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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.19232154 |
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| _version_ | 1866901960029896704 |
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| author | Office, Editorial |
| author_facet | Office, Editorial |
| contents | <p>Gravel roads serving South Sudan's oil field access network are subject to concurrent deterioration from seasonal Sudd wetland flooding — with inundation depths reaching 0.8–1.4 m for periods of 15–45 days per year — and extreme axle loadings from crude oil tanker combinations routinely exceeding 48 tonnes gross vehicle weight. Conventional deterministic pavement design methods applied in the region embed safety implicitly through empirical design catalogues calibrated to Central African subgrade conditions, without quantifying residual failure probability or the relative contribution of individual uncertainty sources. This paper presents the first probabilistic structural reliability analysis framework calibrated specifically to South Sudan oil field gravel road conditions, applying First-Order Reliability Method (FORM), Second-Order Reliability Method (SORM), and Monte Carlo Simulation (MCS) to two coupled limit-state functions representing structural rutting failure and flood washout failure. Eight random variables — subgrade CBR, gravel layer thickness, 24-hour design rainfall, flood inundation duration, gross vehicle weight, daily traffic volume, material grading index, and drainage coefficient — are characterised from field datasets comprising 142 DCP tests, 620 weigh-in-motion records, 30 years of rainfall gauge data, and satellite-derived flood extent mapping across six study routes totalling 1,065 km. FORM analysis yields system reliability indices of β = 2.48–3.75 across the six routes, with two routes (Routes B and D) falling below the ISO 2394 target of β_T = 3.5 and one route (Route D) approaching the serviceability threshold of β = 2.5. Sobol variance decomposition identifies subgrade CBR and flood inundation duration as the two dominant uncertainty source</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19232154 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Reliability Analysis of Oil Field Gravel RoadsUnder Seasonal Flooding and Heavy Vehicle Office, Editorial reliability analysis FORM SORM Monte Carlo simulation gravel roads South Sudan seasonal flooding heavy vehicle loading <p>Gravel roads serving South Sudan's oil field access network are subject to concurrent deterioration from seasonal Sudd wetland flooding — with inundation depths reaching 0.8–1.4 m for periods of 15–45 days per year — and extreme axle loadings from crude oil tanker combinations routinely exceeding 48 tonnes gross vehicle weight. Conventional deterministic pavement design methods applied in the region embed safety implicitly through empirical design catalogues calibrated to Central African subgrade conditions, without quantifying residual failure probability or the relative contribution of individual uncertainty sources. This paper presents the first probabilistic structural reliability analysis framework calibrated specifically to South Sudan oil field gravel road conditions, applying First-Order Reliability Method (FORM), Second-Order Reliability Method (SORM), and Monte Carlo Simulation (MCS) to two coupled limit-state functions representing structural rutting failure and flood washout failure. Eight random variables — subgrade CBR, gravel layer thickness, 24-hour design rainfall, flood inundation duration, gross vehicle weight, daily traffic volume, material grading index, and drainage coefficient — are characterised from field datasets comprising 142 DCP tests, 620 weigh-in-motion records, 30 years of rainfall gauge data, and satellite-derived flood extent mapping across six study routes totalling 1,065 km. FORM analysis yields system reliability indices of β = 2.48–3.75 across the six routes, with two routes (Routes B and D) falling below the ISO 2394 target of β_T = 3.5 and one route (Route D) approaching the serviceability threshold of β = 2.5. Sobol variance decomposition identifies subgrade CBR and flood inundation duration as the two dominant uncertainty source</p> |
| title | Reliability Analysis of Oil Field Gravel RoadsUnder Seasonal Flooding and Heavy Vehicle |
| topic | reliability analysis FORM SORM Monte Carlo simulation gravel roads South Sudan seasonal flooding heavy vehicle loading |
| url | https://doi.org/10.5281/zenodo.19232154 |