Integrating Fuzzy AHP And Monte Carlo-Simulation for Robust Financial Risk Assessment in Road Infrastructure Projects: A Case Study of the Naya Sarai Four-Lane ROB, Jharkhand.

Integrating Fuzzy AHP And Monte Carlo-Simulation for Robust Financial Risk Assessment in Road Infrastructure Projects: A Case Study of the Naya Sarai Four-Lane ROB, Jharkhand.

Pintu Kumar

M.Tech Student, Jharkhand University of Technology, Ranchi Science & Technology Campus, Sirkhatoli, Namkum, Ranchi Jharkhand-834010

Dr. Ramesh Murmu

Assistant Professor Department of Project Engineering and Management, J.U.T Ranchi

Abstract – Road infrastructure projects in developing economies are routinely exposed to multidimensional financial risks arising from administrative delays, cash-flow disruptions, land-acquisition bottlenecks, and market uncertainties. Traditional risk-management frameworks, typically reliant on qualitative judgment or deterministic estimates, struggle to capture the combined effects of expert subjectivity and probabilistic cost variability. This study proposes a hybrid Fuzzy Analytic Hierarchy ProcessMonte Carlo Simulation (FAHPMCS) model to quantify and simulate financial-risk escalation for the Naya Sarai Four-Lane Road Over Bridge (ROB) project in Jharkhand, India. Eight major financial risks were identified through documentation review and expert consultation. FAHP was employed to derive uncertainty-sensitive importance weights, which were then integrated into a Monte Carlo cost-escalation model using tailored probability distributions. Results demonstrate that cash-flow failure, delayed payments, land-acquisition delays, material price inflation, and utility-shifting issues dominate the project's financial-risk structure. The probabilistic simulation indicates an 84.84% likelihood of exceeding the approved cost of 28.74 crore, with the expected cost centred at 31.18 crore. Sensitivity analysis highlights liquidity-related risks as primary cost drivers. The study contributes a robust hybrid modelling framework for improving financial forecasting, risk-based budgeting, and governance of public infrastructure projects.

Keywords: Financial Risk, Road Infrastructure, Fuzzy AHP, Monte Carlo Simulation, Risk Modelling.

1. INTRODUCTION

   Road infrastructure projects constitute critical assets for regional development, particularly in emerging economies where transport expansion directly supports socio-economic growth (1). Despite their strategic importance, such projects face persistent financial risks arising from geotechnical uncertainties, market fluctuations, and managerial complexities, often leading to significant budget deviations (2). Existing risk assessment practices largely depend on deterministic or purely qualitative techniques, which are insufficient for modelling the nonlinear, interdependent, and stochastic nature of construction risk phenomena (3). These limitations are exacerbated under conditions of linguistic ambiguity and incomplete expert knowledge (4).

   Fuzzy AHP enhances the reliability of risk prioritisation by embedding fuzzy set theory into multicriteria decision-making, enabling the systematic handling of vagueness in expert evaluations (5,6). Conversely, Monte Carlo Simulation provides a probabilistic modelling structure capable of generating frequency distributions for cost outcomes, thereby quantifying uncertainty with higher accuracy (7,8). A critical review of current literature (113) highlights a methodological gap: the absence of integrated frameworks that combine fuzzy-based prioritisation with stochastic simulation for holistic financial risk assessment.

   This study bridges this gap by establishing a hybrid Fuzzy AHPMonte Carlo Simulation model. The Naya Sarai Four-Lane Road Over Bridge (ROB) in Jharkhand is selected as the case context due to its multidimensional uncertainty profile, including challenging geological conditions, fluctuating input prices, and substantial financial exposure (13). The integrated model aims to enhance risk quantification fidelity and support evidence-driven financial decision-making.

2. LITERATURE REVIEW

   Construction projects especially in developing regions operate within highly uncertain environments marked by technical, financial, organisational and external risks that frequently disrupt cost, time and quality performance (1). In India, such risks remain pervasive, with financial, construction and design-related factors driving cost overruns and project delays (2).

