## 5 Discussions  
### Advantages of the Method Proposed  
Based on the above-mentioned parametric studies, the proposed method is more cost-beneficial than the conventional method in the cases listed below. The parameters are categorized in Figure 7 and the conditions referred to in the Introduction were also noted for reference.  
Figure 14: Influence of other parameters on applicability (\(\text{COV}_{\text{Q}}\) = 0.20, \(\text{COV}_{\text{Rc}}\) = 0.35).  
Figure 15: Influence of other parameters on applicability (\(\text{COV}_{\text{Q}}\) = 0.10, \(\text{COV}_{\text{Rc}}\) = 0.50).  
* Parameters of the technique
* Low cost of countermeasure (\(k_{c}\)), which was the same as Condition (2)
* Low uncertainty of estimation of the proposed method (\(\text{COV}_{\text{Rp}}\)), which was the same as Condition (3)
* Parameters of the conventional method
* High uncertainty of estimation of the conventional method (\(\text{COV}_{\text{Rc}}\)), which was the same as Condition (1)
* Conditions of the project
* High failure cost (\(k_{f}\))
* Where the structure requires high reliability (\(\beta_{T}\))
* When the building cost tends to increase with an increase in the SF (\(k_{b}\))
* Low uncertainty of loads (\(\text{COV}_{\text{Q}}\))
* Small scale of the projects  
The conditions required for the OM listed in the Introduction are all included in (A) and (B). Makers of piling techniques can develop and improve the piling technique through flexible responses to differences between the design and observed results, the development of efficient countermeasure methods, and the development and improvement of the estimation methods. Which of these should be prioritized depends on the performance requirements, as stated previously.  
The remaining condition is (C), under which, at first glance, it may seem unusual to decrease the initial SF where the failure cost is large and the structure requires high reliability. However, this is understandable when considering that the conventional method requires a larger SF, whereas the proposed method does not.  
A larger reliability index is better for the proposed method. Initially, it appears that the ULS should be dominant in determining the design parameters. However, this assumes that the \(\text{COV}_{\text{Rp}}\) and \(\text{COV}_{\text{Rc}}\) remain the same, which they generally do not. The estimated variation in deformation is higher than that of the ultimate capacity. Furthermore, the measured deformation during piling could be the initial elastic deformation. Therefore, the \(\text{COV}_{\text{Rc}}\) is expected to be higher and the \(\text{COV}_{\text{Rp}}\) and \(k_{f}\) lower for SLS than for ULS. Hence, it cannot be determined in this paper whether piling data are more important for the ULS or SLS.  
As variations in the loads cannot be handled during construction, it is naturally appropriate that they are smaller; that is, the dead load should be dominant instead of the live load.  
If the project size and number of piles are large, there will be more time to conduct preliminary geotechnical investigations and loading tests. In such cases, the estimated variation \(\text{COV}_{\text{Rc}}\) will be smaller and the likelihood of adopting the proposed method will be lower. The relationship between the number of piles and the cost of conducting loading tests was nonogrammed by [66], showing that around 50 piles would be the threshold to determine whether load tests should be undertaken for the project.  
Assuming that \(k_{c}\) is 1.0 at most by simply adding new piles, \(k_{b}\) is greater than 1.0 and \(\beta_{T}\) is more than 3.0, the proposed method seems highly applicable (Figure 13). To make the proposed method more applicable for the ULS, reducing the \(\text{COV}_{\text{Rp}}\) takes priority over reducing the \(k_{c}\).