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950 results on '"Wan, Huan"'

3. Particle Breakage Prediction of Coral Sand Using Machine Learning Method

Author

Li, Xue, Zhou, Wan-Huan, Wang, Chao, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Lu, Xinzheng, Series Editor, Rujikiatkamjorn, Cholachat, editor, Xue, Jianfeng, editor, and Indraratna, Buddhima, editor

Published
2025
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4. Investigation of small-strain shear modulus of marine sediment treated with different flocculants

Author

Wei-Pin Wu, K. K. Pabodha M. Kannangara, and Wan-Huan Zhou

Subjects
Macau marine sediment, Cationic polyacrylamide (CPAM), Chitosan, Nanoscale zero-valent iron, Small-strain shear modulus, Oedometer, Engineering (General). Civil engineering (General), TA1-2040
Abstract

Abstract Dredged sediments are typically found near coastal areas. Because of its high compressibility potential and low strength, multiple studies have tested various substances that can be mixed with the dredged materials to enhance the overall treatment process while minimizing post-construction settlement. Recent investigations indicated that combining flocculants such as cationic polyacrylamide (CPAM) with marine sediment improves the consolidation and permeability properties compared to untreated soil. Despite its importance, only a few studies have focused on the mechanical properties of marine sediment treated with various flocculant types. This study employs a bender element apparatus to examine the small-strain shear modulus (G max) variation of Macau marine sediment treated with three different flocculants: CPAM; chitosan; and nanoscale zero-valent iron (nZVI). The result revealed that the G max of natural marine sediment increases when blended with chitosan and CPAM with medium cationicity. Interestingly, no apparent change in G max values was observed when the marine sediment samples were treated using CPAM with high cationicity. In contrast, the G max values of soil samples treated with nZVI decreased. The comparison with existing empirical models reveals that the plasticity index is a significant parameter in determining the G max of Macau marine sediments. The findings from this study will support the identification of viable filling materials for future land reclamation projects in the Greater Bay Area of China.

Published
2024
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28. Development and validation of an artificial intelligence-based model for detecting urothelial carcinoma using urine cytology images: a multicentre, diagnostic study with prospective validation

Author

Wu, Shaoxu, Shen, Runnan, Hong, Guibin, Luo, Yun, Wan, Huan, Feng, Jiahao, Chen, Zeshi, Jiang, Fan, Wang, Yun, Liao, Chengxiao, Li, Xiaoyang, Liu, Bohao, Huang, Xiaowei, Liu, Kai, Qin, Ping, Wang, Yahui, Xie, Ye, Ouyang, Nengtai, Huang, Jian, and Lin, Tianxin

Published
2024
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31. Enhanced elastic beam model with BADS integrated for settlement assessment of immersed tunnels

Author

Cong Tang, Shu-Yu He, Zheng Guan, Wan-Huan Zhou, and Zhen-Yu Yin

Subjects
Immersed tunnel, Settlement, Beam on elastic foundation model, Bayesian adaptive direct search, Hong Kong–Zhuhai–Macao tunnel, Engineering geology. Rock mechanics. Soil mechanics. Underground construction, TA703-712
Abstract

Excessive settlement may induce structural damage and water leakage in immersed tunnels, seriously threatening the tunnels’ safety. However, making accurate assessment of the settlement in immersed tunnels is difficult due to the incomplete knowledge of the geotechnical parameters and the inadequacy of the model itself. This paper proposes an effective method to accurately assess the settlement in immersed tunnels. An enhanced beam on elastic foundation model (E-BEFM) is developed for the settlement assessment, with the Bayesian adaptive direct search algorithm adopted to estimate unknown model parameters based on previous observations. The proposed method is applied to a field case of the Hong Kong–Zhuhai–Macao immersed tunnel. The original BEFM is used for comparison to highlight the better assessment performance of E-BEFM, particularly for joints’ differential settlement. Results show that the proposed method can provide accurate predictions of the total settlement, angular distortion (a representation of tubes’ relatively differential settlement), and joints’ differential settlement, which consequently supports the associated maintenance decision-making and potential risk prevention for immersed tunnels in service.

Published
2023
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45. Cluster-based supervised classification

Author

Wan, Huan, Liu, Jun, Scotney, Bryan, and Wang, Hui

Subjects
006.3, Clustering, Classification, LDA, Feature extraction, Subclass
Abstract

Supervised classification is one of our fundamental approaches to understanding the world, and is studied in many research areas. Feature extraction and classification learning are two key processes, which significantly influence the performance of supervised classification. Although impressive progress has been made in supervised classification due to the development of feature extraction methods and classifiers, there are still unsolved problems in supervised classification, such as the class imbalance problem and the few-shot classification problem. In this thesis, we focus on the complex boundary problem — it is hard to obtain high classification accuracy for problems with complex decision boundaries due to the existence of subclass structures. We propose a cluster-based approach to supervised classification and develop cluster-based feature extraction methods and cluster-based classification learning methods. For feature extraction, to find out the importance of considering within-class multimodality for feature extraction, we conduct a study on within-class multimodal data distribution and classification under such a distribution. This study is guided by five important questions about within-class multimodal data. Systematic experiments using a variety of artificial and real data are conducted to answer the five questions, which further lead to some useful findings. In the second study, a new feature extraction method is proposed, called global subclass discriminant analysis (GSDA). To extract discriminative features, GSDA first obtains clusters in a global way by clustering the whole data set and derives class-specific clusters based on these global clusters. Then it seeks to maximise interclass distance and minimise intraclass distance based on these class-specific clusters. GSDA is extensively evaluated on a wide range of data through comparison with the closely related and state-of-the-art feature extraction methods. Experimental results demonstrate GSDA’s superiority in terms of accuracy and run time. For classification learning, in the third study, we propose a cluster-based data relabelling (CBDR) method for improving the classification performance of existing classifiers on nonlinear data. CBDR aims to impel classifiers to find cluster-based decision boundaries rather than class-based decision boundaries. Extensive experimentations demonstrate that CBDR dramatically boosts the classification performance of classifiers on nonlinear data, especially for linear classifiers. In the final study, a novel Gaussian mixture model (GMM) classifier is proposed, called separability criterion based GMM (SC-GMM) classifier. In SC-GMM, the separability criterion is employed to find the optimal number of Gaussian components for GMM. Experiments have been carried out on various classification tasks. Experimental results demonstrate the superiority of the SC-GMM classifier.

Published
2020

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