Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Deep Residual Learning for Image Recognition

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A 3D Feature-based Approach for Mapping Scaling Effects on Stone Monuments

Muñoz-Pandiella, I., Pueyo, X., & Bosch, C. (2024) · Journal on Computing and Cultural Heritage · doi:10.1145/3651988 · Cited by 2

A Data-Driven Method Based on Bidirectional Convolutional Current Neural Network to Detect Structural Damage

Xue, S., Su, T., Xie, Q., Zhao, X., & Zong, Z. (2024) · Iranian Journal of Science and Technology Transactions of Civil Engineering · doi:10.1007/s40996-024-01427-4 · Cited by 2

Tunnel lining quality detection technology based on impulse echo acoustic method from fine management perspective

Song, J., Feng, Y., & Huang, B. (2024) · Wireless Networks · doi:10.1007/s11276-024-03725-1 · Cited by 2

Frequency data driven damage detection of polymeric composite structural health using machine learning models

Kumar, V., Pattanayak, P., Meher, A. K., & Panda, S. K. (2024) · ZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik · doi:10.1002/zamm.202400481 · Cited by 2

Camera-Based Real-Time Damage Identification of Building Structures through Deep Learning

Hormozabad, S. J., Palacio‐Betancur, A., & Soto, M. G. (2025) · Journal of structural design and construction practice. · doi:10.1061/jsdccc.sceng-1600 · Cited by 2

Composite Structural Health Prediction Using Vibroacoustic Responses via Machine Learning Techniques

Pattanayak, P., Kumar, V., Meher, A. K., Akkasali, N. K., Satapathy, S., Kumar, G., Sharma, N., & Panda, S. K. (2025) · International Journal of Applied Mechanics · doi:10.1142/s175882512550019x · Cited by 4

A Distributed Sensing- and Supervised Deep Learning-Based Novel Approach for Long-Term Structural Health Assessment of Reinforced Concrete Beams

Jayawickrema, M., Herath, M., Hettiarachchi, N. K., Sooriyaarachchi, H., Banerjee, S., Epaarachchi, J. A., & Prusty, B. G. (2025) · Metrology · doi:10.3390/metrology5030040 · Cited by 2

Structural Health Monitoring aided by neural networks

Varghese, A., & Koshy, B. I. (2022) · International Conference on Next Generation Intelligent Systems (ICNGIS) · doi:10.1109/icngis54955.2022.10079879 · Cited by 2

Feasible Applicability of Deep Learning for Solid Detection in Concrete Wastewater: An Evaluation

Chen, Y., & Yao, Q. (2023) · Applied Sciences · doi:10.3390/app13158652 · Cited by 2

Advancing Structural Health Monitoring with Deep Belief Network-Based Classification

Vélez, Á. P., Fernández‐Muñiz, Z., & Fernández‐Martínez, J. L. (2025) · Mathematics · doi:10.3390/math13091435 · Cited by 2

MambaU-Light: a lightweight model for bridge surface crack segmentation based on joint distillation of multiple models

Bai, R., Luo, D. M., Chen, L., Jin, H., & Jiang, L. (2025) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2025.2505089 · Cited by 2

A Data-Driven Machine Learning Framework for Predicting Building Seismic Response Using Ambient Vibration Data

Damikoukas, S., & Lagaros, N. D. (2025) · Lecture notes in civil engineering · doi:10.1007/978-3-031-92044-8_14 · Cited by 2

OPTIMIZATION OF STRUCTURAL COMPUTATIONAL MODELS USING NEURAL NETWORKS: A SYSTEMATIC REVIEW OF CURRENT APPROACHES AND PROSPECTS

Getun, S., Ivanchenko, H., Botvinovska, S., Гетун, Г., & Соломін, А. (2025) · APPLIED GEOMETRY AND ENGINEERING GRAPHICS · doi:10.32347/0131-579x.2025.108.72-96 · Cited by 2

A vibration response identification neural network with resilience against missing data anomalies

Zhang, R., Zhou, Q., Tian, L., Zhang, J., & Bai, L. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac5c91 · Cited by 2

