Rishi Jain, Michael Lepech, Andrew Sonta
Analysis and design on the building scale takes place in parallel with that on the urban scale, creating difficulty in understanding how the building fits in with the larger city context early in the design process. Such misalignment can result in delays and cost overruns (~5.7%) during construction of a building.
An Urban Information Model (UIM) that is able to capture the various urban data streams, aspects of a regional plan set by agencies, and information from the virtual design of a building would enable better and earlier decision-making with regard to aspects of the project that lead to delays later in the project life cycle. Structured analysis will be conducted to classify various urban data streams. A method to combine such data in an ontological framework (UIM) will be developed and validated on a case study with a CIFE industry collaborator.
More people than ever are living and working in cities. As the share of the global population living in urban areas grows to 66% by 2050, planners, architects, and builders must strengthen their understanding of the complex dynamics of city life. The many design and construction decisions that go into interventions in the built environment — e.g. buildings, infrastructure projects, or policy decisions — would be better made if they are informed not only by the goals of the owner, conditions of the site, and coordination among stakeholders, but also if they are informed by their potential impact on the surrounding urban environment. To work toward this goal of a robust understanding of the city’s form and its occupants, we must make use of the myriad urban data streams that are continuously being produced in the city.
The urbanist Jane Jacobs’s description of the kind of problem a city is — her “organized complexity” — is beginning to reveal itself through technologic advancements that have increased the availability of the unstructured and structured data describing the urban world. From three dimensional spatial models of the built environment, to traffic patterns, to socioeconomic mappings, urban data is being collected at different scales, by different people, in different time intervals, and through many different systems. For the most part, the current way we think about city-scale data is specific rather than comprehensive: traffic data is used to solve traffic problems; economic data is used to solve economic problems. With all of this fragmented information that describes city life, it is difficult to identify and understand the relevant multi-dimensional effects on our urban environment that come from city-scale interventions.
In modern-day construction, delays caused by unknowns can contribute heavily to increases in costs. One common reason for a project delay is an environmental assessment followed by an environmental impact statement. Chester and Hendrickson found that such critical delays can lead to project cost increases of up to 5.7%. For a project as large as the Salesforce Tower in San Francisco, with an estimated budget of $1.1 billion, a delay for re-assessing environmental air quality or pedestrian flow could have huge cost implications for the project (e.g., $62.7 million).
The proposed research will consist of the following three steps:
- Analysis and classification of the various data and information streams that are already available within urban areas, as well as those becoming more readily available.
- Development of a method for combining these data streams in an elegant ontological framework (i.e., core of the Urban Information Model) that allows for straightforward representation of the many dimensions of the urban environment.
- Validation of the ontological framework on a case study in collaboration with a CIFE industry collaborator.
The biggest anticipated outcome of this research is a novel and elegant multi-dimensional Urban Information Modeling framework that will allow for the synthesis of the many data streams available in cities. This framework will be an ideological extension of the BIM ontology at a larger urban scale. The research team is not aware of an existing framework that works to synthesize the data describing the spatial, temporal, and human dimensions of a city.
To be able to simultaneously analyze various data streams will enable decision-makers to understand impacts that result from policy recommendations or new proposals for urban systems. With this proposed work, the ontological framework of BIM can move beyond design and construction of buildings, and create a new framework that will enable contractors, urbanists, sociologists, policy makers, and architects to leverage integrated engineering tools for better design and analysis of city-scale interventions.