Advances in Civil Engineering

Data Mining in Civil Engineering


Publishing date
01 Jun 2020
Status
Closed
Submission deadline
07 Feb 2020

Lead Editor

1The University of Western Australia, Perth, Australia

2Central South University, Changsha, China

3University of Transport Technology, Hanoi, Vietnam

4Norwegian Geotechnical Institute, Oslo, Norway

5LNCT College, Jabalpur, India

This issue is now closed for submissions.
More articles will be published in the near future.

Data Mining in Civil Engineering

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Triggered by the emergence of new technologies such as automation, smart equipment, and the wide application of mobile technologies, a huge amount of data is being generated in civil engineering. This brings about challenges—such as how to analyze this data—as well as opportunities for governments, organizations, communities, and individuals to utilize this data. Thus, this has led to the emergence of a completely different paradigm for decision making.

Data mining has been widely used in civil engineering, making it a hot research topic due to its importance. For example, data mining techniques such as regression and classification have been used to analyze landslide susceptibility, suspended sediment load modelling, accident severity prediction, and concrete property estimation. Data mining can support decision-making and provide new insights for civil engineers, which inevitably involves experts from both data analytics and specialized civil engineering.

The aim of this Special Issue is to collect state-of-the-art research findings on the latest developments and challenges in the field of data mining for civil engineering. High-quality original research papers that present theoretical frameworks, methodologies, and application case studies from a single- or cross-country perspective are welcome, as well as review articles.

Potential topics include but are not limited to the following:

  • Analyses or meta-analyses of existing data in civil engineering applications
  • Data mining techniques, including tracking patterns, classification, association, outlier detection, clustering, regression, and prediction, for decision-making, used in civil engineering
  • Cutting-edge data mining methods, such as hybrid machine learning techniques, for data mining in civil engineering application
  • Web/internet data mining and application technology for civil engineering, e.g., information retrieval and web search, social network analysis, web crawling, information integration, opinion mining, and sentiment analysis
  • Model updating using large-scale data in civil engineering
  • Real-world civil engineering case studies of data mining, such as slope stability prediction, default detection, material property prediction, and software development for civil practitioners etc.

Articles

  • Special Issue
  • - Volume 2020
  • - Article ID 8478043
  • - Research Article

Stability of a Roadway below a Coal Seam under Dynamic Pressure: A Case Study of the 11123 Floor Gas Drainage Roadway of a Mine in Huainan, China

Pingsong Zhang | Yuanchao Ou | ... | Chong Xu
  • Special Issue
  • - Volume 2020
  • - Article ID 2763863
  • - Research Article

Bayesian Estimation of Resistance Factor for Bored Piles Based on Load Test Database

Zhijun Xu | Ranran Zhang | ... | Mingfang Du
  • Special Issue
  • - Volume 2020
  • - Article ID 4865628
  • - Research Article

A Novel Prediction Method of Dynamic Wall Pressure for Silos Based on Support Vector Machine

Hanhua Yu | Zhijun Xu | ... | Fang Yuan
  • Special Issue
  • - Volume 2020
  • - Article ID 3897215
  • - Research Article

A New Reliability Rock Mass Classification Method Based on Least Squares Support Vector Machine Optimized by Bacterial Foraging Optimization Algorithm

S. Zheng | A. N. Jiang | ... | G. C. Luo
  • Special Issue
  • - Volume 2020
  • - Article ID 4190682
  • - Research Article

A Novel Approach for Automatic Detection of Concrete Surface Voids Using Image Texture Analysis and History-Based Adaptive Differential Evolution Optimized Support Vector Machine

Nhat-Duc Hoang | Quoc-Lam Nguyen
  • Special Issue
  • - Volume 2020
  • - Article ID 2569531
  • - Research Article

Assessment of Waterlogging Risk in the Deep Foundation Pit Projects Based on Projection Pursuit Model

Han Wu | Junwu Wang
  • Special Issue
  • - Volume 2020
  • - Article ID 5921901
  • - Research Article

Strength and Microscopic Damage Mechanism of Yellow Sandstone with Holes under Freezing and Thawing

Huren Rong | Jingyu Gu | ... | Hao Dong
  • Special Issue
  • - Volume 2020
  • - Article ID 1643529
  • - Research Article

Development of an Ensemble Intelligent Model for Assessing the Strength of Cemented Paste Backfill

Yuantian Sun | Guichen Li | ... | Jiahui Xu
  • Special Issue
  • - Volume 2020
  • - Article ID 5143879
  • - Research Article

Mapping BIM Uses for Risk Mitigation in International Construction Projects

Tsenguun Ganbat | Heap-Yih Chong | Pin-Chao Liao
  • Special Issue
  • - Volume 2020
  • - Article ID 6548682
  • - Research Article

Bridge Seismic Damage Assessment Model Applying Artificial Neural Networks and the Random Forest Algorithm

Hanxi Jia | Junqi Lin | Jinlong Liu
Advances in Civil Engineering
 Journal metrics
Acceptance rate41%
Submission to final decision98 days
Acceptance to publication40 days
CiteScore1.100
Impact Factor1.176
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