Towards damage detection using physics-based modeling and data-driven optimization

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Structural Defects Classification and Detection Using Convolutional Neural Network (CNN): A Review

Arafin, P., & Billah, A. H. M. M. (2022) · Lecture notes in civil engineering · doi:10.1007/978-981-19-0507-0_27 · Cited by 2

An FPGA-Based Laser Virtual Scale Method for Structural Crack Measurement

Yuan, M., Fang, Z., Xiao, P., Tong, R., Zhang, M., & Huang, Y. (2023) · Buildings · doi:10.3390/buildings13010261 · Cited by 2

Development of a Deep Learning-Based Anomaly Detection System for Structures

Rasul, M., Kawashima, M., & Khuyen, H. T. (2023) · Lecture notes in civil engineering · doi:10.1007/978-3-031-32519-9_159 · Cited by 2

Disaster impact analysis in Africa: EDDA - UAV building damage dataset of rural and urban Mozambique

Hatić, D., Rauhut, M., Polushko, V., Codastefano, M., Rosa, S., McKay, P., Stompanato, F., Beleza, A., & Hagen, H. (2023) · doi:10.1117/12.2683882 · Cited by 2

The automated segmentation and enhancement of cracks on airport pavements using three-dimensional imaging techniques

Zhai, S., & Xu, Y. (2024) · International Conference on Algorithm, Image Processing and Machine Vision (AIPMV) · doi:10.1117/12.3014473 · Cited by 2

A Cost-Effective Method to Detect Structural Damage by Employing IOT

Nayak, D. S., & Pandey, A. K. (2023) · International Conference on Recent Advances in Science and Engineering Technology (ICRASET) · doi:10.1109/icraset59632.2023.10420356 · Cited by 1

Correlation coefficients of vibration signals and machine learning algorithm for structural damage assessment in beams under moving load

Pham‐Bao, T., & Le-Ngọc, V. (2024) · Frattura ed Integrità Strutturale · doi:10.3221/igf-esis.70.03 · Cited by 1

Railway Substructure Modeling Approach for Modal Analysis Using Multibody Simulation

Morais, J., Fortunato, E., Ribeiro, D. R. F., & Mendes, J. G. (2024) · Applied Sciences · doi:10.3390/app14156510 · Cited by 1

Measured displacement data analysis and modelling for structural health monitoring

Miah, M. S., & Lienhart, W. (2024) · Procedia Structural Integrity · doi:10.1016/j.prostr.2024.09.288 · Cited by 1

Physics-trained artificial intelligence framework to detect chloride induced degradation in concrete

Patel, P., Gupta, A., Bodda, S. S., & Sandhu, H. K. (2025) · Journal of Infrastructure Intelligence and Resilience · doi:10.1016/j.iintel.2025.100139 · Cited by 1

Monitoramento de integridade estrutural utilizando inteligência artificial

Alves, V., Alves, V. A. M., & Cury, A. (2024) · Revista da Universidade Federal de Minas Gerais · doi:10.35699/2965-6931.2023.47533 · Cited by 1

Research on innovative hybrid analysis method for structural seismic response based on neural network restoring force model

Zhu, Y., Wu, J., Xie, L., Wang, K., & Wei, Y. (2025) · ENGINEERING Structure and Civil Engineering · doi:10.1007/s11709-025-1176-5 · Cited by 1

Damage Detection of Seismically Excited Buildings Using Neural Network Arrays with Branch Pruning Optimization

Chou, J., Chang, C., & Liu, C. (2025) · Buildings · doi:10.3390/buildings15122052 · Cited by 2

Data-driven prediction for uniaxial compression failure time of strong bump-prone coal using acoustics-informed neural networks

Wang, S., Zhao, Y., Song, Y., Guo, J., Zhu, G., Gong, K., Zhang, G., Wang, W., & Liu, B. (2025) · Bulletin of Engineering Geology and the Environment · doi:10.1007/s10064-025-04412-x · Cited by 1

A data-lean machine learning approach for damage extent estimation and classification in composite structures under multiple failure modes

