Machine Learning-Based Evaluation of Shear Capacity of Recycled Aggregate Concrete Beams
Recycled aggregate concrete (RAC) is a promising solution to address the challenges raised by concrete production. However, the current lack of pertinent design rules has led to a hesitance to accept structural members made with RAC. It would entail even more difficulties when facing application sce...
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MDPI AG
2020-10-01
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| author | Yong Yu Xinyu Zhao Jinjun Xu Cheng Chen Simret Tesfaye Deresa Jintuan Zhang |
| author_facet | Yong Yu Xinyu Zhao Jinjun Xu Cheng Chen Simret Tesfaye Deresa Jintuan Zhang |
| author_sort | Yong Yu |
| collection | DOAJ |
| description | Recycled aggregate concrete (RAC) is a promising solution to address the challenges raised by concrete production. However, the current lack of pertinent design rules has led to a hesitance to accept structural members made with RAC. It would entail even more difficulties when facing application scenarios where brittle failure is possible (e.g., beam in shear). In this paper, existing major shear design formulae established primarily for conventional concrete beams were assessed for RAC beams. Results showed that when applied to the shear test database compiled for RAC beams, those formulae provided only inaccurate estimations with surprisingly large scatter. To cope with this bias, machine learning (ML) techniques deemed as potential alternative predictors were resorted to. First, a Grey Relational Analysis (GRA) was carried out to rank the importance of the parameters that would affect the shear capacity of RAC beams. Then, two contemporary ML approaches, namely, the artificial neural network (ANN) and the random forest (RF), were leveraged to simulate the beams’ shear strength. It was found that both models produced even better predictions than the evaluated formulae. With this superiority, a parametric study was undertaken to observe the trends of how the parameters played roles in influencing the shear resistance of RAC beams. The findings indicated that, though less influential than the structural parameters such as shear span ratio, the effect of the replacement ratio of recycled aggregate (RA) was still significant. Nevertheless, the value of <i>v</i><sub>c</sub>/(<i>f</i><sub>c</sub>)<sup>1/2</sup> (i.e., the shear contribution from RAC normalized with respect to the square root of its strength) predicted by the ML-based approaches appeared to be insignificantly affected by the replacement level. Given the existing inevitable large experimental scatter, more shear tests are certainly needed and, for safe application of RAC, using partial factors calibrated to consider the uncertainty is feasible when designing the shear strength of RAC beams. Some suggestions for future works are also given at the end of this paper. |
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| series | Materials |
| spelling | doaj.art-ab6afce74521400bbee2fc0045b89baf2023-11-20T16:57:52ZengMDPI AGMaterials1996-19442020-10-011320455210.3390/ma13204552Machine Learning-Based Evaluation of Shear Capacity of Recycled Aggregate Concrete BeamsYong Yu0Xinyu Zhao1Jinjun Xu2Cheng Chen3Simret Tesfaye Deresa4Jintuan Zhang5School of Environment and Civil Engineering, Dongguan University of Technology, Dongguan 523808, ChinaState Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou 510640, ChinaCollege of Civil Engineering, Nanjing Tech University, Nanjing 211816, ChinaHunan Engineering Technology Research Center for High Speed Railway Operation Safety Assurance, Hunan Vocational College of Railway Technology, Zhuzhou 412001, ChinaCollege of Civil Engineering, Nanjing Tech University, Nanjing 211816, ChinaCollege of Architecture and Electrical Engineering, Hezhou University, Hezhou 542899, ChinaRecycled aggregate concrete (RAC) is a promising solution to address the challenges raised by concrete production. However, the current lack of pertinent design rules has led to a hesitance to accept structural members made with RAC. It would entail even more difficulties when facing application scenarios where brittle failure is possible (e.g., beam in shear). In this paper, existing major shear design formulae established primarily for conventional concrete beams were assessed for RAC beams. Results showed that when applied to the shear test database compiled for RAC beams, those formulae provided only inaccurate estimations with surprisingly large scatter. To cope with this bias, machine learning (ML) techniques deemed as potential alternative predictors were resorted to. First, a Grey Relational Analysis (GRA) was carried out to rank the importance of the parameters that would affect the shear capacity of RAC beams. Then, two contemporary ML approaches, namely, the artificial neural network (ANN) and the random forest (RF), were leveraged to simulate the beams’ shear strength. It was found that both models produced even better predictions than the evaluated formulae. With this superiority, a parametric study was undertaken to observe the trends of how the parameters played roles in influencing the shear resistance of RAC beams. The findings indicated that, though less influential than the structural parameters such as shear span ratio, the effect of the replacement ratio of recycled aggregate (RA) was still significant. Nevertheless, the value of <i>v</i><sub>c</sub>/(<i>f</i><sub>c</sub>)<sup>1/2</sup> (i.e., the shear contribution from RAC normalized with respect to the square root of its strength) predicted by the ML-based approaches appeared to be insignificantly affected by the replacement level. Given the existing inevitable large experimental scatter, more shear tests are certainly needed and, for safe application of RAC, using partial factors calibrated to consider the uncertainty is feasible when designing the shear strength of RAC beams. Some suggestions for future works are also given at the end of this paper.https://www.mdpi.com/1996-1944/13/20/4552recycled aggregate concretebeamshear capacityGrey relational analysismachine learning |
| spellingShingle | Yong Yu Xinyu Zhao Jinjun Xu Cheng Chen Simret Tesfaye Deresa Jintuan Zhang Machine Learning-Based Evaluation of Shear Capacity of Recycled Aggregate Concrete Beams Materials recycled aggregate concrete beam shear capacity Grey relational analysis machine learning |
| title | Machine Learning-Based Evaluation of Shear Capacity of Recycled Aggregate Concrete Beams |
| title_full | Machine Learning-Based Evaluation of Shear Capacity of Recycled Aggregate Concrete Beams |
| title_fullStr | Machine Learning-Based Evaluation of Shear Capacity of Recycled Aggregate Concrete Beams |
| title_full_unstemmed | Machine Learning-Based Evaluation of Shear Capacity of Recycled Aggregate Concrete Beams |
| title_short | Machine Learning-Based Evaluation of Shear Capacity of Recycled Aggregate Concrete Beams |
| title_sort | machine learning based evaluation of shear capacity of recycled aggregate concrete beams |
| topic | recycled aggregate concrete beam shear capacity Grey relational analysis machine learning |
| url | https://www.mdpi.com/1996-1944/13/20/4552 |
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