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Unveiling the potential of an evolutionary approach for accurate compressive strength prediction of engineered cementitious composites

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CASE STUDIES IN CONSTRUCTION MATERIALS 2023年 19卷

作者： Davarpanah, T. Q. Amirhossein Masoodi, Amir R. Gandomi, Amir H. Ferdowsi Univ Mashhad Dept Civil Engn Mashhad Iran Univ Technol Sydney Fac Engn & IT Sydney NSW 2007 Australia Obuda Univ Univ Res & Innovat Ctr EKIK H-1034 Budapest Hungary Univ Technol Sydney Fac Engn & Informat Technol Sydney Australia

The different human activities in numerous fields of civil engineering have become possible due to recent development in soft computing. As many researchers have widely extended the use of evolutionary numerical methods to predict the mechanical properties of construction materials, it has become necessary to investigate the performance, accuracy, and robustness of these approaches. gene expression programming (GEP) is a method that stands out among these methods as it can generate highly accurate formulas. In this study, two models of GEP are used to anticipate the compressive strength of engineered cementitious composite (ECC) containing fly ash (FA) and polyvinyl alcohol (PVA) fiber at 28 days. The experimental results for 76 specimens, which are made with ten different mixture properties, are taken from the literature to build the models. Considering the experimental results, four different input variables in the GEP approach are used to arrange the models in two modes: sorted data distribution (SDD) and random data distribution (RDD). Prognosticating the compressive strength values based on the mechanical properties of ECC containing FA and PVA will be possible for the models of the GEP method by using these input variables. The comparison between the experimental results and the results of training, testing, and validation sets of two models (GEP-I and GEP-II), each of which has two distinct distribution modes, is done. It is observed that both modes of RDD and SDD lead to responses with the same accuracy (R-square more than 0.9). Nevertheless, the GEP-I (SDD) model was chosen as the best model in this study based on its performance with the validation data set.

关键词： Engineered cementitious composite (ECC) Fly ash Polyvinyl alcohol Compressive strength gene expression programming

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Plastic concrete mechanical properties prediction based on experimental data

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CASE STUDIES IN CONSTRUCTION MATERIALS 2023年 18卷

作者： Nafees, Afnan Althoey, Fadi Khan, Sherbaz Sikandar, Muhammad Ali Alyami, Saleh H. Rehman, Muhammad Faisal Javed, Muhammad Faisal Eldin, Sayed M. COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Abbottabad 22060 Pakistan Najran Univ Dept Civil Engn Najran Saudi Arabia CECOS Univ IT & Emerging Sci Dept Civil Engn Peshawar Pakistan Univ Engn & Technol Dept Architecture Abbottabad Campus Peshawar Pakistan Future Univ Egypt Fac Engn Ctr Res New Cairo 11835 Egypt

The industrial revolution brought environmental degradation to light. Concrete and plastic degrade the ecosystem and cause unsustainable development. Academic and industrial sectors are interested in lowering carbon emissions associated with concrete. Meanwhile, global sand scar-city worries environmentalists. To reach sustainable development goals, cement and fine aggre-gate must be substituted with other abundant waste/natural materials. This study aimed to develop a green concrete by utilizing plastic waste and creating modelling tool for predicting the mechanical properties of plastic concrete. Different composition of silica fume and super-plasticizers substituted fine aggregate and cement in both irradiated (treated) and regular (un-treated) plastic concrete. Compressive strength (fc') and split tensile strength (fst) of the resulting concrete were studied. Moreover, from literature data, 320 data points each for fc' and fst were used to train gene expression programming (GEP) models. Models' accuracy was evaluated employing various statistical measures. Regular plastic waste concrete has demonstrated a lower fc' and exhibited anomalous behavior for fst. While irradiated plastic waste concrete has demonstrated improved mechanical characteristics, comparatively. Correlation coefficients using GEP models for fc' and fst were found to be 0.92 and 0.88, respectively. Furthermore, sensitivity analysis revealed that plastic was the most significant in the GEP model's development. K fold validation was employed to prevent over-fitting of the models. GEP provides an empirical expression for each outcome to predict future database features. This research improves green concrete's long-term sustainability by reducing carbon emissions and alleviating fine aggregate scarcity.

