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Pavements 2026

Pavement Condition Assessments and Construction Quality Management

AI and Imaging Innovations for Airfield Pavement Evaluation: From PCI Modeling to Robotics

Overcoming Challenges in Using 3D Laser Imaging (LCMS) Data for Airport Pavement Evaluation Based on the Pavement Condition Index (PCI)

Wednesday, July 1, 2026

9:15 AM - 9:30 AM East Coast USA Time

Primary Author(s)

Ali Hafiz, n/a

Project Manager
Advanced Infrastructure Design, Inc.
hamilton, New Jersey, United States

Co-Author(s)

Ali Hafiz, n/a

Project Manager
Advanced Infrastructure Design, Inc.
hamilton, New Jersey, United States

Presenter(s)

Ali Hafiz, n/a

Project Manager
Advanced Infrastructure Design, Inc.
hamilton, New Jersey, United States

Co-Author(s)

Abbas S. Kachwalla, PE

Associate Vice President
AECOM
Chicago, Illinois, United States

Presenter(s)

Abbas S. Kachwalla, PE

Associate Vice President
AECOM
Chicago, Illinois, United States

Co-Author(s)

MC

Manuel Celaya, n/a

Practice Lead
Advanced Infrastructure Design (AID)
Hamilton Township, New Jersey, United States

Presenter(s)

MC

Manuel Celaya, n/a

Practice Lead
Advanced Infrastructure Design (AID)
Hamilton Township, New Jersey, United States

Submitter(s)

Ali Hafiz, n/a

Project Manager
Advanced Infrastructure Design, Inc.
hamilton, New Jersey, United States

Abstract: Pavement Management Systems (PMS) rely on accurate and consistent pavement condition data to support effective maintenance and rehabilitation planning. The Pavement Condition Index (PCI) is a key metric within PMS, providing a standardized method to evaluate pavement surface distress and overall condition. This study discusses the main challenges and proposes practical solutions for applying three-dimensional (3D) laser imaging data collected by a Laser Crack Measurement System (LCMS) to airport pavement evaluation using PCI. A primary challenge is that the LCMS captures only a 13-foot-wide swath, while PCI sample units are typically wider, requiring precise stitching and alignment of multiple scans. Another challenge is that some PCI distress types, particularly surface deformation related distresses such as rutting , depressions, etc. cannot be directly obtained from 2D imagery and must be extracted from 3D point cloud data. Additionally, maintaining straight and stable vehicle movement during data collection is essential to prevent positional drift and ensure geometric accuracy. To overcome these challenges, new data processing algorithms and automated assessment software were developed to integrate image stitching, point cloud analysis, and trajectory correction. The proposed approach was successfully applied to airport pavement evaluations, improving accuracy, consistency, and efficiency in PCI assessment using LCMS data.

Learning Objectives:

Attendees can expect to learn the following from this session:

Understand the key challenges in applying 3D Laser Crack Measurement System (LCMS) data for airport pavement evaluation based on the Pavement Condition Index (PCI).
Learn how advanced data processing techniques such as image stitching, point cloud analysis, and trajectory correction, can enhance the accuracy and consistency of PCI assessments.
Recognize the potential of integrating 3D laser imaging and automated analysis software within Pavement Management Systems (PMS) to modernize airport pavement evaluation practices.