   Similar patterns appear across MSMEs, where informal practices and limited capacity hinder systematic risk management (3), and in fragile environments such as Yemen, where risk processes remain reactive and unstructured (4). Financial risks particularly inflation, currency fluctuation and liquidity constraints emerge as especially destabilising, as evidenced in Lebanese construction projects (5). Broader reviews reaffirm the need for more structured, analytical approaches in Indias construction sector (6). To strengthen assessment practices, advanced decision-support tools have gained prominence. Monte Carlo Simulation (MCS) enables probabilistic modelling of uncertainty, overcoming the linearity and subjectivity of conventional approaches (7). Fuzzy Analytical Hierarchy Process (Fuzzy AHP) improves the robustness of expert-based weighting by incorporating fuzzy set theory into multi- criteria evaluations, particularly for infrastructure investment decisions (8). Applications in geotechnically complex bridge projects further demonstrate Fuzzy AHPs capacity to assess interacting construction risks under ambiguity (9). In transport and bridge infrastructure, MCS has been applied to schedule forecasting and safety-risk quantification, generating probabilistic delay estimates and risk indices (10,11). At a broader methodological level, Monte Carlo is recognised as a core component of quantitative construction risk analysis, though typically applied independently of structured prioritisation tools (12). Meanwhile, fuzzy MCDM research such as Fuzzy AHP integrated with Fuzzy MARCOS demonstrates the effectiveness of hybrid fuzzy systems for complex decision environments (13).

   Overall, the literature reveals three gaps:

   • Limited focus on financial risk assessment frameworks for road infrastructure.

   • Isolated application of Fuzzy AHP and Monte Carlo Simulation.

   • Absence of hybrid fuzzystochastic approaches within transport infrastructure contexts.

3. OBJECTIVE

   This study investigates financial uncertainties in real-world infrastructure projects through the Naya Sarai Four-Lane ROB case. It identifies and prioritises major financial risks including land and utility delays, cost volatility, and cash-flow deviations using Fuzzy AHP to capture ambiguity in expert judgement. These prioritised risks are then analysed through Monte Carlo Simulation to estimate their cost impacts across plausible future scenarios. By integrating both methods into a unified framework, the study aims to deliver a more reliable and practical approach for financial risk assessment applicable to agencies such as the Road Construction Department and Indian Railways.

4. METHODOLOGY

   1. Introduction

      This study employs a hybrid qualitativequantitative framework integrating Fuzzy Analytic Hierarchy Process (FAHP) with Monte Carlo Simulation (MCS) to evaluate financial uncertainties in the Naya Sarai Four-Lane ROB project. The methodology proceeds through four structured phases: (i) literature-based risk identification; (ii) multi-source data collection; (ii) expert-driven fuzzy prioritisation of financial risks; and (iv) probabilistic cost modelling using MCS informed by FAHP weights.

   2. Data Collection

      Primary data were gathered through semi-structured interviews with RCD engineers, contractor managers, and design and site engineers to capture payment delays, fund-flow disruptions, land and utility bottlenecks, and administrative constraints not reflected in official documentation. Secondary data included the 2016 DPR, all revised estimates (20182025), sanction orders, land- acquisition and utility dossiers, BOQs, cost indices and interdepartmental correspondence allowing reconstruction of the projects financial trajectory and drivers of escalation.

   3. Case Study

      The Construction of a Four-Lane ROB on HEC to RanchiGumla Road (NH-23) in lieu of Level Crossing No. RL5A at RYL Km 427/2-3 between Ranchi and Piska Stations. Project located at Naya-Sarai, Ranchi in the state of Jharkhand. Characterised by prolonged delays, multi-agency coordination requirements, design revisions, and escalating costs from 20.19 crore in 2016 to

      28.73 crore in 2025 provides a suitable context for testing a hybrid financial-risk modelling approach.

   4. Financial Risk Identification

      Risk identification combined (i) literature-derived taxonomies of financial risk in infrastructure projects and (ii) practitioner insights. Eight risks were finalised: delayed payments, cash-flow failures, material inflation, inaccurate estimation, schedule delays, contractual disputes, land-acquisition escalation and utility-shifting escalation.

   5. Fuzzy AHP

      A structured fuzzy questionnaire captured expert pairwise comparisons using a six-level linguistic scale (EIESI). Buckleys geometric FAHP method was applied: constructing a TFN-based pairwise matrix, aggregating expert judgements, computing fuzzy weights, de-fuzzifying via centroid method and normalising to obtain crisp priorities. The resulting weights reflect both relative importance and embedded uncertainty in expert judgement.

