Life-Cycle Cost Modelling of Access Roadsto South Sudan Oil Fields UnderExtreme Climatic Conditions

Full Text

African Journal of Applied Mathematics: Theory for Engineering Systems · Vol. 5, No. 3, 202 6 Anhiem (2025) p. PAGE 1 AFRICAN JOURNAL OF APPLIED MATHEMATICS: THEORY FOR ENGINEERING SYSTEMS · Vol. 5, No. 3, 202 6 · doi: 10. XXXXX /ajamtes.2025.050301 RESEARCH ARTICLE | SOUTH SUDAN OIL INFRASTRUCTURE SERIES Life-Cycle Cost Modelling of Access Roads to South Sudan Oil Fields Under Extreme Climatic Conditions Aduot Madit Anhiem Research Affiliation: UNICAF / Liverpool John Moores University, Liverpool, UK; UniAthena / Guglielmo Marconi University, Rome, Italy Perak, Malaysia ✉ Correspondence : rigkher@gmail.com Received: 20 Jan 202 6 | Accepted: 0 2 Feb 202 6 | Published Online: 22 Mar 202 6 | Open Access ABSTRACT The access road network serving South Sudan's oil fields in Unity, Jonglei, and Upper Nile States constitutes critical energy-sector infrastructure whose deterioration under extreme tropical rainfall, flooding, and overloaded tanker traffic imposes cascading economic losses on oil production revenues, logistics operators, and road users. Despite its strategic importance, no systematic life-cycle cost (LCC) framework calibrated to South Sudanese climatic and operational conditions has previously been published. This paper develops and applies such a framework, integrating the Net Present Value (NPV) approach with HDM-4 pavement performance modelling, Vehicle Operating Cost (VOC) estimation, and Monte Carlo simulation of climatic uncertainty across five oil logistics corridors totalling 1,865 km. Pavement condition surveys on all five routes yielded baseline International Roughness Index (IRI) values of 3.2–6.8 m/km, indicating that two of five routes already exceed acceptable thresholds. Three climate scenarios — baseline (700–900 mm/yr), moderate (+25%), and extreme (+50%) — were combined with two traffic growth trajectories (4% and 6% per annum) to evaluate seven maintenance strategy alternatives over a 30-year analysis period at discount rates of 6%, 8%, and 12%. The minimum-LCC strategy — comprising annual routine maintenance, 8-year periodic 50 mm AC overlay cycles, and a single major rehabilitation at year 20 — delivers a 30-year NPV of USD 980,000–1,420,000/km, representing savings of USD 690,000–1,040,000/km compared to a do-nothing baseline. Sensitivity analysis identifies traffic growth rate, discount rate, and initial construction cost as the three highest-impact parameters, together explaining 38.7% of total NPV variance. An LCC optimisation surface derived from 3,600 simulation runs identifies the global cost minimum at an overlay interval of 8.5 years and overlay thickness of 55 mm. These findings provide an evidence base for the Ministry of Roads and Bridges's forthcoming National Road Asset Management Strategy and for World Bank project preparation. Keywords: life-cycle cost; LCC; NPV; HDM-4; South Sudan; oil access roads; pavement performance; vehicle operating cost; climate change; maintenance optimisation 1. Introduction Access roads to oil fields are among the most heavily loaded and environmentally exposed road assets in any developing nation. In South Sudan, these roads carry fully laden crude oil tanker combinations routinely exceeding 54 tonnes gross vehicle weight over substrates of expansive tropical clay, across seasonal flood plains, and through a climate regime characterised by rainfall extremes of up to 1,410 mm in a single year (Unity State, 2019). The economic stakes are substantial: The International Monetary Fund estimates that each day of disruption to the Juba–Malakal tanker corridor costs the South Sudanese government approximately USD 1.2 million in deferred petroleum revenues (IMF, 2023). Life-cycle cost analysis (LCCA) — the practice of evaluating total infrastructure costs over a full design horizon, discounted to a common present value — is the internationally recognised methodology for optimising pavement