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

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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Evaluation of water quality indexes with novel machine learning and SHapley Additive ExPlanation (SHAP) approaches

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JOURNAL OF WATER PROCESS ENGINEERING 2024年 58卷

作者： Aldrees, Ali Khan, Majid Taha, Abubakr Taha Bakheit Ali, Mujahid Prince Sattam bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj 11942 Saudi Arabia COMSATS Univ Islamabad Civil Engn Dept Abbottabad Campus Abbottabad 22060 Pakistan Prince Sattam bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj 11942 Saudi Arabia Silesian Tech Univ Fac Transport & Aviat Engn Dept Transport Syst Traff Engn & Logist Krasinskiego 8 St PL-40019 Katowice Poland

Water quality indexes (WQI) are pivotal in assessing aquatic systems. Conventional modeling approaches rely on extensive datasets with numerous unspecified inputs, leading to time-consuming WQI assessment procedures. Numerous studies have used machine learning (ML) methods for WQI analysis but often lack model interpretability. To address this issue, this study developed five interpretable predictive models, including two gene expression programming (GEP) models, two deep neural networks (DNN) models, and one optimizable Gaussian process regressor (OGPR) model for estimating electrical conductivity (EC) and total dissolved solids (TDS). For the model development, a total of 372 records on a monthly basis were collected in the Upper Indus River at two outlet stations. The efficacy and accuracy of the models were assessed using various statistical measures, such as correlation (R), mean square error (MAE), root mean square error (RMSE), and 5-fold cross-validation. The DNN2 model demonstrated outstanding performance compared to the other five models, exhibiting R-values closer to 1.0 for both EC and TDS. However, the genetic programming-based models, GEP1 and GEP2, exhibited comparatively lower accuracy in predicting the water quality indexes. The SHapely Additive exPlanation (SHAP) analysis revealed that bicarbonate, calcium, and sulphate jointly contribute approximately 78 % to EC, while the combined presence of sodium, bicarbonate, calcium, and magnesium accounts for around 87 % of TDS in water. Notably, the influence of pH and chloride was minimal on both water quality indexes. In conclusion, the study highlights the cost-effective and practical potential of predictive models for EC and TDS in assessing and monitoring river water quality.

关键词： Water quality indexes gene expression programming Deep neural networks Optimizable Gaussian process regressor SHAP

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Introducing an evolutionary-decomposition model for prediction of municipal solid waste flow: application of intrinsic time-scale decomposition algorithm

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ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS 2021年第1期15卷 1159-1175页

作者： Fan, Linyuan Abbasi, Maryam Salehi, Kazhal Band, Shahab S. Chau, Kwok-Wing Mosavi, Amir MinJiang Univ Coll Math & Data Sci Fuzhou Fujian Peoples R China Capital Univ Econ & Business Sch Stat Beijing Peoples R China Shahid Beheshti Univ Fac Civil Water & Environm Engn Tehran Iran Islamic Azad Univ Dept Comp Sci & Software Engn Tabriz Branch Tabriz Iran Natl Yunlin Univ Sci & Technol Coll Future Future Technol Res Ctr Touliu Taiwan Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China Tech Univ Dresden Fac Civil Engn Dresden Germany Obuda Univ John von Neumann Fac Informat Budapest Hungary

Owing to the importance of municipal waste as a determining factor in waste management, developing data-driven models in waste generation data is essential. In the current study, solid waste generation is taken as the function of several parameters, namely month, rainfall, maximum temperature, average temperature, population, household size, educated man, educated women, and income. Two different stand-alone computational models, namely, gene expression programming and optimally pruned extreme machine learning techniques, are used in this study to establish their reliability in municipal solid waste generation forecasting, followed by Mallow's coefficient feature selection method. The lowest Mallow's coefficient defines the optimal parameters in solid waste generation forecasting. The novel hybrid models of intrinsic time-scale decomposition-gene expression programming and intrinsic time-scale decomposition- optimally pruned extreme machine learning methods based on Monte-Carlo resampling are employed, and an empirical equation is presented for solid waste generation prediction. For examining the reliability of these models, five statistical criteria, namely coefficient of determination, root mean square error, percent mean absolute relative error, uncertainty at 95% and Willmott's index of agreement, are implemented. Considering Willmott's index, the Monte Carlo-intrinsic time-scale decomposition-gene expression programming model attains the closest value (0.957) to the ideal value in the training stage and 0.877 in the testing stage. The hybrid ensemble model of intrinsic time-Scale decomposition-gene expression programming presented lower values of root mean square error (12.279) and percent mean absolute relative error (4.310) in the training phase and in the testing, phase compared to gene expression programming with (12.194) and (5.195), respectively. Overall, the prediction results of the hybrid model of intrinsic time-scale decomposition-gene expression prog

