Application of Machine Learning Methods for Shear Capacity of RC Beams
| dc.contributor.advisor | Bedilu, Habte (PhD) | |
| dc.contributor.author | Birhane, Gemeda | |
| dc.date.accessioned | 2022-04-14T06:44:01Z | |
| dc.date.accessioned | 2023-11-11T12:56:45Z | |
| dc.date.available | 2022-04-14T06:44:01Z | |
| dc.date.available | 2023-11-11T12:56:45Z | |
| dc.date.issued | 2021-12 | |
| dc.description.abstract | Accurate determination of the capacity of reinforced concrete (RC) beams in shear remains a demanding problem due to its complex failure mechanism and the nonlinear relationship between different factors influencing the shear capacity. This research employs different types of single and ensemble machine learning (ML) based techniques; namely, decision tree, support vector machine, extremely randomized trees, gradient boosting, random forest, and extreme gradient boosting (xgBoost) to correctly predict the shear capacity of reinforced concrete beams. To this end, a dataset of experimental test results of RC beam with and without stirrups comprised of various beam geometry, concrete strength, reinforcing steel strength, longitudinal and shear reinforcement ratios, and shear span-toeffective depth ratio is used to develop the models. The proposed models were calibrated for different values of hyperparameters to achieve optimized ML models. The results of the analysis evidenced that the xgBoost model can be effectively utilized to predict the shear capacity of RC beams. The comparison of the predictions of the proposed and existing models evidenced that the efficiency of the proposed model is superior to the existing models and guidelines in terms of accuracy, safety, and economic aspects with significantly lowest bias and variability. A solid correlation exists between the shear capacities predicted using the proposed model and the corresponding experimental values as evidenced by the value of ��2 (��2 = 0.99) for RC beams without stirrup and (��2 = 0.995) for RC beams with stirrup. The proposed xgBoost model is deployed into a user-friendly web-based application to facilitate a quick and accurate prediction of capacity of RC beams in shear. The web-based application can be used by both practitioners and researchers to accurately predict the shear capacity of beams. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/31325 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Shear Capacity Prediction | en_US |
| dc.subject | RC Beams | en_US |
| dc.subject | Machine Learning | en_US |
| dc.title | Application of Machine Learning Methods for Shear Capacity of RC Beams | en_US |
| dc.type | Thesis | en_US |