Development of a Model for Predicting Probabilistic Life-Cycle Cost for the Early Stage of Public-Office Construction
Round 1
Reviewer 1 Report
This paper presents the development, validation and application of probabilistic LCC prediction model based on Monte Carlo simulation and case-based reasoning. Data from 74 construction projects are used, 70 cases form the database for model development, 4 cases are used for validation. Cases included construction cost and MR&R costs, as well as 13 project factors. Two alternativ LCC prediction models are developed and tested through multiple regression analysis and statistical analysis. Of these two models, one model is identified as superior. The model is used to predict life-cycle cost in the early project stages.
In the text and the table 1 13 variables are mentioned, while figure 3 (Model I) includes 15 variables and figure 3 includes 14 variables.- are these the same variables, which number is correct?
Figure 4 and Table 5 include the same data, is this necessary, or can one depiction be deleted?
Table 5 includes Models I to IV. Are these the same Models I and II as in figure 2 and 3? It is confusing. Please differentiate.
Figures 2, 3 and 5 should be larger. Please also coordinate the page breaks for the final paper submission.
The data could also be used to identify cost drivers and variables that influence the LCC in construction projects. - Such information would probably be more interesting than the prediction of LCC of designs. In the current form the model just predicts the LCC of building of the same quality and type than the models in the database. New building projects however, should be designed better than existing buildings, if this model is really to be useful in the early project stages.
Author Response
1. This paper presents the development, validation and application of probabilistic LCC prediction model based on Monte Carlo simulation and case-based reasoning. Data from 74 construction projects are used, 70 cases form the database for model development, 4 cases are used for validation. Cases included construction cost and MR&R costs, as well as 13 project factors. Two alternativ LCC prediction models are developed and tested through multiple regression analysis and statistical analysis. Of these two models, one model is identified as superior. The model is used to predict life-cycle cost in the early project stages.
Response: Thank you for the opportunity to revise the present paper. We profusely appreciate all thoughtful comments of reviewers. The manuscript has been carefully revised in response to their comments.
2. In the text and the table 1 13 variables are mentioned, while figure 3 (Model I) includes 15 variables and figure 3 includes 14 variables.- are these the same variables, which number is correct?
Response: Thanks for the comment. In our study, Model I (Figure 2) and Model II (Figure 3) were constructed with 15 variables (X1–X13, Y1, and Y2) and 14 variables (X1–X13, and Y3) respectively. To prevent any confusion, we revised Figure 2, 3, and related explanation in lines 103 to 105, 138 to 139, and 199 to 200 as follows:
“The collected data included the construction costs (Y1), MR&R costs (Y2), and LCC (Y3) obtained from results of the LCC analysis performed during the design phase of these buildings.”
“To select the independent variables for Model I, a significance test based on p-value was performed with 15 variables (X1–X13, Y1, and Y2) using MRA.”
“In the same way with developing Model I, a significance test was performed 14 variables (X1–X13, and Y3) for Model Ⅱ.”
3. Figure 4 and Table 5 include the same data, is this necessary, or can one depiction be deleted?
Response: Thanks for the thoughtful suggestion. Figure 4 was added to visualize the error rate fluctuation of each model. However, it might make readers confused as you commented. Thus, we decided to remove Figure 4.
4. Table 5 includes Models I to IV. Are these the same Models I and II as in figure 2 and 3? It is confusing. Please differentiate.
Response: Thanks for the comment. Model I and Model II included in Table 5 are the same with models presented in Figures 2 and 3. Model III and Model IV are prediction models based on MRA, which were developed to compare and validate the performance of the proposed CBR models (Model I and Model II), then choose the best model as a base model for the probabilistic model. To clarify this, we revised the statement in lines 209, 223 to 227 as follows:
“To validate developed the Model Ⅰ, and Ⅱ, k-fold cross-validation was conducted, …”
“In this study, two MRA models (Model Ⅲ and Model Ⅳ) were constructed. Model Ⅲ predicted the construction and MR&R costs separately and presented the LCC as the sum of the results, while Model Ⅳ directly predicted the LCC. The validation results of the LCC prediction Model Ⅰ, Ⅱ (CBR model) and Model Ⅲ, Ⅳ (MRA model) were summarized in Table 5.”
5. Figures 2, 3 and 5 should be larger. Please also coordinate the page breaks for the final paper submission.
Response: Thanks for the comment. We actively reflected your opinion in this revision.
