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Prediction of the effective moment of inertia for concrete beams reinforced with FRP bars using an evolutionary algorithm

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STRUCTURES 2022年 35卷 684-705页

作者： Arabshahi, Alireza Tavakol, Masoumeh Sabzi, Javad Gharaei-Moghaddam, Nima Ferdowsi Univ Mashhad Sch Engn Civil Engn Dept Mashhad Razavi Khorasan Iran

The previous studies on the bending behavior of Fiber Reinforced Polymers (FRP) Reinforced Concrete (RC) beams proved that there is a distinct difference between the cracking and deflection behavior of these structural members and conventional RC beams with steel reinforcing rebar. Accordingly, the existing relations for the prediction of deflection and crack width of RC members cannot be used for serviceability controls of FRP-RC beams. The present study proposes new and more accurate predictive relations for the effective moment of inertia and the crack width of GFRP-RC beams using a collection of experimental test results from literature an Evolutionary Algorithm (EA) called multi-expression programming (MEP). An EA is a subset of generic population-based metaheuristic optimization, which performs mechanisms inspired by biological operations to find the best solutions for different types of approximating problems. Numerical evaluations proved that the proposed relation for the effective moment of inertia has higher accuracy than the other existing relations with an R-2 value of 0.49 and RMSE, MAE, and IAE error indices of 0.31, 0.72, and 0.51. The proposed relation for the crack width also outperforms the other existing counterparts. Since the proposed relations are developed based on the results of the four-point bending test on simple beams, the accuracy of the proposed relation for the effective moment of inertia was also compared with the experimental and numerical results for continuous beams, which again verified the high accuracy of the proposed model. In addition to the experimental database, a numerical model is also developed to verify the accuracy of the suggested models. The attained results demonstrate considerably higher accuracy of the proposed relations in comparison with the other existing counterparts.

关键词： GFRP RC beam Deflection Crack width Serviceability Evolutionary algorithm multi-expression programming Effective moment of inertia

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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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Energy consumption predictions by genetic programming methods for PCM integrated building in the tropical savanna climate zone

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JOURNAL OF BUILDING ENGINEERING 2023年 68卷

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

The development of energy-efficient buildings by considering early-stage design parameters can help reduce buildings' energy consumption. Machine learning tools are getting popular for forecasting the energy demand of buildings, which play a vital role in improving building energy efficiency. In this research, multi-expression and genetic expression programming were utilized to anticipate the energy consumption of PCM-integrated buildings by taking early-stage design parameters into consideration. The prediction models were developed using the data generated by energy simulations for the PCM-integrated building in eight cities within a tropical savanna climate. The statical parameters were used to evaluate and externally validate the proposed prediction model. The statistical evaluation reveals that the genetic expression programmingbased predictive model gave more accurate energy consumption predictions for PCMintegrated buildings than multi-expression programming. The performance indices of the statistically analyzed gene expression programming-based prediction model (GEP7) showed excellent values: correlation coefficient (R) = 0.961, performance index (& rho;) = 0.169, and Nash-Sutcliffe efficiency (NSE) = 0.108. Thereafter, the sensitivity and parametric analyses were performed. It was unearthed that the roof solar absorptance, window visible transmittance, wall solar absorptance, and the melting temperature of PCM were the influential early-stage design parameters for PCM-integrated buildings. In conclusion, the gene-expression programming-based predictive model can be utilized to predict the influence of early-stage design parameters on the energy consumption of PCM-integrated buildings.

关键词： Phase change materials Early-stage design parameters Energy consumption Genetic programming multi-expression programming Gene-expression programming

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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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Prediction of collapsibility of loess site based on artificial intelligence: comparison of different algorithms

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ENVIRONMENTAL EARTH SCIENCES 2024年第3期83卷 101-101页

作者： Zhu, Xueliang Shao, Shuai Shao, Shengjun Xian Univ Technol Inst Geotech Engn Xian 710048 Shaanxi Peoples R China Xian Univ Technol Dept Architecture & Urban Planning Xian 710048 Shaanxi Peoples R China Xian Univ Technol Shaanxi Prov Key Lab Loess Mech & Engn Xian 710048 Shaanxi Peoples R China

