Autonomous concrete crack detection using deep fully convolutional neural network

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Concrete and Pavement Cracks Detection Leveraging Vision Transformer (ViT) Model

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Release the Crack’CNN : Concrete Crack Detection using Convolutional Neural Networks

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Optimizing deep belief network for concrete crack detection via a modified design of ideal gas molecular dynamics

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Crack Detection and Structural Assessment in Bridges Using Generative Adversarial Networks

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Wind Turbine Surface Crack Detection Based on YOLOv5l-GCB

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Construction Site Segmentation Using Drone-Based Ortho-Image and Convolutional Encoder-Decoder Network Model

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Comparative Analysis of Deep Learning Algorithms Based on Surface Image Classification

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RC column damaged classification based on deep contrasted attention

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AUTOMATIC DETECTION AND DIMENSIONAL MEASUREMENT OF MINOR CONCRETE CRACKS WITH CONVOLUTIONAL NEURAL NETWORK

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Lightweight concrete crack segmentation method based on Deeplabv3+

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Classification of Road Damage Images Considering the Discovery of Unknown Classes

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Nondestructive testing method of Si3N4 ceramic bearing cylindrical roller surface defects based on depthwise separable convolution coupled with identity block ASPP

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An Universal Framework for Pavement Crack Detection

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Evaluation of Transfer Learning for Visual Road Condition Assessment

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WITHDRAWN: Deep Learning-based Fatigue Cracks Detection in Bridge Girders Using Feature Pyramid NetworksPreprint

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Crowdsensing-based Monitoring of Transportation Infrastructure using Moving Vehicles

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SCNet: A Generalized Attention-based Model for Crack Fault SegmentationPreprint

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Automatic Detection and Its Applications in Infrastructure

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شناسایی ترک در اعضاء بتنی با استفاده از مدلهای رمزگذار - رمزگشا بر پایه ی یادگیری عمیق

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Enhanced Accurate Segmentation of Bridge Surface Image Defects Based on Multi-scale Feature Fusion

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EFFICIENCY IMPROVEMENT OF PINNS INVERSE ANALYSIS BY EXTRACTING SPATIAL FEATURES OF DATA

Deguchi, S., SHIBATA, Y., & Asai, M. (2023) · Japanese Journal of JSCE · doi:10.2208/jscejj.22-15011 · Cited by 2

Public Database of Cracks Images in Mortar Coating with Different Types of Surface Finishes

Sobrinho, R. D. A. G., Neto, F. P. L., & Fernandes, H. (2023) · Buildings · doi:10.3390/buildings13071872 · Cited by 1

CrackF-Net: a pixel-level segmentation network for pavement crack detection

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Finding Width, Angle, Endpoint Length, and Actual Path Length of Cracks in Concrete Structures Using CNN and Image Processing

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Bibliometric Analysis of the Development of International Scientific Collaboration of Nguyen Tat Thanh University (Vietnam) over the last Decade

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Design of Convolution Neural Network for Crack Detection

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Advanced Parallel Laser Line-Camera System for Automatic and Precise Crack Width Measurement

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Cross-Domain Robustness of Transformer-Based Keyphrase Generation

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Fractal Characterization of Channel-Lining Concrete Based on U-Net Deep Learning under Freeze–Thaw Cycles

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A Multi-Deep Learning Intelligent Surface Rock Crack Identification Method for Transmission Tower Siting

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Multi-scale feature fusion networks with frequency-domain attention and depth-wise convolution for pavement crack detection

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Influence of data source and volume on CNN applications in construction

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Hybrid CNN and image processing framework for precise characterization of cracks in concrete structures

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Real-Time Multi-Damage Detection and Risk Prioritisation for Aging Buildings Using YOLOv11 and a Damage Criticality Index

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Crack Detection in Concrete Bridges with CNN Model

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Automating the static and seismic design of 2-D multistorey reinforced concrete structures by using Monte Carlo Tree Search and Genetic Algorithm

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CrackGA: Automatic Road Crack Detection Integrating Deep Learning and Genetic Algorithm

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Concrete Crack Identification Through Hu Moment Shape Analysis and K-Nearest Neighbors Models

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Advanced metaheuristic optimization with enhanced dung beetle algorithm for automated crack detection in civil infrastructure

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Research on image acquisition and intelligent detection algorithms for tunnel cracks

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Detection Models for Concrete Crack Using Deep Learning Approaches: A Review

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LiBiNet: Lightweight boundary information enhanced network for crack segmentation

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Real-Time Pavement Crack Detection for Cutting Machines: Enhancing YOLOv5 with Lightweight Modules and Attention Mechanisms

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CrackRenderFormer: transformer and boundary-guided point rendering technique for refined segmentation of high-resolution bridge crack images

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A deep learning based method for reference-free full-field strain measurement

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Unsupervised domain adaptation framework for crack segmentation using consistency regularization based on cross-domain image augmentation

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From molecules to structures: Role of artificial intelligence in fatigue fracture characterization of concrete

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Multiscale Segmentation and Quantitative Grading Detection of Subway Tunnel Crack Images

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Automated Crack Detection on Concrete Surfaces: An Evaluation of Deep Learning Approaches Using YOLOv8

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TAC-TransUNet: A texture-aware fusion network for underwater dam crack detection

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CrackNet: A novel multi-scale architecture for crack segmentation

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Incorporating Structural Prior Knowledge into YOLO for Robust Infrastructure Damage Detection

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Damage Attention-Aware Dense Layered Framework for Surface Crack Classification

