Análise do uso de tecnologias digitais para identificação automatizada de patologias em construções
DOI:
https://doi.org/10.46421/entac.v19i1.2162Palavras-chave:
Inspeção automatizada, Tecnologias digitais, Drone, Inteligência Artificial (IA), Revisão Sistemática da Literatura (RSL)Resumo
Este estudo visa identificar as principais tecnologias digitais em uso para inspeções automatizadas de patologias em elementos de construção e como as informações obtidas podem apoiar na tomada de decisão gerencial nas obras. O método de pesquisa proposto foi a revisão sistemática da literatura. Foram analisados 26 artigos nas bases da Scopus, Web of Science e IEEE Xplore. Como resultados, observou-se a oportunidade de pesquisas para aperfeiçoamento e robustez nos algoritmos de inteligência artificial, bem como a necessidade de incorporação dessas informações durante a etapa de execução, apoiando a tomada de decisão no processo de controle da qualidade.
Referências
ALI, R., CHUAH, J. H., TALIP, M. S. A., MOKHTAR, N., & SHOAIB, M. A. Structural crack detection using deep convolutional neural networks. Automation in Construction, v. 133, p. 103989, 2022.
AYELE, Y. Z.; ALIYARI, M.; GRIFFITHS, D.; DROGUETT, E. L. Automatic crack segmentation for UAV-assisted bridge inspection. Energies, v. 13, n. 23, p. 6250, 2020.
BACKES, A. R.; JUNIOR, J. J. D. M. S. Introdução à visão computacional usando Matlab. Alta Books Editora, 2019.
BHOWMICK, S.; NAGARAJAIAH, S.; VEERARAGHAVAN, A. Vision and deep learning-based algorithms to detect and quantify cracks on concrete surfaces from UAV videos. Sensors, v. 20, n.21, p. 6299, 2020.
BOUZAN, G. B.; FAZZIONI, P. F.; FAISCA, R. G.; SOARES, C. A. Building facade inspection: A system based on automated data acquisition, machine learning, and deep learning image classification methods. ARPN Journal of Engineering and Applied Sciences, v. 16, n. 14, p. 1516, 2021.
CASTAGNO, J.; ATKINS, E. Roof shape classification from LiDAR and satellite image data fusion using supervised learning. Sensors, v. 18, n.11, p. 3960, 2018.
CHA, Y. J.; CHOI, W.; BÜYÜKÖZTÜRK, O. Deep learning-based crack damage detection using convolutional neural networks, Computer-Aided Civil and Infrastructure Engineering, v. 32, n. 5, p. 361– 378, 2017.
CHOI, D.; BELL, W.; KIM, D.; KIM, J. UAV-Driven Structural Crack Detection and Location Determination Using Convolutional Neural Networks. Sensors, v. 21, n. 8, p. 2650, 2021.
DAIS, D.; BAL, I. E.; SMYROU, E.; SARHOSIS, V. Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning. Automation in Construction, v. 125, p. 103606, 2021.
DAMACENO, S. S. Inteligência artificial: uma breve abordagem sobre seu conceito real e o conhecimento popular. Caderno de Graduação-Ciências Exatas e Tecnológicas-UNIT-SERGIPE, v. 5, n. 1, p. 11-11, 2018.
DORAFSHAN, S.; THOMAS, R. J.; MAGUIRE, M. Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete. Construction and Building Materials, v. 186, p. 1031-1045, 2018.
DRESCH, A.; LACERDA, D. P.; JÚNIOR, J. A. V. A. Design Science Research: método de pesquisa para avanço da ciência e tecnologia. Bookman Editora, 2015.
ELLENBERG, A.; KONTSOS, A.; MOON, F.; BARTOLI, I. Bridge related damage quantification using unmanned aerial vehicle imagery. Structural Control and Health Monitoring, v. 23, n. 9, p. 1168-1179, 2016.
FLAH, M.; SULEIMAN, A. R.; NEHDI, M. L. Classification and quantification of cracks in concrete structures using deep learning image-based techniques. Cement and Concrete Composites, v. 114, p. 103781, 2020.
GHOSH MONDAL T.; JAHANSHAHI, M. R.; WU, R. T.; WU, Z. Y. Deep learning‐based multi‐class damage detection for autonomous post‐disaster reconnaissance. Structural Control and Health Monitoring, v. 27, n. 4, p. 2507, 2020.
GUO, J.; WANG, Q.; LI, Y. Evaluation-oriented façade defects detection using rule-based deep learning method. Automation in Construction, v. 131, p. 103910, 2021.
HOANG, N. D. Image Processing-Based Recognition of Wall Defects Using Machine Learning Approaches and Steerable Filters. Computational Intelligence and Neuroscience, v. 2018, 2018.
HOSKERE, V.; NARAZAKI, Y.; HOANG, T. A.; SPENCER JR, B. F. MaDnet: multi-task semantic segmentation of multiple types of structural materials and damage in images of civil infrastructure. Journal of Civil Structural Health Monitoring, v. 10, n.5, p. 757-773, 2020.
ILIN, R.; WATSON, T.; KOZMA, R. Abstraction hierarchy in deep learning neural networks. International Joint Conference on Neural Networks (IJCNN), p. 768-774, 2017.
JIANG, S.; ZHANG, J. Real‐time crack assessment using deep neural networks with wall‐climbing unmanned aerial system. Computer‐Aided Civil and Infrastructure Engineering, v. 35, n. 6, p. 549-564, 2020.
KANG, D.; CHA, Y. J. Autonomous UAVs for structural health monitoring using deep learning and an ultrasonic beacon system with geo‐tagging. Computer‐Aided Civil and Infrastructure Engineering, v. 33, n.10, p. 885-902, 2018.
