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2019, Vol.5, Issue.6

On the cover

Traditionally, advanced materials are found empirically or through experimental trial-and-error approaches. As big data created by modern experimental and computational techniques is becoming more readily available, data-driven or machine learning methods have opened new paradigms for the discovery and rational design of materials. In this issue, Zhou and his colleagues introduced various machine learning methods and related software or tools. Main ideas and basic procedures for employing these approaches in materials research were highlighted. Recent representative applications of machine learning for functional material design were discussed.

Guest Editorial Board

High Performance Structures: Building Structures and Materials

Guest Editors-in-Chief

Zhou, Xuhong, Chongqing University, China

Kareem, Ahsan, University of Notre Dame, USA

Executive Editors-in-Chief

Nie, Jianguo, Tsinghua University, China

Tamura, Yukio, Chongqing University, China


Beroza, Gregory, Stanford University, USA

Gardner, Leroy, Imperial College London, UK

Li, Guoqiang, Tongji University, China

Li, Hui, Harbin Institute of Technology, China

Liu, Jiaping, Southeast University, China

Reddy, Junuthula Narasimha, Texas A&M University, USA

Spencer, Billie F., University of Illinois at Urbana-Champaign, USA

Teng, Jinguang, The Hong Kong Polytechnic University, China

Uy, Brian, The University of Sydney, Australia

Wu, Zhisen, Southeast University, China

Yang, Qingshan, Chongqing University, China

Yang, Yeongbin, Chongqing University, China

Zhang, Jun, Tsinghua University, China

Zhang, Sumei, Harbin Institute of Technology, China

Contents Volume 5 · Issue 6 · 2019. Pages 981-1192 24 articles

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Smart Process Manufacturing Systems: Deep Integration of Artificial Intelligence and Process Manufacturing

Feng Qian

Engineering 2019, Volume 5, Issue 6,   Pages 981-981 doi:10.1016/j.eng.2019.10.002

Abstract 1973 PDF 279

News & Highlights

Mitigating Climate Change Will Depend on Negative Emissions Technologies

Chris Palmer

Engineering 2019, Volume 5, Issue 6,   Pages 982-984 doi:10.1016/j.eng.2019.10.006

Abstract 1316 PDF 114

What if the Global Positioning System Didn’t Work?

Mitch Leslie

Engineering 2019, Volume 5, Issue 6,   Pages 985-986 doi:10.1016/j.eng.2019.10.007

Abstract 950 PDF 72

Engineering Stars at Google Science Fair

Sean O’Neill

Engineering 2019, Volume 5, Issue 6,   Pages 987-988 doi:10.1016/j.eng.2019.10.008

Abstract 1163 PDF 51

Smog Casts a Shadow on Solar Power

Jane Palmer

Engineering 2019, Volume 5, Issue 6,   Pages 989-990 doi:10.1016/j.eng.2019.10.009

Abstract 790 PDF 32

Charger Collaborations Power Global Electric Vehicle Expansion

Peter Weiss

Engineering 2019, Volume 5, Issue 6,   Pages 991-992 doi:10.1016/j.eng.2019.10.010

Abstract 789 PDF 55

Topic Insights

Recent Advances in Smart Process Manufacturing

R.N. Lumley

Engineering 2019, Volume 5, Issue 6,   Pages 993-994 doi:10.1016/j.eng.2019.09.005

Abstract 1315 PDF 251


Smart Process Manufacturing

Opportunities and Challenges of Artificial Intelligence for Green Manufacturing in the Process Industry

Shuai Mao, Bing Wang, Yang Tang, Feng Qian

Engineering 2019, Volume 5, Issue 6,   Pages 995-1002 doi:10.1016/j.eng.2019.08.013

Abstract 2437 PDF 437

Smart Process Manufacturing for Formulated Products Perspective

James Litster, Ian David L. Bogle

Engineering 2019, Volume 5, Issue 6,   Pages 1003-1009 doi:10.1016/j.eng.2019.02.014

Abstract 1313 PDF 196

Data Analytics and Machine Learning for Smart Process Manufacturing: Recent Advances and Perspectives in the Big Data Era Perspective

Chao Shang、 Fengqi You

Engineering 2019, Volume 5, Issue 6,   Pages 1010-1016 doi:10.1016/j.eng.2019.01.019

Abstract 2444 PDF 467

Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design Review

Teng Zhou, Zhen Song, Kai Sundmacher

Engineering 2019, Volume 5, Issue 6,   Pages 1017-1026 doi:10.1016/j.eng.2019.02.011

Abstract 7497 PDF 898

Artificial Intelligence in Steam Cracking Modeling: A Deep Learning Algorithm for Detailed Effluent Prediction Article

