Integrating Risk Assessment and Scheduling in Highway Construction: A Systematic Review of Techniques, Challenges, and Hybrid Methodologies

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This work explores the intersection of risk assessment and scheduling in highway construction projects, examining 13 qualitative and 13 quantitative risk assessment techniques alongside traditional and advanced scheduling methods. To do this, it confronts the limitations of conventional approaches like CPM and PERT and highlights the benefits of integrating probabilistic tools such as Monte Carlo Simulation, Fuzzy Set Theory, and Bayesian Networks. The paper is thoroughly written making it valuable for both academics and practitioners. The paper includes a good amount of literature, including tables that clarify the strengths and weaknesses of each method.

This paper to be published must improve some areas: there are some redundancies thrughut the paper, and it is long that it may overwhelm those who are not familiar with the subject. The manuscript would benefit from a clearer synthesis in the discussion and conclusions, explicitly highlighting practical implications or case-based validations of integrated approaches. Additionally, I suggest adding empirical examples or implementations to the theoretical discussion regarding how hybrid models outperform traditional ones.

Author Response

Comments and Suggestions for Authors by 1 Reviewer:

This work explores the intersection of risk assessment and scheduling in highway construction projects, examining 13 qualitative and 13 quantitative risk assessment techniques alongside traditional and advanced scheduling methods. To do this, it confronts the limitations of conventional approaches like CPM and PERT and highlights the benefits of integrating probabilistic tools such as Monte Carlo Simulation, Fuzzy Set Theory, and Bayesian Networks. The paper is thoroughly written making it valuable for both academics and practitioners. The paper includes a good amount of literature, including tables that clarify the strengths and weaknesses of each method.

This paper to be published must improve some areas: there are some redundancies throughout the paper, and it is long that it may overwhelm those who are not familiar with the subject. The manuscript would benefit from a clearer synthesis in the discussion and conclusions, explicitly highlighting practical implications or case-based validations of integrated approaches. Additionally, I suggest adding empirical examples or implementations to the theoretical discussion regarding how hybrid models outperform traditional ones. –

Author's Notes to Reviewer:

We sincerely thank the reviewer for this comprehensive and constructive feedback. We have conducted a thorough review of the manuscript to remove redundant content and streamline sections for clarity and brevity. Specific refinements were made in the abstract, methodology descriptions, and discussion sections to ensure conciseness without compromising academic rigor. The Discussion and Conclusion sections have been revised to provide a clearer synthesis of findings, with a stronger focus on practical implications for highway construction project management. The revised conclusion now explicitly links the limitations of traditional techniques (such as CPM and PERT) to the need for hybrid approaches like Bayesian Networks (BNs) and Fuzzy-AHP, emphasizing how these tools enhance adaptability and decision-making under uncertainty.

As regards empirical examples and implementation of hybrid models, this is a review-based study/strictly a literature review that summarizes various risk assessment and scheduling techniques along with their applications in Highway Construction. However, while the present manuscript is a literature review, it now includes case-based examples illustrating the limitations of traditional models and the benefits of integrated methods. We have added examples from van Dorp & Duffey (1999), and Oztas & Okmen (2005) that illustrate how rigid Monte Carlo assumptions and PERT's optimism bias can misrepresent real-world outcomes.

The performance comparison with traditional models will be addressed in a future empirical study. We revised Section 4.1 of the manuscript and added a statement that acknowledges a future separate publication focused on demonstrating the empirical superiority of hybrid model. The revised Section 4 now mentions a forthcoming separate publication that will present a detailed empirical evaluation of the developed hybrid model. This model integrates BNs and AHP to demonstrate improved schedule reliability and risk prediction accuracy compared to traditional methods.

Reviewer 2 Report

Comments and Suggestions for Authors

It is noticed that several aspects are still missing or not fully addressed by the current applications of both qualitative and quantitative risk assessment methods, as well as traditional scheduling techniques in highway construction projects.

The review notes that effective risk management integrates both qualitative and quantitative methods. BNs are highlighted for their ability to integrate both data types. The authors could improve the paper by discussing how the proposed integrated methods (especially BNs) or future directions specifically address the challenges in bridging the qualitative-quantitative gap.   How do these methods handle the subjectivity inherent in qualitative data while maintaining analytical rigor?   How can expert judgment, often the basis of qualitative assessment, be reliably incorporated into quantitative models like BNs or AHP?   Highlighting the limitations of qualitative methods (subjectivity, lack of analytical depth) and quantitative methods (data requirements, misrepresenting reality) could further emphasize why a robust qualitative-quantitative integration is essential but challenging.   While the review references numerous studies and their applications, adding more detailed examples or mini case studies directly within the discussion of techniques and their limitations/integrations could make the concepts more tangible. For instance:   Use specific highway construction examples from the cited literature to show how CPM's rigidity caused problems or how Monte Carlo Simulation results differed from PERT's optimistic estimates.   Illustrate how a Fuzzy AHP approach attempts to handle subjective risk prioritization in highway projects and then explain the noted concerns about its reliability using the same or similar examples.   These examples could significantly enhance the reader's understanding of the practical implications of the discussed methods and their limitations.   While Section 6 outlines future research, reinforcing the link between the specific limitations identified in Section 3 and the proposed future directions could strengthen the paper's narrative. For example, explicitly stating that CPM's failure to align with on-site realities and lack of adaptability leads to the need for AI-driven adaptive scheduling and interoperable systems. Or linking the challenges of handling complex dependencies and integrating qualitative/quantitative data directly to the potential of Bayesian Networks.   By expanding on the "how" and "why" of integration, discussing the practical barriers, strengthening the qualitative-quantitative link with concrete examples, and tightening the connections between identified limitations and future research, the authors can transform their already valuable review into an even more impactful resource for the highway construction industry and researchers.  

