Buildings are constantly subjected to environmental changes, material fatigue, and unexpected natural events. Over time, these factors can lead to cracks, which serve as early warning signs of structural deterioration. If left undetected, these cracks can cause safety hazards and lead to expensive repairs. Traditionally, engineers rely on manual inspections to detect cracks, but this process is slow, prone to human error, and inefficient for large structures.
Thanks to advancements in artificial intelligence, deep learning has transformed crack detection into a more efficient and accurate process. This article delves into how AI-driven models improve accuracy, reduce inspection time, and enhance overall building safety. We will explore the reasons behind crack formation, compare traditional methods with AI-based solutions, and discuss the most effective deep learning models currently in use. Furthermore, we will highlight the future of AI in crack detection and the role of emerging technologies in structural health monitoring.
Cracks appear in buildings due to several factors. Material fatigue occurs when structural components degrade over time, while thermal expansion causes materials to contract and expand due to temperature changes. Moisture penetration weakens the building materials, leading to cracks. Excessive weight or stress on a structure compromises its integrity, and seismic activity from earthquakes induces fractures and surface damage. Identifying and addressing these cracks early ensures the longevity and durability of buildings.
In addition to natural factors, human errors in construction and material selection can also lead to crack formation. Poor quality cement, improper curing, and structural design flaws contribute to premature cracking. Moreover, vibrations caused by heavy machinery, transportation, and underground activities can accelerate crack development. Understanding these underlying causes enables engineers to take preventive measures and mitigate risks.
Traditional vs. AI-Based Crack Detection
Traditional Methods
Conventional crack detection methods include visual inspections, ultrasonic testing, and infrared imaging. Visual inspection relies on engineers manually assessing structures, which is subjective and labor-intensive. Ultrasonic testing uses sound waves to detect internal defects, but it requires specialized equipment. Infrared imaging identifies cracks based on heat distribution, but it is costly and sensitive to external factors.
Manual inspections often depend on the experience and expertise of the inspector, making them susceptible to human error. Additionally, these methods are time-consuming, requiring significant manpower to examine large infrastructures. In some cases, cracks may be hidden behind coatings or structural layers, making visual detection even more challenging.
AI-Based Deep Learning Approaches
Deep learning automates crack detection using image recognition techniques, allowing for quick and accurate analysis. AI models process thousands of images, recognize crack patterns, and identify anomalies with high precision. This method is faster, reduces human error, is cost-effective, and can be applied to buildings, roads, and bridges on a large scale.
One of the most significant advantages of AI-based crack detection is its ability to analyze images in real-time. Engineers can deploy drones and mobile devices equipped with AI models to scan structures efficiently. These AI-driven solutions enhance predictive maintenance strategies, allowing for proactive repairs before cracks worsen.
Deep Learning Models for Crack Detection
In recent years, several deep learning models have emerged as effective tools for automated crack detection in various materials and structures. Among these, Convolutional Neural Networks (CNNs) have demonstrated exceptional performance due to their ability to automatically extract relevant features from images. These networks are particularly adept at recognizing patterns and anomalies, making them suitable for identifying cracks that may not be easily visible to the naked eye.
In addition to standalone CNNs, hybrid architectures that combine CNNs with other machine learning techniques have also shown significant promise. By integrating different approaches, these hybrid models can enhance detection accuracy and improve robustness against diverse environmental conditions and varying image quality. Overall, the use of these advanced deep learning strategies is transforming the landscape of crack detection, leading to faster, more reliable assessments in fields such as civil engineering and infrastructure maintenance.
1. Inception V3
One of the most accurate models, Inception V3, enhances performance using auxiliary classifiers and factorized convolutions. It achieves a remarkable 99.98% accuracy in crack classification. The model’s efficiency lies in its ability to analyze high-resolution images with multiple layers of abstraction, ensuring precise identification of cracks of varying sizes.
2. ResNet-50
ResNet-50 is a deep residual network designed to prevent gradient vanishing issues. It is excellent at detecting even the smallest cracks and achieves 99.98% accuracy in large-scale image classification. ResNet-50’s skip connections allow for efficient learning in deep networks, making it ideal for analyzing complex structures with multiple layers.
3. VGG-16 and VGG-19
These simple yet effective CNN architectures are widely used for image recognition. VGG-16 achieves 99.74% accuracy, while VGG-19 attains 99.69% accuracy. However, they require high computational power. These models rely on deeper convolutional layers to improve feature extraction and pattern recognition.
4. CNN-ResMLP
A hybrid model that combines CNNs with Multi-Layer Perceptrons (MLP), CNN-ResMLP achieves 99.15% accuracy, making it suitable for real-time applications. The model integrates spatial information efficiently, allowing it to detect cracks in a variety of surface textures.
5. InceptionV2ResNet
This model merges Inception modules with residual connections, balancing computational efficiency and accuracy at 99.7%. It is an excellent choice for detecting complex cracks. The combination of inception and residual learning improves the model’s adaptability to different structural environments.
Deep learning significantly enhances crack detection through various sophisticated techniques. Initially, image preprocessing and data augmentation play a crucial role in improving model performance. Techniques such as contrast adjustment, noise reduction, and methods like rotation and flipping help the model train on a diverse range of crack patterns. Moreover, synthetic data generation further boosts model robustness by simulating different lighting and environmental conditions.
In terms of feature extraction, convolutional neural networks (CNNs) are employed to identify key features such as edges, corners, texture variations, and depth differences, which are essential for assessing crack severity. The hierarchical structure of CNN layers allows for the extraction of both low-level and high-level visual features, enhancing detection accuracy. Once features are identified, AI models classify cracks as major or minor, enabling engineers to prioritize repairs effectively.
Techniques such as hyperparameter tuning and ensemble learning minimize false positives and negatives, and combining multiple AI models enhances reliability while reducing uncertainty in predictions. Looking ahead, the future of AI in structural health monitoring is promising, with emerging trends including edge AI for real-time detection on mobile devices, AI-powered drones inspecting high-rise buildings, and 3D crack analysis using depth-sensing cameras.
Additionally, IoT sensors monitoring crack growth in real-time are becoming more prevalent. Advancements in AI are also leading to the development of autonomous inspection robots that can perform thorough structural assessments in complex environments, such as tunnels and bridges, thus providing safer and more efficient inspection processes.
Deep learning is transforming crack detection, offering a faster, more precise, and cost-effective alternative to traditional methods. Models like Inception V3, ResNet-50, and CNN-ResMLP enable engineers to identify and mitigate structural weaknesses efficiently. As AI technology advances, automated crack detection will become a standard practice, ensuring safer and more durable infrastructure worldwide. With the continued integration of AI-driven solutions, the construction industry is set to benefit from improved safety standards, reduced maintenance costs, and enhanced longevity of infrastructure.
Reference
Krishnan, S. S. R., Karuppan, M. N., Khadidos, A. O., Khadidos, A. O., Selvarajan, S., Tandon, S., & Balusamy, B. (2025). Comparative analysis of deep learning models for crack detection in buildings. Scientific Reports, 15(1), 2125.https://doi.org/10.1038/s41598-025-85983-3