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Accounting for the uncertainties in the estimation of average shear wave velocity using – correlations

《结构与土木工程前沿（英文）》 2021年 第15卷 第5期   页码 1199-1208 doi: 10.1007/s11709-021-0749-1

摘要： Site-specific seismic hazard analysis is crucial for designing earthquake resistance structures, particularly in seismically active regions. Shear wave velocity ( V S) is a key parameter in such analysis, although the economy and other factors restrict its direct field measurement in many cases. Various V S–SPT– N correlations are routinely incorporated in seismic hazard analysis to estimate the value of V S. However, many uncertainties question the reliability of these estimated V S values. This paper comes up with a statistical approach to take care of such uncertainties involved in V S calculations. The measured SPT– N values from all the critical boreholes were converted into statistical parameters and passed through various correlations to estimate V S at different depths. The effect of different soil layers in the boreholes on the Vs estimation was also taken into account. Further, the average shear wave velocity of the top 30 m soil cover ( V S30) is estimated after accounting for various epistemic and aleatoric uncertainties. The scattering nature of the V S values estimated using different V S– N correlations was reduced significantly with the application of the methodology. Study results further clearly demonstrated the potential of the approach to eliminate various uncertainties involved in the estimation of V S30 using general and soil-specific correlations.

关键词： uncertainties     V S– N correlations     V S30     SPT data     statistical methodology    

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In situ-based assessment of soil liquefaction potential–Case study of an earth dam in Tunisia

Ikram GUETTAYA,Mohamed Ridha EL OUNI

《结构与土木工程前沿（英文）》 2014年 第8卷 第4期   页码 456-461 doi: 10.1007/s11709-014-0259-5

摘要： The present paper examines the evaluation of liquefaction potential of an earth dam foundation in Tunisia. The assessment of soil liquefaction was made using deterministic and probabilistic simplified procedures developed from several case histories. The data collected from the field investigation performed before and after the vibrocompaction are analyzed and the results are reported. The obtained results show that after vibrocompaction, a significant improvement of the soil resistance reduces the liquefaction potential of the sandy foundation. Indeed, in the untreated layers, the factor of safety drops below 1 which means that the soil is susceptible for liquefaction. However, in the compacted horizons, the values of exceed the unit which justifies the absence of liquefaction hazard of the foundation.

关键词： liquefaction     cone penetration test (CPT)     standard penetration test (SPT)     vibrcompaction     sand    

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SPT based determination of undrained shear strength: Regression models and machine learning

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

《结构与土木工程前沿（英文）》 2020年 第14卷 第1期   页码 185-198 doi: 10.1007/s11709-019-0591-x

摘要： The purpose of this study is the accurate prediction of undrained shear strength using Standard Penetration Test results and soil consistency indices, such as water content and Atterberg limits. With this study, along with the conventional methods of simple and multiple linear regression models, three machine learning algorithms, random forest, gradient boosting and stacked models, are developed for prediction of undrained shear strength. These models are employed on a relatively large data set from different projects around Turkey covering 230 observations. As an improvement over the available studies in literature, this study utilizes correct statistical analyses techniques on a relatively large database, such as using a train/test split on the data set to avoid overfitting of the developed models. Furthermore, the validity and consistency of the prediction results are ensured with the correct use of statistical measures like -value and cross-validation which were missing in previous studies. To compare the performances of the models developed in this study with the prior ones existing in literature, all models were applied on the test data set and their performances are evaluated in terms of the resulting root mean squared error ( ) values and coefficient of determination ( ). Accordingly, the models developed in this study demonstrate superior prediction capabilities compared to all of the prior studies. Moreover, to facilitate the use of machine learning algorithms for prediction purposes, entire source code prepared for this study and the collected data set are provided as supplements of this study.

关键词： undrained shear strength     linear regression     random forest     gradient boosting     machine learning     standard penetration test    

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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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Data quality assessment for studies investigating microplastics and nanoplastics in food products: Arecurrent data reliable?

《环境科学与工程前沿（英文）》 2023年 第17卷 第8期 doi: 10.1007/s11783-023-1694-0

摘要：

● Data quality assessment criteria for MP/NPs in food products were developed.

