Document Type
Article
Publication Date
Winter 12-12-2024
Abstract
Abstract:
Defects during the construction phase of projects pose significant challenges, particularly in terms of
safety, cost overruns, delays, and labor inefficiencies. In irrigation concrete canal lining construction
with mega investments, early detection of defects is critical to ensuring quality and project success.
This study explores the application of machine learning (ML) for onsite defect detection, focusing
on the development and deployment of an object detection mobile application specifically tailored
for identifying visible defects in concrete canal lining construction. Leveraging the machine learning
capabilities of Microsoft Azure, a custom object detection model was trained and validated to
recognize defects such as cracks, honeycombing, and steel reinforcement corrosion in canal lining
structures. The results demonstrated that the application effectively identified defects, though
further enhancements to its accuracy and robustness are needed.To evaluate the potential
project-wide impacts of such technology, the study also examined the effects of implementing MLdriven
defect detection on cost, time, and labor productivity through an online survey of experienced
engineers in the Egyptian construction industry. The findings highlighted the promising potential
of machine learning in transforming defect detection processes, improving project performance,
and addressing key construction challenges. However, the study also identified practical barriers,
such as technology adoption and training, that must be addressed for broader implementation.
This research underscores the transformative potential of ML in irrigation infrastructure projects
and its role in advancing construction quality and efficiency.
Recommended Citation
Ahmed Alhady, Aya Hassan, Mohamed Nabawy, Mohamed Hegazy, Paper ID: 556, “Leveraging Machine Learning for Defect Detection in Irrigation Concrete Canal Lining in Egypt: Advancing Construction Quality and Efficiency”, The Second International Conference on Advanced Sustainable Futures (ICASF 2024), Innovation and Digital Transformation for Sustrainable Futures, 12 December 2024, Abu Dhabi, UAE. https://cdn.adu.ac.ae/images-container/docs/default-source/icasf-docs/icasf-2024-proceedings.pdf
