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Special issue: Innovative applications of big data and artificial intelligence

《工程管理前沿（英文）》 2022年第9卷第4期页码 517-519 doi: 10.1007/s42524-022-0234-0

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Realtime prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel constructionbig data

Zaobao LIU; Yongchen WANG; Long LI; Xingli FANG; Junze WANG

《结构与土木工程前沿（英文）》 2022年第16卷第4期页码 401-413 doi: 10.1007/s11709-022-0823-3

摘要： Real-time dynamic adjustment of the tunnel bore machine (TBM) advance rate according to the rock-machine interaction parameters is of great significance to the adaptability of TBM and its efficiency in construction. This paper proposes a real-time predictive model of TBM advance rate using the temporal convolutional network (TCN), based on TBM construction big data. The prediction model was built using an experimental database, containing 235 data sets, established from the construction data from the Jilin Water-Diversion Tunnel Project in China. The TBM operating parameters, including total thrust, cutterhead rotation, cutterhead torque and penetration rate, are selected as the input parameters of the model. The TCN model is found outperforming the recurrent neural network (RNN) and long short-term memory (LSTM) model in predicting the TBM advance rate with much smaller values of mean absolute percentage error than the latter two. The penetration rate and cutterhead torque of the current moment have significant influence on the TBM advance rate of the next moment. On the contrary, the influence of the cutterhead rotation and total thrust is moderate. The work provides a new concept of real-time prediction of the TBM performance for highly efficient tunnel construction.

关键词： hard rock tunnel tunnel bore machine advance rate prediction temporal convolutional networks soft computing construction big data

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A review of systematic evaluation and improvement in the big data environment

Feng YANG, Manman WANG

《工程管理前沿（英文）》 2020年第7卷第1期页码 27-46 doi: 10.1007/s42524-020-0092-6

摘要： The era of big data brings unprecedented opportunities and challenges to management research. As one of the important functions of management decision-making, evaluation has been given more functions and application space. Exploring the applicable evaluation methods in the big data environment has become an important subject of research. The purpose of this paper is to provide an overview and discussion of systematic evaluation and improvement in the big data environment. We first review the evaluation methods based on the main analytic techniques of big data such as data mining, statistical methods, optimization and simulation, and deep learning. Focused on the characteristics of big data (association feature, data loss, data noise, and visualization), the relevant evaluation methods are given. Furthermore, we explore the systematic improvement studies and application fields. Finally, we analyze the new application areas of evaluation methods and give the future directions of evaluation method research in a big data environment from six aspects. We hope our research could provide meaningful insights for subsequent research.

关键词： big data evaluation methods systematic improvement big data analytic techniques data mining

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基于数据互联服务的隧道新奥法施工构想与初探 Article

杜博文, 杜彦良, 徐飞, 贺鹏

《工程（英文）》 2018年第4卷第1期页码 123-130 doi: 10.1016/j.eng.2017.07.002

摘要：

新奥法（NATM）广泛应用于山岭隧道、城市地铁、地下贮库、地下厂房、矿山巷道等地下工程，掌子面前方地质、围岩变形、支护结构受力状态等在施工过程中的动态变化情况，是评价结构稳定程度、优化施工方案，确保隧道施工安全与质量的必要信息。施工过程中获取的大量动态监测信息的不确定性与离散性，给施工方案的选择及灾害事故与险情的准确预测带来了巨大挑战，增加了隧道安全隐患。针对上述问题，本文提出了一种基于互联网大数据支持环境下的隧道施工数据服务系统，通过对已施工案例中各检测器结果进行标记，建立同场景下施工相关参数的关联，利用工程案例的积累不断补充和完善，实现相似环境下的参数提取，为同类场景下施工方案设计、施工资源的合理分配提供数据支撑，为后续工程设计、施工提供依据。

关键词：新奥法大数据环境数据服务隧道施工

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Challenges to Engineering Management in the Big Data Era

Yong Shi

《工程管理前沿（英文）》 2015年第2卷第3期页码 293-303 doi: 10.15302/J-FEM-2015042

摘要： This paper presents a review of the challenges to engineering management in the Big Data Era as well as the Big Data applications. First, it outlines the definitions of big data, data science and intelligent knowledge and the history of big data. Second, the paper reviews the academic activities about big data in China. Then, it elaborates a number of challenging big data problems, including transforming semi-structured and non-structured data into “structured format” and explores the relationship of data heterogeneity, knowledge heterogeneity and decision heterogeneity. Furthermore, the paper reports various real-life applications of big data, such as financial and petroleum engineering and internet business.