Papadopoulos, D. I., Giannakopoulos, G., Karkaletsis, V., & Rekatsinas, C. S. (2025) · Machine Learning Science and Technology · doi:10.1088/2632-2153/adfb20 · Cited by 1

A Configurable Parallel Architecture for Singular Value Decomposition of Correlation Matrices

López, L. E. L., Cruz, D. L., Cota-Ruiz, J., Díaz, J., Sifuentes, E., Silva-Aceves, J., & Aguilera, F. J. E. (2025) · Electronics · doi:10.3390/electronics14163321 · Cited by 1

A load-adaptive multi-level feature fusion method for predicting metal fatigue crack length based on lamb wave and strain signals

Gao, Q., Li, H., Wang, X., Huo, J., & Wang, Y. (2025) · Theoretical and Applied Fracture Mechanics · doi:10.1016/j.tafmec.2025.105268 · Cited by 1

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

Ho, J., Ahn, Y., & Shin, H. (2025) · Sustainability · doi:10.3390/su17219390 · Cited by 3

Population-Driven Meta-transfer Learning: A Physics-Guided Framework for Multi-stage Damage Detection in Heterogeneous Bridge

Wu, Z., Teng, S., Wang, X., He, Y., & Wang, S. (2025) · Journal of Failure Analysis and Prevention · doi:10.1007/s11668-025-02317-1 · Cited by 1

Fleet Monitoring of Bridge Using Direct Calculation of Moving Reference Influence Line

Ren, Y., Tian, J., OBrien, E. J., Zhu, T., Özer, E., Li, W., Cheng, S., He, H., & Feng, K. (2025) · Applied Sciences · doi:10.3390/app152211960 · Cited by 1

Research on Structural Damage Identification Method Based on Convolutional Neural Network

Liu, Z., Teng, S., & Wang, S. (2025) · Open Journal of Applied Sciences · doi:10.4236/ojapps.2025.1511228 · Cited by 1

The Role of Temperature in Static Bridge Responses: Lessons from a Case Study

Morici, M., Gioiella, L., Ierimonti, L., Micozzi, F., Venanzi, I., Ubertini, F., & Dall’Asta, A. (2025) · ce/papers · doi:10.1002/cepa.70009 · Cited by 1

Flange bolt loosening detection using an improved you only look once version 8 method and angle estimation

Gao, Y., Zhou, S., Li, M., Wei, C., & Li, S. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113337 · Cited by 1

A Dynamic Dimension Reduction Method for Structural Health Monitoring Data Based on Sparse Bayesian Inference

Zeng, H., & Zhu, Y. (2025) · Structural Control and Health Monitoring · doi:10.1155/stc/5560897 · Cited by 1

Harnessing machine learning algorithms for benchmarking deterioration prediction in civil infrastructure systems

Ansari, M. A., & Vyas, S. (2025) · Asian Journal of Civil Engineering · doi:10.1007/s42107-025-01566-7 · Cited by 1

Structural Health Monitoring of Mooring Systems for Floating Wind Turbines

Pandit, A. R., Ghiasi, R., & O’Shea, M. (2025) · Lecture notes in civil engineering · doi:10.1007/978-3-031-96114-4_2 · Cited by 1

Accurate Measurement of Segment Dislocation for Shield Tunnels Based on Binocular Vision Technology

Guan, Z., Liu, Y., Lin, Y., & Lin, W. (2026) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-7135 · Cited by 1

A Novel Diagnosis Framework for Maglev Levitation Control Faults Based on Contrastive Self-Supervised Feature Learning

Jiang, G.-F., Wang, S.-M., & Ni, Y. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3670573 · Cited by 1

Robust structural modal identification and damage assessment through novel weighted spectral and cepstral representations

Li, L., Betti, R., Shen, Z., Gan, L., Gu, H., & Zhang, H. (2026) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2026.119753 · Cited by 1

Bridge Health State Assessment and Multiscale Deterioration Modeling: Methods, Challenges, and Future Prospects

Yin, K., Luo, H., Shao, Y., & Liu, W. (2026) · Structural Control and Health Monitoring · doi:10.1155/stc/3578303 · Cited by 1