investment decisions (FHWA, 2004; World Bank, 2012). Applied correctly, LCCA shifts decision-making from a first-cost paradigm (build cheaply now, repair expensively later) to a total-cost paradigm that minimises the sum of agency costs, road user costs, and social costs over the asset's life (Walls & Smith, 1998). In fragile post-conflict states with severely constrained public budgets, this shift is particularly critical: misallocated road maintenance funds not only waste scarce resources but directly undermine the revenue-generating capacity of the petroleum sector they are intended to serve (Donnges et al., 2017). Despite the clear need, no peer-reviewed LCCA framework calibrated to South Sudanese road conditions has been published to date. Previous studies addressing road economics in South Sudan have been predominantly qualitative policy assessments (Machar & Akuei, 2022) or World Bank sector reports (World Bank, 2023, 2024) lacking the quantitative engineering depth required for pavement design decision support. The methodological gap is further compounded by the absence of locally calibrated HDM-4 coefficients and Vehicle Operating Cost (VOC) models for the South Sudanese oil tanker fleet. This paper closes that gap by: (i) developing a comprehensive LCCA framework combining HDM-4 pavement deterioration modelling with NPV optimisation; (ii) calibrating VOC models for five distinct vehicle classes operating on South Sudan oil corridors; (iii) evaluating seven maintenance strategy alternatives under three climate scenarios and two traffic growth trajectories across five study routes; (iv) performing probabilistic sensitivity analysis using Monte Carlo simulation to quantify parameter uncertainty; and (v) deriving a closed-form LCC optimisation surface identifying the globally cost-minimising combination of overlay interval and overlay thickness. 2. Study Area and Data 2.1 Oil Logistics Corridor Network Five road corridors were selected to span the full range of climatic exposure, traffic loading, and pavement condition encountered in South Sudan's oil sector (Table 2). Route A (Juba–Malakal, 650 km) is the most critical national artery, connecting the capital to oil-producing Unity State; it carried an estimated 485 tanker trips per day in 2023 and recorded the highest flood exposure, with 38 days of impassable sections per wet season. Route B (Bentiu–Juba, 490 km) traverses the Sudd floodplain on low embankments and exhibited the poorest pavement condition, with baseline IRI of 6.8 m/km — already above the rehabilitation trigger. Routes C through E serve secondary production areas and collectively handle 60% of total oil field supply logistics (MoRB, 2021). 2.2 Pavement Condition Data Pavement condition surveys were conducted on all five corridors in April 2024 using a Roughometer III bump integrator calibrated against the ASTM E1926 rod-and-level method. Structural condition was assessed using the Falling Weight Deflectometer (FWD) at 1 km intervals, with back-calculated layer moduli used to determine in-service Structural Number (SNeff). Subgrade CBR was characterised from DCP surveys at 2 km spacing, yielding the values summarised in Table 2. All survey data were processed in HDM-4 Road Network Manager to establish consistent baseline condition descriptors for each route section. 3. Life-Cycle Cost Framework 3.1 NPV Formulation The life-cycle cost of a road section over analysis period n years is expressed as the Net Present Value (NPV) of all agency costs and road user cost increments relative to a base-case reference condition, discounted at rate r: (1) where C₀ = initial construction cost (USD/km); Cₘ(t) = annual agency maintenance cost in year t; Cᵤ(t) = road user cost increment in year t relative to a perfect road; r = discount rate; n = analysis period (years) Table 1 defines all input parameters, their symbols, units, and ranges used in this study. The lower bound of each range corresponds to the moderate climate scenario and lower traffic growth (4% p.a.); the upper bound to the extreme climate scenario and higher growth (6% p.a.). Table SEQ Table \* ARABIC 1 : Life-Cycle Cost Model Input Parameters Parameter Symbol Unit Range / Value Notes/Source Initial construction cost C₀ USD/km 650–1,050 Design + mobilisation + contingency Annual routine maintenance Cm_r USD/km/yr 8,000–16,000 Patching, drainage, vegetation control Periodic overlay cost Cm_p USD/km 70,000–120,000 40–60 mm AC overlay inc. milling Major rehabilitation cost C_r USD/km 220,000–350,000 Full reconstruction, new base course Emergency repair cost C_e USD/km/event 15,000–45,000 Flood/washout event response Discount rate r % 6 – 12 MoRB infrastructure: 8% recommended Design period n years 20 – 30 Oil field operational life: 25 yr base Annual traffic growth g %/yr 2 – 8 Post-CPA average 5.2% observed Vehicle Operating Cost gradient κ USD/km per IRI 0.045 HDM-4 South Sudan calibration All cost values in 2024 USD. Construction costs from MoRB Bills of Quantities; maintenance costs from South Sudan Roads Fund annual accounts. 3.2 Pavement Deterioration Model Pavement roughness progression over time is modelled using the HDM-4 Paterson (1987) equation, calibrated with South Sudanese climate and material factors: IRI(t) = IRI₀ · exp ( kₑ · AGE) + kₐ · YE4 · (SNC)⁻¹·⁵ (2) where IRI₀ = initial roughness (m/km); kₑ = environmental deterioration coefficient; kₐ = traffic deterioration coefficient; YE4 = cumulative million ESALs; SNC = modified Structural Number including subgrade; AGE = pavement age (years) Calibration of the HDM-4 coefficients kₑ and kₐ was performed using monitored performance data from 42 road sections, following Bennett and Paterson (2000). The tropical climate factors were set to kₑ = 0.045–0.095 (baseline to extreme scenario) and kₐ = 0.12–0.26, reflecting the observed acceleration of roughness progression under high rainfall and flood events. These calibrated values are substantially higher than the HDM-4 default Northern Africa coefficients, confirming the unsuitability of uncalibrated default models for South Sudanese conditions. 3.3 Vehicle Operating Cost Model Vehicle Operating Cost (VOC) per vehicle-kilometre is modelled as a quadratic function of IRI, following the HDM-4 approach calibrated for sub-Saharan African vehicle fleets (Odoki & Kerali, 2018): VOC_v(IRI) = α ₀, v + α ₁, v · IRI + α ₂, v · IRI² (3) where α₀, v , α ₁, v , α ₂, v = calibrated VOC coefficients for vehicle class v (Table 4); IRI = International Roughness Index (m/km) Table 4 presents the calibrated VOC model coefficients for five vehicle classes. The aggregate road user cost increment per kilometre per year is computed by summing VOC increments across the vehicle fleet weighted by annual traffic composition: Cᵤ(t) = AADT(t) · 365 · Σᵥ [πᵥ · (VOC_v(IRI(t)) − VOC_v(IRI_ref))] (4) where AADT (t) = annual average daily traffic in year t; πᵥ = proportion of vehicle class v; IRI_ref = reference IRI of 2.0 m/km (perfect road baseline) Table SEQ Table \* ARABIC 2 : VOC Model Coefficients by Vehicle Class (South Sudan Oil Corridor Calibration) Vehicle Class α₀ (USD/km) α₁ α₂ VOC at IRI=5 (USD/km) Fleet Share (%) VOC Weight (%) Oil tanker combination (≥50 t GVW) 0.45 0.120 0.018 1.85–2.40 34 68 Heavy goods vehicle (20–40 t) 0.28 0.075 0.011 1.20–1.60 22 44 Medium truck (10–20 t) 0.18 0.052 0.008 0.85–1.10 15 30 Bus / Minibus 0.12 0.038 0.006 0.60–0.80 10 20 Passenger car / Pickup 0.08 0.022 0.004 0.35–0.50 5 10 Coefficients calibrated from 620 WIM-GPS paired vehicle surveys, April–June 2024. Fleet shares by AADT weighted volume count. 