Nonlinear Response Prediction of Spar Platform in Deep Water Using an Artificial Neural Network
Round 1
Reviewer 1 Report
The authors compared the performance of ANNs with respect to FEM in predicting spar platform response and concluded that ANNs perform reasonably well. At the same time, it consumes almost no time in comparison to FEM. The article is well written. However, I have some concerns as follows.
· The authors concluded that ANNs consume a few seconds, while FEM takes a very long time. The same conclusion has been reported in Ref 11. The difference between the present manuscript and Ref 11 is that the considered parameters are different. I wonder if the authors could clarify the reason for considering wave height and wave period instead of the parameters considered in Ref 11 to produce the same conclusion.
· Why is this present study necessary, while it produces the same conclusion as in Ref 11? Does it provide better or quicker prediction than the algorithm shown in Ref 11?
· The literature only discusses FEM and ANNs related studies. I was surprised not to see literature regarding other methods applied for predicting spar platform response.
· Line 234: the authors mention ‘wave height’ and ‘wave period’ in Table 1. But, Table 1 shows no such information. A similar issue applies to Table 2 mentioned in line 235.
· Figure 5: Two figures seems to be overlapped.
Author Response
Thanks! Please see the attachment.
Author Response File: Author Response.docx
Reviewer 2 Report
The authors studied the nonlinear responses of the spar platform subjected to the structural parameter, as well as wave height and wave period, based on artificial neural network, Feed-forward neural networks through the backpropagation algorithm are depleted to train the network. Subsequently the neural network is formed, rapid responses are obtained from a newly forecasted wind force. The results are nicely presented and validated.
Author Response
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Reviewer 3 Report
As per the fig 2, ANN is applied ? Now the first question is that how do you decide to choose the ANN ? Why not Deep Learning or if you wish to optimize, then why you have not applied the other optimization algorithms ? This is most important. Justify.
Few highly indexed Journals from Transactions, SCI journals etc may be included in literature review, preferably from year 2021 and 2022 and accordingly, the modifications are required.
Author Response
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Author Response File: Author Response.docx
Reviewer 4 Report
The article is within the scope of the journal. The topic discussed is interesting.
It is well written and easy to read.
The content of the article addresses an interesting topic from an original perspective. The results shown are interesting. However, some improvements must be made in order to be accepted:
a) The Results and Discussion sections should be separated.
b) With the current content, the Results part is adequately covered. However, the Discussion part must be added. In the discussion part, the results of the presented work must be compared with other similar works, indicating its advances and limitations.
c) The conclusions should be improved indicating lines of future work.
d) A section on materials and methodology should be included, where the methods used and the materials are explicitly indicated.
e) The state of the art of the article should be extended.
Author Response
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Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
I appreciate the efforts by the authors for addressing the comments.
Reviewer 4 Report
The paper can be accepted in current state.