关键词： High density polyethylene Irradiated plastic waste Regular plastic waste expression tree Compressive strength Split tensile strength gene expression programming Coefficient of determination Sensitivity analysis

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Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation

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AGRICULTURAL WATER MANAGEMENT 2021年 244卷 106622-106622页

作者： Ahmadi, Farshad Mehdizadeh, Saeid Mohammadi, Babak Quoc Bao Pham Thi Ngoc Canh Doan Ngoc Duong Vo Shahid Chamran Univ Ahvaz Dept Hydrol & Water Resources Engn Ahvaz Iran Urmia Univ Water Engn Dept Orumiyeh Iran Hohai Univ Coll Hydrol & Water Resources Nanjing 210098 Peoples R China Duy Tan Univ Inst Res & Dev Danang 550000 Vietnam Duy Tan Univ Fac Environm & Chem Engn Danang 550000 Vietnam Univ Danang Univ Econ Fac Stat & Informat Da Nang Vietnam Univ Danang Univ Sci & Technol Da Nang Vietnam

Reference evapotranspiration (ET0) is one of the most important parameters, which is required in many fields such as hydrological, agricultural, and climatological studies. Therefore, its estimation via reliable and accurate techniques is a necessity. The present study aims to estimate the monthly ET0 time series of six stations located in Iran. To achieve this objective, gene expression programming (GEP) and support vector regression (SVR) were used as standalone models. A novel hybrid model was then introduced through coupling the classical SVR with an optimization algorithm, namely intelligent water drops (IWD) (i.e., SVR-IWD). Two various types of scenarios were considered, including the climatic dataand antecedent ET0 data-based patterns. In the climatic data based models, the effective climatic parameters were recognized by using two pre-processing techniques consisting of tau Kendall and entropy. It is worthy to mention that developing the hybrid SVR-IWD model as well as utilizing the tau Kendall and entropy approaches to discern the most influential weather parameters on ET0 are the innovations of current research. The results illustrated that the applied pre-processing methods introduced different climatic inputs to feed the models. The overall results of present study revealed that the proposed hybrid SVR-IWD model outperformed the standalone SVR one under both the considered scenarios when estimating the monthly ET0. In addition to the mentioned models, two types of empirical equations were also used including the Hargreaves-Samani (H-S) and Priestley-Taylor (P-T) in their original and calibrated versions. It was concluded that the calibrated versions showed superior performances compared to their original ones.

关键词： Empirical models gene expression programming Intelligent water drops Reference evapotranspiration Support vector regression

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A realizable and scale-consistent data-driven non-linear eddy viscosity modeling framework for arbitrary regression algorithms

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INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW 2022年 97卷

作者： Mandler, Hannes Weigand, Bernhard Inst Thermodynam Luft & Raumfahrt Pfaffenwaldring 31 D-70569 Stuttgart Germany

A data-driven modeling framework for non-linear eddy viscosity models is presented. In contrast to the majority of similar approaches, it splits the multivariate regression problem into a set of univariate problems and is, furthermore, independent of a specific machine learning algorithm. Instead of inferring the closure equation from high-fidelity data as a whole, the coefficients of the tensor polynomial are learned individually. The target variables are obtained from an efficient field inversion procedure by virtue of successive tensor projections. The hypotheses for each closure coefficient are then fitted separately by employing an arbitrary regression technique. Ordinary curve fitting, neural networks and gene expression programming are considered as examples. The robustness of the model against an extrapolation and the stability of the solver are promoted by coefficient limiters and a full barycentric realizability correction. Additionally, turbulent scale consistency is guaranteed by a..-corrective frozen-RANS method, which is the subject of a companion paper (Mandler and Weigand, 2022). The proposed strategy leads to models which are well suited for the application to the same class of flows they were inferred from, namely separated channel flows. As proven by an extensive extrapolation study, the resulting neuralSST model is robust against geometry and Reynolds number modifications provided the type of flow does not drastically change. It consistently outperforms both the shear stress transport (SST) and a more complex elliptic-blending model and agrees well with the reference data.

