| id |
acadia17_102 |
| authors |
Aparicio, German |
| year |
2017 |
| title |
Data-Insight-Driven Project Delivery: Approach to Accelerated Project Delivery Using Data Analytics, Data Mining and Data Visualization |
| source |
ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 102-109 |
| doi |
https://doi.org/10.52842/conf.acadia.2017.102
|
| summary |
Today, 98% of megaprojects face cost overruns or delays. The average cost increase is 80% and the average slippage is 20 months behind schedule (McKinsey 2015). It is becoming increasingly challenging to efficiently support the scale, complexity and ambition of these projects. Simultaneously, project data is being captured at growing rates. We continue to capture more data on a project than ever before. Total data captured back in 2009 in the construction industry reached over 51 petabytes, or 51 million gigabytes (Mckinsey 2016). It is becoming increasingly necessary to develop new ways to leverage our project data to better manage the complexity on our projects and allow the many stakeholders to make better more informed decisions.
This paper focuses on utilizing advances in data mining, data analytics and data visualization as means to extract project information from massive datasets in a timely fashion to assist in making key informed decisions for project delivery. As part of this paper, we present an innovative new use of these technologies as applied to a large-scale infrastructural megaproject, to deliver a set of over 4,000 construction documents in a six-month period that has the potential to dramatically transform our industry and the way we deliver projects in the future. This paper describes a framework used to measure production performance as part of any project’s set of project controls for accelerated project delivery. |
| keywords |
design methods; information processing; data mining; big data; data visualization |
| series |
ACADIA |
| email |
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| full text |
 file.pdf (1,631,725 bytes) |
| references |
Content-type: text/plain
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Davis, Daniel (2016)
 Evaluating Buildings with Computation and Machine Learning
, ACADIA // 2016: Posthuman Frontiers: Data, Designers, and Cognitive Machines, Proceedings of the 36th Annual Conference of the Association for Computer Aided Design in Architecture, edited by Kathy Velikov, Sean Ahlquist, Matias del Campo, and Geoffrey Thu?n, 116–23. Ann Arbor: ACADIA
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Wise, Simon (2017)
The Rise of Data Analytics in Sport
, Blog. May 17. https://blog.data-8.co.uk/2017/05/17/the-rise-of-data-analytics-in-sport/
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| last changed |
2022/06/07 07:55 |
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