Physics–data fusion of improved modal-strain energy features and acceleration signals within a CNN for offshore structural damage detection

Kumar, R., Yuen, T. Y. P., & Kumar, D. (2026) · Structures · doi:10.1016/j.istruc.2026.111959 · Cited by 1

Incorporating Structural Prior Knowledge into YOLO for Robust Infrastructure Damage Detection

ZHANG, Z., & Guo, C. (2026) · Buildings · doi:10.3390/buildings16112105 · Cited by 1

Catastrophic Jump Phenomena in Infrastructure Deterioration: Overcoming Analytical Breakdown via RNNs and Billion-Scale Simulations

Kawahata, Y., Maeda, N., & Hatadani, S. (2026) · CivilEng · doi:10.3390/civileng7030060 · Cited by 1

Component-aware 3D damage localization on Gaussian splatting for bridge inspection

Wang, Z., & Liang, X. P. (2026) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1016/j.cacaie.2026.100236 · Cited by 1

Experimental machine learning approach for building structural health monitoring application

Mghazli, M. O., Zoubir, Z., Rallu, A., Lamdouar, N., & Mankibi, M. E. (2024) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2647/18/182031 · Cited by 1

Aerial Reconnaissance Utilizing UAV With GNSS-RTK for Computer Vision-Based Analysis of Concrete Cracks

Ofamin, X. J. J. P., Guzman, M. A. R. C. D., Rotor, M. A., & Banluta, J. L. (2025) · AIAA SciTech Forum · doi:10.2514/6.2025-1346 · Cited by 1

Signal Processing Analysis of Lamb Wave Propagation in Stainless Steel Plates with Thickness Defects: Implications for AI-Based Non-Destructive Testing

Laaz, H., Rhimini, H., Nissabouri, S., & Mekkaoui, M. (2025) · International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) · doi:10.1109/iraset64571.2025.11008279 · Cited by 1

Data-Driven Structural Health Monitoring Through Echo State Network Regression

Li, X., Zhu, Y., & Yu, W. (2025) · Information · doi:10.3390/info16080678 · Cited by 1

Benchmarking YOLOv8 Variants with Transfer Learning for Real-Time Detection and Classification of Road Cracks and Potholes

Kurniadi, D., Latif, A. A., Mulyani, A., & Aulawi, H. (2025) · Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) · doi:10.29207/resti.v9i4.6710 · Cited by 1

Deep learning-based predication of the input energy spectra for self-centering systems

Song, G., & Xing, L. (2025) · Soil Dynamics and Earthquake Engineering · doi:10.1016/j.soildyn.2025.109805 · Cited by 1

SHM of RC Bridge Girder for Damage Identification and Localization Using Machine Learning

ULLEGADDI, A., MOHAN, S. C., Mahmoodian, M., & GANESH, V. (2025) · International Workshop on Structural Health Monitoring (IWSHM) · doi:10.12783/shm2025/37526 · Cited by 1

A transformer model for detection of stiffener disbonding in stiffened aluminium panel using Lamb wave

Prajapati, K. K., Ghosh, A., & Mitra, M. (2025) · Journal of Intelligent Material Systems and Structures · doi:10.1177/1045389x251383204 · Cited by 1

Hierarchical Bayesian inference with surrogate modeling for model updating and uncertainty quantification of an operational offshore wind turbine

Moynihan, B., Moaveni, B., & Hines, E. M. (2025) · Wind energy and engineering research. · doi:10.1016/j.weer.2025.100022 · Cited by 1

Cross-domain structural damage identification based on updated FEM and WGAN-GP: Bridging the gap between FEM simulations and actual structural responses

Zhou, X., Yu, W., Liu, Y., & Elchalakani, M. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2025.121888 · Cited by 1

Mechanical Behavior and Performance Degradation of Structural Cables in Buildings: A Comprehensive Review

Xu, C., Zhang, H. F., Liu, H., Wang, J., Zhang, Y., Guo, L., Chen, Z., Kosior‐Kazberuk, M., & Krassowska, J. (2025) · Materials · doi:10.3390/ma18245502 · Cited by 1

