This paper proposes a domain adaptation model for population-based structural health monitoring in an offshore wind farm. The idea is to predict the fatigue damage equivalent moments in the jacket support structure of a wind turbine based on the trained machine learning model on another wind turbine in the same wind farm. The proposed idea is novel. However, there are several issues in the paper that should be addressed:

1. The text is quite confusing for the reader to capture the idea of the proposed method because there are several unclear sentences and a large number of grammatical errors and typos throughout the paper.
2. The paper may not be well organized. For example, why is the data description included in subsection 2.1? The organization of the article is needed.
3. There is no literature review in the introduction section to state the novelty and advantage of this work over the previous research studies, e.g., https://doi.org/10.1016/j.renene.2021.01.143, https://doi.org/10.1016/j.renene.2020.10.121, https://doi.org/10.1016/j.ymssp.2021.108519, https://doi.org/10.3390/en15020558, https://doi.org/10.1016/j.renene.2022.07.117.
4. The motivation for the study should be further emphasized. In particular, the contribution should be highlighted in the introduction and conclusion sections.
5. The reference style is not consistent throughout the text, as well as the numbering of figures and tables (table 3.1 in line 71, figure 3 in line 195, and figure 4 in line 199, for example)
6. The description on the train and test data should be provided. For example, what percentage of source and target domains data is used for training and testing the models?
7. In your domain adaptation model, how do you consider the wind turbine position in the wind farm? Because based on different positions, different severity of loads may be imposed on wind turbines; hence, the trained machine learning model on a wind turbine may not give promising results in another wind turbine. 

The paper presents a novel application of domain adaptation - and a critical focus for the progression of SHM (sharing data/information between systems). However, the reviewer cannot recommend publication on the grounds of two concerns. The first requires more thorough reviewing/referencing, and the second concerns the interpretability of the method/notation.

While the authors reference earlier work in PBSHM, there appears to be an overlap that should be more clearly acknowledged. Especially in the introductory section 2. In the reviewer's opinion, the text doesn't justifiably reference the original framework papers - discussions around figure 1, for example. More referencing is required throughout, especially to definitions of the underlying theory of domain adaptation.

The second (more major concern) is that the paper is rather confusing in terms of notation, to the point that the methodology becomes unclear

• Both feature data and the feature space appear to be defined as 'X'
• The set notation throughout is abnormal - missing brackets? (Maybe a rendering issue.) If the authors are following some other convention it should be clearly defined
• page 4: the summations are misleading to the reviewer, and the statement, Ds = X = Xi,s, Yi,s)^N needs more explanation
• page 5: there appears to be a notation equating 'domains' for homogeneous transfer, but isn't the transformation (into the encoded space) required for the assumption of (approximate) equivalence - and concerning the probability distribution? Are the authors referring to equivalence in the dimensionality of the feature spaces between domains? It seems, from the earlier notation, that the statement implies [Xs, p(Xs)] = [Xt, p(Xt)]. This would imply the two domains are the same, so there is no need for transfer.
• There are few references in the above sections - which should clearly reference established definitions
• The 'encoded space' is discussed but never clearly defined, usually represented with 'phi(X)' or similar
• p16/17 appear to conflict in using similar notation for the labels themselves and the label spaces, also with respect to page 4
• pg 6: 'rewriting' - typo?
• pg 12: broken reference to AdaBoost
• WTG acronym appears undefined

The paper presents a new domain adaptation approach for the PBSHM to estimate the fatigue damage equivalent moments for the jacket support structure of an offshore wind farm under the homogeneous population assumption.

Major shortcomings:

• The paper should be revised both linguistically and structurally. Currently it is very difficult to follow the idea of the paper.
•  The tables and figure numbers in the text do not match the figures/tables (e.g. line 199: Figure 3 is probably Figure 4).
• The citations are not always clear, because years are missing (e.g. line 21) and poorly integrated into the text flow. In addition, literature sources are missing, such as in the definitions section.
• A state of the art is missing, as well as a classification of the proposed methodology in it.
•  The figure are mostly difficult to read and should be revised.

Unfortunately, for the reasons stated above, I recommend a rejection of the paper for publication. For a possible resubmission, a few more comments:

• section 2.1: Where are the wind turbines under consideration located in the wind farm?  Where exactly are the strain gage measurement locations. In section 4.2 describe that the transition piece must be the same for a population. Therefore, it should be mentioned here that the height of the transition piece is the same for the wind turbine. 
•  section 4.2: If I understand correctly, the boxplots in Figure 2 belong to the statistical quantities in Table 2. The minima and maxima definitely differ from each other. I also can't imagine that the standard deviation and the mean are exactly the same. Either correct, or explain more precisely where the difference between the two is.
• section 6.4: Figure 5 and 6 Look exactly the same. What is the reason for this?  The high DEM are often underestimated. Could it be due to the training datasets, since rare events cannot be considered?  What is the impact of general model errors on lifetime prediction?