6. The data could also be used to identify cost drivers and variables that influence the LCC in construction projects. - Such information would probably be more interesting than the prediction of LCC of designs. In the current form the model just predicts the LCC of building of the same quality and type than the models in the database. New building projects however, should be designed better than existing buildings, if this model is really to be useful in the early project stages.
Response: Thanks for the comment. In this study, we derived the variables of the early stage in order to predict LCC. However, qualitative variables such as design quality were not addressed. As you commented, there is a need for future research to deal with the qualitative variables as well as the variables derived in this study. To this end, we are carrying out a follow-up study on an LCC prediction model with qualitative variables such as design quality and energy efficiency rating. To clarify this, we added a limit of this study and a need for future research in lines 408 to 412 as follows:
“The proposed model in this study was not dealt with the qualitative variables such as design quality, hence there is a need for future research to address the qualitative variables. To this end, the authors are carrying out follow-up research to stipulate the range and distribution of each variable, and to handle qualitative variables such as design quality or energy efficiency rating.”
Author Response File: 
Author Response.docx
Reviewer 2 Report
Dear Authors,
The paper treats a very interesting topic, it is well written and organized. Nevertheless some revision are needed.
As an example: in the introduction you cite some paper like [1-6] (or [9-12] etc.) and you wrote "many studies have sought methods etc."
But what did they conclude?? please discuss
In the introduction the influence of the final users is not included. Please include it in your dissertation as maintenance, repair etc are influenced by final users and also form what they expect on building performances. There are new researches on this topic like Caniato and Gasparella, Discriminating People’s Attitude towards Building Physical Features in Sustainable and Conventional Buildings. Energies. 2019; 12(8):1429 doi:10.3390/en12081429
and
V. L. Castaldo , I. Pigliautile , F. Rosso , F. Cotana , F. De Giorgio , A. L. Pisello , How subjective and non-physical parameters affect occupants’ environmental comfort perception, Energy & Buildings (2018), doi:https://doi.org/10.1016/j.enbuild.2018.08.02
and
D.O. Sant’Anna, P.H. Dos Santos, N.S. Vianna, M.A. Romero, Indoor environmental quality perception and users’ satisfaction of conventional and green buildings in Brazil, Sustainable Cities and Society 43 (2018)
712 95–110
and
Thermal and acoustic performance expectations on timber buildings, Building Acoustics, 2017, Vol. 24(4) 219–237, DOI 10.1177/1351010X17740477
Please do include them and other references to your introduction with a brief discussion on this topic.
You use many acronyms in your paper. It would be useful if you insert an apendix with their explaination
Equation one is very basic and already described in the text so its formulation in my opinion is useless.
In paragraph 4.1 you stated that you collected data from 74 constructions. They are a large amount! it is better if you explain how you have such large amount of information (previous researches? agreements with pucli bodies? etc.)
Lines 108-109 are not clear to me at all. Why X11a and X11c have the same values if the describe two different cases?
You never explain WHY you are using such statistic tests (e.g. anova) end not other ones. In statistical analysis you can choose the best rated test whether from your experience or from literature. Please explain
Author Response
1. The paper treats a very interesting topic, it is well written and organized. Nevertheless some revision are needed.
Response: Thank you for the opportunity to revise the present paper. We profusely appreciate all thoughtful comments of reviewers. The manuscript has been carefully revised in response to their comments.
2. As an example: in the introduction you cite some paper like [1-6] (or [9-12] etc.) and you wrote "many studies have sought methods etc." But what did they conclude?? please discuss
Response: Thanks for the comment. In response to your comment, we added more detailed explanation in terms of previous studies in lines 31 to 34 as follows:
“Most of them focused on predicting the construction cost and contributed to improving the prediction accuracy [1–4]. In addition, research has also been conducted on the overhead cost in the construction stage [5], and probability-based approaches have been made to predict construction costs [6].”
