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Assessing the competency of a semi-parametric expert system in the realms of response characterization uncertainty in premixed methanol dual fuel diesel combustion strategies: In critique to RSM

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 185卷 115516-115516页

作者： Kakati, Dipankar Banerjee, Rahul NIT Agartala Dept Mech Engn Agartala 799046 India

Engine response characterization endeavours of the day are faced with the contradictory obligations of developing an accurate engine response map in face of an ever-expanding parametric space and decreasing test-bench based prototype development time cycles. Under such imperatives of sparse experimental regimens, the present study focuses on the pivotal notions of reliability in a data-driven multi-variate response modelling endeavour in the complex realms of a premixed methanol dual fuel diesel operation. In the study, the gene expression programming (GEP) technique with its distinguishing features of adaptivity in model evolution has been critically examined for its credibility as an expert system in such emerging combustion strategies. The relevance and competency of the GEP method has been rationalized to this end by comparing its performance with the traditional Response Surface Methodology which has been the mainstay in such engine response characterization objectives. Model integrity has been analysed and compared through a multitude of conventional and improvised statistical goodness-of-fit measures encompassing absolute and relative error metrics. Model reliability was adjudged through an exhaustive uncertainty analysis. Information theoretic measures of Kullback-Leibler divergence and improvised Thiel uncertainty coefficients were employed to compare the quality of system knowledge gained by the competing techniques. generalization capability was reckoned through all the respective metrics applied across a test data set held blind to the meta-model training sequence. Comparable regression scores across conventional and standardized correlation measures notwithstanding, all GEP evolved meta-models registered substantially lower footprints in all statistical error metrics together with lower uncertainty bandwidths of estimation and information loss across both training and test data simultaneously for all responses explored. The outcome of the study arguably

关键词： Premixed methanol in diesel combustion Semi-parametric vs parametric Characterization Soot-NOx-UHC-CO-BSFC-COVIMEP gene expression programming RSM Theil Uncertainty Information entropy

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SOM-and-GEP-Based Model for the Prediction of Foamed Bitumen Characteristics

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JOURNAL OF TRANSPORTATION ENGINEERING PART B-PAVEMENTS 2021年第2期147卷

作者： Eleyedath, Abhary Kar, Siksha Swaroopa Swamy, Aravind Krishna Indian Inst Technol Delhi Dept Civil Engn Delhi 110016 India Council Sci & Ind Res Cent Rd Res Inst Pavement Engn Area Delhi 110016 India

Due to significant interaction between properties of bitumen and test conditions, prediction of foamed bitumen characteristics [i.e., half-life (HL) and expansion ratio (ER)] is a challenging exercise. This work presents a novel hybrid clustering-gene expression programming (GEP) approach to predict foamed bitumen characteristics. To develop these predictive models, a database consisting of 190 observations (arising out of different combinations of eight distinct binder types, six water contents, and eight test temperatures) was used. The self-organizing map (SOM)-based clustering of this database helped in obtaining homogeneous groups under highly complex interaction. Further, the C5.0 algorithm was used to decipher underlying patterns among clusters identified by SOM. A GEP approach was used to develop four global models to predict HL and ER. Subsequently, hybrid models were obtained through recalibration of these global models but using data from individual clusters. Statistical analysis indicated that hybrid models outperformed corresponding global models in all cases. Global sensitivity analysis indicated that among various parameters, water content had a significant effect on ER prediction. This was followed by temperature and viscosity. However, for predicting HL, this order was ER (if used), water content, temperature, and viscosity.

