Date Approved

1-27-2026

Embargo Period

1-27-2026

Document Type

Thesis

Degree Name

M.S. Civil Engineering

Department

Civil engineering

College

Henry M. Rowan College of Engineering

Advisor

Adriana Trias Blanco, Ph.D.

Committee Member 1

Cheng Zhu, Ph.D.

Committee Member 2

Wade Lin, Ph.D.

Keywords

LiDAR;Point Cloud;Remote Sensing;Soil deformations

Disciplines

Civil and Environmental Engineering | Civil Engineering | Engineering

Abstract

Frost heave and thaw-related deformation threaten the performance of roads, runways, embankments, and buried utilities in cold regions, yet traditional inspections remain reactive and often miss early uplift. This study evaluates the capability of LiDAR (Light Detection and Ranging) to detect small-scale ground deformation and introduces a practical point-cloud framework designed for early-stage characterization. Controlled jack-lift experiments quantified handheld LiDAR accuracy at uplift increments of 6.35, 12.7, 19.05, and 25.4 mm, with corresponding detection accuracies of 88%, 86%, 93.33%, and 94%. Registration quality had a significant impact. The targetless scans showed slight misalignment, whereas spherical targets provided precise, repeatable outputs. Although the framework functions without baselines or targets, the highest fidelity is achieved when both are available and when baseline subtraction is applied. A combined global and local roughness strategy was established to differentiate shallow uplift, with global metrics demonstrating greater sensitivity during the initial stages of deformation. The study enhances LiDAR-based (handheld for small scale; mobile and terrestrial for large-scale) deformation monitoring by quantifying accuracy under controlled uplift, formalizing a robust registration and change-detection framework, establishing a practical thresholding strategy, and identifying an optimal analysis grid. Grid-resolution analysis identified a 5 × 6 grid size (3.33% of the scan area) as the optimal balance between computational cost and accuracy. These results support earlier detection of ground deformation.

Download

Share

COinS