Wei, C-L (2025) Data-centric AI solutions for built environment applications. PhD thesis, Arizona State University, USA.
Abstract
This dissertation explores the application of Data-centric AI to address spatial-dependent challenges in the built environment. Spatial-dependent issues in this field require complex analysis of spatial data to interpret relationships, geometries, and locations of physical objects. Traditional methods that rely on manual or rule-based approaches are labor-intensive, slow, and limited in both accuracy and adaptability. While deep learning techniques have improved accuracy in spatial data interpretation, they often require extensive data, computational resources, and technical expertise. This dissertation introduces Data-centric AI strategies aimed at maintaining high accuracy while reducing these demands, making advanced spatial analysis more accessible. Through three case studies spanning buildings, transportation infrastructure, and industrial facilities, this research demonstrates how Data-centric AI can optimize existing datasets for improved spatial analysis and decision-making. Instead of focusing on model complexity, the study enhances data quality and utility through targeted optimization and augmentation techniques, revealing significant potential for these methods to streamline processes in urban planning and infrastructure management. This work also discusses the limitations of Data-centric approaches, such as dependency on the quality and diversity of available datasets, which can impact the robustness of outcomes in multi-class scenarios. Future research directions are suggested, focusing on expanding Data-centric AI methodologies to diverse fields, including environmental monitoring and disaster response, and encouraging broader adoption within the built environment sector. By providing a comprehensive framework for AI-driven analysis in urban development and infrastructure, this research advocates for a shift towards sustainable, data-driven approaches that foster resilience and innovation in urban planning and construction management.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Eiris, R and Czerniawski, T |
| Uncontrolled Keywords: | accuracy; built environment; complexity; optimization; decision making; infrastructure management; innovation; learning; monitoring; case study |
| Index terms: | environmental monitoring, adaptability, decision-making, methodology, complexity, urban planning, dataset, accuracy, strategy, spatial data, urban development, built environment, monitoring, transportation infrastructure, dissertation, geometry, disaster response, infrastructure management, case study, deep learning |
| Subjects: | professional development, management, systems engineering, infrastructure and transport systems, emergency and crisis management, decision analysis, data management, control systems, mathematical modelling, data collection methods, artificial intelligence, urban design, infrastructure engineering, research methods, spatial and geospatial analysis, environmental impact, user focus, research dissemination and communication, urban planning |
| Topics: | Risk Management, Sustainability, Engineering Principles, Governance, Business Strategy, Information Management, Research Practice, Site Management, Urban Studies, Digital Applications, Design Practice |
| Descriptive scope: | 5 PCTEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here