Augmented Reality for Damage Management with Geometric Shapes

Salgado, D., Balado, J., Calvar, S., & Solla, M. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-10731-2_41 · Cited by 1

Structural health monitoring: Emerging technologies and data analytics

Çatbaş, F. N., & Luleci, F. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-38315-1.00009-x · Cited by 1

Integrating machine learning algorithms for post-fire damage assessment and rehabilitation prioritization of urban structures

Dileep Kumar, M., Chauhan, M., Sharma, K., Sethy, B. P., Ali, G., & Sethi, K. C. (2026) · Journal of Building Pathology and Rehabilitation · doi:10.1007/s41024-026-00775-0 · Cited by 1

Structural damage detection of shear frame model using parallel physics-informed neural network

Baniya, S., & Maity, D. (2026) · ENGINEERING Structure and Civil Engineering · doi:10.1007/s11709-026-1264-1 · Cited by 1

Phy-GCN: A physics-constrained graph convolutional network incorporating structural dynamic response features for structural damage identification and localization

Gao, H., Zhu, L., Wen, S., & Liao, S. (2026) · Journal of Building Engineering · doi:10.1016/j.jobe.2026.115489 · Cited by 1

A crowdsensing platform for structural health monitoring of rural bridge infrastructure

Tantaroudas, N. D., Zamora-Sánchez, D., Armijo, J. A., López-Villarragut, E., Arakistain, I., Katika, T., Tsimiklis, G., & Amditis, A. (2026) · Open Research Europe · doi:10.12688/openreseurope.22915.1 · Cited by 1

Cross-structure domain translation with structural state awareness: AT-StarGAN-GP for multi-level synthetic signal generation in structural health monitoring

Avcı, M. S., Ercan, E., Hızal, Ç., Malekjafarian, A., & Özer, E. (2026) · Structural Health Monitoring · doi:10.1177/14759217261427318 · Cited by 1

Multi-loads response prediction for long-span cable-stayed bridges by integrating physical model updating and deep learning

Wang, H., Wang, Z., Zhang, F., Xu, Y., Zhang, Y., Shen, K., Meng, X., & Wang, J. (2026) · Advances in Structural Engineering · doi:10.1177/13694332261433701 · Cited by 1

Recent progress in AI-enabled compressor structural health monitoring: A review on materials, sensor technologies, and algorithms

Li, Z., Liu, L., Zhong, F., Lu, T., & Wong, K. (2026) · Alexandria Engineering Journal · doi:10.1016/j.aej.2026.03.005 · Cited by 1

Real-Time Structural Health Monitoring of Bridges Using Deep Reinforcement Learning and IoT Sensors

Poongodi, D. K., Udayakumar, R., & Subramaniam, K. (2025) · International Academic Journal of Science and Engineering · doi:10.71086/iajse/v12i3/iajse1269 · Cited by 1

Koopman Operator Methods in Structural Health Monitoring: A Systematic Review Towards Hybrid Physics–Data Frameworks

Alagbada, A. V., & Lahmer, T. (2026) · Applied Sciences · doi:10.3390/app16073392 · Cited by 1

Adaptive UAV path planning in geometrical digital twin generated using neural radiance field

Mahajan, A., & Geetha, G. K. (2026) · Next research. · doi:10.1016/j.nexres.2026.101718 · Cited by 1

Damage localisation based on fusion of multi-source vehicle-induced response of bridges

Zhou, B., Zhu, J., Jiao, X., & Yessoufou, F. (2026) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2026.2656716 · Cited by 1

AI-driven structural health monitoring for UAVs: a review of damage detection and safety enhancement

Kanirai, N. S. A., Mustapha, F., Ma, Y., Abdullah, M. N., & Mustapha, M. (2026) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2026.2670744 · Cited by 1

Automated Rayleigh-Wave Nonlinear Acoustic Platform for Real-Time Fatigue Monitoring in Metallic Materials

Kordatou, T. Z., Farmaki, S. G., Exarchos, D. A., & Matikas, T. E. (2026) · Sensors · doi:10.3390/s26103190 · Cited by 1

