@inproceedings{de2265e154a84d7a88b816731763d5f5,
title = "Real-Time Site Specific Assessment of Cement Mortar Using a Solitary Wave Based Deep Learning",
abstract = "This paper proposes a real-time non-destructive evaluation technique for site-specific assessment of mortar using highly nonlinear solitary waves (HNSWs). This is achieved by studying a deep learning algorithm based on the convolution neural network (CNN) using HNSWs as input data. Of particular interest is to examine the sensitivity of the pre-trained CNN architectures on hydration process of cement mortar. To collect HNSW datasets for training, validation, and testing of the deep learning algorithm, mortar cube samples with various curing ages are prepared and HNSW datasets are generated from a granular crystal sensor. The pre-trained CNN architectures showed excellent performance for identifying the strength development of mortar based on curing age.",
keywords = "Convolution Neural Network, Granular Crystal, Highly Nonlinear Solitary Waves, Machine Learning, Non-destructive Evaluation",
author = "Kim, \{Tae Yeon\} and Sangyoung Yoon and Abdulameer, \{Ahmed Alkhaffaf\} and Yeun, \{Chan Yeob\} and Ernesto Damiani",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 10th International Operational Modal Analysis Conference, IOMAC 2024 ; Conference date: 22-05-2024 Through 24-05-2024",
year = "2024",
doi = "10.1007/978-3-031-61421-7\_32",
language = "British English",
isbn = "9783031614200",
series = "Lecture Notes in Civil Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "326--333",
editor = "Carlo Rainieri and Carmelo Gentile and \{Aenlle L{\'o}pez\}, Manuel",
booktitle = "Proceedings of the 10th International Operational Modal Analysis Conference, IOMAC 2024 - Volume 1",
address = "Germany",
}