AI ROLE IN INITIAL STAGE OF STRUCTURAL HEALTH MONITORING IN CIVIL INFRASTRUCTURES

            Structural integrity assessment is crucial for ensuring the safety and longevity of infrastructure. Cracks, a common structural defect, can originate from various causes, including mechanical stress, environmental factors, and material degradation. These cracks are classified based on their orientation (horizontal, vertical, diagonal), cause (structural, non-structural), and propagation behavior (surface, subsurface, thorough). Traditional crack detection methods such as Visual Inspection Analysis (VIA), Acoustic Emission (AE), and Ultrasonic Testing (UT) have been widely used for in-situ analysis. These methods, while effective, often involve high costs, time-consuming manual inspections, and limited accuracy in complex structures. Digital Image Processing (DIP) and Visual Inspection (VI) offer cost-effective solutions for initial crack detection, but their effectiveness is limited without automation. Recent advancements in Artificial Intelligence (AI) have revolutionized Structural Health Monitoring (SHM) by automating crack detection and classification. Machine Learning (ML) and Deep Learning (DL), key AI subfields, provide robust solutions for automatic crack identification by analyzing image patterns and structural datasets. ML models use predefined features extracted from images or sensor data, while DL models operate directly on raw images or feature vectors to classify cracks accurately. These AI-driven approaches significantly enhance the efficiency and accuracy of crack detection compared to traditional methods. However, challenges such as data availability, model generalization, and real-time implementation need to be addressed for widespread adoption. The integration of AI with existing SHM frameworks holds great potential for improving structural assessment, reducing maintenance costs, and preventing catastrophic failures.

• Publication year

  2025

• Venue

  International Journal of Applied Mathematics

• Publication date

  2025-10-02

• Fields of study

  Not labeled

• Identifiers
  DOI 10.12732/ijam.v38i5.1412
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Non-destructive methodology for crack detection using machine learning-assisted resonant sensor

  2024cited by this paper
• Automatic recognition system for concrete cracks with support vector machine based on crack features

  2024cited by this paper
• Rapid visual assessment of crack on residential building

  2023cited by this paper
• Seismic damage prediction of RC buildings using machine learning

  2023cited by this paper
• A rapid machine learning-based damage detection algorithm for identifying the extent of damage in concrete shear-wall buildings

  2023influential reference
• Automatic image-based brick segmentation and crack detection of masonry walls using machine learning

  2022cited by this paper
• Deep Learning-Based Crack Detection: A Survey

  2022cited by this paper
• A Comprehensive Review of Deep Learning-Based Crack Detection Approaches

  2022cited by this paper
• A Review of Infrared Thermography for Delamination Detection on Infrastructures and Buildings

  2022cited by this paper
• A Systematic Review of Structural Health Monitoring Systems to Strengthen Post-Earthquake Assessment Procedures

  2022cited by this paper
• Machine learning based computational approach for crack width detection of self-healing concrete

  2022cited by this paper
• Multi-Image-Feature-Based Hierarchical Concrete Crack Identification Framework Using Optimized SVM Multi-Classifiers and D-S Fusion Algorithm for Bridge Structures

  2021cited by this paper
• An integrated machine learning model for automatic road crack detection and classification in urban areas

  2021cited by this paper
• Short Review of the Use of Acoustic Emissions for Detection and Monitoring of Cracks

  2021cited by this paper
• Detecting cracks due to steel corrosion in reinforced cement mortar using intermodulation generation of ultrasonic waves

  2021cited by this paper
• Inspecting Buildings Using Drones and Computer Vision: A Machine Learning Approach to Detect Cracks and Damages

  2021cited by this paper
• Detection of multidamage to reinforced concrete using support vector machine‐based clustering from digital images

  2021cited by this paper
• Image-Based Crack Detection Methods: A Review

  2021cited by this paper
• Concrete Cracks Detection and Monitoring Using Deep Learning-Based Multiresolution Analysis

  2021cited by this paper
• Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

  2021cited by this paper
• BUILDING CRACKS: CAUSES AND PREVENTIONS

  2020cited by this paper
• Classifying earthquake damage to buildings using machine learning

  2020cited by this paper
• Concrete crack detection and quantification using deep learning and structured light

  2020cited by this paper
• Automated crack detection and measurement based on digital image correlation

  2020cited by this paper
• Remote inspection of RC structures using unmanned aerial vehicles and heuristic image processing

  2020cited by this paper
• A review on acoustic emission monitoring for damage detection in masonry structures

  2020cited by this paper
• Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

  2019cited by this paper
• Automated Vision-Based Detection of Cracks on Concrete Surfaces Using a Deep Learning Technique

  2018cited by this paper
• Study on Causes and Prevention of Cracks in Building

  2018cited by this paper
• Crack monitoring in historical masonry with distributed strain and acoustic emission sensing techniques

  2018cited by this paper
• Concrete Cracks Detection Based on Deep Learning Image Classification

  2018cited by this paper
• A novel deep learning-based method for damage identification of smart building structures

  2018cited by this paper
• INFRARED THERMOGRAPHY FOR DETECTING DEFECTS IN CONCRETE STRUCTURES

  2018cited by this paper
• Research Progress of Visual Inspection Technology of Steel Products—A Review

  2018cited by this paper
• New Crack Detection Method for Bridge Inspection Using UAV Incorporating Image Processing

  2018cited by this paper
• Statistical Features and Traditional SA-SVM Classification Algorithm for Crack Detection

  2018cited by this paper
• Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks

  2017cited by this paper
• Ultrasonic health monitoring in structural engineering: buildings and bridges

  2016cited by this paper
• Study on control of cracks in a Structure through Visual Identification & Inspection

  2014cited by this paper
• Evaluation of corrosion-induced crack and rebar corrosion by ultrasonic testing

  2014cited by this paper
• Crack detection and characterization techniques—An overview

  2014cited by this paper
• Monitoring Crack Propagation in Reinforced Concrete Shear Walls by Acoustic Emission

  2013cited by this paper
• Combined use of thermography and ultrasound for the characterization of subsurface cracks in concrete

  2010cited by this paper
• Historical brick-masonry subjected to double flat-jack test: Acoustic Emissions and scale effects on cracking density

  2009cited by this paper
• Framework for building defects and their identification technologies: case studies of domestic buildings in Melbourne, Australia

  year unknowncited by this paper

• No citing papers are available for this paper.