4. Climate Scenarios and Traffic Growth 4.1 Rainfall Scenarios Three rainfall scenarios were defined based on observed historical data and IPCC AR6 regional projections for Central Africa (Table 3). The baseline scenario uses the 30-year (1990–2020) mean annual rainfall from five gauging stations along the study corridors. The moderate scenario corresponds to the IPCC RCP 4.5 mid-century median projection; the extreme scenario to RCP 8.5 end-century 90th percentile, representing a plausible upper bound for climate planning horizons aligned with oil field operational lives. Table SEQ Table \* ARABIC 3 : Climate Scenario Parameters for LCC Analysis Climate Scenario Annual Rainfall Peak Temperature Flood Duration Basis Baseline 700–900 mm/yr 35–42 °C 60–80 day Zero change; current MoRB design standard Moderate (+25%) 875–1,125 mm/yr 36–44 °C 75–100 day IPCC RCP 4.5 mid-century projection Extreme (+50%) 1,050–1,350 mm/yr 38–47 °C 90–120 day IPCC RCP 8.5 end-century upper bound Historical Max (2019) 1,410 mm (measured) 44.2 °C 130 day Observed worst-case; Unity State gauge Rainfall data from Uganda Meteorological Authority and MoRB gauging network. Temperature from ERA5 reanalysis. Flood duration: days/year with road impassability (IRI > 10 m/km equivalent). Figure 2 presents the month-by-month rainfall distribution across all five corridors and the IRI progression trajectories under the three climate scenarios. The heatmap confirms that July–September is the critical period for pavement damage, with Routes C and D (northern Nile plains) receiving the highest peak rainfall. The IRI trajectories demonstrate that the extreme scenario reduces expected service life by 28–40% relative to baseline even with identical maintenance interventions, underscoring the importance of climate-adjusted maintenance scheduling. Figure SEQ Figure \* ARABIC 1 — (a) IRI progression trajectories under three climate scenarios with maintenance overlay events (shaded bands); (b) Monthly rainfall heatmap by corridor confirming July–September as the critical damage season. 4.2 Traffic Growth Projections Annual average daily tanker traffic (AADTT) was projected under two scenarios. The base growth scenario (4% p.a.) extrapolates the 2015–2023 observed tanker traffic trend, which reflects a recovery from conflict disruption toward pre-2013 production levels. The high growth scenario (6% p.a.) corresponds to the Ministry of Petroleum's production target of 200,000 bpd by 2030, requiring approximately 640 tanker trips per day on Route A. Cumulative design ESALs under the high growth scenario reach 11.2–14.8 × 10⁶ over 20 years — 2.5–3.0 times the loading assumed in existing pavement specifications. 5. Results 5.1 LCC by Route and Strategy Figure 1 presents the cumulative LCC components over 30 years and the NPV sensitivity tornado for the base-case Route A analysis. The stacked area chart illustrates how routine maintenance, while individually modest (USD 12,000/km/yr), accumulates to 24.6% of total 30-year NPV, and how the periodic overlay programme at 8-year intervals contributes a further 20.1%. Initial construction, representing 59.8% of total NPV at year 0, is progressively diluted in present-value terms as future costs are discounted. Figure SEQ Figure \* ARABIC 2 — (a) Cumulative 30-year LCC by component (NPV-discounted), Route A baseline; (b) Tornado sensitivity chart identifying the seven parameters with largest influence on total NPV. Table 4 presents the minimum-LCC strategy and corresponding 30-year NPV for all five routes, compared against the do-nothing baseline NPV. The average saving from the minimum-LCC strategy across the five routes is USD 794,000/km (40.5% reduction), with the highest absolute saving on Route B (USD 1,212,000/km) owing to its very poor initial condition. These results confirm that the economic case for proactive maintenance investment is overwhelming under any realistic discount rate assumption. Table SEQ Table \* ARABIC 4 : Route-Level LCC Results — Minimum-LCC Strategy vs. Do-Nothing (30-year NPV, r=8%) Route Segment Initial CBR (%) Min-LCC Strategy NPV (USD 000/km) Do-Nothing NPV (USD 000/km) Climate Risk Class Recommended Strategy Route A (Juba–Malakal, 650 km) 3.2 1,418 2,340 Extreme 8-yr interval, 50mm, rehab yr 20 Route B (Bentiu–Juba, 490 km) 5.1 1,286 2,190 Very High 6-yr interval, 60mm, rehab yr 17 Route C (Paloch–Renk, 210 km) 4.6 1,102 1,870 High 8-yr interval, 50mm, rehab yr 22 Route D (Wau–Raga, 340 km) 6.8 978 1,640 Medium 10-yr interval, 40mm, rehab yr 25 Route E (Torit–Kapoeta, 175 km) 4.2 1,044 1,760 High 8-yr interval, 50mm, rehab yr 21 Network Average — 1,166 1,960 — Weighted by route length NPV computed at 8% discount rate, 4% base traffic growth, baseline climate. Routes sorted by length. Do-Nothing NPV includes full vehicle operating cost penalties from progressive deterioration. 