An Appraisal of Building Information Modelling for Overall Project Performance
Chapter One
Purpose of the Study
The purpose of the study was to establish the influence of Building Information Modeling adoption on overall project performance: a case of Lagos state, Nigeria.
Objectives of the Study
This study was be guided by the following research objectives:
- To establish the influence of Building Information Modelling budget estimation on overall project performance;
- To assess the influence of Building Information Modelling time estimation on overall project performance;
- To determine the influence of Building Information Modelling error minimization on overall project performance;
- To establish the influence of Building Information Modelling quality improvement on overall project performance.
CHAPTER TWO
LITERATURE REVIEW
Introduction
With the rapid adoption of BIM in the construction industry, and its gradual implementation in the design industry, careful considerations have to be taken when making the change over from the traditional method of creating construction documents towards a BIM approach. There are plenty of positives, negatives, and unknowns that have to be considered when implementing BIM. This chapter will discuss research that was done about the concept of BIM, its perceived influence on overall project performance, the theoretical and conceptual framework
Archer (2006) defines project Completion as “Controlling process that ensures that project objectives are met within the specified time and budget by monitoring and measuring progress regularly to identify variances from plan, so that corrective action can be taken when necessary” and further identifies controlling process to have links with planning and executing process.
The concept of Building Information Modeling and overall project performance
BIM is not merely a 3D graphic representation of design intent; rather, it is a comprehensive information management tool based on the simulation of design and construction. BIM has its roots in Computer Aided Design (CAD) development from decades ago, yet still has no single, widely-accepted definition in the AEC industry. However, the most comprehensive definition of building information modeling has been defined by Associated General Contractors of America (AGC) which states “BIM is the development and use of a computer software model to simulate the construction and operation of a facility.” The resulting model, a building information model, is a data-rich, object-oriented, intelligent and parametric digital representation of the facility, from which views and data appropriate to various users’ needs can be extracted and analyzed to generate information that can be used to make decisions and improve the process of delivering the facility (Associated General Contractors of America, 2006). This “Model Based “process where buildings will be built virtually before they get built in the field is also referred to as Virtual Design and Construction (VDC).
A. Mortenson Company (Mortenson) thinks of BIM as “an intelligent simulation of architecture,” that must exhibit six (6) key characteristics; digital – enabling simulation of design and construction, spatial – 3D, to better represent complex construction conditions than 2D drawings, measurable – data is quantifiable, dimension-able, and query-able more than visual, comprehensive – encapsulating and communicating design intent, building performance, constructability, and sequential and financial aspects of means and methods, accessible – data made available to the entire project team through interoperable and intuitive interface, including architects, engineers, contractors, fabricators, owners, facility maintenance, and users, and durable – data that reflects as-built conditions and remains usable through all phases of a facility’s life, including design and planning, fabrication and construction, and operations and maintenance (Campbell, 2007).
BIM incorporates the use of 3D visualization techniques with real-time, data driven, object- based imaging as a tool by all facets of the industries (Holness, 2006). This is a change from the current practice of; designs being manifested and put to paper with engineers then designing the structure and other supporting elements of the building. After completion of the design documents, a 3D model can be generated for the owner showing digital walkthroughs and to provide 3D renderings of spatial relationships. These tools are very useful to convey design intentions to owners and clients that who not able to visualize 3D space from 2D drawings. These can be very expensive and time consuming to produce (Autodesk, 2012).
BIM greatly increases the user’s ability to control and manipulate data and information in an unprecedented way and in an interoperable format. Moving from paper-centric information to parametric, model-based information means that the digital design can be used for cost estimations, simulations, scheduling, energy analysis, structural design, GIS integration, fabrication, erection, and facilities management (Seaman, 2006). All of which are relative to each other, and changes in one category will have impacts on the others that are automatically accounted for. Since all of the above information is dynamically linked, productivity from recalculation of simple and minor changes will be greatly increased because the computer program will be able to handle the changes and calculations internally (Davidson, 2009).
