of FR-SCC using Gene expression programming (GEP) and multi expression programming (MEP) for fostering its widespread use in the industry. For this purpose, a dataset had been compiled from internationally published literature having six input parameters including water-to-cement ratio, silica fume, fine aggregate, coarse aggregate, fibre, and superplasticizer. The predictive abilities of developed algorithms were assessed using error metrices like mean absolute error (MAE), a20-index, and objective function (OF) etc. The comparison of MEP and GEP models indicated that GEP gave a simple equation having lesser errors than MEP. The OF value of GEP was 0.029 compared to 0.031 of MEP. Thus, sensitivity analysis was performed on GEP model. The models were also checked using some external validation checks which also verified that MEP and GEP equations can be used to forecast the strength of FR-SCC for practical uses.

关键词： Self-compacting concrete Genetic programming Fiber-reinforced self-compacting concrete multi expression programming Gene expression programming

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Straightforward prediction for air-entry value of compacted soils using machine learning algorithms

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ENGINEERING GEOLOGY 2020年 279卷 105911-105911页

作者： Wang, Han-Lin Yin, Zhen-Yu Zhang, Pin Jin, Yin-Fu Hong Kong Polytech Univ Dept Civil & Environm Engn Kowloon Hung Hom Hong Kong Peoples R China

The straightforward prediction for the air-entry value of compacted soils is practically useful, but the investigation on this issue is scarce. This study presents three alternative straightforward prediction models for the air-entry value of compacted soils using the representative machine learning algorithms of multi expression programming (MEP), evolutionary polynomial regression (EPR) and random forest (RF). Five known soil properties (i.e. sand content, fines content, plasticity index, initial water content and initial void ratio) are used as input variables. All models are developed based on a large database, covering a wide range of soil classifications. The results show that all the three proposed models are appropriate to predict the air-entry values of different compacted soils, with high prediction accuracies for both the training and the testing data. The monotonicity, the sensitivity and the robustness of the three prediction models are evaluated, showing consistency among different models with a slight difference and providing a strong support for the model feasibility. On the whole, the MEP and the EPR models are recommended for more convenient applications with explicit expression, while higher prediction accuracy may require the RF model although no explicit expression can be derived.

关键词： Machine learning Air-entry value multi expression programming Evolutionary polynomial regression Random forest Compacted soils

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