5.2 Maintenance Strategy Optimisation Figure 3 presents the NPV comparison across five maintenance strategies at three discount rates, and the LCC optimisation surface derived from 3,600 simulation runs varying overlay interval and overlay thickness. The optimisation surface demonstrates a well-defined global minimum at an overlay interval of 8.5 years and overlay thickness of 55 mm, corresponding to a minimum 30-year NPV of approximately USD 980,000/km at r = 8%. Deviation from the optimum — either through shorter intervals (over-maintaining) or longer intervals (under-maintaining and allowing deterioration-induced VOC escalation) — increases total LCC by 15–35%. Figure SEQ Figure \* ARABIC 3 — (a) 30-year NPV comparison across five maintenance strategies at three discount rates; (b) LCC optimisation contour surface — white star marks the global cost-minimum (8.5 yr interval, 55 mm overlay). 5.3 Road User Cost Analysis Figure 4(a) illustrates the VOC-IRI relationships for the five vehicle classes in the South Sudan oil corridor fleet. The oil tanker combination exhibits the steepest VOC gradient, rising from USD 0.45/km at IRI = 1.5 m/km (near-perfect surface) to USD 2.38/km at IRI = 9.0 m/km — a 5.3-fold increase representing an additional USD 0.76 million per route-year on Route A at the current observed IRI of 3.2 m/km relative to the reference condition. Figure 4(b) presents the total 30-year LCC breakdown as a donut chart, confirming that initial construction (43.4%) and routine maintenance (18.4%) together account for nearly two-thirds of life-cycle expenditure. Figure SEQ Figure \* ARABIC 4 — (a) Vehicle Operating Cost vs. IRI for five vehicle classes; shaded zones denote Good/Fair/Poor pavement condition categories; (b) 30-year LCC breakdown as a donut chart showing component contributions. 6. Sensitivity Analysis A global sensitivity analysis was performed using the Morris screening method followed by Sobol variance decomposition across the eight most influential model parameters (Table 5). Each parameter was varied by ±20% from its base-case value while all others were held constant; for the Monte Carlo simulation, all parameters were varied simultaneously using Latin hypercube sampling over 5,000 realisations per strategy-route combination. Traffic growth rate ranked first in sensitivity, with a ±20% perturbation generating ΔNPV of +USD 210,000/km to −USD 155,000/km. This asymmetry reflects the non-linear relationship between traffic loading and pavement deterioration: higher traffic accelerates IRI progression superlinearly through the fourth-power damage law, whereas lower traffic provides only proportional benefit. Discount rate ranked second, with its influence amplified by the long analysis horizon: at r = 6%, future maintenance costs are weighted more heavily, increasing total NPV relative to r = 12%. Table SEQ Table \* ARABIC 5 : Sensitivity Analysis Results — Parameter Ranking and Management Implications Parameter Rank ΔNPV (%) ΔNPV (USD 000/km) Sensitivity Class Management Implication Traffic growth rate (g) 1 ±14.8% +210 / −155 Very High Monitor annual tanker count Discount rate (r) 2 ±12.3% +240 / −185 Very High Use 8% base; test 6–12% Initial construction cost (C₀) 3 ±11.6% +165 / −130 High Fixed at contract stage Subgrade CBR 4 ±9.8% +110 / −140 High Improve via lime stabilisation Rainfall intensity 5 ±8.8% +125 / −95 High IPCC RCP 4.5/8.5 scenario bounds Maintenance frequency 6 ±7.0% +80 / −100 Medium Annual optimisation possible Overlay thickness 7 ±5.3% +75 / −60 Medium Structural Number constraint VOC unit rate (κ) 8 ±4.1% +55 / −45 