关键词： Waste management artificial intelligence intrinsic time-scale decomposition (ITD) algorithm gene expression programming machine learning circular economy (CE)

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Artificial intelligence-based optimized models for predicting the slump and compressive strength of sustainable alkali-derived concrete

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CONSTRUCTION AND BUILDING MATERIALS 2023年 409卷

作者： Zou, Baoping Wang, Yanbing Amin, Muhammad Nasir Iftikhar, Bawar Khan, Kaffayatullah Ali, Mujahid Althoey, Fadi Zhejiang Univ Sci & Technol Sch Civil Engn & Architecture Hangzhou 310023 Peoples R China China Univ Min & Technol Beijing Sch Mech & Architecture Engn Beijing 100083 Peoples R China King Faisal Univ Coll Engn Dept Civil & Environm Engn Al Hasa 31982 Saudi Arabia Univ Teknol Malaysia Sch Civil Engn Johor Baharu 81310 Johor Malaysia Silesian Tech Univ Fac Transport & Aviat Engn Dept Transport Syst Traff Engn & Logist Krasinskiego 8 St PL-40019 Katowice Poland Najran Univ Coll Engn Dept Civil Engn Najran Saudi Arabia

Alkali-activated materials (AAMs) are a potential class of construction materials that are well-known for their versatility and capacity for long-term sustainability. As a result of its ability to lessen the negative effects that the building industry has on the environment, AAMs have become increasingly popular in recent years. However, it can be difficult and time-consuming to figure out what proportions of alkali-activated concrete (AAC) would work best for a given project. Compressive strength (CS) and slump, both of which are important properties of AAC's viability in construction, were predicted using machine learning (ML) techniques, such as multi-expression programming (MEP) and gene expression programming (GEP) in this study. The mathematical formulations of AAC for both slump and CS for the AAC were effectively derived with the application of these ML approaches. According to the study's findings, MEP models performed better than GEP models in making accurate predictions, with MEP achieving R2 values of 0.92 and 0.93 for slump and CS in AAC, respectively, whereas GEP provided R2 values of 0.86 and 0.89. The hyper-parameters of the AI models were fine-tuned, and the models were verified with statistical measurements and Taylor diagrams. It's possible that using the findings from sensitivity analysis to estimate the relative importance of factors impacting the slump and CS of AAC might be helpful. The artificial intelligence-based models that were built showed a strong connection with the desired outcomes, suggesting that they might be used to estimate the slump and CS of AAC for different values of the input components.

关键词： Alkali-activated concrete Compressive strength Slump Multi-expression programming gene expression programming Sensitivity analysis Taylor diagram

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Estimation of strength, rheological parameters, and impact of raw constituents of alkali-activated mortar using machine learning and SHapely Additive exPlanations (SHAP)

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CONSTRUCTION AND BUILDING MATERIALS 2023年第1期377卷

作者： Nazar, Sohaib Yang, Jian Wang, Xing-Er Khan, Kaffayatullah Amin, Muhammad Nasir Javed, Mohammad Faisal Althoey, Fadi Ali, Mujahid Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn State Key Lab Ocean Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai Key Lab Digital Maintenance Bldg & Infras Shanghai 200240 Peoples R China Univ Birmingham Sch Civil Engn Birmingham B15 2TT England Comsats Univ Islamabad Dept Civil Engn Abbottabad Campus Abbottabad Pakistan King Faisal Univ Coll Engn Dept Civil & Environm Engn Al Hasa 31982 Saudi Arabia Najran Univ Dept Civil Engn Najran 55461 Saudi Arabia Silesian Tech Univ Fac Transport & Aviat Engn Dept Transport Syst Traff Engn & Logist Krasinskiego 8 St PL-40019 Katowice Poland

