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Of Mills and Malls: The Future of Urban Industrial Heritage in Neoliberal Mumbai

Chalana, Manish. (2012). Of Mills and Malls: The Future of Urban Industrial Heritage in Neoliberal Mumbai. Future Anterior: Journal Of Historic Preservation, History, Theory, And Criticism, 9(1), 1 – 15.

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Abstract

The mandate of historic preservation is to maintain vestiges of diverse cultural heritage, a task that is becoming increasingly difficult in rapidly globalizing India. Much of the country's urban heritage outside of the “monument-and-site” framework is threatened by massive restructuring of cities facilitated by neoliberal urban policies. Mumbai has a rich cultural heritage, associated with diverse sociocultural and economic groups. Much of this is threatened by development practices pursued by various forces with a particular vision of Mumbai as an emerging “global city.” In this work Chalana examines Girangaon, an early industrial district of Mumbai, currently being transformed by forces of domestic and global capital. He argues that Girangaon's urban industrial heritage is a significant piece of the city's development history, which future visions of a global metropolis should embrace. While the expansion of Mumbai's economy has benefited some avenues of preservation practice in Mumbai, in Girangaon its consequences have also been negative, as a working-class neighborhood is restructured into a hypermodern district for the elite. The current forms of preservation practice in the city have been insufficient in addressing the complexity around managing heritage in low-income neighborhoods. Girangaon, and Mumbai overall, reveal the many ways that economic, cultural, and political globalization can impact historic preservation practice.]

Associations between Neighborhood Built Environment, Residential Property Values, and Adult BMI Change: The Seattle Obesity Study III

Buszkiewicz, James H.; Rose, Chelsea M.; Ko, Linda K.; Mou, Jin; Moudon, Anne Vernez; Hurvitz, Philip M.; Cook, Andrea J.; Drewnowski, Adam. (2022). Associations between Neighborhood Built Environment, Residential Property Values, and Adult BMI Change: The Seattle Obesity Study III. SSM-Population Health, 19.

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Abstract

Objective: To examine associations between neighborhood built environment (BE) variables, residential property values, and longitudinal 1-and 2-year changes in body mass index (BMI). Methods: The Seattle Obesity Study III was a prospective cohort study of adults with geocoded residential addresses, conducted in King, Pierce, and Yakima Counties in Washington State. Measured heights and weights were obtained at baseline (n = 879), year 1 (n = 727), and year 2 (n = 679). Tax parcel residential property values served as proxies for individual socioeconomic status. Residential unit and road intersection density were captured using Euclidean-based SmartMaps at 800 m buffers. Counts of supermarket (0 versus. 1+) and fast-food restaurant availability (0, 1-3, 4+) were measured using network based SmartMaps at 1600 m buffers. Density measures and residential property values were categorized into tertiles. Linear mixed-effects models tested whether baseline BE variables and property values were associated with differential changes in BMI at year 1 or year 2, adjusting for age, gender, race/ethnicity, education, home ownership, and county of residence. These associations were then tested for potential disparities by age group, gender, race/ethnicity, and education. Results: Road intersection density, access to food sources, and residential property values were inversely associated with BMI at baseline. At year 1, participants in the 3rd tertile of density metrics and with 4+ fast-food restaurants nearby showed less BMI gain compared to those in the 1st tertile or with 0 restaurants. At year 2, higher residential property values were predictive of lower BMI gain. There was evidence of differential associations by age group, gender, and education but not race/ethnicity. Conclusion: Inverse associations between BE metrics and residential property values at baseline demonstrated mixed associations with 1-and 2-year BMI change. More work is needed to understand how individual-level sociodemographic factors moderate associations between the BE, property values, and BMI change.

Keywords

Body-mass Index; Physical-activity; Food Environment; Socioeconomic-status; Weight-gain; Health; Quality

Automated Extraction of Geometric Primitives with Solid Lines from Unstructured Point Clouds for Creating Digital Buildings Models

Kim, Minju; Lee, Dongmin; Kim, Taehoon; Oh, Sangmin; Cho, Hunhee. (2023). Automated Extraction of Geometric Primitives with Solid Lines from Unstructured Point Clouds for Creating Digital Buildings Models. Automation In Construction, 145.

