Statistical evaluation of a machine learning model as shear strength prediction on reinforced concrete beam
The shear strength (Vs) computation of reinforced concrete (RC) beams has been a major topic in structural engineering. Several methodologies have been introduced for the Vs prediction; however, the modeling accuracy is relatively low owing to the complex character of the resistance mechanism inv...
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my-upm-ir.1133702024-11-13T02:58:06Z Statistical evaluation of a machine learning model as shear strength prediction on reinforced concrete beam 2022-06 Mohammed, Mohammed Hayder Riyadh The shear strength (Vs) computation of reinforced concrete (RC) beams has been a major topic in structural engineering. Several methodologies have been introduced for the Vs prediction; however, the modeling accuracy is relatively low owing to the complex character of the resistance mechanism involving the dowel effect of longitudinal reinforcement, concrete in the compression zone, the contribution of the stirrups if existed, and the aggregate interlock. It is difficult, if not impossible, to shear design RC beams with and without stirrups utilizing laboratory trials. The span-todepth proportion, web width, and reinforcement proportion are only a few of the various factors that must be considered concurrently. Additionally, empirical techniques for shear design are developed within the confines of their testing regimes owing to the complicated shear failure process. As a result, these methodologies have limited generalizability and application. To overcome this problem, this work applies machine learning strategies for shear design. The current thesis is adopting the developing the Random Forest (RF) model as a robust machine learning (ML) predictive model for Vs prediction for reinforced concrete beams. The proposed ML model is developed based on collected experimental data 349, including the beam geometric and concrete properties parameters. Nine input combinations are constructed based on the associated input parameters for the proposed predictive model. The validation was conducted against the support vector machine (SVM) model, considered a well-established ML model introduced in the literature. In addition, several empirical formulations (EFs) are calculated for comparison. Research findings evidenced the potential of the proposed RF model for modeling the Vs reinforced concrete beams. Based on quantitative metric for the testing phase modeling, the RF model achieved the best results of the seventh input combination with root mean square error (RMSE = 89.68 KN), mean absolute error (MAE = 35.59 KN), mean absolute percentage error (MAPE = 0.16). The modeling accuracy performance comparison with the established ML models and the EFs confirmed the capacity of the proposed model. Results indicated that all the parameters utilized beam geometric and concrete properties are significant for the development of the predictive model. However, the model structure emphasizes the incorporation of seven predictors by excluding (beam flange thickness and coefficient). In general, the research provided a reliable a robust soft computing model for Vs of RC beams computation that contributes to the basic knowledge of structural engineering design and sustainability. Machine learning Prestressed concrete beams Structural analysis (Engineering) 2022-06 Thesis http://psasir.upm.edu.my/id/eprint/113370/ http://psasir.upm.edu.my/id/eprint/113370/1/113370.pdf text en public masters Universiti Putra Malaysia Machine learning Prestressed concrete beams Structural analysis (Engineering) Ismail, Sumarni |
| institution |
Universiti Putra Malaysia |
| collection |
PSAS Institutional Repository |
| language |
English |
| advisor |
Ismail, Sumarni |
| topic |
Machine learning Prestressed concrete beams Structural analysis (Engineering) |
| spellingShingle |
Machine learning Prestressed concrete beams Structural analysis (Engineering) Mohammed, Mohammed Hayder Riyadh Statistical evaluation of a machine learning model as shear strength prediction on reinforced concrete beam |
| description |
The shear strength (Vs) computation of reinforced concrete (RC) beams has been a
major topic in structural engineering. Several methodologies have been introduced for
the Vs prediction; however, the modeling accuracy is relatively low owing to the
complex character of the resistance mechanism involving the dowel effect of
longitudinal reinforcement, concrete in the compression zone, the contribution of the
stirrups if existed, and the aggregate interlock. It is difficult, if not impossible, to shear
design RC beams with and without stirrups utilizing laboratory trials. The span-todepth
proportion, web width, and reinforcement proportion are only a few of the
various factors that must be considered concurrently. Additionally, empirical
techniques for shear design are developed within the confines of their testing regimes
owing to the complicated shear failure process. As a result, these methodologies have
limited generalizability and application. To overcome this problem, this work applies
machine learning strategies for shear design. The current thesis is adopting the
developing the Random Forest (RF) model as a robust machine learning (ML)
predictive model for Vs prediction for reinforced concrete beams. The proposed ML
model is developed based on collected experimental data 349, including the beam
geometric and concrete properties parameters. Nine input combinations are constructed
based on the associated input parameters for the proposed predictive model. The
validation was conducted against the support vector machine (SVM) model, considered
a well-established ML model introduced in the literature. In addition, several empirical
formulations (EFs) are calculated for comparison. Research findings evidenced the
potential of the proposed RF model for modeling the Vs reinforced concrete beams.
Based on quantitative metric for the testing phase modeling, the RF model achieved the
best results of the seventh input combination with root mean square error (RMSE =
89.68 KN), mean absolute error (MAE = 35.59 KN), mean absolute percentage error
(MAPE = 0.16). The modeling accuracy performance comparison with the established
ML models and the EFs confirmed the capacity of the proposed model. Results
indicated that all the parameters utilized beam geometric and concrete properties are
significant for the development of the predictive model. However, the model structure
emphasizes the incorporation of seven predictors by excluding (beam flange thickness
and coefficient). In general, the research provided a reliable a robust soft computing
model for Vs of RC beams computation that contributes to the basic knowledge of
structural engineering design and sustainability. |
| format |
Thesis |
| qualification_level |
Master's degree |
| author |
Mohammed, Mohammed Hayder Riyadh |
| author_facet |
Mohammed, Mohammed Hayder Riyadh |
| author_sort |
Mohammed, Mohammed Hayder Riyadh |
| title |
Statistical evaluation of a machine learning model as shear strength prediction on reinforced concrete beam |
| title_short |
Statistical evaluation of a machine learning model as shear strength prediction on reinforced concrete beam |
| title_full |
Statistical evaluation of a machine learning model as shear strength prediction on reinforced concrete beam |
| title_fullStr |
Statistical evaluation of a machine learning model as shear strength prediction on reinforced concrete beam |
| title_full_unstemmed |
Statistical evaluation of a machine learning model as shear strength prediction on reinforced concrete beam |
| title_sort |
statistical evaluation of a machine learning model as shear strength prediction on reinforced concrete beam |
| granting_institution |
Universiti Putra Malaysia |
| publishDate |
2022 |
| url |
http://psasir.upm.edu.my/id/eprint/113370/1/113370.pdf |
| _version_ |
1818586151354630144 |