Infrared Thermography for Defect Detectability in Structural Health Monitoring of Concrete Structures: Heat Transfer Physics, Resolution Limits, and AI-Assisted Diagnostics

Wciślik, W., & Wciślik, S. (2026) · Materials · doi:10.3390/ma19122596 · Cited by 1

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

Health monitoring of long-span bridges using deep learning driven by sensor measured and numerical response data

Xue, Z., Sebastian, W., & D'Ayala, D. F. (2023) · CRC Press eBooks · doi:10.1201/9781003323020-462 · Cited by 1

Diagnostics of Concrete Box Beam Bridges Using Wireless Sensors and Finite Element Analysis

Rizzo, P., Enshaeian, A., & Ghahremani, B. (2023) · Journal of Nondestructive Evaluation Diagnostics and Prognostics of Engineering Systems · doi:10.1115/1.4063804 · 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

Harnessing data from benchmark testing for the development of spalling detection techniques using deep learning

Ravuri, N. L. C., & Mantawy, I. M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-824073-1.00017-4 · Cited by 1

Inception Time Model for Structural Damage Detection Using Vibration Measurements

Singh, V., Bharali, K., Kalita, I., Roy, M., Debnath, N., Saharia, M., & Das, A. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-9040-5_7 · Cited by 1

Multi-location fault detection with piezoelectric arrays and multi-task CNN learning

Chiu, Y.-C., Lo, Y., & Shu, Y.-C. (2024) · Active and Passive Smart Structures and Integrated Systems · doi:10.1117/12.3009663 · Cited by 1

Localizing structural damage based on auto-regressive with exogenous input model parameters and residuals using a support vector machine based learning approach

Güneş, B. (2024) · Frontiers of Structural and Civil Engineering · doi:10.1007/s11709-024-1107-x · Cited by 1

Development of a data visualization program for changes in measured temperature, humidity, and pressure inside concrete

Cho, J., Kim, C., Kim, J., Kim, H. H., & Yeon, J. (2025) · KSCE Journal of Civil Engineering · doi:10.1016/j.kscej.2024.100042 · Cited by 1

A State-of-art on Damage Detection of Composite Structure Using Lamb Wave and Deep Learning Techniques

Munyaneza, O., Yuk, D.-G., & Sohn, J. W. (2025) · International Journal of Precision Engineering and Manufacturing-Smart Technology · doi:10.57062/ijpem-st.2024.00185 · Cited by 1

A Deep Learning Framework for Guided Wave-Based Structural Health Monitoring of a Composite Plate

Prajapati, K. K., Rai, A., Sharan, A., Yegurla, S., Ghosh, A. K., & Mitra, M. (2025) · AIAA SciTech Forum · doi:10.2514/6.2025-1804 · Cited by 1

Enhancing field applicability of unsupervised vibration-based damage assessment via time–frequency pattern differentiation

Park, S., & Kim, S.-J. (2025) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-025-01009-6 · Cited by 3

Accelerometer Mass Loading Study Based on a Damage Identification Method using Fundamental Laws in Closed Systems

TUFAN, T., & Köten, H. (2021) · Journal of Polytechnic · doi:10.2339/politeknik.810768 · Cited by 1

Towards an automated machine learning and image processing supported procedure for crack monitoring

Parente, L., Castagnetti, C., Falvo, E., Grassi, F., Mancini, F., Rossi, P., & Capra, A. (2022) · Joint International Symposium on Deformation Monitoring (JISDM) · doi:10.4995/jisdm2022.2022.13828 · Cited by 1

Parametric Damage Mechanics Empowering Structural Health Monitoring of 3D Woven Composites

Jacot, M., Champaney, V., Chinesta, F., & Cortial, J. (2023) · Sensors · doi:10.3390/s23041946 · Cited by 1

Algoritma Pendeteksi Kerusakan Struktur Bangunan Berbasis Korelasi Jarak dan Metode Kuadrat Terkecil Parsial

Tjen, J. (2022) · Jurnal Edukasi dan Penelitian Informatika (JEPIN) · doi:10.26418/jp.v8i3.56011 · Cited by 1

Influence of equipment, weather and personnel experience on the digitalization and damage detection of concrete bridges

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