   6. Fuzzy AHP-Monte Carlo Simulation Integration

      FAHP weights were used as impact multipliers within the probabilistic model. For each iteration, the cost impact of risk was calculated as:

      = ×

      Where is drawn from the assigned probability distribution. Summing these adjustments with the deterministic base cost yields the total cost per simulation run.

   7. Probability Distribution Assignment

      Distribution families were selected based on behavioural risk characteristics:

      • Lognormal for cash-flow failures and utility shifting.

      • Triangular for payment delays and land acquisition.

      • Truncated Normal for material price inflation.

        Distribution parameters were derived from escalation shares proportional to FAHP-weighted importance.

   8. Monte Carlo Simulation

      A 10,000-iteration MCS was performed to obtain stable probabilistic cost outcomes. Each iteration sampled risk impacts, applied FAHP scaling and computed the resulting total project cost. The final distribution represents the stochastic behaviour of cost escalation and the relative influence of each financial risk.

5. RESULTS

   1. FAHP: Prioritisation of Financial Risks

      Fuzzy pairwise comparisons that were created by experts were aggregated, de-fuzzified, and normalised to calculate final FAHP weights. These weights reflect the relative importance of every risk in determining the possible cost escalation.

      Table 5.1: Risk Weights and Rankings generated with FAHP.

      Risk Code	Financial Risk	Weight	Rank
      R2	Cash Flow Failures	0.2576	1
      R1	Delayed Payments	0.1431	2
      R7	Land Acquisition Delays	0.1421	3
      R3	Material Price Inflation	0.1218	4
      R8	Utility Shifting Cost	0.1143	5
      R5	Schedule Delays	0.0846	6
      R4	Inaccurate Cost Estimation	0.0719	7
      R6	Contractual Disputes	0.0644	8

      The results of the FAHP show that the importance is concentrated heavily in the risks relating to liquidity and administrative risks with R2 (Cash Flow Failures) taking a lead in the hierarchy.

   2. Allocation of Escalation Shares

      The maximum project escalation was allocated proportionately to the five highest rated risks with FAHP weights as multipliers in the allocation.

      Table 5.2: Escalation Share Allocation to Top Five Risks.

      Risk	Weight	Escalation Share ()
      R2 Cash Flow Failures	0.2576	28,270,179.88
      R1 Delayed Payments	0.1431	15,704,436.10
      R7 Land Acquisition Delays	0.1421	15,594,691.62
      R3 Material Price Inflation	0.1218	13,366,878.53
      R8 Utility Shifting Issues	0.1143	12,543,794.88

      The increase is also obviously concentrated under liquidity, administrative and statutory coordination risks.

   3. Probability Distribution Modelling

      All risks were modelled by a distribution family based on behavioural aspects.

      Table 5.3: Probability Distribution Parameters.

      Risk Distribution Parameterisation

      R2 Cash Flow Failures	Lognormal	Mean = 2.827×10; SD = 0.6M
      R1 Delayed Payments	Triangular	Min = 0.3M; Mode = M; Max = 2.5M
      R7 Land Acquisition Delays	Triangular	Min = 0.3M; Mode = M; Max = 2.5M
      R3 Material Price Inflation	Truncated Normal	Mean = M; SD = 0.25M; LB = 0
      R8 Utility Shifting Issues	Lognormal	Mean = 1.254×10; SD = 0.6M

      This moves guaranteed coherence in the methodology of behavioural realism and statistical tractability.

   4. Monte Carlo Simulation

      A simulation of 10,000 iterations was run, and a probabilistic distribution of the overall project cost was obtained.

      Figure 5.1: Simulated Total Project Cost Histogram.

      Table 5.4. Summary of Simulation statistics.

      Statistic	Value ()	Value ( crore)
      Mean	311,833,259.56	31.18
      Median (P50)	308,017,440.81	30.80
      Standard Deviation	26,139,378.57	2.61
      P05	277,075,473.50	27.71
      P95	359,170,512.16	35.92

      The findings indicate the bell-shaped skewed to the right distribution indicating the long-tail behaviour of heavy administrative risks.

   5. Cumulative Probability (S-Curve)

      Figure 5.2: Cumulative Distribution Function

      Probability of Exceedance

      ( > ) = 84.84%

      Table 5.5: Probability of Not Exceeding Cost Thresholds

      Cost Threshold	Probability of Not Exceeding
      Baseline Cost (20.19 Cr)	0%
      Baseline + 5%	0%
      Baseline + 10%	0%
      Approved Cost (28.74 Cr)	15.16%

      The probability of not exceeding the re-estimated cost of 28.74 crore is only 15.16%, demonstrating a structural underestimation of risk in the sanctioned estimate.

