Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Modeling of soil-structure interaction for reinforced breakwater foundation subjected to earthquake and tsunami loading(CRC Press/Balkema, 2019) Hazarika, H.; Chaudhary, B.; Nozu, A.; Kohama, E.; Suzuki, K.; Murakami, A.; Fujisawa, K.This paper describes the soil-structure interaction behavior of a new reinforcing technique for breakwater foundation against earthquake and tsunami induced damage. The technique involves the use of gabion on the top of rubble mound, reinforcing the foundation soil with steel sheet piles, and the use of sealing material between the sheet piles and breakwater. The effectiveness of the proposed technique and the soil structure interaction during earthquake and tsunami loading were evaluated through physical and numerical modeling. As part of the physical modeling for such soil-structure interaction problem, a series of 1g shaking table tests and centrifuge model tests were performed. In addition, a hydraulic model testing apparatus was developed, which can simulate the soil-structure interaction during tsunami overflow, the resulting seepage as well as the scouring. Numerical simulation was also performed for evaluating the effect of tsunami overflow. Soil-structure interaction behaviors were made clear through comparisons of conventional foundation and reinforced foundation models. © 2019 Associazione Geotecnica Italiana, Rome, Italy.Item Physical Model Tests for Newly Developed Breakwater Foundation Subjected to Earthquake and Tsunami(Springer Science and Business Media Deutschland GmbH, 2021) Chaudhary, B.; Hazarika, H.; Murakami, A.; Fujisawa, K.Many breakwaters damaged due to earthquake and tsunami in the past. For example, several breakwaters collapsed by the 2011 off the Pacific Coast of Tohoku Earthquake and subsequent tsunami. It was found that these breakwaters damaged mainly due to the failure of their foundations. Therefore, countermeasures are urgently needed to be developed for breakwater foundation in order to make the breakwater safe against earthquake and tsunami. Recently, new countermeasures were developed by the authors for breakwater foundation in order to make it resilient against an earthquake and tsunami. This paper deals with evaluation of effectiveness of the developed foundation model by conducting shaking table tests and tsunami overflow tests. As reinforcing countermeasures, steel sheet piles and gabions are provided in the breakwater foundation. To see the performance of the developed model, comparisons are made between the developed foundation model and conventional foundation. Through the tests, it was found that the reinforced foundation performed well in reducing damage of the breakwater caused by the earthquake and tsunami. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Tsunami Resilient Foundation for Breakwater: Centrifuge Model Tests(Springer Science and Business Media Deutschland GmbH, 2021) Chaudhary, B.; Hazarika, H.; Murakami, A.; Fujisawa, K.Many coastal protection structures collapsed due to the past earthquakes and tsunamis. For example, several breakwaters damaged during the 2011 Great East Japan Earthquake and Tsunami in Japan. Due to the failure of the breakwaters, the tsunami waves could not be blocked by the breakwaters. Thus, the tsunami entered in the coastal areas; and imposed deep devastation there. It was found that the breakwaters damaged mainly due to their foundation failures. In order to mitigate such damage of breakwater caused by earthquake and tsunami, new techniques were developed by the authors for breakwater foundation. In the technique, gabions and sheet piles are used in breakwater foundation. Effectiveness of the developed foundations model of breakwater were evaluated by conducting centrifuge model tests. It was observed that the developed models could mitigate damage, and make the breakwater resilient against earthquake and tsunami-induced damage. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Comparative Analysis of Machine Learning and Deep Learning Models for Ship Classification from Satellite Images(Springer Science and Business Media Deutschland GmbH, 2022) Hazarika, H.; Jidesh, P.; Smitha, A.The automatic detection of the ship from satellite image analysis is the limelight of research in recent years due to its widespread applications. In this paper, a handful of traditional machine learning and deep learning models are compared based on their performance to classify the satellite images available in the public repository as a ship or other categories. The Support Vector Machine(SVM), Decision Trees, Random Forest, K-Nearest Neighbor (KNN), Gaussian Naive Bayes (GaussianNB), and Logistic Regression are machine learning models used in the present work. Histogram of Gradient (HoG) features are used as feature descriptors considering the diverse size and shape of ships in the satellite image dataset. Transfer learning is applied using the deep learning models namely, Inception and ResNet, that are fine-tuned for various learning rates and optimizers. The meticulous experimentation carried out reveals that traditional machine learning performs well when trained and tested on a single dataset. However, there is a drastic change in the performance of machine learning models when tested on a different ship dataset. The results show that the deep learning models have better feature detection and thus have better performance when transfer learning is used on various datasets. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Detection and Classification of Ships using a self-attention Residual Network(Institute of Electrical and Electronics Engineers Inc., 2022) Hazarika, H.; Jidesh, P.The categorization and detection of ships is crucial in maritime applications such as marine surveillance, traffic monitoring etc., which are extremely crucial for ensuring national security. There has been extensive amount of research happening in this area since the last decade. The acceleration of computational power and increasing number of satellite images collected over the years paved the way for such an extensive research. However, high-resolution images of ships that capture the texture details for an effective categorization are still very limited. In the recent years, researchers collected images over various sources to create datasets which are made publicly available for use. In our work, we use the ShipRSImageNet dataset which was recently released. Many state-of-the-art object detection methods were employed on this dataset for different levels of ship detection. But due to the complexity of the dataset, the state-of-the-art models are not capable enough to perform a full categorization of the ships in these dataset. In this work, a residual attention based convolutional neural network (CNN) model is employed for feature extraction, which can be fed in to the state-of-the-art object detection models for the extraction of the features. Usually, other backbone networks which can be employed are complex and consists of multiple layers. Due to the need for a large number of training parameters, these models and approaches have heavy-weight designs. Therefore, they need more storage and costlier training processes. © 2022 IEEE.