关键词： Foamed bitumen Expansion ratio Half-life gene expression programming Self-organizing map Decision tree Global sensitivity analysis Clustering

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Machine learning based prediction models for spilt tensile strength of fiber reinforced recycled aggregate concrete

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CASE STUDIES IN CONSTRUCTION MATERIALS 2024年 20卷

作者： Alarfaj, Mohammed Qureshi, Hisham Jahangir Shahab, Muhammad Zubair Javed, Muhammad Faisal Arifuzzaman, Md Gamil, Yaser King Faisal Univ Coll Engn Dept Elect Engn Al Hufuf 31982 Saudi Arabia King Faisal Univ Coll Engn Dept Civil & Environm Engn Alahsa 31982 Saudi Arabia COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Abbottabad 22020 Pakistan Ghulam Ishaq Khan Inst Engn Sci & Technol Dept Civil Engn Topi 23640 Pakistan Monash Univ Malaysia Sch Engn Dept Civil Engn Jalan Lagoon Selatan Bandar Sunway 47500 Selangor Malaysia

The demand for concrete production has led to a significant annual requirement for raw materials, resulting in a substantial amount of waste concrete. In response, recycled aggregate concrete has emerged as a promising solution. However, it faces challenges due to the vulnerability of the hardened mortar attached to natural aggregates, leading to susceptibility to cracking and reduced strength. This study focuses on predicting the split tensile strength of fiber reinforced recycled aggregate concrete using five prediction models, including two deep neural network models DNN1 and DNN2, one optimizable Gaussian process regression (OGPR), and two genetic programming based GEP1 and GEP2 models. The models exhibited high accuracy in predicting spilt tensile strength with robust R2 , RMSE, and MAE values. DNN2 has the highest R2 value of 0.94 and GEP1 has the lowest R2 value of 0.76. DNN2 model R2 was 3.3% and 13.5% higher than OGPR and GEP2. Similarly, DNN2 and GEP2 model performed 9.3% and 9.21% better than DNN1 and GEP1 respectively in terms of R2 . DNN2 model performed 20.32% and 31.5% better than OGPR and GEP2 in terms of MAE. Similarly, GEP2 and DNN2 MAE were 13.1% and 31.5% better than GEP1 and DNN1. Sensitivity analysis using the relevance factor and permutation feature importance revealed that the most significant positive factors are cement, natural coarse aggregates, density of recycle aggregates, and superplasticizer while recycle aggregate concrete, max size, and water content of recycle aggregates and water content have the most negative effect on STS values. The proposed ML methods, especially DNN2 and OGPR can be effectively utilized in practical projects, saving time and cost for eco-friendly fiber reinforced recycled aggregate concrete mixes. However, it is required to study more input variables and utilize hybrid models to further enhance the accuracy and reliability of the models.

关键词： Fiber reinforced Recycled Aggregate Concrete Machine Learning Sustainability Eco-friendly Concrete Spilt Tensile Strength gene expression programming Deep neural networks Optimizable gaussian process regression

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Metaheuristic artificial intelligence (AI): Mechanical properties of electronic waste concrete

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CONSTRUCTION AND BUILDING MATERIALS 2023年第1期394卷

作者： Khan, Mohsin Ali Usman, Mian Muhammad Alsharari, Fahad Yosri, Ahmed M. Aslam, Fahid Alzara, Majed Nabil, Marwa CECOS Univ IT & Emerging Sci Dept Civil Engn Peshawar 25000 Pakistan Jouf Univ Civil Engn Dept Sakaka 72388 Jouf Saudi Arabia Delta Univ Sci & Technol Fac Engn Civil Engn Dept Belkas Egypt Prince Sattam Bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj Saudi Arabia Zagazig Univ Fac Engn Dept Struct Engn Zagazig Egypt

The appropriate disposal of electronic waste (E-waste) is becoming a serious concern on a global scale. The purpose of present work is to establish a link between the mix design factors and mechanical strength using the metaheuristic based artificial intelligence (AI) Technique known as gene-expression-programming (GEP). The developed dataset includes several input variables i.e., the percentage of e-waste partial substitute, water to cement ratio, specimen age, water absorption and specific gravities of the aggregates, while the compressive strength (CS), flexural strength (FS) and tensile strength (STS) are used as predictive outcome. The established models were assessed using the root mean square error (RMSE), mean absolute error (MAE), objective function, and performance index as well as the regression measure known as the coefficient of correlation (R2). All strength models showed a significant correlation (R2 = 0.94), with the minimum statistical errors (MAE 2.04, RMSE 2.54), (MAE 0.36, RMSE 0.47), and (MAE 0.43, RMSE 0.54) for CS, FS and STS respectively. Furthermore, the parametric and sensitivity analyses were considered for analyzing impact of particular input variables on the performance of outcome. The established machine learning based metaheuristic models can be utilized confidently to use e-waste concrete in a variety of construction purposes.