关键词： Data-driven turbulence modeling RANS closure Neural network gene expression programming Univariate regression

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Predicting the mechanical properties of plastic concrete: An optimization method by using genetic programming and ensemble learners

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CASE STUDIES IN CONSTRUCTION MATERIALS 2024年 20卷

作者： Asif, Usama Javed, Muhammad Faisal Abuhussain, Maher Ali, Mujahid Khan, Waseem Akhtar Mohamed, Abdullah Nazarbayev Univ Dept Civil Engn Astana Kazakhstan Ghulam Ishaq Khan Inst Engn Sci & Technol Dept Civil Engn Topi Pakistan Univ Louisiana Lafayette Dept Civil Engn Lafayette LA 70503 USA Silesian Tech Univ Fac Transport & Aviat Engn Dept Transport Syst Traff Engn & Logist Krasinskiego 8 St PL-40019 Katowice Poland Umm Al Qura Univ Coll Engn & Comp Al Qunfudah Dept Civil & Environm Engn Mecca Saudi Arabia Future Univ Egypt Res Ctr New Cairo 11835 Egypt

This study presents a comparative analysis of individual and ensemble learning algorithms (ELAs) to predict the compressive strength (CS) and flexural strength (FS) of plastic concrete. Multilayer perceptron neuron network (MLPNN), Support vector machine (SVM), random forest (RF), and decision tree (DT) were used as base learners, which were then combined with bagging and Adaboost methods to improve the predictive performance. In addition, gene expression programming (GEP) was used to develop computational equations that can be used to predict the CS and FS of plastic concrete. An extensive database containing 357 and 125 data points was obtained from the literature, and the eight most impactful ingredients were used in the model's development. The accuracy of all models was assessed using several statistical measures, including an error matrix, Akaike information criterion (AIC), K-fold cross-validation, and other external validation equations. Furthermore, sensitivity and SHAP analysis were performed to evaluate input variables' relative significance and impact on the anticipated CS and FS. Based on statistical measures and other validation criteria, GEP outpaces all other individual models, whereas, in ELAs, the SVR ensemble with Adaboost and RF modified with the Bagging technique demonstrated superior performance. SHapley Additive exPlanations (SHAP) and sensitivity analysis reveal that plastic, cement, water, and the age of the specimens have the highest influence, while superplasticizer has the lowest impact, which is consistent with experimental studies. Moreover, GUI and GEP-based simple mathematical correlation can enhance the practical scope of this study and be an effective tool for the pre-mix design of plastic concrete.

关键词： Plastic concrete Machine learning Compressive strength Flexural strength Sustainability Ensemble learning algorithms gene expression programming

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A comprehensive and reliable investigation of axial capacity of Sy-CFST columns using machine learning-based models

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ENGINEERING STRUCTURES 2023年第1期284卷

作者： Memarzadeh, Armin Sabetifar, Hassan Nematzadeh, Mahdi Univ Mazandaran Fac Engn & Technol Dept Civil Engn Babolsar Iran

The literature on predicting the load-carrying capacity of symmetrical concrete-filled steel tube (Sy-CFST) columns using different machine learning methods has mainly focused on a single method or cross-section type in each study. Sy-CFST column has been widely used in the engineering field because of its several benefits such as increased strength due to confinement generation, better ductility due to high steel ratio, and less construction cost and time as compared to the encased reinforced concrete. This study attempted to evaluate the load-carrying capacity of these columns with circular and square cross-sections based on the simultaneous use of the two gene expression programming (GEP) and artificial neural network (ANN) approaches. The database required for extracting GEP and ANN models was based on the empirical results of 993 specimens. Variables considered here include the compressive strength of concrete (fc), yield stress of steel (fy), cross-sectional areas of concrete (Ac) and steel (As), diameter to thickness ratio of the steel tube (D/t or B/t), and slenderness ratio of Sy-CFST columns (lambda). Moreover, parametric and sensitivity analyses were conducted separately to assess the contribution of each effective parameter to the axial capacity. To validate the efficiency of the models, prediction values of GEP and ANN were compared with the predictions of existing codes (6 codes) and different studies (8 studies). The results indicated that the developed models provide accurate predictions for the load-carrying capacity of SyCFST columns. In addition, the variation of parameters in the proposed models is consistent with experimental trends observed in other studies, which confirms the consistency of the proposed numerical models with physical observations.

