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Jinying Xu

Bio: Jinying Xu is an academic researcher from University of Hong Kong. The author has contributed to research in topics: Blockchain & Computer science. The author has an hindex of 7, co-authored 21 publications receiving 114 citations.

Papers
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Journal ArticleDOI
TL;DR: Design for manufacture and assembly (DfMA) has become a buzzword amid the global resurgence of prefabrication and construction industrialization as mentioned in this paper. But design for manufacturing and assembly is hardly new, as there are c...
Abstract: Design for manufacture and assembly (DfMA) has become a buzzword amid the global resurgence of prefabrication and construction industrialization. Some argued that DfMA is hardly new, as there are c...

45 citations

Journal ArticleDOI
TL;DR: In this article, a trustworthy Internet of Things (IoT)-enabled building information modeling platform (IBP) is proposed for modular construction to ensure transparency, traceability, and traceability.
Abstract: Configuring a trustworthy Internet of Things (IoT)–enabled building information modeling (BIM) platform (IBP) is significant for modular construction to ensure transparency, traceability, a...

43 citations

Journal ArticleDOI
TL;DR: The pursuit of modern product sophistication and production efficiency has bolstered design for manufacture and assembly (DfMA) around the world as discussed by the authors, which is both a design philosophy and a method.
Abstract: The pursuit of modern product sophistication and production efficiency has bolstered design for manufacture and assembly (DfMA) around the world. Being both a design philosophy and a method...

42 citations

Journal ArticleDOI
TL;DR: In this paper, the authors evaluated the effects of prefabrication on construction waste minimization by exploiting a quantitative dataset stemmed from 114 sizable high-rise building projects in Hong Kong.
Abstract: Prefabrication has long been recognized as a green production technology to minimize construction's adverse environmental impacts such as waste, noise, dust, and air pollution. Previous studies reported the effects of prefabrication on construction waste minimization. However, these studies relied primarily on small data obtained by ethnographic methods such as interviews and questionnaire surveys. Research to evaluate the effects using bigger, more objective quantitative data is highly desired. This research aims to re-evaluate the effects of prefabrication on construction waste minimization by exploiting a quantitative dataset stemmed from 114 sizable high-rise building projects in Hong Kong. It was discovered that the average waste generation rates of conventional and prefabrication building projects were 0.91 and 0.77 ton/m² respectively. Compared with conventional construction, prefabrication logged a 15.38% waste reduction. Further probing into specific prefabricated components adopted in the samples, it is discovered that precast windows and walls are more conducive to waste minimization. This is coincident with the fact that these components are also widely adopted in the sample buildings. This study reconfirms the positive effects of prefabrication on waste minimization and articulates that two types of prefabricated components play relatively bigger role in minimizing construction waste. The strengths of this study lie in its statistical analyses of a valuable and objective quantitative dataset measuring prefabrication and waste generation rates. Future studies are recommended to prove the corollary - it is not what category of prefabricated component, but the actual proportion of prefabrication in the total construction volume that matters to waste minimization.

40 citations

Journal ArticleDOI
TL;DR: This work presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of building information modeling for construction projects.
Abstract: Building information modeling (BIM), as a technological artifact, has been acclaimed to have significant effects on construction projects by overcoming inherent problems such as poor commun...

31 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Book
01 Jan 1988
TL;DR: In this paper, the evolution of the Toyota production system is discussed, starting from need, further development, Genealogy of the production system, and the true intention of the Ford system.
Abstract: * Starting from Need* Evolution of the Toyota Production System* Further Development* Genealogy of the Toyota Production System* The True Intention of the Ford System* Surviving the Low-Growth Period

1,793 citations

Journal Article
Wade H. Shaw1
TL;DR: Could it be that the authors will reach a time when it is quicker to discover something from scratch than from facts they can sort out from a library or the web?
Abstract: WHEN Alvin Toffler coined the term 'information overload' in his classic 1970 book, Future Shock, it is doubtful that even he imagined the formidable array of information sources that we now have access to. Speculation quickly surfaced that such an information supply could lead to poor decisions as the capacity of our human processing ability becomes taxed. Research has established that job productivity and performance can be severely reduced due to excessive information and the intrusion of data sources into our lives. Could it be that we will reach a time when it is quicker to discover something from scratch than from facts we can sort out from a library or the web?

152 citations

ReportDOI
01 Feb 1988
TL;DR: In this paper, the Department of Energy published a Data Book that provides energy-related information under the following headings: Characteristics of residential buildings in the US; Characteristic of new single-family construction in US; characteristics of new multi-family constructions in United States; Household Appliances; Residential Sector Energy Consumption, Prices, and Expenditures; Characteristics in US Commercial Buildings; Commercial Buildings Energy Consumption and Prices, Expenses; and Additional Buildings and Community Systems Information.
Abstract: This Data Book updates and expands the previous Data Book originally published by the Department of Energy in September, 1986 (DOE/RL/01830/16). Energy-related information is provided under the following headings: Characteristics of Residential Buildings in the US; Characteristics of New Single Family Construction in the US; Characteristics of New Multi-Family Construction in the US; Household Appliances; Residential Sector Energy Consumption, Prices, and Expenditures; Characteristics of US Commercial Buildings; Commercial Buildings Energy Consumption, Prices, and Expenditures; and Additional Buildings and Community Systems Information. 12 refs., 59 figs., 118 tabs.

110 citations