3. In the introduction the influence of the final users is not included. Please include it in your dissertation as maintenance, repair etc are influenced by final users and also form what they expect on building performances. There are new researches on this topic like Caniato and Gasparella, Discriminating People’s Attitude towards Building Physical Features in Sustainable and Conventional Buildings. Energies. 2019; 12(8):1429 doi:10.3390/en12081429 and V. L. Castaldo , I. Pigliautile , F. Rosso , F. Cotana , F. De Giorgio , A. L. Pisello , How subjective and non-physical parameters affect occupants’ environmental comfort perception, Energy & Buildings (2018), doi:https://doi.org/10.1016/j.enbuild.2018.08.02 and D.O. Sant’Anna, P.H. Dos Santos, N.S. Vianna, M.A. Romero, Indoor environmental quality perception and users’ satisfaction of conventional and green buildings in Brazil, Sustainable Cities and Society 43 (2018) 712 95–110 and Thermal and acoustic performance expectations on timber buildings, Building Acoustics, 2017, Vol. 24(4) 219–237, DOI 10.1177/1351010X17740477
Please do include them and other references to your introduction with a brief discussion on this topic.
Response: Thanks for the comment. In response to your comment, we added the contents with respect to the impact of final users in lines 36 to 40 as follows:
“The LCC of the building is affected by the final users since it is completed. Attitudes, habits, and perceptions of users influence the physical characteristics and LCC of the building [9,10]. Conversely, the building environment affects users’ productivity and psychological comfort [11]. Decision making in early stage also has a great impact on LCC [12]. It could be as large as the impact of the final user on the LCC.”
4. You use many acronyms in your paper. It would be useful if you insert an apendix with their explaination
Response: Thanks for the comment. In accordance with the comment, we added an appendix in terms of acronyms and their explanation in lines 418 to 444.
5. Equation one is very basic and already described in the text so its formulation in my opinion is useless.
Response: Thanks for the comment. In accordance with the comment, we removed equation one and revised the statement in lines 70 to 71 as follows.
“The total cost of a facility is the LCC, which includes planning costs, design costs, construction costs, operation and maintenance costs, and disposal costs.”
6. In paragraph 4.1 you stated that you collected data from 74 constructions. They are a large amount! it is better if you explain how you have such large amount of information (previous researches? agreements with pucli bodies? etc.)
Response: Thanks for the comment. The data was collected from LCCA reports submitted to the public clients in Korea together with the detailed design documents. To be better explained, we revised the statement in lines 102 to 103 as follows:
“To develop LCC prediction models, 74 LCCA reports that were submitted to the public clients in Korea together with the detailed design documents.”
7. Lines 108-109 are not clear to me at all. Why X11a and X11c have the same values if the describe two different cases?
Response: Thanks for the comment. There are numerical and categorical variables in this study. X11 is a categorical variable. To conduct MRA, categorical variables should be converted into dummy variables, and a categorical variable having n categories needs n-1 dummy variables. For example, foundation type variable (X11) consisting of three categories (X11a mat, X11b pile, and X11c pile + mat) can be converted with two dummy variables (1, 0). If a building has a mat type foundation, X11a, X11b, and X11c will be set as 1, 0, and 0, respectively. Related statement was added in lines 111 to 117 as follows:
“Variables used in this study were classified into numerical and categorical types. While numerical variables take numerical values and represent some kind of measured values [42], categorical variables should be converted into dummy variables (0 or 1) in order to conduct regression analysis [1]. For example, foundation type variable (X11) consisting of three categories (X11a mat, X11b pile, and X11c pile + mat) can be converted with two dummy variables. If a building has a mat type foundation, X11a, X11b, and X11c will be set as 1, 0, and 0, respectively.”
8. You never explain WHY you are using such statistic tests (e.g. anova) end not other ones. In statistical analysis you can choose the best rated test whether from your experience or from literature. Please explain
Response: Thanks for the comment. In this study, we selected the independent variables based on the significance (p-value) presented by MRA because the p-value represents a significant probability of the independent variable. In this study, variables having p-value above 0.05 were excluded. Thus, to clarify this, we removed the word ANOVA, revised Table 2, 3, 4, and added statements in lines 138 to 142 and 199 to 200 as follows:
“To select the independent variables for Model I, a significance test based on p-value was performed with 15 variables (X1–X13, Y1, and Y2) using MRA. The p-value represents a significant probability of the independent variables, and the significance level of 0.05 is generally considered to have statistical significance [23,44]. In this study, thus, variables having p-value above 0.05 were excluded.”
“In the same way with developing Model I, a significance test was performed 14 variables (X1–X13, and Y3) for Model Ⅱ.”
Author Response File: Author Response.docx
Round 2
Reviewer 2 Report
Dear Authors I think the paper should be published
