Abstract

The rapid expansion of Egypt’s road infrastructure presents significant challenges in terms of maintenance and monitoring. These difficulties are mainly due to the lack of current data on road conditions and limited resources to gather new data. In such contexts, relying on traditional survey-based approaches for assessing road conditions proves to be both costly and time consuming. As a result, there is a growing need for more efficient and accessible methods to address the maintenance burden imposed by the expanding network. The objective of this thesis is to develop a resilient, data-driven framework that predicts pavement condition using open-access satellite imagery integrated with low-cost International Roughness Index (IRI) measurements, enabling local road authorities to prioritize maintenance more efficiently.

To meet this objective, the study focuses on a 1405‑segment network in New Cairo, Egypt, covering arterial, collector, and local roads, for which IRI readings were collected via a smartphone-based application and matched with Google Earth Pro satellite images. Five convolutional neural network architectures—LeNet, EfficientNet, VGG‑16, AlexNet, and ResNet‑50—were trained and evaluated under three‑category and binary condition rating schemes across three datasets (482, 809, and 1405 segments) to analyze the influence of data volume and model choice on predictive performance.

The results show that the best three‑category model (VGG‑16) achieved a test accuracy of about 64%, whereas the best binary models (ResNet‑50 and AlexNet) reached approximately 77% accuracy, indicating greater robustness for binary classification under the available data quality. These findings suggest that open-access satellite imagery combined with smartphone collected IRI measurements can deliver performance comparable to studies using commercial high-resolution satellite Imagery more accurate IRI measurements. Moreover, the study recommends that binary models can be utilized as a practical decision-support tool while pursuing future enhancements through higher-resolution satellite imagery, expanded and more variable datasets, and more advanced hybrid deep learning architectures.

School

School of Sciences and Engineering

Department

Construction Engineering Department

Degree Name

MS in Construction Engineering

Graduation Date

Spring 6-5-2026

Submission Date

2-12-2026

First Advisor

Mohamed Abou-zeid

Second Advisor

Maram Saudy

Committee Member 1

May Haggag

Committee Member 2

Ehab Nour Eldin

Committee Member 3

Ezzeldin Yazeed

Extent

84 p.

Document Type

Master's Thesis

Institutional Review Board (IRB) Approval

Not necessary for this item

Disclosure of AI Use

Thesis text drafting; Code/algorithm generation and/or validation

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Available for download on Friday, February 12, 2027

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