Collapsibility affects loess engineering stability;the straightforward prediction for the self-weight collapsibility coefficient of loess is useful to determine the collapsibility type of loess site. In this study, three representative machine learning algorithms: multi-expression programming (MEP), random forest (RF) and support vector machine (SVM) are used to develop three straightforward prediction models for the loess self-weight collapsibility coefficient values, aiming to evaluate the collapsibility of loess sites according to the basic physical properties. Considering soil depth and compression modulus, a large database including five input variables, i.e., initial water content, initial void ratio, plasticity index, soil depth and compression modulus is established. Genetic algorithm (GA) is used to optimize the hyper-parameters of the RF and SVM models. The results show that the three models developed for the training set and the test set have high prediction accuracy for the self-weight collapsibility coefficient of loess. The monotonicity, sensitivity and robustness of the three prediction models are analyzed, showing the consistency between different models, but slightly different, which verifies the feasibility of the model. On the whole, the high prediction accuracy of the RF model is first recommended, but no explicit expression. The MEP model with explicit expression is also recommended for ease of application. The SVM model is the last option according to the situation.

关键词： Loess collapsibility parameters Machine learning multi-expression programming Random forest Support vector machine Genetic algorithm

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Effect of magnetized mixing water on the fresh and hardened state properties of steel fibre reinforced self-compacting concrete

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CONSTRUCTION AND BUILDING MATERIALS 2020年 248卷 118660-118660页

作者： Ghorbani, Saeid Sharifi, Sohrab Rokhsarpour, Hamed Shoja, Sara Gholizadeh, Mostafa Rahmatabad, Mohammad Ali Dashti de Brito, Jorge Univ Ghent Fac Engn & Architecture Magnel Vandepitte Lab Dept Struct Engn & Bldg Mat Tech Lane Ghent Sci PkCampus A B-9052 Ghent Belgium Univ Alberta Dept Civil & Environm Engn Edmonton AB Canada Islamic Azad Univ Civil Engn Dept Yazd Branch Yazd Iran Ferdowsi Univ Mashhad Fac Econ & Adm Sci Econ Dept Mashhad Razavi Khorasan Iran Ferdowsi Univ Mashhad Fac Sci Chem Dept Mashhad Razavi Khorasan Iran Univ Lisbon Inst Super Tecn Dept Civil Engn Architecture & Georresources CERIS Lisbon Portugal

The objective of this research is to investigate the effect of magnetized water on the fresh and hardened properties of self-compacting concrete (SCC) reinforced with different ratios of steel fibres. For this purpose, a total of 12 mixes were produced with different contents of steel fibres (0%, 0.35%, 1%, and 1.65% by volume of concrete) and the mixing water passed through a permanent magnetic field (with a strength of 0.65 T), 1 and 15 times, at a flow speed of 0.75 m/s. Slump flow, V-funnel, compressive strength, splitting tensile strength and flexural strength tests, as well as a statistical analysis, were conducted. The results of the workability, compressive strength, splitting tensile strength and flexural strength tests indicate that the SCC mixes are considerably enhanced by magnetizing the mixing water. Finally, a novel evolutionary programming technique called multi-expression programming was used to generate mathematical models. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Magnetized mixing water Self-compacting concrete Steel fibres Fresh state properties Hardened state properties multi-expression programming

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Proposition of an equivalent reduced thickness for composite steel plate shear walls containing an opening

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JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH 2020年 168卷 105985-105985页

作者： Meghdadian, Mohammad Gharaei-Moghaddam, Nima Arabshahi, Alireza Mahdavi, Navid Ghalehnovi, Mansour Ferdowsi Univ Mashhad Sch Engn Dept Civil Engn Mashhad Razavi Khorasan Iran

The composite steel plate shear wall (CSPSW) is a relatively new structural system which attracted many researchers in recent years. The main idea for developing the CSPSW system is to improve out-of-plain strength of steel plate shear walls by addition of one or two layers of concrete. This configuration provides high strength and acceptable ductility for this system in comparison with the other lateral load bearing systems. Sometimes due to architectural considerations, it is required to include openings in the shear walls. These openings change performance of the CSPSW and reduce its strength and stiffness. Moreover, inclusion of openings offers difficulty in analysis and design of the CSPSW system. The main goal of this study is to develop accurate numerical models to analyze CSPSW containing opening. Based on the obtained result and using an evolutionary algorithm named multi-expression programming (MEP), an empirical relation is proposed to calculate equivalent reduced thickness to be used in the analysis of the CSPSW instead of incorporating the opening in the developed models. Accuracy of the developed model and the proposed relation are verified experimentally. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Composite steel plate shear wall Opening Evolutionary algorithms multi-expression programming Reduced equivalent thickness

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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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