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A comparative study of LightGBM, XGBoost, and GEP models in shear strength management of SFRC-SBWS

STRUCTURES 2024年 61卷

作者： Alshboul, Odey Almasabha, Ghassan Shehadeh, Ali Al-Shboul, Khaled Hashemite Univ Fac Engn Dept Civil Engn POB 330127 Zarqa 13133 Jordan Yarmouk Univ Hijjawi Fac Engn Technol Dept Civil Engn Irbid 21163 Jordan Jordan Univ Sci & Technol Dept Nucl Engn POB 3030 Irbid 22110 Jordan

Steel fiber reinforced concrete (SFRC) was revealed to have superior mechanical properties compared to plain concrete, especially under shear loads. However, Due to a lack of prediction models for SFRC-Slender Beams without Stirrups (SFRC-SBWS) in the available literature, the application of SFRC-SBWS in reinforced concrete (RC) constructions is limited. This research aims to fill a literature gap by analyzing different Machine Learning (ML) algorithms, including Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and gene expression programming (GEP), to forecast the shear strength of SFRC-SBWS specimens. A database of 333 tested SFRC-SBWS specimens was collected and analyzed. The results revealed the LightGBM and XGBoost algorithms surpassed other studied algorithms with Coefficient of determination (R2) = 95.74% and 93.27%, Root Mean Square Error (RMSE) = 20.04 kN and 25.19 kN, respectively. A GEP-based closed-form model was suggested to forecast the shear strength, considering the most important factors, including beam depth effect, beam width, and longitudinal steel reinforcement ratio. The proposed GEP model has an R2 = 91.12% and RMSE = 101.44 kN, showing a slightly lower prediction accuracy. The effectiveness of the studied models was evaluated from various angles, and the results demonstrate the importance of managing new technological advancements to create reliable support methods. Engineers that use these approaches get a dependable tool for estimating shear resistance, which is crucial in guaranteeing the safety and lifespan of RC structures. The successful combination of classic engineering approaches and revolutionary ML strategies indicates the potential for improving accuracy and reducing bias in prediction-oriented investigations. As the engineering field supports technological improvements, combining robust and efficient prediction models, as described in this paper, can contribute considerably to resilient structure d

关键词： SFRC Machine learning Shear strength Beams without stirrups gene expression programming

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Predicting maximum deflection of N-Edged thin-shelled hyperbolic-Paraboloid umbrella using machine learning techniques

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THIN-WALLED STRUCTURES 2024年 205卷

作者： Eldarwich, Hamid Mansouri, Iman Garlock, Maria Wang, Shengzhe Princeton Univ Dept Civil & Environm Engn Princeton NJ 08544 USA Univ Colorado Denver Dept Civil Engn Denver CO 80217 USA

Many thin-shelled hyperbolic paraboloid (hypar) umbrella forms have been built in the last 60 years as roof coverings. While the stresses in these forms remain relatively low, the deflections are a critical design parameter, and one that must be considered by architects and engineers in the conceptual design phase, meaning the design stage when the scale and form is given to the structure. To-date, there is no closed-form solution that can predict the maximum deflection of umbrellas due to their highly varying and complex geometry. The best option for predicting deflection is via finite element analysis, which is time-consuming for conceptual (preliminary) design purposes. In response, this paper uses machine learning via genetic programming (GP) and gene expression programming (GEP) to develop closed-form equations that predict the maximum corner deflection of N-edged hypar umbrellas - where N = 3, 4, 5, 6, 7, and 8. For a given a boundary condition and material, geometry is the most significant parameter influencing a shell's stiffness;thus, elastic finite element (FE) models use geometric properties as input variables (N, projected area, normalized rise, and shell thickness). The maximum corner deflection is recorded as an output and the FE analyses generate a large dataset of 53,754 results. It is observed that both GP and GEP can effectively parameterize the maximum deflection of N-edged hypar umbrellas, with GEP producing more concise, but relatively less accurate, equations than GP. While the formulations are trained using concrete material, a material factor multiplier transforms the results to other material properties within the assumption of elastic limits. The results of the study can be used to assist with conceptual design of hypar umbrellas and to validate complex FE models of hypar umbrellas. This research also illustrates the use of machine learning techniques as applied to the conceptual design of structures with highly varying geometries.

