A software for predicting Pavement Condition Index (PCI) using machine learning for practical decision‑making with an exclusion approach
Year: 2025
Type: software-paper
Abstract: 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.
Source: SoftwareX
Authors: Huthaifa I. Ashqar, Amjad Issa, Sari Masri
Institutions: Arab American University, An-Najah National University
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,200
OpenAlex ID: W6958948366