Validation of an LSDF-Adjusted Inspection Framework for Pipeline Maintenance Planning in the Niger Delta

Static inspection programmes may apply uniform inspection intervals across heterogeneous asset populations, whereas risk-based inspection (RBI) seeks to differentiate inspection priorities according to assessed risk. However, location-specific environmental, biological and socio-security exposure is not always represented explicitly in inspection scheduling. This study evaluates and internally validates a Location-Specific Degradation Factor (LSDF) as a decision-support layer for inspection planning across twelve Niger Delta pipeline locations. LSDF scores were converted to an effective corrosion rate using the calibrated amplification model CR eff =CR base (1+k⋅LSDF), with k≈4.74 estimated by root-mean-square error (RMSE) minimisation, and subsequently mapped to LSDF severity classes and study-specific maximum inspection intervals. In-sample predicted-versus-observed degradation rates showed strong agreement (Pearson r=0.998, predictive R 2 =0.993, RMSE = 0.064 mm/yr, and classification accuracy = 91.7%, 11/12; exact 95% confidence interval (CI): 61.5-99.8%). Leave-one-location-out cross-validation (LOLO-CV) preserved classification accuracy at 91.7% (11/12; exact 95% CI: 61.5-99.8%) while giving a more conservative RMSE of 0.073 mm/yr, and stable refitted k values of 4.710-4.804. Relative to a uniform 36-month static inspection benchmark, the LSDF-based policy assigned shorter inspection intervals to locations with greater degradation exposure and longer intervals to locations with lower exposure. This reallocation increased total scheduled inspection events over five years from 24 to 62 (+158.3%), reflecting intensified surveillance of higher-severity locations rather than an overall reduction in inspection activity. No economic-benefit claim is made because location-specific inspection, mobilisation, failure-consequence and discount-rate data were unavailable. The LSDF is therefore proposed as a location-sensitive decision-support layer that complements, rather than replaces, established RBI and condition-based integrity assessment.