   6. Sensitivity Analysis

The risks that had the strongest impact on total cost were detected with the help of the Spearman rank correlation.

Figure 5.3: Spearman Rank Correlation Sensitivity Plot

Table 5.6: Sensitivity Ranking

Risk	Spearman	Interpretation
R2 Cash Flow Failures	0.7322	Dominant driver
R7 Land Acquisition Delays	0.3558	Moderate influence
R1 Delayed Payments	0.3500	Moderate influence
R8 Utility Shifting Issues	0.3090	Noticeable influence
R3 Material Price Inflation	0.1619	Smaller contribution

Such diagnostic shows what risks have the most monotonic contribution to the total cost.

5.6. DISCUSSION

This studys hybrid FAHPMCS analysis provides a comprehensive understanding of financial-risk behaviour in the Naya Sarai Four-Lane ROB project. The FAHP results revealed a highly skewed risk hierarchy, with liquidity-driven risks dominating the financial landscape. Cash Flow Failures (0.2576) emerged as the most critical driver, followed by Delayed Payments and Land- Acquisition Delays. Technical uncertainties played a comparatively minor role, underscoring the systemic nature of institutional and administrative disruptions.

Escalation allocation reinforced this pattern: the highest-ranked risks absorbed the majority of the escalation burden, confirming their disproportionate influence on cost trajectories. Probability-distribution modelling assigned behavioural-appropriate distributions lognormal for administrative shocks, triangular for bounded bureaucratic delays, and truncated normal for material price volatility ensuring realism and methodological robustness.

Monte Carlo Simulation (10,000 iterations) projected a mean project cost of 31.18 crore, substantially higher than the sanctioned

28.74 crore. The distribution exhibited right-skewed behaviour, capturing the long-tail exposure to severe but sporadic administrative failures. The S-curve further demonstrated that the probability of completing the project within the approved estimate is only 15.16%, highlighting a systemic underestimation of risk in DPR-based budgeting.

Sensitivity analysis using Spearman correlations confirmed that liquidity and administrative risks (R2, R1, R7) exert the strongest monotonic influence on total cost. The alignment between expert-based FAHP rankings and probabilistic sensitivity outputs validates the hybrid modelling approach and demonstrates the internal coherence of expert assessments with real stochastic behaviour.

Collectively, the results reveal a governance-driven risk system where cost escalation is shaped primarily by institutional inefficiencies rather than technical failures. This demands targeted reforms, including digitised payment systems, coordinated land and utility clearance mechanisms, and risk-adjusted DPR methodologies. The study thereby establishes a data-driven foundation for strengthening financial resilience in public infrastructure planning and execution.

1. CONCLUSION

   This study demonstrates the effectiveness of a hybrid Fuzzy AHPMonte Carlo Simulation framework for characterising and quantifying financial uncertainty in large-scale public infrastructure projects. Applied to the Naya Sarai Four-Lane ROB, the approach reveals that cost escalation is driven primarily by liquidity disruptions and administrative delays rather than by technical or engineering uncertainties. The dominance of Cash Flow Failures, Delayed Payments and Land-Acquisition Delays validated through FAHP weights, escalation allocation and MCS sensitivity analysis highlights the structural nature of governance-related risk pathways.

   The probabilistic findings show a substantial misalignment between approved estimates and actual risk exposure. With only a 15.16% likelihood of completing the project within the sanctioned budget, traditional deterministic DPR-based estimation frameworks clearly understate systemic financial vulnerabilities. The heavy-tailed cost distribution further indicates susceptibility to infrequent but high-impact administrative shocks, underscoring the importance of uncertainty-aware planning.

   The strong convergence between expert judgement and stochastic cost behaviour confirms the methodological robustness of integrating FAHP with MCS. The study provides clear evidence that addressing financial instability, administrative delays and inter- agency coordination bottlenecks is essential for enhancing cost predictability in public works.

   In conclusion, the hybrid FAHPMCS framework offers a scientifically rigorous and practically deployable tool for financial-risk assessment. It supports the transition from deterministic budgeting toward risk-adjusted, data-driven decision-making an essential transformation for improving governance, financial resilience and delivery outcomes in Indias infrastructure sector.