关键词： big data data science intelligent knowledge engineering management real-life applications

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Clinical research of traditional Chinese medicine in big data era

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《医学前沿（英文）》 2014年第8卷第3期页码 321-327 doi: 10.1007/s11684-014-0370-y

摘要：

With the advent of big data era, our thinking, technology and methodology are being transformed. Data-intensive scientific discovery based on big data, named “The Fourth Paradigm,” has become a new paradigm of scientific research. Along with the development and application of the Internet information technology in the field of healthcare, individual health records, clinical data of diagnosis and treatment, and genomic data have been accumulated dramatically, which generates big data in medical field for clinical research and assessment. With the support of big data, the defects and weakness may be overcome in the methodology of the conventional clinical evaluation based on sampling. Our research target shifts from the “causality inference” to “correlativity analysis.” This not only facilitates the evaluation of individualized treatment, disease prediction, prevention and prognosis, but also is suitable for the practice of preventive healthcare and symptom pattern differentiation for treatment in terms of traditional Chinese medicine (TCM), and for the post-marketing evaluation of Chinese patent medicines. To conduct clinical studies involved in big data in TCM domain, top level design is needed and should be performed orderly. The fundamental construction and innovation studies should be strengthened in the sections of data platform creation, data analysis technology and big-data professionals fostering and training.

关键词： big data traditional Chinese medicine clinical evaluation evidence based medicine

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Appreciating the role of big data in the modernization of environmental governance

《工程管理前沿（英文）》 2022年第9卷第1期页码 163-169 doi: 10.1007/s42524-021-0185-x

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Scientific computation of big data in real-world clinical research

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《医学前沿（英文）》 2014年第8卷第3期页码 310-315 doi: 10.1007/s11684-014-0358-7

摘要：

The advent of the big data era creates both opportunities and challenges for traditional Chinese medicine (TCM). This study describes the origin, concept, connotation, and value of studies regarding the scientific computation of TCM. It also discusses the integration of science, technology, and medicine under the guidance of the paradigm of real-world, clinical scientific research. TCM clinical diagnosis, treatment, and knowledge were traditionally limited to literature and sensation levels; however, primary methods are used to convert them into statistics, such as the methods of feature subset optimizing, multi-label learning, and complex networks based on complexity, intelligence, data, and computing sciences. Furthermore, these methods are applied in the modeling and analysis of the various complex relationships in individualized clinical diagnosis and treatment, as well as in decision-making related to such diagnosis and treatment. Thus, these methods strongly support the real-world clinical research paradigm of TCM.

关键词： big data real world clinical research Chinese medicine medical computing

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城市大数据与城市智能化发展 Review

潘云鹤,田沄,刘晓龙,顾德道,华岗

《工程（英文）》 2016年第2卷第2期页码 171-178 doi: 10.1016/J.ENG.2016.02.003

摘要：

本文探讨了城市大数据的概念，明确了城市大数据的特点，并依据处理方法和应用目标对中国城市大数据进行了分类；分析了中国城市智能化同其他国家“智慧城市”的区别，给出了中国城市智能化的定义，并提出了中国城市智能化的模型；明确了城市大数据在城市智能化中的作用，即城市大数据是城市智能化的基础和核心，是城市智能化持续发展的不竭动力；指出了中国城市大数据发展面临的问题和挑战；针对城市大数据在城市智能化中的支撑和核心作用，提出了城市大数据的建设重点，包括：基础支撑、城市治理、公共服务以及经济和产业发展等；指出了城市智能化的应用是推动中国城市发展的极妙抓手。当前，中国具备天时、地利、人和的独特优势，应充分借助城市大数据，促进城市智能化向高水平发展。

关键词：城市大数据城市智能化三元空间建设重点

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我国智能建造关键领域技术发展的战略思考

陈珂,丁烈云

《中国工程科学》 2021年第23卷第4期页码 64-70 doi: 10.15302/J-SSCAE-2021.04.007

摘要：

智能建造作为新一代信息技术和工程建造的有机融合，是实现我国建筑业高质量发展的重要依托。本文阐述了智能建造的基本概念与重要性，归纳了面向全产业链一体化的工程软件、面向智能工地的工程物联网、面向人机共融的智能化工程机械、面向智能决策的工程大数据等四类关键领域技术；通过问卷调研与专家访谈，分析了我国智能建造关键领域技术在市场环境、企业部署、核心资源储备等方面的现状和短板。在此基础上，明确了关键领域技术的发展目标，提出了建立健全标准体系、推动“产学研用”协同、加大知识产权保护、开展典型工程试点示范等重点任务，继而从管理机构、企业、高校等多个主体的角度形成对策建议。

关键词：智能建造，工程软件，工程物联网，工程机械，工程大数据

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A study on specialist or special disease clinics based on big data

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《医学前沿（英文）》 2014年第8卷第3期页码 376-381 doi: 10.1007/s11684-014-0356-9

摘要：

Correlation analysis and processing of massive medical information can be implemented through big data technology to find the relevance of different factors in the life cycle of a disease and to provide the basis for scientific research and clinical practice. This paper explores the concept of constructing a big medical data platform and introduces the clinical model construction. Medical data can be collected and consolidated by distributed computing technology. Through analysis technology, such as artificial neural network and grey model, a medical model can be built. Big data analysis, such as Hadoop, can be used to construct early prediction and intervention models as well as clinical decision-making model for specialist and special disease clinics. It establishes a new model for common clinical research for specialist and special disease clinics.