关键词： Compressive strength Electronic waste Flexural strength gene expression programming Machine learning Metaheuristic Parametric analysis Sensitivity analysis Tensile strength

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Predicting the Ultimate Axial Capacity of Uniaxially Loaded CFST Columns Using Multiphysics Artificial Intelligence

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MATERIALS 2022年第1期15卷 39页

作者： Khan, Sangeen Ali Khan, Mohsin Zafar, Adeel Javed, Muhammad Faisal Aslam, Fahid Musarat, Muhammad Ali Vatin, Nikolai Ivanovich Natl Univ Sci & Technol NUST Mil Coll Engn MCE Dept Struct Engn Islamabad 44000 Pakistan CECOS Univ IT & Emerging Sci Civil Engn Dept Peshawar 25000 Pakistan COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Abbottabad 22060 Pakistan Prince Sattam bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj 11942 Saudi Arabia Univ Teknol PETRONAS Dept Civil & Environm Engn Bandar Seri Iskandar 32610 Malaysia Peter Great St Petersburg Polytech Univ St Petersburg 195291 Russia

The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop a prediction Multiphysics model for the circular CFST column by using the Artificial Neural Network (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the gene expression Program (GEP). The database for this study contains 1667 datapoints in which 702 are short CFST columns and 965 are long CFST columns. The input parameters are the geometric dimensions of the structural elements of the column and the mechanical properties of materials. The target parameters are the bearing capacity of columns, which determines their life cycle. A Multiphysics model was developed, and various statistical checks were applied using the three artificial intelligence techniques mentioned above. Parametric and sensitivity analyses were also performed on both short and long GEP models. The overall performance of the GEP model was better than the ANN and ANFIS models, and the prediction values of the GEP model were near actual values. The PI of the predicted N-st by GEP, ANN and ANFIS for training are 0.0416, 0.1423, and 0.1016, respectively, and for N-lg these values are 0.1169, 0.2990 and 0.1542, respectively. Corresponding OF values are 0.2300, 0.1200, and 0.090 for N-st, and 0.1000, 0.2700, and 0.1500 for N-lg. The superiority of the GEP method to the other techniques can be seen from the fact that the GEP technique provides suitable connections based on practical experimental work and does not rely on prior solutions. It is concluded that the GEP model can be used to predict the bearing capacity of circular CFST columns to avoid any laborious and time-consuming experimental work. It is also recommended that further research should be performed on the data to develop a prediction equation using other techniques such as Random Forest Regression and Multi expression Program.

关键词： concrete filled steel tube artificial neural network multi-physics model Random Forest Regression Adaptive Neuro-Fuzzy Inference System gene expression programming bearing capacity of columns

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Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms

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CONSTRUCTION AND BUILDING MATERIALS 2021年 308卷 125021-125021页

作者： Song, Hongwei Ahmad, Ayaz Farooq, Furqan Ostrowski, Krzysztof Adam Maslak, Mariusz Czarnecki, Slawomir Aslam, Fahid Dalian Minzu Univ Coll Civil Engn Dalian 116600 Peoples R China COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Abbottabad 22060 Pakistan Cracow Univ Technol Fac Civil Engn 24 Warszawska Str PL-31155 Krakow Poland Wroclaw Univ Sci & Technol Dept Bldg Engn PL-50370 Wroclaw Poland Prince Sattam bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj 11942 Saudi Arabia