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Abstract

Point clouds produced by laser scanners are an invaluable source of data for reconstructing multi-dimensional digital models that reflect the as-is conditions of built facilities. However, previous studies aimed to reconstruct models by overlaying the dataset on top of ground-truth reference models to manually adjust the accuracy of the output. Therefore, this paper describes the extraction of geometric primitives with solid lines—the simplest form of objectified data that computer-aided design systems can handle—from unorganized data points and creation of digital models of built facilities in a form of floor plan. The geometric primitives are extracted from 3D points by hybridizing machine learning algorithms, which are mean-shift clustering, non-convex hull, and random sample and consensus (RANSAC). This paper provides a solution for creating a new form of as-built model with high accuracy and robustness from scratch without the involvement of ground-truth solutions or manual adjustments. © 2022 Elsevier B.V.

Keywords

Computer Aided Design; Geometry; Laser Applications; Learning Algorithms; Machine Learning; Scanning; As-build Model Creation; Build Facility; From-point-to-line; Geometric Primitives; Laserscanners; Model Creation; Outline Extractions; Point-clouds; Point-to-line; Solid Lines

Lingzi Wu

Lingzi Wu is an Assistant Professor with the Department of Construction Management (CM) at the University of Washington (UW). Prior to joining UW in September 2022, Dr. Wu served as a postdoctoral fellow in the Department of Civil and Environmental Engineering at University of Alberta, where she received her MSc and PhD in Construction Engineering and Management in 2013 and 2020 respectively. Prior to her PhD, Dr. Wu worked in the industrial construction sector as a project coordinator with PCL Industrial Management from 2013 to 2017.

An interdisciplinary scholar focused on advancing digital transformation in construction, Dr. Wu’s current research interests include (1) integration of advanced data analytics and complex system modeling to enhance construction practices and (2) development of human-in-the-loop decision support systems to improve construction performance (e.g., sustainability and safety). Dr. Wu has published 10 papers in top journals and conference proceedings, including the Journal of Construction Engineering and Management, Journal of Computing in Civil Engineering, and Automation in Construction. Her research and academic excellence has received notable recognition, including a “Best Paper Award” at the 17th International Conference on Modeling and Applied Simulation, and the outstanding reviewer award from the Journal of Construction Engineering and Management.

As an educator and mentor, Dr. Wu aims to create an inclusive, innovative, and interactive learning environment where students develop personal, technical, and transferable skills to grow today, tomorrow, and into the future.

Narjes Abbasabadi

Narjes Abbasabadi, Ph.D., is an Assistant Professor in the Department of Architecture at the University of Washington. Dr. Abbasabadi also leads the Sustainable Intelligence Lab. Abbasabadi’s research centers on sustainability and computation in the built environment. Much of her work focuses on advancing design research efforts through developing data-driven methods, workflows, and tools that leverage the advances in digital technologies to enable augmented intelligence in performance-based and human-centered design. With an emphasis on multi-scale exploration, her research investigates urban building energy flows, human systems, and environmental and health impacts across scales—from the scale of building to the scale of neighborhood and city.

Abbasabadi’s research has been published in premier journals, including Applied Energy, Building and Environment, Energy and Buildings, Environmental Research, and Sustainable Cities and Society. She received honors and awards, including “ARCC Dissertation Award Honorable Mention” (Architectural Research Centers Consortium (ARCC), 2020), “Best Ph.D. Program Dissertation Award” (IIT CoA, 2019), and 2nd place in the U.S. Department of Energy (DOE)’s Race to Zero Design Competition (DOE, 2018). In 2018, she organized the 3rd IIT International Symposium on Buildings, Cities, and Performance. She served as editor of the third issue of Prometheus Journal, which received the 2020 Haskell Award from AIA New York, Center for Architecture.