2. LIMITATIONS AND FUTURE SCOPE

   1. Limitations

      Despite its robust analytical framework, the study carries several limitations. The risk prioritisation relies on a limited expert panel, and the Monte Carlo model assumes independence among risks due to the absence of empirical correlation data. Additionally, probability distributions were calibrated using a single-project cost trajectory, which may not fully capture broader market or

      macroeconomic variability. These constraints, while carefully managed through FAHP and behaviourally justified distribution modelling, highlight areas for methodological enhancement.

   2. Future Scope

Future research can broaden the applicability of this hybrid FAHPMCS framework by incorporating larger and more diverse expert panels across government, contractor and regulatory agenies; modelling interdependencies among financial and administrative risks through correlation structures or Bayesian networks; integrating dynamic cost indices and real-time market data to refine distribution parameters; and validating the model across multiple infrastructure categories such as highway corridors, urban metro systems and bridge rehabilitation projects to strengthen generalisability. Advancing the framework in these directions will contribute to more resilient, uncertainty-aware financial planning in Indias rapidly expanding infrastructure sector.

REFERENCES

[1]. Al-Mukahal, A. A. M. (2020). Risk management of construction projects. Engineering Management Research, 9(1), 1527. https://doi.org/10.5539/emr.v9n1p15 [2]. Muthukrishnan, N., & Ganapathi, R. (2021). A study on risk management practices in construction projects in India. Turkish Online Journal of Qualitative

Inquiry, 12(7), 88048816

[3]. Singla, H. K., & Phadtare, M. (2025). Risk management practices in construction projects: A qualitative exploration of MSMEs in India. Journal of Advances in Management Research. Advance online publication. https://doi.org/10.1108/JAMR-06-2024-0217

[4]. Bahamid, R. A., Doh, S. I., Khoiry, M. A., Kassem, M. A., & Al-Sharafi, M. A. (2022). The current risk management practices and knowledge in the construction industry. Buildings, 12(7), 1016. https://doi.org/10.3390/buildings12071016

[5]. Shibani, A., Hasan, D., Saaifan, J., Sabboubeh, H., Eltaip, M., Saidani, M., & Gherbal, N. (2024). Financial risk management in the construction projects. Journal of King Saud University Engineering Sciences, 36(4), 552561. https://doi.org/10.1016/j.jksues.2022.05.001

[6]. Singh, M. K., Deep, S., & Banerjee, R. (2017). Risk management in construction projects as per Indian scenario. International Journal of Civil Engineering and Technology, 8(3), 127136

[7]. Senova, A., Tobisova, A., & Rozenberg, R. (2023). New approaches to project risk assessment utilizing the Monte Carlo method. Sustainability, 15(1), 1006. https://doi.org/10.3390/su15021006

[8]. Zhao, X., Xue, W., Liu, K., Guo, F., & Chen, M. (2025). Fuzzy analytic hierarchy process-based investment risk evaluation for infrastructure construction projects. Buildings, 15(5), 756. https://doi.org/10.3390/buildings15050756

[9]. Han, J., Lu, G., & Yang, J. (2025). The risk assessment of bridge pile foundation construction in karst regions based on the fuzzy analytic hierarchy process.

Buildings, 15(7), 1059. https://doi.org/10.3390/buildings15071059

[10]. Ichsan, M., Isvara, W., & Karim, S. (2025). Monte Carlo simulation for enhancing the schedule completion forecast of Jakarta Central Railway Station construction project. Applied Sciences, 15(13), 7464. https://doi.org/10.3390/app15137464

[11]. Hartono, W., Kristiawan, S. A., Handayani, D., & Sutopo, W. (2025). Risk assessment with Monte Carlo simulation to improve bridge construction safety.

Engineering Proceedings, 84, 56. https://doi.org/10.3390/engproc2025084056

[12]. Sharma, M. K. (2020). Monte Carlo simulation applications for construction project management. International Journal of Civil Engineering and Technology, 11(2), 88100.

[13]. Bakr, M., & Altok, Ö. (2021). Application of fuzzy AHP and fuzzy MARCOS approach for the evaluation of e-service quality in the airline industry. Decision Making: Applications in Management and Engineering, 4(1), 127152. https://doi.org/10.31181/dmame2104127b