CHAPTER THREE
RESEARCH METHODOLOGY
Introduction
This chapter gives an outline of how the study was carried out. It describes the research design, the target population, the sample and sampling procedure, research instruments, validity and reliability of instruments, data collection procedures and data analysis techniques that were used. It also entails ethical considerations and operational definition of variables.
Research Design
A descriptive survey research design was used because descriptive research design does not involve modifying the situation under study nor to determine the cause-effect relationship. It involved acquiring information about a certain segment of the population and getting information on their characteristics, opinions or attitudes (Orodho, 2003). This research design has been chosen for this study because it enabled the researcher to obtain the opinions of Architects, Engineers and project managers involved in construction projects in their natural setting. It was also useful in summarizing the data collected in a way that provided descriptive information. Churchill and Brown (2004) also observe that descriptive research design is appropriate where the study seeks to describe the characteristics of certain groups, estimate the proportion of people who have certain characteristics and make predictions.
Target population
A target population is the total composition of elements from which the sample is drawn; it is the specific population about which information is desired, (Gerber-Nel, et al, 2011). Burns and Burns (2008), further describe the population as all elements or subjects that meet the criteria for inclusion in a study. The target population is 30 of registered architectural, engineering, project management and construction companies. The study population included project managers, architects, civil and structural engineers, mechanical engineers, electrical engineers and contractors working on the design and construction firms based in Lagos state, Nigeria. Five firms were selected from each category.
CHAPTER FOUR
DATA ANALYSIS PRESENTATION AND INTERPRETATION
Introduction
In this chapter, the findings of the study are presented and discussed in thematic subsections in line with the study objectives. The thematic areas include: questionnaire return rate, demographic characteristics of the respondents, Building Information Modelling budget estimation and completion of construction project, Building Information Modelling time estimation and completion of construction project, Building Information Modelling error elimination and completion of construction project and Building Information Modelling quality improvement and completion of construction project.
CHAPTER FIVE
SUMMARY OF FINDINGS, DISCUSSIONS, CONCLUSION AND RECOMMENDATIONS
Introduction
This chapter covers summary of the findings, discussion of the findings and conclusion drawn from the study as well as the recommendations based on the study findings and suggestions for further study.
Summary of the Findings
The first objective was to establish the influence of Building Information Modelling budget estimation on overall project performance. To this end, three variables were tested namely project cost, project cost predictability and quantity take offs. On project cost, out of the 26 respondents, 2(7.7%) disagreed, 4 (15.4%) of them were impartial, 8 (30.8%) agreed and 12 (46.1%) strongly agreed that the use of BIM reduces the overall cost. On cost predictability, 2(7.7%) disagreed, 4 (15.04%) of them had no Influence, 12(46.1%) agreed and 8(30.8%) strongly agreed that predicting project cost is made easier while using BIM .On quantity take offs, out of the 26 respondents, 10 (38.5%) agreed and 16 (61.5%) strongly agreed quantity take offs using BIM were easier and more accurate using than conventional ways.. The mean score on a 5 point likert scale on this objective is 4.25 which was rounded off to 4. It was therefore determined that Building information modeling budget estimation positively influences overall project performance.
The second objective of the study to assess the influence of Building Information Modelling time estimation on overall project performance. To this end, three variables were tested namely construction time, time predictability and material supply schedule. On construction period, out of the 26 respondents, 2(7.7%) disagreed, 10 (38.5%) agreed and 14 (53.8%) strongly agreed that use of BIM increases accuracy of time projection for the project.
This translates to a mean of 4.47 (rounded off to 4) on a 5 point likert scale. On time predictability, 4(15.4%) disagreed, 14 (53.8%) agreed and 8(30.8%) strongly agreed that BIM affects the project time predictability. This translates to a mean of 4.0 on a 5 point Likert scale .On material supply schedule, Out of the 26 respondents, 8 (30.8%) agreed and 18 (69.2%) strongly agreed that material schedule are easily derived when using BIM. This translates to a mean of 4.7 (rounded off to 5) on a 5 point Likert scale.
