Information integration and management through the design and construction phases of a project life cycle

Information management is a key aspect of successful construction projects. Having inaccurate measurements and conflicting data can lead to costly mistakes, and vague quantities can ruin estimates and schedules. Building information modeling (BIM) augments a 3D model with a wide variety of information, which reduces many sources of error and can detect conflicts before they occur. Because new technology is often more complex, it can be difficult to effectively integrate it with existing business practices. In this paper, we will answer two questions: How can BIM add value to construction projects? and What lessons can be learned from other companies that use BIM or other similar technology? Previous research focused on the technology as if it were simply a tool, observing problems that occurred while integrating new technology into existing practices. Our research instead looks at the flow of information through a company and its network, seeing all the actors as part of an ecosystem. Building upon this idea, we proposed the metaphor of an information supply chain to illustrate how BIM can add value to a construction project. This paper then concludes with two case studies. The first case study illustrates a failure in the flow of information that could have prevented by using BIM. The second case study profiles a leading design firm that has used BIM products for many years and shows the real benefits of using this program.

Adaptive Sampling with Mobile Sensor Networks

Mobile sensor networks have unique advantages compared with wireless sensor networks. The mobility enables mobile sensors to flexibly reconfigure themselves to meet sensing requirements. In this dissertation, an adaptive sampling method for mobile sensor networks is presented. Based on the consideration of sensing resource constraints, computing abilities, and onboard energy limitations, the adaptive sampling method follows a down sampling scheme, which could reduce the total number of measurements, and lower sampling cost. Compressive sensing is a recently developed down sampling method, using a small number of randomly distributed measurements for signal reconstruction. However, original signals cannot be reconstructed using condensed measurements, as addressed by Shannon Sampling Theory. Measurements have to be processed under a sparse domain, and convex optimization methods should be applied to reconstruct original signals. Restricted isometry property would guarantee signals can be recovered with little information loss. While compressive sensing could effectively lower sampling cost, signal reconstruction is still a great research challenge. Compressive sensing always collects random measurements, whose information amount cannot be determined in prior. If each measurement is optimized as the most informative measurement, the reconstruction performance can perform much better. Based on the above consideration, this dissertation is focusing on an adaptive sampling approach, which could find the most informative measurements in unknown environments and reconstruct original signals. With mobile sensors, measurements are collect sequentially, giving the chance to uniquely optimize each of them. When mobile sensors are about to collect a new measurement from the surrounding environments, existing information is shared among networked sensors so that each sensor would have a global view of the entire environment. Shared information is analyzed under Haar Wavelet domain, under which most nature signals appear sparse, to infer a model of the environments. The most informative measurements can be determined by optimizing model parameters. As a result, all the measurements collected by the mobile sensor network are the most informative measurements given existing information, and a perfect reconstruction would be expected. To present the adaptive sampling method, a series of research issues will be addressed, including measurement evaluation and collection, mobile network establishment, data fusion, sensor motion, signal reconstruction, etc. Two dimensional scalar field will be reconstructed using the method proposed. Both single mobile sensors and mobile sensor networks will be deployed in the environment, and reconstruction performance of both will be compared.In addition, a particular mobile sensor, a quadrotor UAV is developed, so that the adaptive sampling method can be used in three dimensional scenarios.

Governmentality in higher education : a critical analysis of the National Survey of Student Engagement (NSSE)

In this dissertation, the National Survey of Student Engagement (NSSE) serves as a nodal point through which to examine the power relations shaping the direction and practices of higher education in the twenty-first century. Theoretically, my analysis is informed by Foucault’s concept of governmentality, briefly defined as a technology of power that influences or shapes behavior from a distance. This form of governance operates through apparatuses of security, which include higher education. Foucault identified three essential characteristics of an apparatus—the market, the milieu, and the processes of normalization—through which administrative mechanisms and practices operate and govern populations. In this project, my primary focus is on the governance of faculty and administrators, as a population, at residential colleges and universities. I argue that the existing milieu of accountability is one dominated by the neoliberal assumption that all activity—including higher education—works best when governed by market forces alone, reducing higher education to a market-mediated private good. Under these conditions, what many in the academy believe is an essential purpose of higher education—to educate students broadly, to contribute knowledge for the public good, and to serve as society’s critic and social conscience (Washburn 227)—is being eroded. Although NSSE emerged as a form of resistance to commercial college rankings, it did not challenge the forces that empowered the rankings in the first place. Indeed, NSSE data are now being used to make institutions even more responsive to market forces. Furthermore, NSSE’s use has a normalizing effect that tends to homogenize classroom practices and erode the autonomy of faculty in the educational process. It also positions students as part of the system of surveillance. In the end, if aspects of higher education that are essential to maintaining a civil society are left to be defined solely in market terms, the result may be a less vibrant and, ultimately, a less just society.