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Recent Advances and Applications of Big Data and Distributed Computing...
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Recent Advances and Applications of Big Data and Distributed Computing Systems

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2040

Special Issue Editors

Dr. Jiayao Chen

E-Mail Website
Guest Editor
School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China
Interests: data fusion; computer vision; underground space; tunneling; rock engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Qian Fang

E-Mail Website1 Website2
Guest Editor
Key Laboratory for Urban Underground Engineering of Ministry of Education, Beijing Jiaotong University, Beijing 100044, China
Interests: big data; excavation; tunneling; numerical simulation; numerical modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dongming Zhang

E-Mail Website
Guest Editor
Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China
Interests: machine learning; big data in tunneling; tunnel mechanics
Special Issues, Collections and Topics in MDPI journals
Dr. Mingliang Zhou

E-Mail Website
Guest Editor
Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China
Interests: neural network model; 3D computer vision; numerical modeling; geotechnical engineering; distributed computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development of data collection technology in construction has attracted the attention of industry, academia, and governments, leading to an explosive growth of infrastructure data. The volume of big data thus far exceeds the processing capacity of traditional computer technology and information systems. Big data offer insights that cannot be derived from small data sets. Intelligent analysis and the interpretation of big data can greatly benefit science and industry, improve management decision making, and bring economic gains. Distributed computing systems are also an important means of processing big data. With this technology, computing resources can be effectively allocated according to data characteristics and computational efficiency, achieving optimized big data processing. Therefore, this Special Issue will focus on scientific issues and applications related to big data and distributed computing system, especially those technologies that aid in infrastructure construction and management.

Original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Collection and analyzing of big data;
  • Development and capabilities of distributed computing systems;
  • Data optimization;
  • Data fusion technology;
  • Machine learning technologies and applications;
  • Data security;
  • Deep learning technologies and applications;
  • Intelligent collection and analysis of infrastructure data;
  • Distributed computing;
  • Data for infrastructure engineering life cycle;
  • Artificial intelligence analysis;
  • Technical application of distributed computing;
  • Optimization of computing networks.

We look forward to receiving your contributions.

Dr. Jiayao Chen
Prof. Dr. Qian Fang
Prof. Dr. Dongming Zhang
Dr. Mingliang Zhou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data optimization
  • data fusion
  • artificial intelligence
  • machine learning
  • infrastructure engineering
  • distributed computing

Published Papers (1 paper)

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Research

19 pages, 3591 KiB  
Article
Evaluation of Short-Term Rockburst Risk Severity Using Machine Learning Methods
by Aibing Jin, Prabhat Basnet and Shakil Mahtab
Big Data Cogn. Comput. 2023, 7(4), 172; https://doi.org/10.3390/bdcc7040172 - 07 Nov 2023
Viewed by 1503
Abstract
In deep engineering, rockburst hazards frequently result in injuries, fatalities, and the destruction of contiguous structures. Due to the complex nature of rockbursts, predicting the severity of rockburst damage (intensity) without the aid of computer models is challenging. Although there are various predictive [...] Read more.
In deep engineering, rockburst hazards frequently result in injuries, fatalities, and the destruction of contiguous structures. Due to the complex nature of rockbursts, predicting the severity of rockburst damage (intensity) without the aid of computer models is challenging. Although there are various predictive models in existence, effectively identifying the risk severity in imbalanced data remains crucial. The ensemble boosting method is often better suited to dealing with unequally distributed classes than are classical models. Therefore, this paper employs the ensemble categorical gradient boosting (CGB) method to predict short-term rockburst risk severity. After data collection, principal component analysis (PCA) was employed to avoid the redundancies caused by multi-collinearity. Afterwards, the CGB was trained on PCA data, optimal hyper-parameters were retrieved using the grid-search technique to predict the test samples, and performance was evaluated using precision, recall, and F1 score metrics. The results showed that the PCA-CGB model achieved better results in prediction than did the single CGB model or conventional boosting methods. The model achieved an F1 score of 0.8952, indicating that the proposed model is robust in predicting damage severity given an imbalanced dataset. This work provides practical guidance in risk management. Full article
(This article belongs to the Special Issue Recent Advances and Applications of Big Data and Distributed Computing Systems)
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