Fuzzy Sensitivity Analysis of Structural Performance

Round 1

Reviewer 1 Report

SUSTAINABILITY

TITLE:   FUZZY SENSITIVITY ANALYSIS OF STRUCTURAL PERFORMANCE

Review comments

This is a well-written paper that presents a brief review of the application of sensitivity analyses in structural engineering is provided, and then the concept of local sensitivity analysis is developed for the fuzzy randomness theory. Despite the versatility and widespread application of fuzzy randomness in structural and mechanical engineering, less attention has been paid to the formulation of sensitivity analysis for this uncertainty model. Several sensitivity tests based on the classical probability theory are extended to this uncertainty model, namely Monte Carlo simulation (MCS), tornado diagram analysis (TDA), and first-order second-moment method (FOSM). The multidisciplinary application of these methods in engineering is shown using a numerical example, a truss structure, and finally seismic performance evaluation of a framed structure from a full-scale experimental test. The way of visualizing the results is also provided, which helps the interpretation and better understanding. The results show that the established tools can provide detailed insight into the uncertainty of fuzzy random models. Furthermore, efficient methods like TDA and FOSM can substantially reduce the computational time compared to the MCS while providing an acceptable trade-off for accuracy.  This paper is well-organized and useful. It can be published after addressing the following changes. I have a few comments for you to consider:

1.     In the keywords expand the term FOSM.

2.     In Fig.4 “Fuzzy sensitivity analysis using α-level optimization”, Check the spelling for minimum and similar typo errors throughout the manuscript.

3.     All the terminologies used in the manuscript can be placed in a tabular form which is easily understandable for the readers.

4.     In Fig.6 the maximum samples taken shows 1000. Will the accuracy of the analysis gets increased if the sample sizes are increased?

5.     What are the differences between Fuzzy fragility analysis, Fuzzy global sensitivity analysis while comparing with MCS, FOSM and TDS methods?

6.     Fig. 18: Separate each figure as a,b,c etc..

7.     Please conduct an extensive literature review. Some of the most relevant references which are not in the literature review section are:

(1)        Estimation of Probability on Delay in Desalination Plant Construction Projects in Lakshadweep Island.

(2)        Damage Detection Of Structures Based On Neural Network Approach.

Author Response

attached

Author Response File: Author Response.pdf

Reviewer 2 Report

The paper is devoted to local sensitivity analysis of fuzzy random models. However, I had serious difficulties with understanding the manuscript. Below I mention some questions and recommendations for the authors:

1. Lines 52-53. You need to add the references for MCS, TDA and FOSM methods.

 
2. Line 60. I recommend to add Regional Sensitivity Analysis to the list of global sensitivity approaches.

3. Line 126. Should it be lambda(alpha_1)=1 or lambda(alpha_2)=1?

4. Line 146. Term “fuzzy failure probability” should be strictly defined.

5. Lines 149-160. Could you briefly explain why alpha-optimization improves efficiency.

6. Line 214. What is “virtually infinite originals”?

7. Subsection 3.2. I'm sure it's necessary to describe the computational issues of TDA in more detail.

8. Line 286. What means “minute perturbation”?

9. Line 315. I recommend to write in the caption to fig. 5 that different plots correspond to 3 different methods (MCS, TDA and FOSM).

10. Line 380. Term "reliability index" is used before it is defined in Line 570.

11. Line 436. Could you specify why there are 2,606,500 simulations here?

12. As I understand the main results are resented in Figs. 9, 14, 21. However, I did not quite understand how the minimum and maximum values in Equation 6 are used to plot these figures.

 
Due to these issues, I recommend the major revision of the presented manuscript before it can be published in Sustainability.

Author Response

attached

Author Response File: Author Response.pdf

Reviewer 3 Report

The article reviewed focused on the idea of local sensitivity analysis founded on the fuzzy randomness principle. The authors applied the Monte Carlo simulation, tornado diagram analysis, and first-order second-moment method to prove their arguments. A good effort was made by the authors to explain how the established tools offer a comprehensive understanding into the uncertainty of fuzzy random models, which is commendable. However, some issues need to be addressed to make the work to a high standard of the journal. Consequently, the authors could address the following:

1. Specify the novelty of the work.

2. State the advantages of the method proposed.

3. Add 3 to 5 additional relevant literature in the 2020-2022 year range

Author Response

attached

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

I encourage the authors' efforts to revise the paper. The authors answered my questions and have revised the manuscript according to my comments. The resubmitted manuscript is essentially improved. Therefore, I recommend the presented paper for publication in Sustainability.