One-part alkali-activated material (AAM) is a new eco-friendly developed low-carbon binder that utilizes alkaline activators in solid form. This study deals with the experimental synthesis of one-part alkali-activated mortar (AAM) based on the partial replacement of fly ash (FA) with hydraulic lime (LM) as a precursor, and machine learning-based gene expression modeling (GEP) modeling for the optimization of the developed AAM. The datasets were established by the experimental work performed during this current study. The chosen input parameters were fly ash, hydraulic lime, sodium silicate, sodium hydroxide, sand/binder, water/binder, curing age, and time after mixing. The experimental results showed greater compressive strength and rheological pa-rameters for the specimens having a high quantity of hydraulic lime. The GEP model has shown a strong generalization capability and prediction capacity for the future estimation of compressive strength, plastic vis-cosity, and yield strength. All the models showed a strong correlation of 0.92, 0.89, and 0.96 for compressive strength, plastic viscosity, and yield stress respectively. SHapely Additive exPlanations (SHAP) were employed to explore the effect of each input parameter of AAM on the predicted outcomes. The results revealed a strong interaction and positive effect of LM on the YS and PV while a negative impact was observed for the compressive strength. While fly ash has shown a negative impact on three outcomes of PV, YS, and CS respectively. The addition of LM and SS-activator leads to earlier structural build-up due to the flocculation of particles caused by the faster geopolymerization reactions.

关键词： Alkali -activated material gene expression programming Yield stress Plastic viscosity SHAP analysis

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Machine learning-driven web-post buckling resistance prediction for high-strength steel beams with elliptically-based web openings

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RESULTS IN ENGINEERING 2024年 21卷

作者： Rabi, Musab Jweihan, Yazeed S. Abarkan, Ikram Ferreira, Felipe Piana Vendramell Shamass, Rabee Limbachiya, Vireen Tsavdaridis, Konstantinos Daniel Santos, Luis Fernando Pinho Jerash Univ Dept Civil Engn Jerash 26150 Jordan Mutah Univ Coll Engn Civil & Environm Engn Dept POB 7 Al Karak 61710 Jordan Abdelmalek Essaadi Univ Fac Sci Dept Phys Tetouan 93002 Morocco Univ Fed Uberlandia Fac Civil Engn Campus Santa Monica Uberlandia MG Brazil Brunel Univ London Dept Civil & Environm Engn London England London South Bank Univ Sch Build Environm & Architecture Div Civil & Bldg Serv Engn London England City Univ London Sch Sci & Technol Dept Engn Northampton Sq London EC1V 0HB England

The use of periodical elliptically -based web (EBW) openings in high strength steel (HSS) beams has been increasingly popular in recent years mainly because of the high strength -to -weight ratio and the reduction in the floor height as a result of allowing different utility services to pass through the web openings. However, these sections are susceptible to web -post buckling (WPB) failure mode and therefore it is imperative that an accurate design tool is made available for prediction of the web -post buckling capacity. Therefore, the present paper aims to implement the power of various machine learning (ML) methods for prediction of the WPB capacity in HSS beams with (EBW) openings and to assess the performance of existing analytical design model. For this purpose, a numerical model is developed and validated with the aim of conducting a total of 10,764 web -post finite element models, considering S460, S690 and S960 steel grades. This data is employed to train and validate different ML algorithms including Artificial Neural Networks (ANN), Support Vector Machine Regression (SVR) and gene expression programming (GEP). Finally, the paper proposes new design models for WPB resistance prediction. The results are discussed in detail, and they are compared with the numerical models and the existing analytical design method. The proposed design models based on the machine learning predictions are shown to be powerful, reliable and efficient design tools for capacity predictions of the WPB resistance of HSS beams with periodical (EBW) openings.