关键词： big data correlation analysis medical information integration data analysis clinical model

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Study on Big Data-based Behavior Modification in Metro Construction

Lie-yun Ding,Sheng-yu Guo

《工程管理前沿（英文）》 2015年第2卷第2期页码 131-136 doi: 10.15302/J-FEM-2015037

摘要： With the rapid development of metro construction in China, construction accidents frequently happen, which are significantly attributable to workers’ unsafe behavior. Behavior-based safety (BBS) is an effective method to modify workers’ unsafe behavior. This paper introduces the study on big data-based metro construction behavior modification, aiming to solve the problem of current research without consideration of workers’ personal characters. First, the behavior modification pushing mechanism based on content-based personalized recommendation is studied. Secondly, the development of behavior modification system of metro construction (BMSMC) is introduced. Thirdly, BMSMC practical applications using the unsafe behavior rate, as a measuring indicator is implemented. Observations at one metro construction site in Wuhan indicate that the unsafe behavior rate of modified scaffolders at this work place decreased by 69.3%. At the same time, as of unmodified scaffolders at another work place for comparison, the unsafe behavior rate decreased by 56.9%, which validates the effectiveness of this system.

关键词： big data unsafe behavior behavior modification behavior-based safety (BBS) unsafe behavior rate

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Slope stability analysis based on big data and convolutional neural network

Yangpan FU; Mansheng LIN; You ZHANG; Gongfa CHEN; Yongjian LIU

《结构与土木工程前沿（英文）》 2022年第16卷第7期页码 882-895 doi: 10.1007/s11709-022-0859-4

摘要： The Limit Equilibrium Method (LEM) is commonly used in traditional slope stability analyses, but it is time-consuming and complicated. Due to its complexity and nonlinearity involved in the evaluation process, it cannot provide a quick stability estimation when facing a large number of slopes. In this case, the convolutional neural network (CNN) provides a better alternative. A CNN model can process data quickly and complete a large amount of data analysis in a specific situation, while it needs a large number of training samples. It is difficult to get enough slope data samples in practical engineering. This study proposes a slope database generation method based on the LEM. Samples were amplified from 40 typical slopes, and a sample database consisting of 20000 slope samples was established. The sample database for slopes covered a wide range of slope geometries and soil layers’ physical and mechanical properties. The CNN trained with this sample database was then applied to the stability prediction of 15 real slopes to test the accuracy of the CNN model. The results show that the slope stability prediction method based on the CNN does not need complex calculation but only needs to provide the slope coordinate information and physical and mechanical parameters of the soil layers, and it can quickly obtain the safety factor and stability state of the slopes. Moreover, the prediction accuracy of the CNN trained by the sample database for slope stability analysis reaches more than 99%, and the comparisons with the BP neural network show that the CNN has significant superiority in slope stability evaluation. Therefore, the CNN can predict the safety factor of real slopes. In particular, the combination of typical actual slopes and generated slope data provides enough training and testing samples for the CNN, which improves the prediction speed and practicability of the CNN-based evaluation method in engineering practice.

关键词： slope stability limit equilibrium method convolutional neural network database for slopes big data

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Utilizing big data to build personalized technology and system of diagnosis and treatment in traditional

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《医学前沿（英文）》 2014年第8卷第3期页码 272-278 doi: 10.1007/s11684-014-0364-9

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Big Data to support sustainable urban energy planning: The EvoEnergy project

Moulay Larbi CHALAL, Benachir MEDJDOUB, Nacer BEZAI, Raid SHRAHILY

《工程管理前沿（英文）》 2020年第7卷第2期页码 287-300 doi: 10.1007/s42524-019-0081-9

摘要： Energy sustainability is a complex problem that needs to be tackled holistically by equally addressing other aspects such as socio-economic to meet the strict CO emission targets. This paper builds upon our previous work on the effect of household transition on residential energy consumption where we developed a 3D urban energy prediction system (EvoEnergy) using the old UK panel data survey, namely, the British household panel data survey (BHPS). In particular, the aim of the present study is to examine the validity and reliability of EvoEnergy under the new UK household longitudinal study (UKHLS) launched in 2009. To achieve this aim, the household transition and energy prediction modules of EvoEnergy have been tested under both data sets using various statistical techniques such as Chow test. The analysis of the results advised that EvoEnergy remains a reliable prediction system and had a good prediction accuracy (MAPE  5%) when compared to actual energy performance certificate data. From this premise, we recommend researchers, who are working on data-driven energy consumption forecasting, to consider merging the BHPS and UKHLS data sets. This will, in turn, enable them to capture the bigger picture of different energy phenomena such as fuel poverty; consequently, anticipate problems with policy prior to their occurrence. Finally, the paper concludes by discussing two scenarios of EvoEnergy development in relation to energy policy and decision-making.

关键词： urban energy planning sustainable planning Big Data household transition energy prediction