Comments on the Quality of English Language

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a potential minor spelling inconsistency within a figure caption ("Ashey et al." in the figure caption versus "Ashley et al." in the reference to the source.

 

 

 

Author Response

It is noticed that several aspects are still missing or not fully addressed by the current applications of both qualitative and quantitative risk assessment methods, as well as traditional scheduling techniques in highway construction projects. – Noted.

The review notes that effective risk management integrates both qualitative and quantitative methods. BNs are highlighted for their ability to integrate both data types. The authors could improve the paper by discussing how the proposed integrated methods (especially BNs) or future directions specifically address the challenges in bridging the qualitative-quantitative gap.   How do these methods handle the subjectivity inherent in qualitative data while maintaining analytical rigor?   How can expert judgment, often the basis of qualitative assessment, be reliably incorporated into quantitative models like BNs or AHP?   Highlighting the limitations of qualitative methods (subjectivity, lack of analytical depth) and quantitative methods (data requirements, misrepresenting reality) could further emphasize why a robust qualitative-quantitative integration is essential but challenging.   While the review references numerous studies and their applications, adding more detailed examples or mini case studies directly within the discussion of techniques and their limitations/integrations could make the concepts more tangible. For instance:   Use specific highway construction examples from the cited literature to show how CPM's rigidity caused problems or how Monte Carlo Simulation results differed from PERT's optimistic estimates.   Illustrate how a Fuzzy AHP approach attempts to handle subjective risk prioritization in highway projects and then explain the noted concerns about its reliability using the same or similar examples.   These examples could significantly enhance the reader's understanding of the practical implications of the discussed methods and their limitations.   While Section 6 outlines future research, reinforcing the link between the specific limitations identified in Section 3 and the proposed future directions could strengthen the paper's narrative. For example, explicitly stating that CPM's failure to align with on-site realities and lack of adaptability leads to the need for AI-driven adaptive scheduling and interoperable systems. Or linking the challenges of handling complex dependencies and integrating qualitative/quantitative data directly to the potential of Bayesian Networks.   By expanding on the "how" and "why" of integration, discussing the practical barriers, strengthening the qualitative-quantitative link with concrete examples, and tightening the connections between identified limitations and future research, the authors can transform their already valuable review into an even more impactful resource for the highway construction industry and researchers. 

Author's Notes to Reviewer:

We thank the reviewer for their constructive and insightful feedback. To address the challenges in bridging qualitative and quantitative data, Section 4 has been updated to explain how Bayesian Networks (BNs) convert expert judgment into probabilistic structures using conditional probability tables (CPTs). Similarly, the reliability of expert input in AHP is supported through structured pairwise comparison, consistency checking, and aggregation methods.

As regards of examples, this is a review-based study/strictly a literature review that summarizes various risk assessment and scheduling techniques along with their applications in Highway Construction. However, while the present manuscript is a literature review, it now includes case-based examples illustrating the limitations of traditional models and the benefits of integrated methods. We have added examples from van Dorp & Duffey (1999), and Oztas & Okmen (2005) that illustrate how rigid Monte Carlo assumptions and PERT's optimism bias can misrepresent real-world outcomes. In contrast, BNs can model dependencies more realistically. Additional examples from Zhang & Zou (2020) and Tüysüz & Kahraman (2006) demonstrate how Fuzzy AHP has been used to prioritize subjective highway construction risks while also addressing concerns about consistency and validation.

The performance comparison with traditional models will be addressed in a future empirical study. We revised Section 4.1 of the manuscript and added a statement that acknowledges a future separate publication focused on demonstrating the empirical superiority of hybrid model. The revised Section 4 now mentions a forthcoming separate publication that will present a detailed empirical evaluation of the developed hybrid model. This model integrates BNs and AHP to demonstrate improved schedule reliability and risk prediction accuracy compared to traditional methods.

Reviewer 3 Report

Comments and Suggestions for Authors

The paper corresponds to the journal's scope and represents a concrete contribution and review about new technologies in this area.

Perform a technical review of the work. Check the citation method.

Percent match of 9% is very good.

Some references in the paper are not linked to the literature; the link does not work.

Improve the quality of figures. Show the specification of equipment and software used in the research and analysis of research methods.