关键词： Finite element modelling Web -post buckling resistance Elliptically -based web openings High strength steel beams Artificial neural network gene expression programming Support vector machine regression

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Intelligent prediction modeling for flexural capacity of FRP-strengthened reinforced concrete beams using machine learning algorithms

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HELIYON 2024年第1期10卷 e23375页

作者： Khan, Majid Khan, Adil Khan, Asad Ullah Shakeel, Muhammad Khan, Khalid Alabduljabbar, Hisham Najeh, Taoufik Gamil, Yaser COMSATS Univ Islamabad Abbottabad Campus Abbottabad 22060 Pakistan Univ Bradford Civil & Struct Engn Dept Bradford BD7 1DP West Yorkshire England Univ Engn & Technol Dept Civil Engn Peshawar 25120 Pakistan Prince Sattam Bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj 11942 Saudi Arabia Lulea Univ Technol Dept Civil Environm & Nat Resources Engn Operat Maintenance & Acoust Lulea Sweden Monash Univ Malaysia Sch Engn Dept Civil Engn Jalan Lagoon Selatan Bandar Sunway 47500 Selangor Malaysia

Fiber-reinforced polymers (FRP) are widely utilized to improve the efficiency and durability of concrete structures, either through external bonding or internal reinforcement. However, the response of FRP-strengthened reinforced concrete (RC) members, both in field applications and experimental settings, often deviates from the estimation based on existing code provisions. This discrepancy can be attributed to the limitations of code provisions in fully capturing the nature of FRP-strengthened RC members. Accordingly, machine learning methods, including gene expression programming (GEP) and multi-expression programming (MEP), were utilized in this study to predict the flexural capacity of the FRP-strengthened RC beam. To develop data-driven estimation models, an extensive collection of experimental data on FRP-strengthened RC beams was compiled from the experimental studies. For the assessment of the accuracy of developed models, various statistical indicators were utilized. The machine learning (ML) based models were compared with empirical and conventional linear regression models to substantiate their superiority, providing evidence of enhanced performance. The GEP model demonstrated outstanding predictive performance with a correlation coefficient (R) of 0.98 for both the training and validation phases, accompanied by minimal mean absolute errors (MAE) of 4.08 and 5.39, respectively. In contrast, the MEP model achieved a slightly lower accuracy, with an R of 0.96 in both the training and validation phases. Moreover, the ML-based models exhibited notably superior performances compared to the empirical models. Hence, the ML-based models presented in this study demonstrated promising prospects for practical implementation in engineering applications. Moreover, the SHapley Additive exPlanation (SHAP) method was used to interpret the feature's importance and influence on the flexural capacity. It was observed that beam width, section effective depth, and the tensile

关键词： Fiber-reinforced polymers Flexural capacity Machine learning gene expression programming Multi-expression programming

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Machine learning techniques for predicting the peak response of reinforced concrete beam subjected to impact loading

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RESULTS IN ENGINEERING 2024年 24卷

作者： Husnain, Ali Iqbal, Munir Waqas, Hafiz Ahmed El-Meligy, Mohammed Javed, Muhammad Faisal Ullah, Rizwan Ghulam Ishaq Khan Inst Engn Sci & Technol Dept Civil Engn Swabi 23640 Khyber Pakhtoon Pakistan Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Jadara Univ Jadara Univ Res Ctr POB 733 Irbid Jordan Western Caspian Univ Baku Azerbaijan Comsats Univ Islamabad Abbottabad Campus Dept Civil Engn Islamabad Pakistan