The third objective of the study was to determine the influence of Building Information Modelling error minimization on overall project performance. To this end, four variables were tested namely design accuracy, defects and RFIs. On design accuracy, 2(7.7%) disagreed, 20(76.9%) agreed and 4 (15.4%) of them strongly agreed that BIM accelerates and increases the design process. This translates to a mean of 4.0 on a 5 point likert scale. On defects, out of the 26 respondents, 10(38.5%) agreed and 16(61.5.0%) of the strongly agreed that the use of BIM reduces the number and nature of defects. This translates to a mean of 4.62(rounded off to 5) on a 5 point Likert scale. On RFIs, Out of the 26 respondents, 4 (15.4%) agreed and 22(84.6%) of them strongly agreed that the use of BIM reduces the number of RFIs. This translates to a mean of 4.49(rounded off to 4) on a 5 point likert scale.
The fourth objective of the study to establish the influence of Building Information Modelling quality improvement on overall project performance. To this end, three variables were tested namely 3D models, clash detections and project cycle. On 3D models, out of the 26 respondents, 5 (19.2%) agreed and 21(80.8%) strongly agreed that BIM 3D models increases visualization .This translates to a mean of 4.81 (rounded off to 5) on a 5 point likert scale. On clash detections, 11 (42.3%) agreed and 15(57.7%) strongly agreed that BIM application reduces discrepancies. This translates to a mean of 4.58 (rounded off to 5) on a 5 point likert scale. On project life, Out of the 26 respondents, 6 (23.1%) disagreed, 16 (61.5%) agreed and 4(15.4%) strongly agreed that BIM application allows for project life evacuations. This translates to a mean of 3.7 (rounded off to 4) on a 5 point likert scale.
The findings of the study answered the research questions since the influence Building Information Modelling adoption on overall project performance within budget, time and to acceptable quality have been quantified by descriptive statistics. The discussion and related literature were presented for each of the four variables of the study.
Discussion of Findings
The study was conducted to investigate the influence of Building Information Modelling adoption on successful overall project performance. The finding derived from the study are discussed in this section.
BIM budget estimation and completion of construction project
On the influence of BIM budget estimation on the completion of construction, the study revealed that that the various dimensions of project budget considered in the study which includes the project cost, project cost predictability and quantity take offs have been greatly influenced the overall project performance. The findings also in conforming to literature review reveals that major projects that employed BIM, found cost benefits including a reduction of unbudgeted change, accuracy of cost estimation and projections, and clash detections resulting in savings Azhar et al (2008)
BIM time estimation and completion of construction project
For the influence of BIM time estimation on the completion of construction, the study revealed that that the various dimensions of project time frame considered in the study which includes the project construction time, project time predictability and material supply schedule significantly influenced overall project performance. The findings also in conforming to literature review referring to the results of the survey of 185 construction companies; 70 percent of the respondents claimed they had realized performances in terms of time during construction, the time savings attendant upon a BIM-oriented approach during the construction phase of projects is evident. (Azhar, 2009)
BIM error elimination and completion of construction project
On the influence of BIM error elimination on the completion of construction, the study revealed that that the various dimensions of project errors considered in the study which includes the design accuracy, defects and RFIs have been greatly influenced overall project performance. The findings also in conforming to literature review reveals that the development of 3D building information modeling (BIM) combined with quantity information management, quantity and cost progress can be monitored and controlled in real- time, with accuracy and with transparency. Discrepancies, cost overflows and problems are seen earlier and steps can be taken to rectify them or at least minimize the consequences (Gren, 2008).
BIM quality improvement and completion of construction project
On the influence of BIM quality improvement on the completion of construction, the study revealed that that the various dimensions of project quality considered in the study which includes the 3D models, clash detection and project life greatly influence overall project performance. The findings also in conforming to literature review reveals BIM adoption is an efficient contributor to better project quality than was ordinarily possible under the traditional order of things (Wong 2008).