The cementitious composites have different properties in the changing environment. Thus, knowing their mechanical properties is very important for safety reasons. The most important in the case of concrete is the Compressive strength (CS). To predict the CS of concrete Machine learning (ML) approaches has been essential. This study includes the collection of data from the experimental work and the application of ML techniques to predict the CS of concrete containing fly ash. The chemical and physical properties of all the materials used in this study were evaluated. Although, the emphasis of this research is on the use of supervised machine learning algorithms to forecast the CS of concrete. The gene expression programming (GEP), Artificial neural network (ANN), and Decision tree (DT) algorithms were investigated for the prediction of outcome (CS). Concrete samples (cylinders) with different mix ratios were cast and tested at various ages to maintain the required data for applying it to run the models. Total 98 data points were collected from the experimental approach, in which seven parameters namely cement, fly ash, superplasticizer, coarse aggregate, fine aggregate, water, and days were taken as input to predict the output which was CS parameter. The experimental data is further validated by mean of k-fold cross-validation using R-2, root mean error (RME), and Root mean square error (RMSE). In addition, statistical checks were incorporated to evaluate the model performance. In comparison, the bagging algorithm shows high accuracy towards the prediction of outcome as indicated by its high coefficient correlation (R-2) value equals to 0.95, while R-2 value for GEP, ANN and DT comes to 0.86, 0.81 and 0.75 respectively.

关键词： Individual algorithms Concrete Compressive strength Fly ash Predictions gene expression programming Decision tree Artificial neural network Bagging regressor

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Flexural strength prediction of concrete beams reinforced with hybrid FRP and steel bars based on machine learning

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STRUCTURES 2024年 65卷

作者： Zhang, Tao Gao, Danying Xue, Chengcheng Zhengzhou Univ Sch Water Conservancy Engn 100 Sci Rd Zhengzhou 450001 Henan Peoples R China Zhengzhou Univ Sch Civil Engn 100 Sci Rd Zhengzhou 450001 Henan Peoples R China

In the past decade, hybrid fiber-reinforced polymer (FRP) and steel reinforced concrete (hybrid FRP-steel RC) beams have attracted significant interest due to their relatively superior flexural behavior compared with the pure FRP bars reinforced concrete (FRP-RC) beams. The recent trend in research involves the utilization of machine learning (ML) tools to accurately predict the flexural behavior of RC beams. In this study, the two artificial intelligence technologies, namely artificial neural network (ANN) and gene expression programming (GEP), through a data-driven approach, were used to establish reliable and accurate ML models for predicting the flexural strength of hybrid FRP-steel RC beams. To build the ML models, a huge database of 134 data points was collected from the peer-reviewed published work. In both ANN and GEP models, eight main parameters were considered as input variables, including the beam width (b) b ) and depth (d), d ), steel and FRP reinforcement area (As A s and A f ), yield strength of steel bar (fy), f y ), concrete compressive strength (fc), f c ), the elasticity modulus of FRP bar (Ef) E f ) and the ultimate tensile strength of FRP bar (ffu). f f u ). The results demonstrated that the proposed ML models had lower mean absolute error (MAE), MAE ), mean absolute percentage error ( MAPE ) and root mean squared error ( RMSE ) as well as higher correlation coefficient (R2) R 2 ) than the ACI 440.11-22 equations. Finally, a parametric study was conducted using the suggested ML models to explore the effect of each input variable on the flexural strength of hybrid FRP-steel RC beams.

关键词： Flexural strength Hybrid FRP-steel reinforcement Machine learning Artificial neural network gene expression programming Data-driven

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Study Using Machine Learning Approach for Novel Prediction Model of Liquid Limit

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BUILDINGS 2022年第10期12卷 1551页

作者： Nawaz, Muhammad Naqeeb Qamar, Sana Ullah Alshameri, Badee Karam, Steve Codur, Merve Kayaci Nawaz, Muhammad Muneeb Riaz, Malik Sarmad Azab, Marc Natl Univ Sci & Technol NUST Inst Civil Engn Islamabad 44000 Pakistan Amer Univ Middle East Coll Engn & Technol Egaila 54200 Kuwait Erzurum Tech Univ Fac Engn & Architecture Dept Ind Engn TR-25050 Erzurum Turkey Natl Univ Technol NUTECH Civil Engn Dept Islamabad 44000 Pakistan