KIM, H.; LEE, S.; AHN, E.; SHIN, M.; SIM, S. H. Crack identification method for concrete structures considering angle of view using RGB-D camera-based sensor fusion. Structural Health Monitoring, v. 20, n.2, p. 500-512, 2021.
KITCHENHAM, B. Procedures for performing systematic reviews. Keele, UK, Keele University, v. 33, p. 1-26, 2004.
KUMARAPU, K.; SHASHI, M.; KEESARA, V. R. UAV in Construction Site Monitoring and Concrete Strength Estimation. Journal of the Indian Society of Remote Sensing, v. 49, n. 3, p. 619-627, 2021.
KUNG, R. Y.; PAN, N. H.; WANG, C. C.; LEE, P. C. Application of Deep Learning and Unmanned Aerial Vehicle on Building Maintenance. Advances in Civil Engineering, v. 2021, 2021.
LE, T. T.; NGUYEN, V. H.; LE, M. V. Development of deep learning model for the recognition of cracks on concrete surfaces. Applied Computational Intelligence and Soft Computing, v. 2021, 2021.
LIU, Y.; YEOH, J. K.; CHUA, D. K. Deep learning–based enhancement of motion blurred UAV concrete crack images. Journal of Computing in Civil Engineering, v. 34, n. 5, p. 04020028, 2020.
LUO, C.; YU, L.; YAN, J.; LI, Z.; REN, P.; BAI, X.; LIU, Y. Autonomous detection of damage to multiple steel surfaces from 360° panoramas using deep neural networks. Computer‐Aided Civil and Infrastructure Engineering, v. 36, n. 12, p. 1585-1599, 2021.
MOHER, D.; LIBERATI, A.; TETZLAFF, J.; ALTMAN, D. G.; PRISMA GROUP. Reprint—preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Physical therapy, v. 89, n. 9, p. 873-880, 2009.
MORGENTHAL, G.; HALLERMANN, N.; KERSTEN, J.; TARABEN, J.; DEBUS, P.; HELMRICH, M.; & RODEHORST, V. Framework for automated UAS-based structural condition assessment of bridges. Automation in Construction, v. 97, p. 77-95, 2019.
MÜLLER, A. C.; GUIDO, S. Introduction to machine learning with Python: a guide for data scientists. " O'Reilly Media, Inc.", 2016.
OH, S.; HAM, S.; LEE, S. Drone-Assisted Image Processing Scheme using Frame-Based Location Identification for Crack and Energy Loss Detection in Building Envelopes. Energies, v. 14, n. 19, p. 6359, 2021.
PERRY, B. J.; GUO, Y.; MAHMOUD, H. N. Automated site-specific assessment of steel structures through integrating machine learning and fracture mechanics. Automation in Construction, v. 133, p. 104022, 2022.
POTENZA, F.; RINALDI, C.; OTTAVIANO, E.; GATTULLI, V. A robotics and computer-aided procedure for defect evaluation in bridge inspection. Journal of Civil Structural Health Monitoring, v. 10, n. 3, p. 471-484, 2020.
RIBEIRO, D.; SANTOS, R.; SHIBASAKI, A.; MONTENEGRO, P.; CARVALHO, H.; CALÇADA, R. Remote inspection of RC structures using unmanned aerial vehicles and heuristic image processing. Engineering Failure Analysis, v. 117, p. 104813, 2020.
ROCHA, R. L. Redes neurais convolucionais aplicadas à inspeção de componentes do vagão ferroviário. Dissertação (Mestrado em Computação Aplicada do Núcleo de Desenvolvimento Amazônico em Engenharia) - Universidade Federal do Pará, Tucuruí-Pará, 2020.
STOCHINO, F.; FADDA, M. L.; MISTRETTA, F. Low cost condition assessment method for existing RC bridges. Engineering Failure Analysis, v. 86, p. 56-71, 2018.
SZELISKI, R. Computer vision: algorithms and applications. Springer Science & Business Media, 2010.
VALENÇA, J.; PUENTE, I.; JÚLIO, E. N. B. S.; GONZÁLEZ-JORGE, H.; ARIAS-SÁNCHEZ, P. Assessment of cracks on concrete bridges using image processing supported by laser scanning survey. Construction and Building Materials, v. 146, p. 668-678, 2017.
VETRIVEL, A.; GERKE, M.; KERLE, N.; NEX, F.; VOSSELMAN, G. Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning. ISPRS Journal of Photogrammetry and Remote Sensing, v. 140, p. 45-59, 2018.
VIJAYANANDH, R.; SENTHIL KUMAR, M.; VASANTHARAJ, C.; RAJ KUMAR, G.; SOUNDARYA, S. Numerical study on structural health monitoring for unmanned aerial vehicle. Journal of Advanced Research in Dynamical and Control Systems, v. 9, n. 6, p. 1937-1958, 2017.
WU, Y.; QIN, Y.; QIAN, Y.; GUO, F.; WANG, Z.; JIA, L. Hybrid deep learning architecture for rail surface segmentation and surface defect detection. Computer‐Aided Civil and Infrastructure Engineering, v. 37, n. 2, p. 227-244, 2021.
YEUM, C. M.; CHOI, J.; DYKE, S. J. Automated region-of-interest localization and classification for vision-based visual assessment of civil infrastructure. Structural Health Monitoring, v. 18, v. 3, p. 675-689, 2019.
ZHU, Q.; DINH, T. H.; PHUNG, M. D.; HA, Q. P. Hierarchical convolutional neural network with feature preservation and autotuned thresholding for crack detection. IEEE Access, v. 9, p. 60201-60214, 2021.
