A software for predicting Pavement Condition Index (PCI) using machine learning for practical decision‑making with an exclusion approach

In Palestine and other resource‑constrained settings, determining the Pavement Condition Index (PCI) requires exhaustive visual surveys of up to 19 distress types, which is a process that is both time‑consuming and costly to obtain. Despite advances in PCI prediction (2023–2025), existing methods still depend on full‑distress assessments, failing to reduce fieldwork burden. We present an open‑source machine learning software that classifies pavement into PCI categories (Good, Satisfactory, Fair, Poor, Impassable) by systematically excluding low‑utility distresses, reducing inspection effort by up to 40% while achieving an overall accuracy of 82%. The framework integrates features such as pavement age, layer thickness, right‑of‑way (ROW), average daily traffic (ADT), and heavy‑duty vehicle percentage.