Estimating CPT Parameters at Unsampled Locations Based on Kriging Interpolation Method

Round 1

Reviewer 1 Report

Comments:

The Kriging method was adopted for interpolation of the unsampled locations of the cone penetrometer test (CPT) results. The topic is novel and meaningful for potential readers, and the paper is, in general, well-written. Therefore, it is recommended that the following minor comments are revised before it being considered for publication. Please see the detailed comments below.

The major technical comments are given as follows:

Major comments:

1. The authors stated “However, there are only few case studies on adopting Kriging method to predict the soil parameters at the unsampled locations, and the data used in previous study is often scarce, which is not enough to prove the accuracy and effectiveness of the Kriging method.” Please give a brief discussion on the difference and novelty of the current work compared with the existing works, such as:
   • Wang, Y., & Zhao, T. (2017). Statistical interpretation of soil property profiles from sparse data using Bayesian compressive sampling. Géotechnique, 67(6), 523-536.
   • Zhao, T., Xu, L., & Wang, Y. (2020). Fast non-parametric simulation of 2D multi-layer cone penetration test (CPT) data without pre-stratification using Markov Chain Monte Carlo simulation. Engineering Geology, 273, 105670.
   • Zhao, T., & Wang, Y. (2020). Interpolation and stratification of multilayer soil property profile from sparse measurements using machine learning methods. Engineering Geology, 265, 105430.
   • Guo, Yimu, Guozhu Zhang, and Songyu Liu. (2020) Temperature effects on the in-situ mechanical response of clayey soils around an energy pile evaluated by CPTU. Engineering Geology 276: 105712.
2. Section 1.1. The filtering of the outlier data points also removed some of the features of the data points, e.g., the extreme q_c values at the turning point of the curve. This can lead to a certain level of inaccuracy of the following processing. Please explain how this was considered during the filtering.
3. Section 2.1 Set up semivariogram (The original numbering of the manuscript is wrong. Please revise). The authors stated “Figure 7 shows some commonly used semivariogram models in the literature such as spherical model, exponential model, Gaussian model, etc.”. Please delete etc.
4. Equation (2). Please define “E”.
5. Figure 9. This is one of the most critical figures that presented the results, however, the authors adopted a rather unconventional presentation form, which hindered a good understanding of the purpose of the figure. Please revise to make the accuracy of your interpolation explicit to the readers.

Editorial/languate issues of the manuscript:

The English writing of the paper needs extensive polishing. Some representative editorial issues are given below, but they are far from exhaust:

1. Should be “unsampled” instead of “unsamled”. Please check.
2. Two lines above Eq. (3). Should be “upon” instead of “uopn”.
3. One line below Eq. (3). Should be “discrete” instead of “dicrete”.
4. The paragraph below Eq. (8). There is an extra “the” in the sentence.
5. Some of the symbols are italic while others are not in the text. Please keep consistent.

Comments for author File: Comments.pdf

Author Response

Dear Editor and Reviewers:

On behalf of my co-authors, we are very grateful to you for giving us an opportunity to revise our manuscript. We appreciate you very much for your positive and constructive comments and suggestions on our manuscript entitled “ Estimating CPT parameters at unsampled locations based on Kriging Interpolation Method”(ID: applsci-1432132).

We sincerely thank the reviewer for thoroughly examining our manuscript and providing helpful comments to guide our revision. We have provided a point-by-point response to the reviewer’s comments as shown below.

1. The authors stated “However, there are only few case studies on adopting Kriging method to predict the soil parameters at the unsampled locations, and the data used in previous study is often scarce, which is not enough to prove the accuracy and effectiveness of the Kriging method.” Please give a brief discussion on the difference and novelty of the current work compared with the existing works, such as:

response: Thanks for your kind suggestions.  The fourth article is related to the temperature effect and does not involve spatial prediction.At present, there have been several studies using Markov chain and machine learning methods combined with CPT test to predict the soil parameters at the unsampled locations, however, they need to meet some strong prerequisites. For example, Markov chain method is suitable when the unsampled location and the sampled CPT boreholes are located on the same horizontal line, while the machine learning method needs to obtain the soil layer information in advance and perform extensive training to get a better prediction result. Similarly, the  Bayesian compressive sampling method is computationally expensive and cannot be used conveniently. Compared with them, the Kriging method is suitable for two-dimensional situations, especially when the sampled CPT boreholes are distributed around the unsampled location as shown in Figure 2, and it is much simpler and more convenient to use for practical engineering. More discussion have been included in the revised manuscript.

2. The filtering of the outlier data points also removed some of the features of the data points, e.g., the extreme q_c values at the turning point of the curve. This can lead to a certain level of inaccuracy of the following processing. Please explain how this was considered during the filtering.

response: The abnormal point is caused by the error of the CPT device itself and improper human operation. The abnormal point itself is not the characteristic value of the soil layer. If it is considered, errors will be introduced. The method used in this paper is to replace the abnormal point with the  weighted average value of its nearby points.

3. The original numbering of the manuscript is wrong. Please revise

response: the numbering has been revised.

4. Figure 7 shows some commonly used semivariogram models in the literature such as spherical model, exponential model, Gaussian model, etc.”. Please delete etc.

response: etc. has been deleted.

5.  Equation (2). Please define “E”.

response: "E" has been defined.

6.  Figure 9. This is one of the most critical figures that presented the results, however, the authors adopted a rather unconventional presentation form, which hindered a good understanding of the purpose of the figure. Please revise to make the accuracy of your interpolation explicit to the readers.

response: To address the reviewer's concern ,Figure 9 has been revised,and the result has been shown more explicitly.

7. The English writing of the paper needs extensive polishing.

response: Thank you for your constructive suggestion, which is highly appreciated. We have carefully scrutinized the manuscript,and the spelling errors have been revised. In addition, the manuscript has been improved with the help of a native English speaker.

We sincerely hope that this revised manuscript has addressed all your comments and suggestions. We appreciated for reviewer’s warm work earnestly, and hope that the correction will meet with approval. Once again, thank you very much for your comments and suggestions. If you have any question, please don’t hesitate to connect us.

JinHao Liu

E-mail: [email protected]

Reviewer 2 Report

The findings of the paper are very interesting for the geotechnical engineers.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

See attached file

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf