Development of a Fuzzy Inference System Based Rapid Visual Screening Method for Seismic Assessment of Buildings Presented on a Case Study of URM Buildings
Round 1
Reviewer 1 Report
(1)The title cannot accurately reflect the research content of the manuscript.
(2) Introduction needs to further improve the research status.
(3) The quality of Figs and Tables in manuscripts need to be improved.
(4) The typesetting of the manuscript is not beautiful.
(5) Conclusions of the manuscript need to be improved.
(6) Although I am not a native English speaker, the language of the manuscript needs to be modified.
Author Response
Firstly, we would like to express our gratitude to Reviewer – 1 for his/her time and significant feedback. The modifications and/or additions made in response to the given comments below are explained one by one.
- The typesetting of the manuscript is not beautiful.
The manuscript style was altered to MDPI format.
- The title cannot accurately reflect the research content of the manuscript.
The title has been modified as the reviewer suggested.
- Introduction needs to further improve the research status.
As stated by the reviewer, the introduction has been improved by adding nearly one more page explanation.
- The quality of Figs and Tables in manuscripts need to be improved.
- The legend in Figure 1 is altered to have a better-quality.
- Figure 6's blurry fonts have been corrected by altering the current figure.
- Conclusions of the manuscript need to be improved.
As the reviewer highlighted, the Conclusion section of the manuscript has been altered.
- Although I am not a native English speaker, the language of the manuscript needs to be modified.
The manuscript is going to be submitted to the university for proofreading before publication.
The adjustments and additions made in this study are shown in green text in the paper, making it easy to follow all of the changes.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Dear Authors
The presented work shows a nice efforts but needs further significant corrections and considerations to become an acceptable paper on this journal.
1- Abstract does not present important points. It should be between 250 to 300 words and concisely mention the problems of previous works and novelties in this paper.
2- Similarly, the introduction has very poor structure and lack of literature review. Usually in the chapter of introduction the background and needs of study of seismic damage and vulnerability assessment methods have to be highlighted and prepare readers to go further. Then your second chapter should be literature review where you present an overview on the previous works and the main problem statements of work and how it can be improved or overcome on it.
3- Please provide more information about the selected location and data repository and how they have been collected.
4- Results and discussion are not properly organized and it has to show the significant achievements of the proposed method and discuss each table and figure properly and in detail.
5- As you have lots of abbreviations, I recommend to provide a table of abbreviations according to MDPI style.
6-It could be great if you do a comparison between your proposed method and some of the available or common other methods to show the efficiency of it.
7- It would be useful if you provide a general framework or flowchart that how others can implement or use your proposed method for their assessment purposes. However Fig. 9 is showing the concept but it is not the novelty of your work.
8- In general, your conclusion needs further improvements and you can discuss a bit again about the achievements and novelty of your proposed method.
9- In total, the main problem of you paper is the lack of literature review, novelty and you can present some new developed methods for vulnerability and damage assessment of buildings, roads and infrastructures to attract the attention of readers and show a wide view of your works. I found, there are many works which are similar to your works and therefore the novelty of your paper is not much in compare to them however you have to cite them as beforehand works. Below are some of the recent works, where I found them new and useful to add and make your paper much more interesting:
-A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings
-Seismic vulnerability assessment for Montreal
-Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings
-Buildings' seismic vulnerability assessment methods: a comparative study
-Assessment of Seismic Building Vulnerability Using Rapid Visual Screening Method through Web-Based Application for Malaysia
-The Evaluation of Existing Buildings In Bitlis Province Using A Visual Screening Method
10- What makes your work more significant and better than FEMA or any other methods. Did you just modify the same factors of FEMA 154 and made it localized for Albania?
11- In General, what is the novelty of your work? is it just adjustment of fuzzy logic rules to your selected data? is it robust ? What about considering other important parameters that affect vulnerability of buildings?
Author Response
Initially, we would like to thank the Reviewer – 2 for his/her time and significant comments. Changes and/or additions made in the light of the comments made are explained separately under each of the comments listed below.
The presented work shows a nice efforts but needs further significant corrections and considerations to become an acceptable paper on this journal.
- Abstract does not present important points. It should be between 250 to 300 words and concisely mention the problems of previous works and novelties in this paper.
As stated by the reviewer, a new sentence was added to the abstract to address the issue with earlier work. The corresponding sentence begins from line 17 of the Abstract.
- Similarly, the introduction has very poor structure and lack of literature review.
As stated by the reviewer, the introduction has been improved by adding nearly one more page explanation. The paragraphs in the introduction restructured and further literature review added. [Lines 37 to 40, & 69 to 84, & 93 to 129]
- Usually in the chapter of introduction the background and needs of study of seismic damage and vulnerability assessment methods have to be highlighted and prepare readers to go further.
As stated by the reviewer, further explanation added to the first paragraph of the introduction for enhancing explanation on assessment needs of existing buildings. [Lines 37 to 40]
- Then your second chapter should be literature review where you present an overview on the previous works and the main problem statements of work and how it can be improved or overcome on it.
As requested by the reviewer, for presenting previous work, the third and fourth paragraphs of the introduction have been extended, and the fifth and sixth paragraphs have been added. [Lines 69 to 84 & 93 to 129]
The main problems of the conventional RVS methods are extensively discussed by adding new explanation in fourth paragraph of the Introduction. [Lines 76 to 84]
The explanation for overcoming the main problems is provided in the fourth paragraph of introduction’ last sentence. [Lines 76 to 84] Then, fuzzy logic based S-RVS methods developed in the literature explained in the fifth paragraph. [Lines 101 to 115] In the 6th paragraph of the Introduction, main problems of the developed S-RVS methods explained. [Lines 116 to 125] The considered building type for the developed S-RVS method and validation of the developed method as clearly different from previous studies for development of an S-RVS method for URM buildings are described in paragraph 7. [Lines 126 to 129]
- In General, What is the novelty of your work?
The novelty of the work has been explained with the sentence “To this end, this study developed a new … 2019 Albania earthquake.” At the beginning of the last paragraph of the Introduction. [Line 126]
- Is it just adjustment of fuzzy logic rules to your selected data?
The developed S-RVS method not only adjusts fuzzy logic rules but also demonstrates the evaluation way of the input parameters (such as construction year in Table 1), develops membership functions (such as for site seismic hazard, increase in demand, decrease in resistance, structural deficiency), optimizing the transformation values as shown in Table 3.
Additionally, sensitivity assessment is applied in terms of selecting defuzzification techniques, to enhance the accuracy and applicability of the developed method. The largest of maxima defuzzification method is found more suitable for this study. Finally, the developed method was compared with a representative conventional RVS method and post-earthquake screening data to demonstrate its applicability.
- Is it robust ?
Though it is not expected RVS methods are as robust as Detailed Vulnerability Assessment methods, the developed method (67.5% accuracy) has shown to be highly accurate even than a well-developed conventional RVS method (FEMA P-154 – 25% accuracy), which is considered a highly developed and accepted representative of them. The developed method is robust, even if the small changes in present parameters.
- What about considering other important parameters that affect vulnerability of buildings?
Because of the simplicity and fast trackable capability of RVS methods, in this study limited amount of highly effective parameters are employed. However, further parameters can also be used to modify the developed method. Additionally, the idea behind the developed method could be utilized to develop proper methods in different civil engineering sub-fields such as for visual inspection of pavement, bridges, and railways. [Lines 123 – 125]
- In total, the main problem of you paper is the lack of literature review, novelty and you can present some new developed methods for vulnerability and damage assessment of buildings, roads and infrastructures to attract the attention of readers and show a wide view of your works. I found, there are many works which are similar to your works and therefore the novelty of your paper is not much in compare to them however you have to cite them as beforehand works. Below are some of the recent works, where I found them new and useful to add and make your paper much more inter
- Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings ïƒ
- Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings ïƒ
- Assessment of Seismic Building Vulnerability Using Rapid Visual Screening Method through Web-Based Application for Malaysia ïƒ
- The Evaluation of Existing Buildings In Bitlis Province Using A Visual Screening Method ïƒ
- Seismic vulnerability assessment for Montreal ïƒ Since this study did not found directly related to the RVS methods, it was not cited in the manuscript.
- Buildings' seismic vulnerability assessment methods: a comparative study ïƒ
As suggested by the reviewer, in addition to the above-suggested publications for citation, further improvement has been performed in the Introduction and correspondingly further related studies are cited. Additionally, as the reviewer pointed out, an additional explanation has been provided in a sentence beginning at line 123 to give the reader a broad view of the applicability of the fuzzy logic-based S-RVS methods. [Lines 123 to 125]
- Please provide more information about the selected location and data repository and how they have been collected.
- Four new sentences were added to the paragraph titled Study Area and Building Stock to provide more information about the location. [Lines 141, 146, 148, 152]
- The data was collected by the team dispatched by the Hungarian Government, as explained in the second paragraph titled Study Area and Building Stock. This paper's third author was also a member of that team. [Lines 168 to 169]
- As noted by the reviewer, another sentence added to the second paragraph under the title Study Area and Building Stock to further explanation of data collection process. [Lines 169 to 171]
- Further explanation was added about the building stock of Albania and 2019 post-earthquake reconnaissance findings. [Lines 162 to 166]
- Results and discussion are not properly organized and it has to show the significant achievements of the proposed method and discuss each table and figure properly and in detail.
Based on the reviewer’s comment following modifications has performed in the manuscript:
- A flowchart (Figure 13) was added at the beginning of the results section to help the reader follow the results section. [Line 485] Also, an explanation of the corresponding flowchart was added. [Lines 479 to 483]
- Results section reorganized by adding new subtitles.
- Further explanation added under the headings numbered as 6.3.3 [Lines from 575] and 6.3.4 [Lines from 588].
- It would be useful if you provide a general framework or flowchart that how others can implement or use your proposed method for their assessment purposes. However, Fig. 9 is showing the concept but it is not the novelty of your work.
Because the majority of the model's parameters can be collected in parallel, a data collection form for the proposed S-RVS method was created, as shown in Figure 18. The created form can be used to collect the building screening data required to use the developed S-RVS method. [Line 627] Additionally corresponding explanation was added as a subtitle. [Lines 611 to 625]
- It could be great if you do a comparison between your proposed method and some of the available or common other methods to show the efficiency of it.
Since many RVS methods' accuracy did not demonstrate by comparing findings with post-earthquake building screening data or DVA findings, there are limited studies to compare. However, in the 3rd paragraph of the Discussion section the comparison of the developed method with an S-RVS method developed for reinforced concrete structures is provided in terms of accuracy level as noted by the reviewer. [Lines 643 to 645 & 652 to 654]
- In general, your conclusion needs further improvements and you can discuss a bit again about the achievements and novelty of your proposed method.
- The achievements of this study are explained with the sentence:
- “The main accomplishment … from 2019 Albania earthquake” [Lines 693 to 696] and
- “Eventually, this study has demonstrated … 67.5 percent accuracy” [Lines 708 to 710]
- As stated by the reviewer, the conclusion has been modified to explain the novelty of the study with the sentence “In contrast to existing fuzzy logic based S-RVS methods, … post-earthquake screening data”. [Lines 686 to 688, & 689 to 693, & 696 to 701]
- The achievements of this study are explained with the sentence:
- What makes your work more significant and better than FEMA or any other methods. Did you just modify the same factors of FEMA 154 and made it localized for Albania?
As it is presented comparison and review of the RVS methods by the previous study of the authors [1], the parameters used in RVS differ. Also, the RVS methods have different evaluation methodologies. Since some of the RVS methods were developed based on expert opinion, it is difficult to modify them, and also based on the experience of the authors FEMA shows building damage states higher than in reality they are. Additionally, even it is difficult to compare different FEMA versions based on findings. Therefore, one of the important aspects of this study providing mathematical model-based calculations, that can be easily adjusted and modified for further requirements. To this end, the developed method is more robust than conventional RVS methods.
The FEMA RVS method uses site seismicity to determine the RVS form, while the current method uses site seismicity to calculate spectral acceleration values for the building. For this purpose, building height, average story height, site conditions, and site seismicity are used. In FEMA screening form is selected based on site seismicity. Since building height is not considered in FEMA, buildings with different heights could have the same score. However, an acceleration response spectrum is used for the considered building site in the developed method to consider each building's height seperately.
In addition, the construction year in the FEMA RVS method only considered being classified as the considered design code for deciding pre-code or post-benchmark. However, the developed method considers both the design code development timeline and construction year relation as a continuous grading, as shown in Table 1.
Overall, the developed S-RVS method differs significantly from traditional RVS methods.
- As you have lots of abbreviations, I recommend to provide a table of abbreviations according to MDPI style.
As the reviewer noted, a table of abbreviations has been provided after the conclusion. [Line 724]
The adjustments and additions made in this study are shown in green text in the paper, making it easy to follow all of the changes.
References:
[1] N. BektaÅŸ, O. Kegyes-Brassai, Conventional RVS Methods for Seismic Risk Assessment for Estimating the Current Situation of Existing Buildings: A State-of-the-Art Review, Sustainability. 14 (2022) 2583. https://doi.org/10.3390/su14052583.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments for author File: Comments.pdf
Author Response
Firstly, we would like to express our gratitude to Reviewer – 3 for his/her time and significant feedback. The modifications and/or additions made in response to the given comments below are explained one by one.
The authors discussed a fuzzy logic-based soft rapid visual screening method that could produce accurate responsive features of buildings under examination, and elegantly revised the existing conventional RVS methods. They used fuzzy inference system-based S-RVS method developed by considering post-earthquake building screening data of some selected structures located in Albania. Within this context, the results are novel and may attract the interest of researchers working in related fields. The paper is comprehensive, very well-written, and the analyses appear to be appropriate. However, it is hoped that the authors can reorganize some of the ideas and highlighted points to further improve the readability.
- The authors should employ subsections in the structure for better expression of ideas and to further enhance readability.
The manuscript was restructured as the reviewer recommended. Additionally, flowcharts and further explanations added to the manuscript. [Lines 205 to 210 & 478 to 485]
- The legend in Figure 1 and the text fonts in Figure 9 appear blurred. Also, the presentation of all figures, in higher resolutions, would enhance the quality of the paper.
As the reviewer noted, the legend in Figure 1 is altered to have a better-quality [Line 53]; (in the modified manuscript Figure 6) Figure 9's blurry fonts have been corrected by altering the current figure [Line 230].
- The authors mentioned that the most used techniques of fuzzy inference methods are that of Sugeno [41] and Mamdani [42]. Thus, the Mamdani type fuzzy model was utilized. Since, this study employed the Mamdani method, it is imperative that the authors emphasize the benefits of the method over others, rather than just stating ‘it is the most used’.
As highlighted by the reviewer, the last sentence of the paragraph after Figure 10 was altered to contain the explanation of the reasons for choosing the Mamdani fuzzy inference method. [Lines 373 to 374]
- The statement “The authors discovered that no building was in the collapse damage state after assessing the post-earthquake screening image data based on the EMS-98 [11] damage state classification explanation Figure 11” should be rechecked after correcting the figure numberings to specifically clarify points and ideas. (i.e., The statement “if the FS score is greater than 2.5, the building's damage state is classified as the probability of grade 1 damage which means negligible to slight damage, as illustrated in Figure 11” is well presented with the appropriate figure number).
As the reviewer stated, the numbering of the corresponding Figure 11 (in the modified manuscript Figure 12) was checked and approved that it is correct.
- The subsequent paragraph that follows the discussion of Figure 11 should read “Figure 12 depicts the damage states determined employing FEMA P-154, which may be compared to the post-earthquake damage states. The determined damage states and post-earthquake damage states are illustrated as green x and red + in Figure 12, respectively. The x-axis of Figure 12 illustrates the building indexes, and the y-axis depicts building damage states as Low – 0, Moderate – 1, and High – 2.”
The corresponding places have been modified as the reviewer highlighted.
- The description of Figure 11 is totally missing in the paper. I suggest that this be included as “Figure 11 presents the Damage classification based on the FEMA:”
Figure 12's location has been altered, and further explanation has been added about the figure. [Lines 465 to 468]
Author Response File: Author Response.pdf
Reviewer 4 Report
This paper presents a rapid visual screen method by employing fuzzy logic algorithm. Some general conclusions are given and potentially helpful for authorities of unreinforced masonry buildings. In the reviewer's opinion, the paper should be minor modified before the paper is accepted, and the authors were suggested to clarify the following aspects:
1. The study considers six input parameters: vertical irregularity, plan irregularity, construction quality, year of construction, structural system, and site seismic hazard analysis. However, these parameters seem to be qualitative, not quantitative information. The authors are suggested to give more descriptions or references on the judging criteria for defining these parameters.
2. In figure 5, the study uses specific seismic data to generate the site-specific acceleration response spectrum. The authors are suggested to provide the reasons why they chose this record data of the station. Are there no other seismic stations or representative earthquake data near the site?
3. Figure10, the authors are suggested to present the appropriateness of using actual seismic data compared to the design code response (ASCE), are the results of such a comparison representative?
4. In applying this method, how do the authors select a representative earthquake record, and is there a directional consideration or near-fault effect of earthquake input?
5. The amount of data available may limit the current approach there any suggestion that the authors can state in conclusion about the minimum amount of data recommended for applying fuzzy theory?
Author Response
Initially, we would like to thank the Reviewer – 4 for his/her time and significant comments. Changes and/or additions made in the light of the comments made are explained separately under each of the comments listed below.
