IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i7p2709-d338854.html
   My bibliography  Save this article

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

Author

Listed:
  • Hai-Bang Ly

    (University of Transport Technology, Hanoi 100000, Vietnam)

  • Tien-Thinh Le

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam)

  • Huong-Lan Thi Vu

    (University of Transport Technology, Hanoi 100000, Vietnam)

  • Van Quan Tran

    (University of Transport Technology, Hanoi 100000, Vietnam)

  • Lu Minh Le

    (Faculty of Engineering, Vietnam National University of Agriculture, Gia Lam, Hanoi 100000, Vietnam)

  • Binh Thai Pham

    (University of Transport Technology, Hanoi 100000, Vietnam)

Abstract

Understanding shear behavior is crucial for the design of reinforced concrete beams and sustainability in construction and civil engineering. Although numerous studies have been proposed, predicting such behavior still needs further improvement. This study proposes a soft-computing tool to predict the ultimate shear capacities (USCs) of concrete beams reinforced with steel fiber, one of the most important factors in structural design. Two hybrid machine learning (ML) algorithms were created that combine neural networks (NNs) with two distinct optimization techniques (i.e., the Real-Coded Genetic Algorithm (RCGA) and the Firefly Algorithm (FFA)): the NN-RCGA and the NN-FFA. A database of 463 experimental data was gathered from reliable literature for the development of the models. After the construction, validation, and selection of the best model based on common statistical criteria, a comparison with the empirical equations available in the literature was carried out. Further, a sensitivity analysis was conducted to evaluate the importance of 16 inputs and reveal the dependency of structural parameters on the USC. The results showed that the NN-RCGA (R = 0.9771) was better than the NN-FFA and other analytical models (R = 0.5274–0.9075). The sensitivity analysis results showed that web width, effective depth, and a clear depth ratio were the most important parameters in modeling the shear capacity of steel fiber-reinforced concrete beams.