关键词： Microplastic     Nanoplastic     Food products     Data quality     Human health risk    

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Blockchain application in healthcare service mode based on Health Data Bank

Jianxia GONG, Lindu ZHAO

《工程管理前沿（英文）》 2020年 第7卷 第4期   页码 605-614 doi: 10.1007/s42524-020-0138-9

摘要： Blockchain is commonly considered a potential disruptive technology. Moreover, the healthcare industry has experienced rapid growth in the adoption of health information technology, such as electronic health records and electronic medical records. To guarantee data privacy and data security as well as to harness the value of health data, the concept of Health Data Bank (HDB) is proposed. In this study, HDB is defined as an integrated health data service institution, which bears no “ownership” of health data and operates health data under the principal–agent model. This study first comprehensively reviews the main characters of blockchain and identifies the blockchain-based healthcare industry projects and startups in the areas of health insurance, pharmacy, and medical treatment. Then, we analyze the fundamental principles of HDB and point out four challenges faced by HDB’s sustainable development: (1) privacy protection and interoperability of health data; (2) data rights; (3) health data supervision; (4) and willingness to share health data. We also analyze the important benefits of blockchain adoption in HDB. Furthermore, three application scenarios including distributed storage of health data, smart-contract-based healthcare service mode, and consensus-algorithm-based incentive policy are proposed to shed light on HDB-based healthcare service mode. In the end, this study offers insights into potential research directions and challenges.

关键词： Health Data Bank     blockchain     data assets     smart contract     incentive mechanism    

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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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Intelligent data analytics is here to change engineering management

Jonathan Jingsheng SHI, Saixing ZENG, Xiaohua MENG

《工程管理前沿（英文）》 2017年 第4卷 第1期   页码 41-48 doi: 10.15302/J-FEM-2017003

摘要： A great deal of scientific research in the world aims at discovering the facts about the world so that we understand it better and find solutions to problems. Data enabling technology plays an important role in modern scientific discovery and technologic advancement. The importance of good information was long recognized by prominent leaders such as Sun Tzu and Napoleon. Factual data enables managers to measure, to understand their businesses, and to directly translate that knowledge into improved decision making and performance. This position paper argues that data analytics is ready to change engineering management in the following areas: 1) by making relevant historical data available to the manager at the time when it’s needed; 2) by filtering out actionable intelligence from the ocean of data; and 3) by integrating useful data from multiple sources to support quantitative decision-making. Considering the unique need for engineering management, the paper proposes researchable topics in the two broad areas of data acquisition and data analytics. The purpose of the paper is to provoke discussion from peers and to encourage research activity.

关键词： engineering management     project management     big data     data analytics     planning     execution    

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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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Anensemble method for data stream classification in the presence of concept drift

Omid ABBASZADEH,Ali AMIRI,Ali Reza KHANTEYMOORI

《信息与电子工程前沿（英文）》 2015年 第16卷 第12期   页码 1059-1068 doi: 10.1631/FITEE.1400398

摘要： One recent area of interest in computer science is data stream management and processing. By ‘data stream’, we refer to continuous and rapidly generated packages of data. Specific features of data streams are immense volume, high production rate, limited data processing time, and data concept drift; these features differentiate the data stream from standard types of data. An issue for the data stream is classification of input data. A novel ensemble classifier is proposed in this paper. The classifier uses base classifiers of two weighting functions under different data input conditions. In addition, a new method is used to determine drift, which emphasizes the precision of the algorithm. Another characteristic of the proposed method is removal of different numbers of the base classifiers based on their quality. Implementation of a weighting mechanism to the base classifiers at the decision-making stage is another advantage of the algorithm. This facilitates adaptability when drifts take place, which leads to classifiers with higher efficiency. Furthermore, the proposed method is tested on a set of standard data and the results confirm higher accuracy compared to available ensemble classifiers and single classifiers. In addition, in some cases the proposed classifier is faster and needs less storage space.

关键词： Data stream     Classificaion     Ensemble classifiers     Concept drift    

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Methodological considerations for redesigning sustainable cropping systems: the value of data-mininglarge and detailed farm data sets at the cropping system level

Nicolas MUNIER-JOLAIN, Martin LECHENET

《农业科学与工程前沿（英文）》 2020年 第7卷 第1期   页码 21-27 doi: 10.15302/J-FASE-2019292

摘要：

Redesigning cropping and farming systems to enhance their sustainability is mainly addressed in scientific studies using experimental and modeling approaches. Large data sets collected from real farms allow for the development of innovative methods to produce generic knowledge. Data mining methods allow for the diversity of systems to be considered holistically and can take into account the diversity of production contexts to produce site-specific results. Based on the very few known studies using such methods to analyze the crop management strategies affecting pesticide use and their effect on farm performance, we advocate further investment in the development of large data sets that can support future research programs on farming system design.