关键词： Ultimate Bearing Capacity Sy-CFST Columns gene expression programming Artificial Neural Network Parametric Study Sensitive Analysis Design Codes

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Joint shear strength models for exterior RC beam-column connections exposed to biaxial and uniaxial cyclic loading

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JOURNAL OF BUILDING ENGINEERING 2020年 30卷

作者： Murad, Yasmin Univ Jordan Civil Engn Dept Amman 11942 Jordan

Joint shear strength models available in the literature can predict the shear strength for reinforced concrete (RC) beam-column connections exposed to uniaxial cyclic loading, while an accurate biaxial joint shear strength model is still lacking. gene expression programming (GEP) is used in this research to develop uniaxial and biaxial joint shear strength models for exterior RC beam-to-column connections exposed to uniaxial and biaxial cyclic loading respectively. The GEP models are developed based on an experimental database available in the literature, where the models are randomly trained and tested. Uniaxial joint shear strength is also predicted using the ACI 352 and ASCE 41 formulations for connections exposed to uniaxial cyclic loading. The performance of the GEP models is statistically evaluated using the coefficient of determination R-squared. The R-squared values are 79%, 79%, 95% and 93% for the ACI, ASCE, uniaxial GEP and biaxial GEP models, respectively. The R-squared values of the GEP models are high, which confirms their accuracy and indicates that they are more fitting to the experimental results than the ACI and ASCE formulations.

关键词： Joint shear strength Biaxial cyclic loading Uniaxial cyclic loading Exterior RC beam-column connections ACI 352 ASCE 41 gene expression programming

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Prediction of Flow Duration Curve in Ungauged Catchments Using genetic expression programming

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Procedia Engineering 2016年 154卷 1431-1438页

作者： Salaudeen Abdul Razaq Shamsuddin Shahid Tarmizi Ismail Eun-Sung Chung Morteza Mohsenipour Xiao-jun Wang Faculty of Civil Engineering Universiti Teknologi Malaysia Johor Bahru 81310 Malaysia Department of Civil Engineering Seoul National University of Science and Technology Seoul 139-743 Korea State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Nanjing Hydraulic Research Institute Nanjing 210029 China Research Center for Climate Change Ministry of Water Resources Nanjing 210029 China

A set of multivariate equations have been developed using gene expression programming (GEP) based symbolic regression technique to generate the flow quantiles of flow duration curve (FDC) in the ungauged catchments in the East Coast of Peninsular Malaysia. The equations were derived from four to seven candidate explanatory variables prepared from climatic, geomorphologic, geographic characteristics, soil properties, and land use and land cover information. Support vector machine (SVM) was used to optimize the best combinations for calibration and validation of GEP models from the data available in thirteen gauged catchments in the study area. Seven flow percentiles namely 0.05, 0.10, 0.25, 0.50, 0.75, 0.90, and 0.95 as well as extreme, maximum, minimum and mean annual flows were identified to develop a framework for predicting various flow metrics. Obtained results revealed that nonlinear regression equations developed using GEP can generate FDCs in ungauged catchments of East Coast of Peninsular Malaysia with an efficiency of up to 0.92.