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Damage Assessment of RC Beam–Column Joints via Digital Image Correlation and Fractal Dimension Analysis

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Lightweight deep learning for sustainable adobe construction: a block-level baseline aligned with circular economy principles and sustainable development goals

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A high-precision deep learning concrete thin crack segmentation network

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Vehicular road crack detection with deep learning: An online benchmark for comprehensive evaluation of existing algorithms

Ma, N., Song, Z., Hu, Q., Liu, C.-W., Han, Y., Zhang, Y., Fan, R., & Xie, L. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.105158 · Cited by 4

Crack Detection and Location Estimation Using a Convolutional Neural Network

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Automatic Crack Segmentation in Pavements using a Dilated Encoder-Decoder Network

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Classification of Ground-Truth Fire Debris Samples Using Neural Networks

Thurn, N. A. (2021) · Journal of International Crisis and Risk Communication Research · Cited by 1

Development of a Neural Network Embedding for Quantifying Crack Pattern Similarity in Masonry Structures

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Concrete Crack Detection Based on Hybrid Residual Network and Graph Convolutional Network

Huang, X., Zhou, X., & Duan, R. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-9913-9_9 · Cited by 1

An Investigation of the Use of Personal Protective Equipment on the Jobsite using Transfer Learning Approach

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Classification of Surface Fracture in Plastics Using Convolutional Neural Networks

Jung, D., Oh, W. J., Kyeong, J. S., & Lee, S.-J. (2023) · MATERIALS TRANSACTIONS · doi:10.2320/matertrans.mt-mi2022002 · Cited by 1

Crack Detection on Brick Walls by Convolutional Neural Networks Using the Methods of Sub-dataset Generation and Matching

Talukder, M. H., Ota, S., Takanokura, M., & Ishii, N. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-37320-6_7 · Cited by 1

Prediction of Hardness and Fracture Toughness for WC-FeAl from Its Microstructural Images via Convolutional Neural Network

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Fissura_Net: An Enhanced Crack Detection and Prediction Network

Jawahar, M., Neeraja, R., Anbarasi, L. J., & Raj, B. E. (2023) · International Conference on Information Technology Trends (ITT) · doi:10.1109/itt59889.2023.10184265 · Cited by 1

SMART METHOD RECOMMENDATIONS FOR THE DETECTION OF POST-EARTHQUAKE DAMAGES IN RC BUILDINGS

Doğan, G., Arslan, M. H., & İlki̇, A. (2023) · COMPDYN Proceedings · doi:10.7712/120123.10470.20866 · Cited by 1

IUCNet: A Novel Semantic Segmentation Network for Overlapping Industrial Parts Detection

Wu, K., & Yang, Q. (2023) · International Conference on Computers, Information Processing and Advanced Education (CIPAE) · doi:10.1109/cipae60493.2023.00078 · Cited by 1

Segmentation Method of Concrete Small Cracks Based on UAV Images

Pan, Y., Zou, W., Xv, Q., Zhao, Y., Liang, Q., & Zhao, T. (2023) · Recent Advances in Computer Science and Communications · doi:10.2174/0126662558276323231129053808 · Cited by 1

Registration Based Feature Core-Set Model for Defect Detection

Jing, X. (2023) · China Automation Congress (CAC) · doi:10.1109/cac59555.2023.10450867 · Cited by 1

Pavement Crack Segmentation Network Based on Local Diagonal Gray Scale Transformation Algorithm

Xie, T., Wang, S., Li, T., & Chang, C. J. (2023) · International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI) · doi:10.1109/cei60616.2023.10528159 · Cited by 1

U2Net pipeline crack detection method with improved self-attention

Li, Y. T., & Zuo, X. (2024) · International Conference on Electronic Information Engineering and Data Processing (EIEDP) · doi:10.1117/12.3033198 · Cited by 1

Visual Recognition and Prediction of Crack Propagation Trajectories

Ai, D. (2024) · IntechOpen eBooks · doi:10.5772/intechopen.113152 · Cited by 1

Beton Yüzey Çatlaklarının YOLOv8 Derin Öğrenme Algoritması ile Tespit Edilmesi

Altun, M. G., & Altun, A. H. (2024) · Çukurova Üniversitesi Mühendislik Fakültesi Dergisi · doi:10.21605/cukurovaumfd.1560104 · Cited by 1

Coupling x-ray computed tomography and gray-level co-occurrence matrix to assess the evolution of microstructural damage in coal treated with supercritical CO2 fluid

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Data-driven approaches in concrete science: applications, challenges and future prospects

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Rapid construction of large-scale bridge crack dataset using residual neural network

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A Multi-Resolution Attention U-Net for Pavement Distress Segmentation in 3D Images: Architecture and Data-Driven Insights

Gong, H., Tao, J., Luo, X., & Wang, F. (2025) · Mathematics · doi:10.3390/math13172752 · Cited by 1

Automatic Pavement Crack Detection using Multi-Scale Image & Neighborhoods Information

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Road Crack Detection and Recognition Based on Image Correction of Horizontal Camera and Convolutional Neural Network

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CRACK DETECTION FROM EXTERNAL WALLS OF NATURAL STONES USING DEEP CONVOLUTIONAL NEURAL NETWORK

Suzuki, A., YANAGIMOTO, T., NAKAMURA, R., Sakamoto, S., & Sakanashi, H. (2021) · AIJ Journal of Technology and Design · doi:10.3130/aijt.27.1086 · Cited by 1

EXTRACTING TEXTUAL INFORMATION FROM BRIDGE INSPECTION RECORDS USING DEEP LEARNING

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