Detection and Application of Concrete Compressive Strength with Machine Vision Technology

[1] Kim, S., Son, S., & Lee, D. (2021). Development of sustainable production technology of free-form concrete panels using a multi-point press cnc machine. Sustainability, 13. Search in Google Scholar

[2] Gibert, X., Patel, V. M., & Chellappa, R. (2017). Deep multi-task learning for railway track inspection. IEEE Transactions on Intelligent Transportation Systems, 1-12. Search in Google Scholar

[3] Lech, & CZARNECKI. (2019). Would recycled plastics be a driving force in concrete technology?. Journal of Zhejiang University-Science A(Applied Physics & Engineering), v.20(05), 77-81. Search in Google Scholar

[4] Pan, X., Sun, Y., Zheng, Z., Zhai, Y., & Zhang, L. (2024). Seismic performance evaluation of fiber-reinforced prestressed concrete containments subject to earthquake ground motions. Nuclear Engineering and Technology, 56(5), 1638-1653. Search in Google Scholar

[5] Davoudi, Rouzbeh, Miller, Gregory, R., & Kutz, et al. (2018). Structural load estimation using machine vision and surface crack patterns for shear-critical rc beams and slabs. Journal of Computing in Civil Engineering, 32(4), 4018024.1. Search in Google Scholar

[6] Nguyen, H., & Hoang, N. D. (2022). Computer vision-based classification of concrete spall severity using metaheuristic-optimized extreme gradient boosting machine and deep convolutional neural network. Automation in construction(Aug.), 140. Search in Google Scholar

[7] Wang, B., Bai, S., Wang, J., Zhao, W., Zhang, Y., & Zhang, Q. (2022). A novel concrete crack damage detection method via sparse correlation model. Structural control and health monitoring(7), 29. Search in Google Scholar

[8] Baohua, S., Yunfeng, P., Xiaoyang, H., & Guijun, X. (2018). Detection of slip for CFRP-concrete interface using stereovision method corrected by epipolar constraint. Structural Control and Health Monitoring, 25, e2212-. Search in Google Scholar

[9] Shang, G., & Chen, J. (2022). Classification of multistates of internal damage in concrete based on convolutional neural network analysis of time-frequency images. Journal of Performance of Constructed Facilities. Search in Google Scholar

[10] Wang, R., Wang, Y., Devadiga, S., Perkins, I., Kong, Z. J., & Yue, X. (2021). Structured-light three-dimensional scanning for process monitoring and quality control in precast concrete production. PCI journal (6), 66. Search in Google Scholar

[11] Ratthaphong, M., & Sakdirat, K. (2017). Vibration characteristics of micro-engineered crumb rubber concrete for railway sleeper applications. Journal of Advanced Concrete Technology, 15(2), 55-66. Search in Google Scholar

[12] Oyamoto, T., Nimura, A., Okayasu, T., Sawada, S., & Miyazaki, T. (2017). Experimental and analytical study of structural performance of rc share walls with corroded reinforcement. Journal of Advanced Concrete Technology, 15(11), 662-683. Search in Google Scholar

[13] Yang, Z., Li, Y., Sang, X., Ding, Y., Ma, B., & Chen, Q., et al. (2023). Concrete implantable bar enabled smart sensing technology for structural health monitoring. Cement & concrete composites. Search in Google Scholar

[14] David, S., Boris, W., & Shimon, B. (2018). Apparent activation energy and strengthening during hardening of cement-concrete compositions. Journal of Advanced Concrete Technology, 16(3), 136-144. Search in Google Scholar

[15] Zhiwu, Y., Ying, X., Zhi, S., & Xiao, L. (2018). Fatigue performance of crts iii slab ballastless track structure un-der high-speed train load based on concrete fatigue damage constitu-tive law. Journal of Advanced Concrete Technology, 16(5), 233-249. Search in Google Scholar

[16] Mahmood, H. Z. S., & Amir, Y. (2017). Predicting bond strength between frp plates and concrete substrate: applications of gmdh and mnlr approaches. Journal of Advanced Concrete Technology, 15(11), 644-661. Search in Google Scholar

[17] Kapadia, H. K., Patel, P. V., & Patel, J. B. (2022). Monitoring and analysis of crack developments in concrete using machine vision. Journal of Structural Engineering. Search in Google Scholar

[18] Ni, F. T., Zhang, J., & Chen, Z. Q. (2019). Zernike-moment measurement of thin-crack width in images enabled by dual-scale deep learning. Computer-Aided Civil and Infrastructure Engineering, 34(5), 367-384. Search in Google Scholar

[19] Li, Y., Pang, Q., & Hu, Z. L. (2024). Research on the accuracy of dieless single point incremental forming based on machine vision. Journal of Manufacturing Processes, 114, 59-66. Search in Google Scholar

[20] Su, C., & Wang, W. (2020). Concrete cracks detection using convolutional neuralnetwork based on transfer learning. Mathematical Problems in Engineering, 2020(13), 1-10. Search in Google Scholar

[21] Hamidia, M., Kaboodkhani, M., & Bayesteh, H. (2024). Vision-oriented machine learning-assisted seismic energy dissipation estimation for damaged rc beam-column connections. Engineering structures (Feb.15), 301. Search in Google Scholar

[22] Sun, Z., Caetano, E., SérgioPereira, & Moutinho, C. (2023). Employing histogram of oriented gradient to enhance concrete crack detection performance with classification algorithm and bayesian optimization. Engineering Failure Analysis. Search in Google Scholar