To meet the growing need for resilient structures in seismic and high-impact zones, accurate prediction of the response of reinforced concrete (RC) beams under impact loads is essential. Traditional methods, such as experimental testing and high fidelity finite element models, are often time consuming and resource intensive. To address these challenges, this study investigates various ensemble and non-ensemble machine learning techniques-including support vector machine, gaussian process regression (GPR), k-nearest neighbor (KNN), gene expression programming, random forest, decision tree, boosted tree, adaptive boosting tree, gradient boosting algorithm, stochastic gradient descent, and artificial neural network-for predicting the peak response of RC beams under impact loads. A set of 145 experimental data points from 12 different sources is used to train and evaluate these machine learning models. Key parameters in the data include beam width and depth, span, reinforcement ratios, concrete strength, steel yield strength, deflection, and impact characteristics. Except for KNN, all models showed satisfactory generalization capabilities with R2 values over 0.8. Statistical errors such as RMSE, a-10 index, MAE, and a-20 index are within acceptable limits. The GPR model is the most effective with R2 value of 0.95. Moreover, Shapely analysis identified beam depth, impact velocity, and beam breadth as critical factors. Overall, this study demonstrates the efficacy of machine learning in accurately predicting the behavior of RC structures under impact loads, providing valuable tools for civil engineers in design and analysis.

关键词： Machine learning Peak response gene expression programming Shapley

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Multi-expression Based gene expression programming

Multi-Expression Based Gene Expression Programming

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2013年中国智能自动化学术会议

作者： Wei Deng Pei He Zhi Huang School of Computer and Communication Engineering Changsha University of Science and Technology Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology

Among the variants of GP,GEP stands out for its simplicity of encoding method and MEP catches our attention for its multi-expression *** this paper,a novel GP variant-MGEP(Multi-expression based gene expression programming) is proposed to combine these two *** new method preserves the GEP structure,however unlike the traditional GEP,its genes,like those of MEP,can be disassembled into many *** in MGEP,the traditional GEP gene can contain multiple solutions for a *** experimental result shows the MGEP is more effective than the traditional GEP and MEP in solving problems.

关键词： genetic programming gene expression programming Multi expression programming

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Innovative models for predicting post-fire bond behavior of steel rebar embedded in steel fiber reinforced rubberized concrete using soft computing methods

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STRUCTURES 2021年 31卷 1141-1162页

作者： Nematzadeh, Mahdi Shahmansouri, Amir Ali Zabihi, Reza Univ Mazandaran Dept Civil Engn Babolsar Iran Mazandaran Univ Sci & Technol Dept Civil Engn Babol Iran

Incorporating crumb rubber (CR) as a substitution for aggregate in concrete is a strategy to mitigate environmental damages caused by this waste. Here, the bond behavior between the steel rebar and high-strength concrete containing CR after exposure heat was studied. Steel fibers (SF) was added in the mix design to evaluate its effect on improving the bond behavior. 108 specimens were fabricated, in which the variables were the volume percentage of CR (0, 5, and 10%), SF volume ratio (0, 0.5, and 1%), applied temperature (200, 400, and 600 degrees C). Three specimens were fabricated for each composite mixture. Afterward, the pullout test was performed on the hardened specimens to evaluate the bond strength, bond stress-slip behavior, failure mode, and bond strengthcompressive strength relationship. The observations showed that incorporating CR lowered the bond strength, and with increasing temperature, this reducing effect increased. In this regard, at the applied temperature of 600 degrees C, the bond strength of specimens with 5% and 10% CR declined by around 24% and 40%, respectively, compared to that of the reference specimen at the same temperature. Moreover, after testing all the heated specimens, it was found that the reducing impact of temperature rise on the bond strength was negligible up to 200 degrees C (around 5%), significant up to 400 degrees C (around 20%), and destructive up to 600 degrees C (around 65%). Additionally, for the specimens failing in the splitting mode, the effect of incorporating SF was positive, and for the specimens failing in the pullout mode, this effect was negligible and, in some cases, even negative. The empirical results suggest that for the specimen incorporating CR, a greater embedment length must be applied compared to the same specimen without CR. In the end, soft computing methods including gene expression programming (GEP) and artificial neural network (ANN), as two promising techniques, were utilized to predict the bond

关键词： Post-fire behavior Steel fiber reinforced concrete Crumb rubber Bond strength evaluation Bond-slip model gene expression programming Artificial neural network

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