## 6 Conclusions  
In this research, prediction models were developed to analyze and predict crack width in reinforced concrete beam-column joints area subjected to lateral cyclic loadings. The prediction models were developed considering the cracks observed at each drift ratio level with numbers of additional shear links and length of the anchorage within the joint region using Rapidminer machine learning tools. The results have shown that the support vector machine (SVM) model can provide accurate and precise performance on the crack widths prediction process. The following conclusions are drawn in this study:  
1. SVM--dot kernel prediction model exhibited 30% fewer discrepancy values between measured and predicted crack width compared to DL--max-out (56%), DL--rectifier (55%), and SVM--neural (48%) and was therefore chosen.
2. For the data splitting ratio, 70:30 split of testing and training smaller number dataset was comparable to the experimental range, thus being a decent and reasonable choice in this research.
3. The higher number of input variables (eleven inputs) being pre-processed by the SVM--dot model was recommended, where the small dataset for this research with an increased number of inter-related inputs parameter presented errors less than 20% consistency of predicted points varied along the reference lines compared to over-fitting result found in seven inputs (52-58%) and less under-fit found in nine inputs (21-25%), respectively.
4. Finally, based on conclusion (2) and (3), further optimization was made where the root mean square error (RMSE) performance index was adapted, and the SVM--dot prediction model further reduced the error between measured and predicted crack width up to 5% compared to absolute error (AE), 37%, and prediction average (PA), 50%, respectively.  
Although the proposed model uses SVM--dot with RMSE optimization, it has been proven to provide a reliable accuracy for the current application. However, it is only applicable for the lateral cyclic loading, which simulating the earthquake loading, as this is the main scope of the study. In the case for the different types of RC material or loading programs, the new dataset is required to improve the model, which may be considered for future work.  
Conceptualization, methodology and project administration, R.G., C.G.T. and A.E.-S.; software, R.G. and A.E.-S.; validation, R.G., A.E.-S. and Z.I.; formal analysis, data curation and writing--original draft preparation, R.G.; investigation, R.G. and C.G.T.; resources, R.G., C.G.T., A.E.-S. and Z.I.; writing--review and editing, M.M.S. and F.M.N.; visualization, R.G., F.M.N. and A.E.-S.; supervision, C.G.T., Z.I. and A.E.-S.; funding acquisition, C.G.T., A.E.-S. and M.M.S. All authors have read and agreed to the published version of the manuscript.  
Supported by University of Malaya RU Grant--Faculty Program (GPF007A-2019).  
Not applicable.  
The experimental results were taken and cited from author's own paper (in press); [[https://doi.org/10.1139/cjce-2019-0578](https://doi.org/10.1139/cjce-2019-0578)]([https://doi.org/10.1139/cjce-2019-0578](https://doi.org/10.1139/cjce-2019-0578)) (accessed on 30 October 2020).  
This study was supported by the Research Fund Assistance from the University of Malaya under RU Grant--Faculty Program (GPF007A-2019). The author thanks the Universite Tun Hussein Onn Malaysia for providing scholarship under S.T.A.B scheme (2017) to perform this work.  
The authors declare no conflict of interest.