Improve and present the research outline. Include a discussion of the results of the systematic research. Highlight clear and concrete conclusions and proposals. On what basis were the best mathematical models selected and proposed? Should the risks of modern roads with autonomous and electric vehicles be included? Systematically present the relevant standards and regulations in this area.

It is necessary to highlight a clear algorithm for choosing a risk management method. Analyze the authors' works in this area. Also point out the importance of machine learning methods that are applied in other areas, namely in the field of electric and another modern vehicle and road infrastructure. In this regard, include the work (doi: 10.46793/adeletters.2024.3.3.5).

Once again, be sure to technically edit and proofread the completed manuscript. Check the way of citing and presenting the literature inside. You must use the available journal template form.

Required information is missing: conflict of interest statement, finances, co-author contribution, etc.

There is a reference to Figure 8 that does not exist in the paper. Is it a reference to Figure 3 (lines 779-786)? Check all sketches, numbering, titles, etc.

The work is of high quality, but it needs to be brought into the framework of the journal to be explicit and understandable. Only the most important details and suggestions should be highlighted.

Also indicate the possibility of applying other methods of communication between systems. Include the work .

Reword the paper in accordance with the reviewers' requests. Make the paper more concise and clearer. After the additions, technically check the paper, which should be accepted because it represents systematized research in this very important field.

Author Response

The paper corresponds to the journal's scope and represents a concrete contribution and review about new technologies in this area. – Thank you

Perform a technical review of the work. Check the citation method. – Noted.

Percent match of 9% is very good. – Thank you

Some references in the paper are not linked to the literature; the link does not work. – It has been corrected.

Improve the quality of figures. Show the specification of equipment and software used in the research and analysis of research methods. – The manuscript includes three figures, all of which are adapted or reprinted from original sources as cited directly in the figure captions and referenced in the main text. These figures were selected to illustrate key methodological concepts and are consistent with the permissions and citation practices required by the original publishers.

Regarding the specification of equipment and software, as this is a review-based study, no specific experimental equipment was used. For literature synthesis, qualitative analysis, and conceptual framework development, the following software tools were utilized: Zotero, Microsoft Excel, Microsoft Word.

Improve and present the research outline. Include a discussion of the results of the systematic research. Highlight clear and concrete conclusions and proposals. On what basis were the best mathematical models selected and proposed? Should the risks of modern roads with autonomous and electric vehicles be included? Systematically present the relevant standards and regulations in this area. - Thank you for your valuable feedback. The research outline has been clarified, and the results of the systematic review are now more clearly discussed in the revised discussion and conclusion sections. Clear conclusions and practical proposals have been highlighted, with hybrid models proposed based on their frequency in the literature, ability to manage uncertainty, and relevance to highway construction challenges. While the scope of this review is focused on current highway construction project practices, we agree that risks associated with autonomous and electric vehicles are important and have noted this as a future research direction.

It is necessary to highlight a clear algorithm for choosing a risk management method. Analyze the authors' works in this area. Also point out the importance of machine learning methods that are applied in other areas, namely in the field of electric and another modern vehicle and road infrastructure. In this regard, include the work (doi: 10.46793/adeletters.2024.3.3.5). – We have mentioned the possibility of applying the ML in Discussion and Future Research Directions section.

Once again, be sure to technically edit and proofread the completed manuscript. Check the way of citing and presenting the literature inside. You must use the available journal template form. – Noted.

 

Required information is missing: conflict of interest statement, finances, co-author contribution, etc. – Noted.

There is a reference to Figure 8 that does not exist in the paper. Is it a reference to Figure 3 (lines 779-786)? Check all sketches, numbering, titles, etc. – Sorry, supposed to be Figure 3 (automatic mistake) - it has been corrected.

The work is of high quality, but it needs to be brought into the framework of the journal to be explicit and understandable. Only the most important details and suggestions should be highlighted. – Thank you for your positive feedback. We appreciate your suggestion. This manuscript is a literature review-based study that discusses a range of established techniques in highway construction risk and scheduling. Given the vast number of available methodologies in the field, we have made a conscious effort to focus on those that are most widely cited and frequently applied in both academic literature and practical settings. For the sake of clarity, the original manuscript exceeded 40 pages, and considerable effort was made to streamline the content while retaining the most relevant and impactful techniques for discussion.

Also indicate the possibility of applying other methods of communication between systems. Include the work. – Not clear.

Reword the paper in accordance with the reviewers' requests. Make the paper mre concise and clearer. After the additions, technically check the paper, which should be accepted because it represents systematized research in this very important field. - Noted

 

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

Based on the author's notes and the revised paper, the authors appear to have directly addressed the reviewer's comments. 

They have explained the mechanisms for integrating qualitative and quantitative data, provided specific examples from the literature to support their points about method limitations and applications (while also including criticisms where relevant), and explicitly stated that the detailed empirical evaluation will be presented in a future publication.

Reviewer 3 Report

Comments and Suggestions for Authors

The authors have acted in accordance with the review. The paper may be accepted after final technical review by the editorial board.