关键词： Felix candela Hyperbolic paraboloid (hypar) Concrete shell Umbrella genetic programming gene expression programming Machine learning Symbolic regression conceptual design

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Data-Driven Predictive Modeling of Steel Slag Concrete Strength for Sustainable Construction

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BUILDINGS 2024年第8期14卷 2476页

作者： Albostami, Asad S. Al-Hamd, Rwayda Kh. S. Al-Matwari, Ali Ammar Oryx Universal Coll Partnership Liverpool John Moo Sch Engn & Construct POB 12253 Doha Qatar Abertay Univ Fac Social & Appl Sci Dept Built Environm & Life Sci Dundee DD1 1HQ Scotland

Conventional concrete causes significant environmental problems, including resource depletion, high CO2 emissions, and high energy consumption. Steel slag aggregate (SSA), a by-product of the steelmaking industry, offers a sustainable alternative due to its environmental benefits and improved mechanical properties. This study examined the predictive power of four modeling techniques-gene expression programming (GEP), an Artificial Neural Network (ANN), Random Forest Regression (RFR), and Gradient Boosting (GB)-to predict the compressive strength (CS) of SSA concrete. Using 367 datasets from the literature, six input variables (cement, water, granulated furnace slag, superplasticizer, coarse aggregate, fine aggregate, and age) were utilized to predict compressive strength. The models' performance was evaluated using statistical measures such as the mean absolute error (MAE), root mean squared error (RMSE), mean values, and coefficient of determination (R2). Results indicated that the GB model consistently outperformed RFR, GEP, and the ANN, achieving the highest R2 values of 0.99 and 0.96 for the training and testing dataset, respectively, followed by RFR with R2 values of 0.97 (training) and 0.93 (testing), GEP with R2 values of 0.85 (training) and 0.87 (testing), and ANN with R2 values of 0.61 (training) and 0.82 (testing). Additionally, the GB model had the lowest MAE values of 0.79 MPa (training) and 2.61 MPa (testing) and RMSE values of 1.90 MPa (training) and 3.95 MPa (testing). This research aims to advance predictive modeling in sustainable construction through analysis and well-defined conclusions.

关键词： gene expression programming artificial neural network random forest regression gradient boosting soft computing artificial intelligence steel slag aggregate sustainable construction compressive strength of concrete

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Modeling wax disappearance temperature using robust white-box machine learning

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FUEL 2024年 376卷

作者： Amar, Menad Nait Zeraibi, Noureddine Benamara, Chahrazed Djema, Hakim Saifi, Redha Gareche, Mourad Sonatrach Dept Etud Thermodynam Div Labs Ave 1 er Novembre Boumerdes 35000 Algeria Univ MHamed Bougara Boumerdes Fac Hydrocarbons & Chem Lab Hydrocarbons Phys Engn Ave 1er Novembre Boumerdes 35000 Algeria

Wax deposition is one of the major operational problems encountered in the upstream petroleum production system. The deposition of this undesirable scale can cause a variety of challenging problems. In order to avoid the latter, numerous parameters associated with the mechanism of wax deposition should be determined precisely. In this study, a new smart correlation was proposed for the accurate prediction of Wax disappearance temperature (WDT) using a robust explicit-based machine learning (ML) approach, namely gene expression programming (GEP). The correlation was developed using comprehensive experimental measurements. The obtained results revealed the promising degree of accuracy of the suggested GEP-based correlations. In this context, the newly- introduced correlations provided excellent statistical metrics (R2 2 = 0.9647 and AARD = 0.5963 %). Furthermore, performance of the developed correlation outperformed that of many existing approaches for predicting WDT. In addition, the trend analysis performed on the outcomes of the proposed GEP-based correlations divulged their physical validity and consistency. Lastly, the findings of this study provide a promising benefit, as the newly developed correlations can notably improve the adequate estimation of WDT, thus facilitating the simulation of wax deposition-related phenomena. In this context, the proposed correlations can supply the effective management of the production facilities and improvement of project economics since the provided correlation is a simple-to-use decision-making tool for production and chemical engineers engaged in the management of organic deposit-related issues.

关键词： Wax Wax disappearance temperature Petroleum production system Machine learning gene expression programming

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Data-driven prediction method for flexural performance of ECC composite sandwich panels

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STRUCTURES 2024年 70卷

作者： Xiong, Feng Bian, Yu Liu, Ye Ge, Qi Deng, Chubing Sichuan Univ Coll Architecture & Environm Key Lab Deep Underground Sci & Engn Minist Educ Chengdu 610065 Peoples R China

To address the inadequate load-carrying capacity and ductility of precast concrete sandwich panels (PCSPs), as well as their inability to meet the current trend of lightweight and high-strength structures, this study proposes a novel engineered cementitious composite (ECC) composite sandwich panel (ESP) by applying ECC to the outer wythe. Existing design methods are unable to accurately quantify either the composite action between the inner and outer wythes or fully consider the complex design parameters when predicting the flexural performance of this new type of sandwich panel. In response, two normal concrete sandwich panels (NSPs) and two ESPs were first tested to reveal their flexural performance using a four-point bending test. Test results indicate that the application of ECC to the outer wythe can significantly improve the flexural performance of the sandwich panel, especially in terms of crack resistance and ductility. Subsequently, finite element parametric analysis demonstrated the impact of connector arrangement on the flexural performance, and design formulas for the degree of composite action (DCA) based on connector arrangement were proposed. Finally, a data-driven prediction model integrating experiments, finite element analysis, and neural networks was developed to accurately predict the flexural carrying capacity and ductility of the sandwich panel. By utilizing gene expression programming, the explicit function can be derived for the prediction model, which provides practical guidance for engineering applications.