Pieter P. Plehiers, Steffen H. Symoens, Ismaël Amghizar, Guy B. Marin, Christian V. Stevens, Kevin M. Van Geem

Engineering 2019, Volume 5, Issue 6,   Pages 1027-1040 doi:10.1016/j.eng.2019.02.013

Abstract 1462 PDF 155

A Knowledge Base System for Operation Optimization: Design and Implementation Practice for the Polyethylene Process Article

Weimin Zhong, Chaoyuan Li, Xin Peng, Feng Wan, Xufeng An, Zhou Tian

Engineering 2019, Volume 5, Issue 6,   Pages 1041-1048 doi:10.1016/j.eng.2019.09.004

Abstract 882 PDF 86

A Multi-Objective Optimal Experimental Design Framework for Enhancing the Efficiency of Online Model Identification Platforms Article

Arun Pankajakshan, Conor Waldron, Marco Quaglio, Asterios Gavriilidis, Federico Galvanin

Engineering 2019, Volume 5, Issue 6,   Pages 1049-1059 doi:10.1016/j.eng.2019.10.003

Abstract 841 PDF 84

A Data and Knowledge Collaboration Strategy for Decision-Making on the Amount of Aluminum Fluoride Addition Based on Augmented Fuzzy Cognitive Maps Article

Weichao Yue、 Weihua Gui、 Xiaofang Chen、 Zhaohui Zeng、 Yongfang Xie

Engineering 2019, Volume 5, Issue 6,   Pages 1060-1076 doi:10.1016/j.eng.2019.10.005

Abstract 1778 PDF 139

Optimal Antibody Purification Strategies Using Data-Driven Models Article

Songsong Liu, Lazaros G. Papageorgiou

Engineering 2019, Volume 5, Issue 6,   Pages 1077-1092 doi:10.1016/j.eng.2019.10.011

Abstract 1401 PDF 105

High Performance Structures: Building Structures and Materials

Research and Implementations of Structural Monitoring for Bridges and Buildings in Japan—A Review Review

Yozo Fujino, Dionysius M. Siringoringo, Yoshiki Ikeda, Tomonori Nagayama, Tsukasa Mizutani

Engineering 2019, Volume 5, Issue 6,   Pages 1093-1119 doi:10.1016/j.eng.2019.09.006

Abstract 3123 PDF 422

Thoughts on the Development of Bridge Technology in China Review

Xuhong Zhou, Xigang Zhang

Engineering 2019, Volume 5, Issue 6,   Pages 1120-1130 doi:10.1016/j.eng.2019.10.001

Abstract 5019 PDF 517

Material Mechanical Properties Necessary for the Structural Intervention of Concrete Structures Article

Tamon Ueda

Engineering 2019, Volume 5, Issue 6,   Pages 1131-1138 doi:10.1016/j.eng.2019.02.012

Abstract 1430 PDF 129

Multiscale Homogenization Analysis of Alkali–Silica Reaction (ASR) Effect in Concrete Article

Roozbeh Rezakhani, Mohammed Alnaggar, Gianluca Cusatis

Engineering 2019, Volume 5, Issue 6,   Pages 1139-1154 doi:10.1016/j.eng.2019.02.007

Abstract 1740 PDF 113

AI for Precision Medicine

Information Science Should Take a Lead in Future Biomedical Research Perspective

Kenta Nakai

Engineering 2019, Volume 5, Issue 6,   Pages 1155-1158 doi:10.1016/j.eng.2019.07.023

Abstract 917 PDF 85

4D Printing

Preliminary Investigation of the Reversible 4D Printing of a Dual-Layer Component Article

Amelia Yilin Lee, Jia An, Chee Kai Chu, Yi Zhang

Engineering 2019, Volume 5, Issue 6,   Pages 1159-1170 doi:10.1016/j.eng.2019.09.007

Abstract 1190 PDF 105

Vehicle Engineering

Explicit–Implicit Co-Simulation Techniques for Dynamic Responses of a Passenger Car on Arbitrary Road Surfaces Article

Hongzhou Hu, Zhihua Zhong

Engineering 2019, Volume 5, Issue 6,   Pages 1171-1178 doi:10.1016/j.eng.2019.09.003

Abstract 736 PDF 81


Privacy Computing: Concept, Computing Framework, and Future Development Trends Article

Fenghua Li, Hui Li, Ben Niu, Jinjun Chen

Engineering 2019, Volume 5, Issue 6,   Pages 1179-1192 doi:10.1016/j.eng.2019.09.002

Abstract 19821 PDF 1374