The liquid limit (LL) is considered the most fundamental parameter in soil mechanics for the design and analysis of geotechnical systems. According to the literature, the LL is governed by different particle sizes such as sand content (S), clay content (C), and silt content (M). However, conventional methods do not incorporate the effect of all the influencing factors because traditional methods utilize material passing through a # 40 sieve for LL determination (LL40), which may contain a substantial number of coarse particles. Therefore, recent advancements suggest that the LL must be determined using material passing from a # 200 sieve. However, determining the liquid limit using # 200 sieve material, referred to as LL200 in the laboratory, is a time-consuming and difficult task. In this regard, artificial-intelligence-based techniques are considered the most reliable and robust solutions to such issues. Previous studies have adopted experimental routes to determine LL200 and no such attempt has been made to propose empirical correlation for LL200 determination based on influencing factors such as S, C, M, and LL40. Therefore, this study presents a novel prediction model for the liquid limit based on soil particle sizes smaller than 0.075 mm (# 200 sieve) using gene expression programming (GEP). Laboratory experimental data were utilized to develop a prediction model. The results indicate that the proposed model satisfies all the acceptance requirements of artificial-intelligence-based prediction models in terms of statistical checks such as the correlation coefficient (R-2), root-mean-square error (RMSE), mean absolute error (MAE), and relatively squared error (RSE) with minimal error. Sensitivity and parametric studies were also conducted to assess the importance of the individual parameters involved in developing the model. It was observed that LL40 is the most significant parameter, followed by C, M, and S, with sensitivity values of 0.99, 0.93, 0.88, and 0.78

关键词： artificial intelligence gene expression programming liquid limit sensitivity and parametric studies

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An evolutionary computing approach for reducing bias in the dynamic modulus predictive models of hot mix asphalt

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CONSTRUCTION AND BUILDING MATERIALS 2022年第0期350卷

作者： Eleyedath, Abhary Swamy, Aravind Krishna IIT Delhi Dept Civil Engn New Delhi 110016 India

Various published literature has indicated significant bias issues with dynamic modulus (|E*|) predictive models for the hot mix asphalt. This study proposes gene expression programming (GEP) based approach to reduce such bias. Bias analysis was carried out using database developed during national cooperative highway research program. GEP based predictive models for bias correction factors were developed and subsequently used in conjunction with |E*| predictive models to update |E*| values. Application of correction factors and subsequent statistical analysis showed improvement in the accuracy of predicted |E*| values. The extent of improvement (using sum of squared error) was dependent on model under consideration and ranged between 19 and 36%.

关键词： Dynamic modulus Local bias Bias correction factor gene expression programming Hot Mix Asphalt

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Discharge and flow field simulation of open-channel sewer junction using artificial intelligence methods

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SCIENTIA IRANICA 2019年第1期26卷 178-187页

作者： Zaji, A. H. Bonakdari, H. Razi Univ Dept Civil Engn Kermanshah Iran

One of the most important parameters in designing sewer structures is the ability to accurately simulate their discharge and velocity field. Among the various sewer receiving inflow methods, open-channel junctions are the most frequently utilized ones. Because of the existence of separation and contraction zones in the open-channel junctions, the fluid flow has a complex behavior. Modeling is carried out by Radial Basis Function (RBF) neural network, gene expression programming (GEP), and Multiple Non-Linear Regression (MNLR) methods. Finding the optimum situation for CEP and RBF models is done by examining various mathematical and linking functions for GEP, different numbers of hidden neurons, and various spread amounts for RBF. In order to use the models in practical situations, three equations were conducted by using the RBF, GEP, and MNLR methods in modeling the longitudinal velocity. Then, the surface integral of the presented equations was used to simulate the flow discharge. The results showed that the GEP and RBF methods performed significantly better than the MNLR in open-channel junction characteristics simulations. The GEP method had better performance than the RBF in modeling the longitudinal velocity field. However, the RBF presented more reliable results in the discharge simulations. (C) 2019 Sharif University of Technology. All rights reserved.

关键词： Discharge prediction gene expression programming Multiple non-linear regression Open channel Radial basis neural network Sewer junction Velocity field

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