| Author: | Zhou, Xin |
| Title: | The impacts of BIM implementation on construction project productivity : experiences from China |
| Advisors: | Wong, K. W. Johnny (BRE) Chan, P. C. Albert (BRE) |
| Degree: | M.Phil. |
| Year: | 2017 |
| Subject: | Hong Kong Polytechnic University -- Dissertations Building information modeling -- China Construction industry -- Management Project management |
| Department: | Department of Building and Real Estate |
| Pages: | xii, 130 pages |
| Language: | English |
| Abstract: | Construction productivity has long been a concern in both industry and academia, for it can be improved to foster sustaining economic growth and generate substantial social wealth and welfare. Being the most dominating factor contributing to the remarkable economic profitability of most construction projects, productivity is receiving incessantly increasing concern with respect to the production efficiency of the whole construction industry. However, construction industry worldwide has been undergoing a substantial and continuous decrease in construction productivity over the past several decades. During the past ten years, the construction productivity growth rate of China has decreased dramatically combined with the decline of the average growth rate of labor productivity of China. Driving by the increasing pressure of improving productivity, over the recent several decades, the architecture, engineering, and construction (AEC) industry has long been making every effort to seek effective approaches to reduce cost, shorten project duration, enhance the quality of construction projects thus to improve productivity. BIM was most commonly perceived as a visualization tool for coordinating and promoting communication of AEC sector in order to reduce rework, predict collisions, enhance project productivity, shorten project time, decrease project costs, and improve quality and safety of construction projects. Generally, BIM is regarded as an emerging, promising, and innovative technology and process, dramatically transformed the way of a building from the original conception onwards to demolition. It allows multiple disciplinary information to be encapsulated within one model, and dramatically transform the conventional design formats and communication approaches of AEC sector whereby players depend heavily on 2D CAD-based model towards a 3D digital interacted model. However, research on showing a clear understanding of the impacts of BIM on construction productivity through BIM implementation is scarce. A comprehensive review of existing research in BIM implementation and construction productivity reveals the research gaps. First, as BIM has been evidenced by many researchers as an effective means for facilitating design processes, reducing design error, thus to achieve productivity gains, numerous previous researchers have investigated the attributable factors affecting design error, attempting to seek out effective strategies to prevent or mitigate design errors. However, rare empirical research has been placed on quantifying the impacts of BIM on design error reduction, and quantitatively measuring the extent to which attributable factors could have the better ability to contain design error. In addition, due to the great potential of BIM for addressing construction inefficiencies and lower productivity in the construction projects, the past decade has witnessed an increasing research interest in BIM both in design and construction stage. Nevertheless, the large majority of prior studies have primarily concentrated on identifying incentive factors and barriers of BIM adoption in the construction industry, or on reporting the business value or potential profitability of applying BIM. Sparse scholarly attention has been focused on quantitatively demonstrating the principal impacts of BIM implementation on construction productivity at project level during the construction stage. To fill this research gap, this research aims to identify the impacts of BIM implementation on construction productivity. The following objectives are achieved in this research: (1) to conduct a comprehensive review of the extant research theories related to the status of BIM implementation and basic characteristics of construction productivity; (2) to theoretically develop a BIM-enabled design error reduction (DER) model during design stage, as well as build up a conceptual framework regarding BIM-based construction productivity gains model; (3) to examine the impacts of BIM implementation in reducing design error by using the conceptual model based on the different design error reduction (DER) indicators; (4) to test the conceptual model for probing deeper into how and to what extent the implementation of BIM can influence the project-level construction productivity based on the empirical data from BIM-based construction projects. Through document analysis, research gaps, as well as the related definition of construction productivity and BIM, were identified in achieving objective 1. By a subsequently further literature review, a design error reduction model and BIM-enabled construction productivity gains model have been developed. Questionnaire survey and semi-structured interview were used to collect project-based data in order to test the proposed model. Descriptive statistics and multiple regression analysis were utilized to investigate and analyze the data for achieving objectives 3 and 4. The primary findings obtained in this study include the following aspects. First, research gaps on quantifying the impacts of BIM implementation on construction productivity has been identified through a comprehensive literature review. Then, a conceptual framework of design error reduction model is developed to evaluate the impacts of BIM implementation in reducing design error during the design stage. Furthermore, BIM-enabled construction productivity gains model has also been built up to assess the impacts of BIM implementation on construction productivity during the construction stage. After the development of these models, empirical data is utilized to test the proposed models. For the DER model, six attributable factor (including clash detection, design system coordination, drawing error, teamwork and cooperation, constructability, and practicality, and knowledge and information management) are found to be positively statistically associated with the aggregate impacts of BIM implementation on design error reduction, among which clash detection has the best ability to positively affect design error reduction. For the BIM-enabled construction productivity gains model, reflective constructs (incorporating labor productivity, communication and coordination, site resource planning and management, simulate master schedule and construction sequences, shorten project duration, quantity takeoff and cost estimation, and minimize project cost) are all positively statistically significant with productivity performance ratio, suggesting that productivity performance ratio increases with these seven reflective factors. This research can enrich theoretical development in the fields of BIM and construction productivity by reviewing the existing research. The research findings and gaps identified in previous studies could serve as the basis for recommending future research in relevant fields. As an exploratory effort to build up the relationship between BIM and construction productivity, a design error reduction model and BIM-enabled construction productivity gains model have been developed to identify the potential relationship between BIM implementation and construction productivity both in design and construction stage. This model could also be used by researchers for future investigation. Furthermore, the findings derived from this research could help to develop a more comprehensive understanding of the reasons why construction organizations implement BIM in construction projects and provide a more dynamic picture of how construction productivity may vary as the attributable factors change. |
| Rights: | All rights reserved |
| Access: | open access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 991021952844403411.pdf | For All Users | 1.59 MB | Adobe PDF | View/Open |
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