关键词： Engineered cementitious composite (ECC) Sandwich panel Finite element analysis Data-driven Neural network gene expression programming

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Predictive modeling for depth of wear of concrete modified with fly ash: A comparative analysis of genetic programming-based algorithms

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CASE STUDIES IN CONSTRUCTION MATERIALS 2024年 20卷

作者： Khan, Adil Khan, Majid Ali, Mohsin Khan, Murad Khan, Asad Ullah Shakeel, Muhammad Fawad, Muhammad Najeh, Taoufik Gamil, Yaser Univ Bradford Dept Adv Civil & Struct Engn Bradford BD7 1DP England COMSATS Univ Dept Civil Engn Islamabad 22060 Pakistan Southeast Univ Nanjing Sch Civil Engn Nanjing Peoples R China Tianjin Univ Sch Civil Engn Tianjin 300354 Peoples R China Univ Engn & Technol Dept Civil Engn Peshawar 25120 Pakistan Silesian Univ Technol Poland Gliwice Poland Budapest Univ Technol & Econ Hungary Budapest Hungary Lulea Univ Technol Dept Civil Environm & Nat Resources Engn Operat Maintenance & Acoust Lulea Sweden Monash Univ Malaysia Sch Engn Dept Civil Engn Jalan Lagoon SelatanBandar Sunway Subang Jaya 47500 Selangor Malaysia

There has been increasing growth in incorporating fly ash as a supplementary cementitious material in concrete mixtures due to its potential to enhance the durability and strength properties of concrete. However, there is a lack of research on predicting the depth of wear of fly ash-based concrete. The laboratory methods available for estimating the depth of wear often involve destructive and expensive tests. Therefore, to avoid costly and laborious tests, this study utilized two machine learning methods, including multi-expression programming (MEP) and gene expression programming (GEP), to predict the depth of wear of fly ash-modified concrete. A comprehensive dataset of 216 experimental records was compiled from published studies for model training and validation. This extensive dataset encompasses the depth of wear as the target variable, along with nine explanatory parameters, namely fly ash, cement content, fine and coarse aggregate, water content, plasticizer, age of concrete, air-entraining agent, and testing time. The models were trained with 70% of the data, and the remaining 30% of data was used for validating the models. The models were developed by a continuous trial-and-error process and iterative refinement of hyperparameters until optimal results were achieved. The efficacy of the models was assessed via multiple statistical indicators. Furthermore, the SHapley Additive exPlanation (SHAP) was utilized for the interpretability of the model prediction from both global and local perspectives. The GEP model exhibited excellent accuracy with a correlation coefficient (R) of 0.989 (training) and 0.992 (validation). Similarly, the MEP model provided prediction accuracy with R values of 0.965 and 0.968 for training and validation sets, respectively. In addition, the MEP and GEP models outperformed the traditional multi-linear regression model. The SHAP interpretation revealed that testing time and age have a higher contribution in determining the depth of wea

关键词： Fly ash Abrasion resistance gene expression programming Multi-expression programming SHAP

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Machine learning for optimal design of circular hollow section stainless steel stub columns: A comparative analysis with Eurocode 3 predictions

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 132卷

作者： Abarkan, Ikram Rabi, Musab Ferreira, Felipe Piana Vendramell Shamass, Rabee Limbachiya, Vireen Jweihan, Yazeed S. Santos, Luis Fernando Pinho Abdelmalek Essaadi Univ Fac Sci Dept Phys Tetouan 93002 Morocco Jerash Univ Dept Civil Engn Irbid Jordan Univ Fed Uberlandia Fac Civil Engn Campus Santa Mon Uberlandia MG Brazil Brunel Univ London Dept Civil & Environm Engn London England London South Bank Univ Sch Built Environm & Architecture Div Civil & Bldg Serv Engn London England Mutah Univ Coll Engn Civil & Environm Engn Dept Mutah 61710 Karak Jordan