关键词： flow duration curve gene expression programming multivariate equations symbolic regression ungauged basin

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Development of soft computing-based models for forecasting water quality index of Lorestan Province, Iran

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SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Singh, Balraj Sepahvand, Alireza Sihag, Parveen Singh, Karan Prabha, Chander Nag, Anindya Hassan, Md. Mehedi Vimal, S. Kang, Dongwann Panipat Inst Engn & Technol Panipat 132102 India Lorestan Univ Khorramabad *** Iran Chandigarh Univ Mohali 140413 India Graphic Era Dept Civil Engn Dehra Dun 248002 India Chitkara Univ Chitkara Univ Inst Engn & Technol Rajpura Punjab India Khulna Univ Comp Sci & Engn Discipline Khulna Bangladesh Sri Eshwar Coll Engn Ctr Excellence Data Sci Dept Artificial Intelligence & Data Sci Coimbatore 641202 India Seoul Natl Univ Sci & Technol Dept Comp Sci & Engn Seoul 01811 South Korea

The Water Quality Index (WQI) is widely used as a classification indicator and essential parameter for water resources management projects. WQI combines several physical and chemical parameters into a single metric to measure the status of Water Quality. This study explores the application of five soft computing techniques, including gene expression programming, Gaussian Process, Reduced Error Pruning Tree (REPt), Artificial Neural Network with FireFly (ANN-FFA), and combinations of Reduced Error Pruning Tree with bagging. These models aim to predict the WQI of Khorramabad, Biranshahr, and Alashtar sub-watersheds in Lorestan province, Iran. The dataset consists of 124 observations, with input variables being sulfate (SO4), total dissolved solids (TDS), the potential of Hydrogen (pH), chloride (Cl), electrical conductivity (EC), Potassium (K), bicarbonate (HCO), magnesium (Mg), sodium (Na), and calcium (Ca), and WQI as the output variable. For model creation (train subset) and model validation (test subset), the data were split into two subsets (train and test) in a ratio of 70:30. The performance evaluation parameters values of training and testing stages of various models indicate that the ANN-FFA based data-driven model performs better than the other modeling techniques applied with the values of coefficient of correlation 0.9990 & 0.9989;coefficient of determination 0.9612 & 0.9980;root mean square error 0.3036 & 0.3340;Nash-Sutcliffe error 0.9980 & 0.9979;and Mean average percentage error 0.7259% & 0.7969% for the train and test subsets, respectively. Taylor diagram results also suggest that ANN-FFA is the best-performing model, followed by the GEP model. This study introduces a novel model for predicting WQI using advanced soft computing models that have not been previously applied in this study area, highlighting its novelty and relevance. The proposed model significantly enhances predictive accuracy and efficiency, offering real-time, cost-effective WQI predi

关键词： Water quality index Artificial neural network FireFly algorithm gene expression programming Reduced error pruning tree Lorestan Province

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Forecasting residual mechanical properties of hybrid fibre-reinforced self-compacting concrete (HFR-SCC) exposed to elevated temperatures

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HELIYON 2024年第12期10卷 e32856页

作者： Bin Inqiad, Waleed Dumitrascu, Elena Valentina Dobre, Robert Alexandru 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

The use of hybrid fibre-reinforced Self-compacting concrete (HFR-SCC) has escalated recently due to its significant advantages in contrast to normal concrete such as increased ductility, crack resistance, and eliminating the need for compaction etc. The process of determining residual strength properties of HFR-SCC after a fire event requires rigorous experimental work and extensive resources. Thus, this study presents a novel approach to develop equations for reliable prediction of compressive strength (cs) and flexural strength (fs) of HFR-SCC using gene expression programming (GEP) algorithm. The models were developed using data obtained from internationally published literature having eight inputs including water-cement ratio, temperature, fibre content etc. and two output parameters i.e., cs and fs. Also, different statistical error metrices like mean absolute error (MAE), coefficient of determination (R2) and objective function (OF) etc. were employed to assess the accuracy of developed equations. The error evaluation and external validation both approved the suitability of developed models to predict residual strengths. Also, sensitivity analysis was performed on the equations which revealed that temperature, water-cement ratio, and superplasticizer are some of the main contributors to predict residual compressive and flexural strength.

关键词： Residual compressive strength Flexural strength Fibres gene expression programming Fly ash Slag Elevated temperature

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