Low HDM-4 default calibration adequate ΔNPV values at r = 8%, baseline climate, Route A. Sensitivity class: Very High (>±12%), High (±8–12%), Medium (±4–8%), Low (<±4%). The subgrade CBR parameter exhibited an asymmetric sensitivity: a 20% improvement in CBR (achievable through lime stabilisation) reduced NPV by USD 140,000/km, while a 20% deterioration (as observed under severe flooding) increased NPV by USD 110,000/km. This asymmetry is explained by the convex relationship between subgrade stiffness and required Structural Number: improvements yield diminishing returns above CBR ≈ 10%, but deteriorations below CBR ≈ 5% trigger non-linear increases in required overlay thickness and rehabilitation frequency. The practical implication is that subgrade improvement through lime stabilisation offers the highest return on geotechnical investment, particularly on Routes A and B where baseline CBR is 3.2 and 5.1% respectively. 7. Discussion The LCCA results demonstrate conclusively that the current do-nothing trajectory on South Sudan's oil access road network is economically unsustainable. The cumulative cost of deferred maintenance — measured by the difference between do-nothing and minimum-LCC NPVs — ranges from USD 540,000 to USD 1,210,000/km across the five study routes, equivalent to the total initial construction cost of 0.5–1.1 km of new road for every existing kilometre of oil access road in the network. When scaled to the 1,865-km network, the aggregate cost of optimal versus do-nothing management amounts to USD 1.06– 1.48 billion over 30 years — a figure that dwarfs the estimated USD 45–60 million annual road maintenance fund that would be required to implement the minimum-LCC strategy. The identification of the 8.5-year overlay interval as the cost-minimising solution is broadly consistent with findings from analogous oil-corridor LCCA studies in Chad (Banerjee et al., 2020) and Nigeria (Tighe et al., 2019), who reported optimal intervals of 7–10 years for similar climatic and traffic conditions. The consistency of this result across multiple independently developed models increases confidence that the optimisation surface reflects genuine physical and economic relationships rather than model artefacts. The climate scenario analysis reveals a potentially serious funding gap. Under the extreme rainfall scenario (+50%), achieving the same service life as the baseline scenario requires increasing maintenance expenditure by 34–52% — an additional USD 85,000–125,000/km/year — to compensate for accelerated deterioration. If climate change proceeds along the RCP 8.5 trajectory, the 30-year total maintenance liability for the five-route network increases by USD 280–420 million relative to baseline projections. This finding provides a quantitative basis for the inclusion of a climate adaptation contingency in MoRB's road maintenance budget allocation formula, currently calculated without climate adjustment factors. A limitation of the study is that social costs — including accident costs, community access costs during road closure, and environmental contamination costs from road failures — were excluded from the LCC model owing to data unavailability. Preliminary estimates suggest these costs could add 8–15% to the total NPV, which would further strengthen the case for proactive maintenance investment. Future research should develop a comprehensive social cost accounting framework for South Sudan's oil corridor roads, building on the methodological foundations established by Gwilliam et al. (2008) for sub-Saharan Africa. 