Assessment of shear strength of reinforced concrete beams without shear reinforcement: A comparative study between codes of practice and artificial neural network
Despite 70 years of investigations in understanding the shear behaviour of reinforced concrete members, it is again gaining attention among structural engineers as the recently issued Australian concrete design standard, AS 3600 updated its shear provisions and ACI 318 unveiled its new one-way shear...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | Case Studies in Construction Materials |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509522002340 |
| _version_ | 1818547924810858496 |
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| author | Thushara Jayasinghe Tharaka Gunawardena Priyan Mendis |
| author_facet | Thushara Jayasinghe Tharaka Gunawardena Priyan Mendis |
| author_sort | Thushara Jayasinghe |
| collection | DOAJ |
| description | Despite 70 years of investigations in understanding the shear behaviour of reinforced concrete members, it is again gaining attention among structural engineers as the recently issued Australian concrete design standard, AS 3600 updated its shear provisions and ACI 318 unveiled its new one-way shear design equation. This study investigates the shear design equations in ACI 318–19 and AS 3600–2018 highlighting their strengths and weaknesses. A detailed parametric study is performed on a database of 1237 shear tests of point loaded RC slender beams without shear reinforcement. An Artificial neural network (ANN) was built, trained and validated with a subset of this database. Further, a very few experimental tests were conducted isolating the effect of a single variable on the shear failure load of RC beams without shear reinforcement. Thus, an ANN is an effective tool to investigate the influence of each variable individually. This study concludes that the introduction of size effect and ρw1/3 terms into the new ACI 318–19 code have resulted in greater accuracy compared to ACI 318–14 which it replaced. The study further demonstrates that AS 3600–2018 agrees well with all ranges of test parameters. The ANN demonstrated more accurate predictions compared to the codes of practice within the range of input parameters considered. |
| first_indexed | 2024-12-12T08:13:09Z |
| format | Article |
| id | doaj.art-f25d240ef45f418a8cb0a5ee608aa58a |
| institution | Directory Open Access Journal |
| issn | 2214-5095 |
| language | English |
| last_indexed | 2024-12-12T08:13:09Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj.art-f25d240ef45f418a8cb0a5ee608aa58a2022-12-22T00:31:42ZengElsevierCase Studies in Construction Materials2214-50952022-06-0116e01102Assessment of shear strength of reinforced concrete beams without shear reinforcement: A comparative study between codes of practice and artificial neural networkThushara Jayasinghe0Tharaka Gunawardena1Priyan Mendis2Corresponding author.; Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, AustraliaFaculty of Engineering and Information Technology, The University of Melbourne, Melbourne, AustraliaFaculty of Engineering and Information Technology, The University of Melbourne, Melbourne, AustraliaDespite 70 years of investigations in understanding the shear behaviour of reinforced concrete members, it is again gaining attention among structural engineers as the recently issued Australian concrete design standard, AS 3600 updated its shear provisions and ACI 318 unveiled its new one-way shear design equation. This study investigates the shear design equations in ACI 318–19 and AS 3600–2018 highlighting their strengths and weaknesses. A detailed parametric study is performed on a database of 1237 shear tests of point loaded RC slender beams without shear reinforcement. An Artificial neural network (ANN) was built, trained and validated with a subset of this database. Further, a very few experimental tests were conducted isolating the effect of a single variable on the shear failure load of RC beams without shear reinforcement. Thus, an ANN is an effective tool to investigate the influence of each variable individually. This study concludes that the introduction of size effect and ρw1/3 terms into the new ACI 318–19 code have resulted in greater accuracy compared to ACI 318–14 which it replaced. The study further demonstrates that AS 3600–2018 agrees well with all ranges of test parameters. The ANN demonstrated more accurate predictions compared to the codes of practice within the range of input parameters considered.http://www.sciencedirect.com/science/article/pii/S2214509522002340Shear strengthSize effectStrain effectBack-propagation neural networkSigmoid function |
| spellingShingle | Thushara Jayasinghe Tharaka Gunawardena Priyan Mendis Assessment of shear strength of reinforced concrete beams without shear reinforcement: A comparative study between codes of practice and artificial neural network Case Studies in Construction Materials Shear strength Size effect Strain effect Back-propagation neural network Sigmoid function |
| title | Assessment of shear strength of reinforced concrete beams without shear reinforcement: A comparative study between codes of practice and artificial neural network |
| title_full | Assessment of shear strength of reinforced concrete beams without shear reinforcement: A comparative study between codes of practice and artificial neural network |
| title_fullStr | Assessment of shear strength of reinforced concrete beams without shear reinforcement: A comparative study between codes of practice and artificial neural network |
| title_full_unstemmed | Assessment of shear strength of reinforced concrete beams without shear reinforcement: A comparative study between codes of practice and artificial neural network |
| title_short | Assessment of shear strength of reinforced concrete beams without shear reinforcement: A comparative study between codes of practice and artificial neural network |
| title_sort | assessment of shear strength of reinforced concrete beams without shear reinforcement a comparative study between codes of practice and artificial neural network |
| topic | Shear strength Size effect Strain effect Back-propagation neural network Sigmoid function |
| url | http://www.sciencedirect.com/science/article/pii/S2214509522002340 |
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