Suggested Citation

  • Hai-Bang Ly & Tien-Thinh Le & Huong-Lan Thi Vu & Van Quan Tran & Lu Minh Le & Binh Thai Pham, 2020. "Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams," Sustainability, MDPI, vol. 12(7), pages 1-34, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2709-:d:338854
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/7/2709/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/7/2709/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hamacher, Kay, 2005. "On stochastic global optimization of one-dimensional functions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 354(C), pages 547-557.
    2. Danial Jahed Armaghani & Panagiotis G. Asteris & Behnam Askarian & Mahdi Hasanipanah & Reza Tarinejad & Van Van Huynh, 2020. "Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness," Sustainability, MDPI, vol. 12(6), pages 1-17, March.
    3. Binh Thai Pham & Chongchong Qi & Lanh Si Ho & Trung Nguyen-Thoi & Nadhir Al-Ansari & Manh Duc Nguyen & Huu Duy Nguyen & Hai-Bang Ly & Hiep Van Le & Indra Prakash, 2020. "A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil," Sustainability, MDPI, vol. 12(6), pages 1-16, March.
    4. Binh Thai Pham & Trung Nguyen-Thoi & Hai-Bang Ly & Manh Duc Nguyen & Nadhir Al-Ansari & Van-Quan Tran & Tien-Thinh Le, 2020. "Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination," Sustainability, MDPI, vol. 12(6), pages 1-29, March.
    5. Thendiyath Roshni & Madan K. Jha & Ravinesh C. Deo & A. Vandana, 2019. "Development and Evaluation of Hybrid Artificial Neural Network Architectures for Modeling Spatio-Temporal Groundwater Fluctuations in a Complex Aquifer System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(7), pages 2381-2397, May.
    6. Dong Van Dao & Hojjat Adeli & Hai-Bang Ly & Lu Minh Le & Vuong Minh Le & Tien-Thinh Le & Binh Thai Pham, 2020. "A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation," Sustainability, MDPI, vol. 12(3), pages 1-22, January.
    7. Pedroni, N. & Zio, E. & Apostolakis, G.E., 2010. "Comparison of bootstrapped artificial neural networks and quadratic response surfaces for the estimation of the functional failure probability of a thermal–hydraulic passive system," Reliability Engineering and System Safety, Elsevier, vol. 95(4), pages 386-395.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Van Quan Tran & Hai-Van Thi Mai & Thuy-Anh Nguyen & Hai-Bang Ly, 2021. "Investigation of ANN architecture for predicting the compressive strength of concrete containing GGBFS," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-21, December.
    2. Xin Wei & Niaz Muhammad Shahani & Xigui Zheng, 2023. "Predictive Modeling of the Uniaxial Compressive Strength of Rocks Using an Artificial Neural Network Approach," Mathematics, MDPI, vol. 11(7), pages 1-17, March.
    3. Muhammad Rashid & Muhammad Attique Khan & Majed Alhaisoni & Shui-Hua Wang & Syed Rameez Naqvi & Amjad Rehman & Tanzila Saba, 2020. "A Sustainable Deep Learning Framework for Object Recognition Using Multi-Layers Deep Features Fusion and Selection," Sustainability, MDPI, vol. 12(12), pages 1-21, June.
    4. Hai-Bang Ly & Thuy-Anh Nguyen & Binh Thai Pham, 2022. "Investigation on factors affecting early strength of high-performance concrete by Gaussian Process Regression," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-17, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Phong Tung Nguyen & Duong Hai Ha & Abolfazl Jaafari & Huu Duy Nguyen & Tran Van Phong & Nadhir Al-Ansari & Indra Prakash & Hiep Van Le & Binh Thai Pham, 2020. "Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam," IJERPH, MDPI, vol. 17(7), pages 1-20, April.
    2. Binh Thai Pham & Chongchong Qi & Lanh Si Ho & Trung Nguyen-Thoi & Nadhir Al-Ansari & Manh Duc Nguyen & Huu Duy Nguyen & Hai-Bang Ly & Hiep Van Le & Indra Prakash, 2020. "A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil," Sustainability, MDPI, vol. 12(6), pages 1-16, March.
    3. Saeideh Samani & Meysam Vadiati & Farahnaz Azizi & Efat Zamani & Ozgur Kisi, 2022. "Groundwater Level Simulation Using Soft Computing Methods with Emphasis on Major Meteorological Components," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3627-3647, August.
    4. Qun Yu & Masoud Monjezi & Ahmed Salih Mohammed & Hesam Dehghani & Danial Jahed Armaghani & Dmitrii Vladimirovich Ulrikh, 2021. "Optimized Support Vector Machines Combined with Evolutionary Random Forest for Prediction of Back-Break Caused by Blasting Operation," Sustainability, MDPI, vol. 13(22), pages 1-15, November.
    5. Pin-Chun Huang & Kuo-Lin Hsu & Kwan Tun Lee, 2021. "Improvement of Two-Dimensional Flow-Depth Prediction Based on Neural Network Models By Preprocessing Hydrological and Geomorphological Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 1079-1100, February.
    6. Zio, E. & Pedroni, N., 2012. "Monte Carlo simulation-based sensitivity analysis of the model of a thermal–hydraulic passive system," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 90-106.
    7. Chengning Zhou & Ning-Cong Xiao & Ming J Zuo & Xiaoxu Huang, 2020. "AK-PDF: An active learning method combining kriging and probability density function for efficient reliability analysis," Journal of Risk and Reliability, , vol. 234(3), pages 536-549, June.
    8. Mohamed K. Abdel-Fattah & Elsayed Said Mohamed & Enas M. Wagdi & Sahar A. Shahin & Ali A. Aldosari & Rosa Lasaponara & Manal A. Alnaimy, 2021. "Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    9. Jyh-Woei Lin, 2022. "Generalized two-dimensional principal component analysis and two artificial neural network models to detect traveling ionospheric disturbances," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(2), pages 1245-1270, March.
    10. Sun, Zhili & Wang, Jian & Li, Rui & Tong, Cao, 2017. "LIF: A new Kriging based learning function and its application to structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 152-165.
    11. Zhi-Hua Xu & Guang-Liang Feng & Qian-Cheng Sun & Guo-Dong Zhang & Yu-Ming He, 2020. "A Modified Model for Predicting the Strength of Drying-Wetting Cycled Sandstone Based on the P-Wave Velocity," Sustainability, MDPI, vol. 12(14), pages 1-17, July.
    12. Dilip Kumar Roy & Sujit Kumar Biswas & Kowshik Kumar Saha & Khandakar Faisal Ibn Murad, 2021. "Groundwater Level Forecast Via a Discrete Space-State Modelling Approach as a Surrogate to Complex Groundwater Simulation Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1653-1672, April.
    13. Liu, Xing & Ferrario, Elisa & Zio, Enrico, 2019. "Identifying resilient-important elements in interdependent critical infrastructures by sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 423-434.
    14. Qinghe Zhao & Zifang Zhang & Yuchen Huang & Junlong Fang, 2022. "TPE-RBF-SVM Model for Soybean Categories Recognition in Selected Hyperspectral Bands Based on Extreme Gradient Boosting Feature Importance Values," Agriculture, MDPI, vol. 12(9), pages 1-16, September.
    15. Li He & Yongming Zhao & Lin Yin & Dongwang Zhong & Haitao Xiong & Shasha Chen & Xinyue Zhang, 2023. "Research on a Non-Synchronous Coordinated Reduction Method for Slopes Based on the Hoek–Brown Criterion and Acoustic Testing Technology," Sustainability, MDPI, vol. 15(21), pages 1-21, November.
    16. Zhang, Jinhao & Xiao, Mi & Gao, Liang, 2019. "An active learning reliability method combining Kriging constructed with exploration and exploitation of failure region and subset simulation," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 90-102.
    17. Ahmed Salih Mohammed & Panagiotis G. Asteris & Mohammadreza Koopialipoor & Dimitrios E. Alexakis & Minas E. Lemonis & Danial Jahed Armaghani, 2021. "Stacking Ensemble Tree Models to Predict Energy Performance in Residential Buildings," Sustainability, MDPI, vol. 13(15), pages 1-22, July.
    18. Shinyoung Kwag & Daegi Hahm & Minkyu Kim & Seunghyun Eem, 2020. "Development of a Probabilistic Seismic Performance Assessment Model of Slope Using Machine Learning Methods," Sustainability, MDPI, vol. 12(8), pages 1-22, April.
    19. Javed Mallick & Saeed Alqadhi & Swapan Talukdar & Majed AlSubih & Mohd. Ahmed & Roohul Abad Khan & Nabil Ben Kahla & Saud M. Abutayeh, 2021. "Risk Assessment of Resources Exposed to Rainfall Induced Landslide with the Development of GIS and RS Based Ensemble Metaheuristic Machine Learning Algorithms," Sustainability, MDPI, vol. 13(2), pages 1-30, January.
    20. Chen, Zequan & Li, Guofa & He, Jialong & Yang, Zhaojun & Wang, Jili, 2022. "Adaptive structural reliability analysis method based on confidence interval squeezing," Reliability Engineering and System Safety, Elsevier, vol. 225(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2709-:d:338854. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.