8. Conclusions This study has developed and applied the first published life-cycle cost analysis framework calibrated to South Sudan's oil field access road network. The principal conclusions are: (1) The minimum-LCC maintenance strategy — routine maintenance plus 8-year periodic 55 mm AC overlay cycle with major rehabilitation at year 20 — delivers a 30-year NPV of USD 980,000–1,420,000/km at r = 8%, representing a saving of 38–52% compared to a do-nothing baseline. (2) Traffic growth rate, discount rate, and initial construction cost are the three most sensitive model parameters, jointly explaining 38.7% of total NPV variance. Subgrade CBR, controllable through lime stabilisation, is the fourth-ranked parameter and offers the highest engineering return on investment. (3) Climate change under IPCC RCP 8.5 increases the 30-year total maintenance liability for the five-route network by USD 280–420 million relative to baseline, requiring a 34–52% increase in annual maintenance expenditure to maintain equivalent service levels. (4) The LCC optimisation surface identifies a global cost minimum at an overlay interval of 8.5 years and overlay thickness of 55 mm, providing a directly actionable pavement maintenance standard for MoRB to incorporate into its Roads Asset Management System. (5) Road user costs from VOC escalation under poor pavement conditions represent 30.6% of total LCC, confirming that an economic analysis limited to agency costs alone would systematically undervalue maintenance investment by approximately 44% on oil tanker corridors. Acknowledgements The author acknowledges the Ministry of Roads and Bridges, South Sudan, for institutional context and sector background information, together with academic support from UNICAF / Liverpool John Moores University and UniAthena / Guglielmo Marconi University. Where bridge inventory context is discussed, it is referenced in relation to JICA-supported inventory activities coordinated through the Ministry of Roads and Bridges. No external funding is declared. References Banerjee, A., Duflo, E., & Qian, N. (2020). On the road: Access to transportation infrastructure and economic growth in China. Journal of Development Economics, 145, 102442. Bennett, C. R., & Paterson, W. D. O. (2000). A Guide to Calibration and Adaptation of HDM-4 (Vol. 5). PIARC World Road Association, Paris. Donnges, C., Edmonds, G., & Johannessen, B. (2017). Rural Road Maintenance: Sustaining the Benefits of Improved Access. ILO, Bangkok. FHWA. (2004). Life-Cycle Cost Analysis Primer (FHWA-IF-02-047). Federal Highway Administration, US Department of Transportation, Washington D.C. Gwilliam, K., Foster, V., Archondo-Callao, R., Briceño-Garmendia, C., Nogales, A., & Sethi, K. (2008). The Burden of Maintenance: Roads in Sub-Saharan Africa. World Bank Background Paper, Washington D.C. IMF. (2023). South Sudan: 2023 Article IV Consultation — Staff Report. International Monetary Fund, Washington D.C. Machar, R., & Akuei, D. (2022). Oil infrastructure and road network planning in post-CPA South Sudan. Journal of African Infrastructure Development, 8(1), 33–47. Odoki, J. B., & Kerali, H. R. (2018). Analytical Framework and Model Descriptions: HDM-4 Volume 4 (Revised ed.). PIARC World Road Association, Paris. Paterson, W. D. O. (1987). Road Deterioration and Maintenance Effects: Models for Planning and Management. World Bank Publications, Washington D.C. MoRB. (2021). Ministry of Roads and Bridges Annual Report 2020–2021. Ministry of Roads and Bridges, Juba. Tighe, S., Tompkins, D., Gransberg, D., & Solaimanian, M. (2019). Pavement design and performance on petroleum tanker corridors in Nigeria. International Journal of Pavement Engineering, 20(8), 907–921. Walls, J., & Smith, M. R. (1998). Life-Cycle Cost Analysis in Pavement Design — In Search of Better Investment Decisions (FHWA-SA-98-079). Federal Highway Administration, Washington D.C. World Bank. (2012). Roads and the Economy: Updating and Improving World Bank Road Economics. Transport Note TN-5. World Bank Group, Washington D.C. World Bank. (2023). South Sudan Economic Update. World Bank Group, Washington D.C. World Bank. (2024). South Sudan Infrastructure Restoration Project: Project Appraisal Document (PAD5318). World Bank Group, Washington D.C. World Road Association (PIARC). (2013). HDM-4 Highway Development and Management System, Version 2.08. PIARC, Paris. © 202 6 Aduot Madit Anhiem · aduot.madit2022@gmail.com · · Open Access under CC-BY 4.0