Work

AI-Driven Detection of Alkali-Silica Reaction in Concrete Structures Using Feature-Enhanced Deep Learning Models

Year: 2025

Type: article

Abstract: Concrete’s affordability, adaptability, and resilience make it a cornerstone of construction, yet its vulnerability to degradation, particularly Alkali-Silica Reaction (ASR), poses significant challenges. ASR induces cracking and structural instability, necessitating efficient detection methods to mitigate its impacts. This study explores the use of Artificial Intelligence (AI) techniques for ASR crack identification, employing image enhancement and advanced models such as ResNet-18, InceptionV3, and AlexNet. A dataset of ASR-affected images was developed and augmented through feature enhancement processes, improving crack visibility and classification accuracy. Among the tested models, InceptionV3 demonstrated superior performance with high accuracy and robustness. The findings reveal that AI-based approaches, combined with image enhancement, effectively identify ASR cracks without requiring structural surface treatment. This research offers a scalable, automated solution to ASR detection, advancing structural health monitoring technologies and contributing to the preservation of critical infrastructure.

Source: IEEE Access

DOI: 10.1109/access.2025.3591865

Authors: Yujie Wu, Mengze Wu, Tianyi Cui, Jiani Lin, Qingke Liao, Jinqiu Shu

Institutions: Chongqing University, Xi’an Jiaotong-Liverpool University, Cardiff University

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,075