关键词： data mining     holistic     Integrated Pest Management     economics     DEPHY network.    

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Multi-timescale optimization scheduling of interconnected data centers based on model predictive control

《能源前沿（英文）》 doi: 10.1007/s11708-023-0912-6

摘要： With the promotion of “dual carbon” strategy, data center (DC) access to high-penetration renewable energy sources (RESs) has become a trend in the industry. However, the uncertainty of RES poses challenges to the safe and stable operation of DCs and power grids. In this paper, a multi-timescale optimal scheduling model is established for interconnected data centers (IDCs) based on model predictive control (MPC), including day-ahead optimization, intraday rolling optimization, and intraday real-time correction. The day-ahead optimization stage aims at the lowest operating cost, the rolling optimization stage aims at the lowest intraday economic cost, and the real-time correction aims at the lowest power fluctuation, eliminating the impact of prediction errors through coordinated multi-timescale optimization. The simulation results show that the economic loss is reduced by 19.6%, and the power fluctuation is decreased by 15.23%.

关键词： model predictive control     interconnected data center     multi-timescale     optimized scheduling     distributed power supply     landscape uncertainty    

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Big data and machine learning: A roadmap towards smart plants

《工程管理前沿（英文）》   页码 623-639 doi: 10.1007/s42524-022-0218-0

摘要： Industry 4.0 aims to transform chemical and biochemical processes into intelligent systems via the integration of digital components with the actual physical units involved. This process can be thought of as addition of a central nervous system with a sensing and control monitoring of components and regulating the performance of the individual physical assets (processes, units, etc.) involved. Established technologies central to the digital integrating components are smart sensing, mobile communication, Internet of Things, modelling and simulation, advanced data processing, storage and analysis, advanced process control, artificial intelligence and machine learning, cloud computing, and virtual and augmented reality. An essential element to this transformation is the exploitation of large amounts of historical process data and large volumes of data generated in real-time by smart sensors widely used in industry. Exploitation of the information contained in these data requires the use of advanced machine learning and artificial intelligence technologies integrated with more traditional modelling techniques. The purpose of this paper is twofold: a) to present the state-of-the-art of the aforementioned technologies, and b) to present a strategic plan for their integration toward the goal of an autonomous smart plant capable of self-adaption and self-regulation for short- and long-term production management.

关键词： big data     machine learning     artificial intelligence     smart sensor     cyber–physical system     Industry 4.0     intelligent system     digitalization    

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Clinical research of traditional Chinese medicine in big data era

null

《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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标题 作者 时间 类型 操作

Accounting for the uncertainties in the estimation of average shear wave velocity using – correlations

期刊论文

In situ-based assessment of soil liquefaction potential–Case study of an earth dam in Tunisia

Ikram GUETTAYA,Mohamed Ridha EL OUNI

期刊论文

SPT based determination of undrained shear strength: Regression models and machine learning

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

期刊论文

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

Feng YANG, Manman WANG

期刊论文

Data quality assessment for studies investigating microplastics and nanoplastics in food products: Arecurrent data reliable?

期刊论文

Blockchain application in healthcare service mode based on Health Data Bank

Jianxia GONG, Lindu ZHAO

期刊论文

Challenges to Engineering Management in the Big Data Era

Yong Shi

期刊论文

Intelligent data analytics is here to change engineering management

Jonathan Jingsheng SHI, Saixing ZENG, Xiaohua MENG

期刊论文

Special issue: Innovative applications of big data and artificial intelligence

期刊论文

Anensemble method for data stream classification in the presence of concept drift

Omid ABBASZADEH,Ali AMIRI,Ali Reza KHANTEYMOORI

期刊论文

Methodological considerations for redesigning sustainable cropping systems: the value of data-mininglarge and detailed farm data sets at the cropping system level

Nicolas MUNIER-JOLAIN, Martin LECHENET

期刊论文

Multi-timescale optimization scheduling of interconnected data centers based on model predictive control

期刊论文

Big data and machine learning: A roadmap towards smart plants

期刊论文

Clinical research of traditional Chinese medicine in big data era

null

期刊论文

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

期刊论文