Stainless steel has many advantages when used in structures, however, the initial cost is high. Hence, it is essential to develop reliable and accurate design methods that can optimize the material. As novel, reliable soft computation methods, machine learning provided more accurate predictions than analytical formulae and solved highly complex problems. The present study aims to develop machine learning models to predict the crosssection resistance of circular hollow section stainless steel stub column. A parametric study is conducted by varying the diameter, thickness, length, and mechanical properties of the column. This database is used to train, validate, and test machine learning models, Artificial Neural Network (ANN), Decision Trees for Regression (DTR), gene expression programming (GEP) and Support Vector Machine Regression (SVMR). Thereafter, results are compared with finite element models and Eurocode 3 (EC3) to assess their accuracy. It was concluded that the EC3 models provided conservative predictions with an average Predicted-to-Actual ratio of 0.698 and Root Mean Square Error (RMSE) of 437.3. The machine learning models presented the highest level of accuracy. However, the SVMR model based on RBF kernel presented a better performance than the ANN, GEP and DTR machine learning models, and RMSE value for SVMR, ANN, GEP and DTR is 22.6, 31.6, 152.84 and 29.07, respectively. The GEP leads to the lowest level of accuracy among the other three machine learning models, yet, it is more accurate than EC3. The machine learning models were implemented in a user-friendly tool, which can be used for design purposes.

关键词： Circular hollow sections Stainless steel Finite element model Artificial neural network Support vector machine regression gene expression programming Decision Trees for Regression

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Study on Dynamic Modulus Prediction Model of In-Service Asphalt Pavement

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BUILDINGS 2024年第8期14卷 2550页

作者： Wang, Duanyi Luo, Chuanxi Li, Jian He, Jun South China Univ Technol Sch Civil Engn & Transportat Guangzhou 510000 Peoples R China Xiaoning Inst Roadway Engn Guangzhou 510000 Peoples R China

The dynamic modulus of in-service asphalt pavements serves as a critical parameter for the computation of residual life and the design of overlays. However, its acquisition is currently limited to laboratory dynamic modulus testing using a limited number of core samples, necessitating a reassessment of its representativeness. To facilitate the prediction of dynamic modulus design parameters through Falling Weight Deflectometer (FWD) back-calculated modulus data, an integrated approach encompassing FWD testing, modulus back-calculation, core sample dynamic modulus testing, and asphalt DSR testing was employed to concurrently acquire dynamic modulus at identical locations under varying temperatures and frequencies. Dynamic modulus prediction models for in-service asphalt pavements were developed utilizing fundamental model deduction and gene expression programming (GEP) techniques. The findings indicate that GEP exhibits superior efficacy in the development of dynamic modulus prediction models. The dynamic modulus prediction model developed can enhance both the precision and representativeness of asphalt pavement's dynamic modulus design parameters, as well as refine the accuracy of residual life estimations for in-service asphalt pavements. Concurrently, the modulus derived from FWD back-calculation can be transmuted into the dynamic modulus adhering to a uniform standard criterion, facilitating the identification of problematic segments within the asphalt structural layer. This is of paramount importance for the maintenance or reconstruction of in-service asphalt pavements.

关键词： in-service asphalt pavement dynamic modulus prediction model modulus back-calculation gene expression programming

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An optimized prediction of FRP bars in concrete bond strength employing soft computing techniques

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JOURNAL OF BUILDING ENGINEERING 2024年 86卷

作者： Al-Hamd, Rwayda Kh S. Albostami, Asad S. Alzabeebee, Saif Al -Bander, Baidaa Abertay Univ Sch Appl Sci Dundee Scotland Univ Petra Fac Engn Civil Engn Dept Amman Jordan Oryx Universal Coll Partnership Liverpool John Moo Sch Engn & Construct Doha Qatar Univ Al Qadisiyah Coll Engn Dept Rd & Transport Engn Al Diwaniyah Al Qadisiyah Iraq Keele Univ Sch Comp Newcastle Upon Tyne ST5 5BG Keele England

The precise estimation of the bonding strength between concrete and fiber-reinforced polymer (FRP) bars holds significant importance for reinforced concrete structures. This study introduces a new methodology that utilizes soft computing methods to enhance the prediction of FRP bars' bonding strength. A significant compilation of experimental bond strength tests is assembled, covering various variables. Significant variables that affect bonding strength are found in the study of this database. The prediction process is optimized using soft computing methods, particularly gene expression programming (GEP) and the Multi-Objective genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR). The proposed soft computing approaches accommodate complex relationships and optimize prediction accuracy depending on the input variables. Results demonstrate its effectiveness in predicting bond strength and comparing it with existing codes and other models from the literature. The results have shown that the MOGA-EPR and the GEP models have high R2 values between 0.91 and 0.94. The proposed new models enhance the reliability and efficiency of designing and assessing FRP-reinforced concrete.

关键词： Multi -objective genetic algorithm evolutionary polynomial regression gene expression programming Soft computing Fiber reinforcement polymer Bond strength

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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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