Conclusion of study
The study was conducted to investigate the influence of Building Information Modelling adoption on overall project performance, from the summary of the finding, the study makes the following conclusions:
On the influence of BIM budget estimation on overall project performance, the study concludes that the adoption of BIM reduces the overall cost due to the accuracy of the models, project cost estimation is made easier, accurate and quicker and project cost projections can be easily done since all the necessary information is well stored in the 3D BIM models.
For the influence of BIM time estimation on overall project performance, the study concludes that the adoption of BIM increases the accuracy of time projections for the project, reduces the project period because most of the discrepancies are addressed before actual commencement of the project, enhance time predictability of the various components of the project and derives material supply schedules accurately and easily.
On the influence of BIM error elimination on overall project performance, the study concludes that the adoption of BIM reduces the design period for the design consortium, reduces the risk of mistakes or discrepancies, and abortive costs are minimized, defines and elaborates the functionality of the end product, reduces the number and nature of defects during and after construction as well as reducing the number of RFIs which in turn ensure productivity and efficiency since so construction works need to reworked and wastage of materials is minimized
For the influence of BIM quality improvement on overall project performance, the study concludes that the adoption of BIM increases visualization and understanding of the project, enables clash detections and subsequent solution when identified and ensure that project can be monitored through its entire life including post construction period.
Recommendations.
Based on the findings and conclusions of the study, the following recommendations were drawn;
- The study recommends that the government and key players in the construction industry need to adopt BIM 5D for effective budget estimation, monitoring and
- The study recommends that the ACE industry both from private and public institutions embraces BIM 4D to allow them view the planned construction projects over time and review the planned versus actual status time
- The study recommends use of BIM clash detection tool in order to minimize errors and discrepancies between the BIM models and the actual
- Further the study recommends that the stake holders in the construction industry need to invest on 3D printers to enable printing of the 3D models for better visualization.
REFERENCES
- Ali, A. S, Anbari, F. T., and Money, W. H. (2008). Impact of Organizational and Project Factors on Acceptance and Usage of Project Management Software and Perceived Project Success. Project Management Journal, 39 (2), 5-33.
- Atkinson, R. (1990). Project Management: Cost, Time and Quality, Two Best Guesses and A Phenomenon, It’s Time to Accept Other Success Criteria. International Journal of Project Management, 17, 337-420.
- Azhar, S. (2011). Building Information Modeling (BIM): Trends, Benefits, Risks, and Challenges for the AEC Industry. Leadership and Management in Engineering, 11 (3), 241–252.
- Besner C., & Hobbs, B. (2006). An Empirical Investigation of Project Management Practice, A Summary of the Survey Results: PMI.
- Burns, R. B., & Burns, R. A. (2008), Business Research Methods and Statistics using SPSS. New Delhi: SAGE Publications Ltd.
- Caldwell, R. (2004). Project Management Information System: Guidlines for Planning, Implementing, and Managing a DME Project Information System (1st edn). New York: CARE,
- Caniëls, M.C., & Bakens, R. J. (2011). The Influence of Project Management Information Systems on decision making in a multi project environment. International Journal of Project Management.
- Cant, C., Gerber-Nel, D., Nel, M., and Kotze, T. (2011). Marketing Research. Claremont: New Africa Education.
- Caruana, A. (2002). Service loyalty: The Influence of service quality and the mediating role of customer satisfaction. European Journal of Marketing, 36 (7/8), 811–828.
- Chung, B.Y., Skibniewski, M. J., & Kwak, Y. H. (2008). Developing ERP Systems Success Model for the Construction Industry. Journal of Construction Engineering and Management, 135 (3), 207–216.
- Chung, B.Y., Skibniewski, M. J., Lucas, H. C., & Kwak, Y. H. (2008). Analyzing Enterprise Resource Planning System Implementation Success Factors in the Engineering, Construction Industry. Journal of Computing in Civil Engineering, 22 (6), 373–382.