This paper presents a rapid visual screen method by employing fuzzy logic algorithm. Some general conclusions are given and potentially helpful for authorities of unreinforced masonry buildings. In the reviewer's opinion, the paper should be minor modified before the paper is accepted, and the authors were suggested to clarify the following aspects:
- The study considers six input parameters: vertical irregularity, plan irregularity, construction quality, year of construction, structural system, and site seismic hazard analysis. However, these parameters seem to be qualitative, not quantitative information. The authors are suggested to give more descriptions or references on the judging criteria for defining these parameters.
By adding another sentence, the authors' earlier work is cited for providing further explanation to the reader. There, more thorough explanation of these parameters can be found. [Lines 257 to 258]
- In figure 5, the study uses specific seismic data to generate the site-specific acceleration response spectrum. The authors are suggested to provide the reasons why they chose this record data of the station. Are there no other seismic stations or representative earthquake data near the site?
As stated by the reviewer, a new explanation has been added to highlight the reason for selecting the ground motion data corresponding to the site-specific response spectrum given in Figure 8. [Lines 305 to 307]
- Figure10, the authors are suggested to present the appropriateness of using actual seismic data compared to the design code response (ASCE), are the results of such a comparison representative?
Figure 11 represents the compatibility of the site-specific response spectra and the design response spectra. Even though, only design spectra needed to be considered to select the screening form; this study also highlights the compatibility between design spectra and the determined site-specific response spectra by classifying the seismicity of the site as moderate based on both spectrums.
Another sentence added to explain correlation between the response spectrums in Figure 11. [Lines 453 to 454]
Further explanation was added to clarify the usage of the response spectra in this study. [Lines 458 to 462]
- In applying this method, how do the authors select a representative earthquake record, and is there a directional consideration or near-fault effect of earthquake input?
Since the considered building screening data were collected after the 2019 Albania earthquake, the nearest recorded ground motion data of the 2019 Albania earthquake (in Tirana station) was considered in this study.
Since the station in Tirana city is approximately 32 km far away from the epicenter of the 2019 Albania earthquake (as shown in below figure), this study and the corresponding site-specific response spectrum do not comprise the effect of the near-field ground motion.
- The amount of data available may limit the current approach there any suggestion that the authors can state in conclusion about the minimum amount of data recommended for applying fuzzy theory?
Since the amount of data required for further development is vague (it is not specific), it is not mentioned. However, because there are thousands of possible combinations for the input parameters under consideration, it would be enough to have 10,000 data rows to take into account every variation of input parameters. Additionally, the results also depend on how the data is representing the combinations.
The adjustments and additions made in this study are shown in green text in the paper, making it easy to follow all of the changes.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
I have no more comments.
Author Response
We want to let you know that after the manuscript is accepted by the journal, a native speaker in our university is going to check it in terms of language. Finally, we would like to thank the reviewer for accepting the changes we have made and the relevant responses in light of the review comments.
Sincerely,
Nurullah
Reviewer 2 Report
Dear Authors
Many thanks for the significant changes. After a careful study of your paper, it has been figured out that prevent to use a new name for RVS and remove all the S-RVS. The reasons are as below:
1- The application and implementation of soft computing methods are not a novelty of your works and as you have cited many works in the past have done it.
2- As you have just used one of the soft computing methods, you can not generalize it to all methods.
3- The proposed method is not robust and therefore the validation of your method is still vague.
4- Amount of data and buildings you have used and the accuracy is not in general acceptable and how can you prove the overfitting of your method and is totally data and location dependant.
Author Response
Initially, we would like to thank the Reviewer – 2 for his/her time and significant comments. Changes and/or additions made in the light of the comments made are explained separately under each of the comments listed below.
Many thanks for the significant changes. After a careful study of your paper, it has been figured out that prevent to use a new name for RVS and remove all the S-RVS. The reasons are as below:
- The application and implementation of soft computing methods are not a novelty of your works and as you have cited many works in the past have done it.
- As you have just used one of the soft computing methods, you can not generalize it to all methods.
- The proposed method is not robust and therefore the validation of your method is still vague.
- Amount of data and buildings you have used and the accuracy is not in general acceptable and how can you prove the overfitting of your method and is totally data and location dependant.
The acronym S-RVS, which stands for soft rapid visual screening, was changed to RVS throughout the manuscript, as the reviewer noted. Additionally, changes were made to Figures 5 and 13 because of the modifications in the acronym.
The adjustments and additions made in this study are shown in green text in the paper, making it easy to follow all of the changes.
Author Response File: Author Response.pdf
Round 3
Reviewer 2 Report
Many thanks for your understanding and significant changes. For sure all reviewers tried to give their best and constructive comments to improve your works.