Prior to joining the University of Washington, she taught at the University of Texas at Arlington and the Illinois Institute of Technology. She also has practiced with several firms and institutions and led design research projects such as developing design codes and prototypes for low-carbon buildings. Most recently, she practiced as an architect with Adrian Smith + Gordon Gill Architecture (AS+GG), where she has been involved in major projects, including the 2020 World Expo. Abbasabadi holds a Ph.D. in Architecture from the Illinois Institute of Technology and Master’s and Bachelor’s degrees in Architecture from Tehran Azad University.

College of Built Environments’ Research Restart Fund Awards Four Grants in First of Two Cycles

The College of Built Environments launched a funding opportunity for those whose research has been affected by the ongoing pandemic. The Research Restart Fund, with awards up to $5,000, has awarded 4 grants in its first of two cycles. A grant was awarded to Real Estate faculty member Arthur Acolin, who is partnering with the City of Seattle’s Office of Planning and Community Development to understand barriers that homeowners, particularly those with lower incomes, face to building Accessory Dwelling Units…

A Case Study of the Failure of Digital Communication to Cross Knowledge Boundaries in Virtual Construction

Neff, Gina; Fiore-Silfvast, Brittany; Dossick, Carrie Sturts. (2010). A Case Study of the Failure of Digital Communication to Cross Knowledge Boundaries in Virtual Construction. Information Communication & Society, 13(4), 556 – 573.

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Abstract

When can digital artefacts serve to bridge knowledge barriers across epistemic communities? There have been many studies of the roles new information and communication technologies play within organizations. In our study, we compare digital and non-digital methods of inter-organizational collaboration. Based on ethnographic fieldwork on three construction projects and interviews with 65 architects, engineers, and builders across the USA, we find that IT tools designed to increase collaboration in this setting instead solidify and make explicit organizational and cultural differences between project participants. Our study suggests that deeply embedded disciplinary thinking is not easily overcome by digital representations of knowledge and that collaboration may be hindered through the exposure of previously implicit distinctions among the team members' skills and organizational status. The tool that we study, building information modelling, reflects and amplifies disciplinary representations of the building by architects, engineers, and builders instead of supporting increased collaboration among them. We argue that people sometimes have a difficult time overcoming the lack of interpretive flexibility in digital coordinating tools, even when those tools are built to encourage interdisciplinary collaboration.

Keywords

Digital Communications; Data Transmission Systems; Communication & Technology; Digital Electronics; System Analysis; Building Information Modelling; Collaboration; Qualitative Methods; Teams; Civil Engineering Computing; Digital Communication; Groupware; Knowledge Representation; Organisational Aspects; Virtual Reality; Case Study; Virtual Construction; Knowledge Barriers; Epistemic Community; Interorganizational Collaboration; Ethnographic Fieldwork; Interpretive Flexibility; Digital Coordinating Tool; Digital Collaboration; Technology; Objects; Design; Representations; Organizations

Automated Task-Level Activity Analysis through Fusion of Real Time Location Sensors and Worker’s Thoracic Posture Data

Cheng, Tao; Teizer, Jochen; Migliaccio, Giovanni C.; Gatti, Umberto C. (2013). Automated Task-Level Activity Analysis through Fusion of Real Time Location Sensors and Worker’s Thoracic Posture Data. Automation In Construction, 29, 24 – 39.

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Abstract

Knowledge of workforce productivity and activity is crucial for determining whether a construction project can be accomplished on time and within budget. Significant work has been done on improving and assessing productivity and activity at task, project, or industry levels. Task level productivity and activity analysis are used extensively within the construction industry for various purposes, including cost estimating, claim evaluation, and day-to-day project management. The assessment is mostly performed through visual observations and after-the-fact analyses even though previous studies show automatic translation of operations data into productivity information and provide spatial information of resources for specific construction operations. An original approach is presented that automatically assesses labor activity. Using data fusion of spatio-temporal and workers' thoracic posture data, a framework was developed for identifying and understanding the worker's activity type over time. This information is used to perform automatic work sampling that is expected to facilitate real-time productivity assessment. Published by Elsevier B.V.

Keywords

Detectors; Construction Projects; Labor Supply; Real-time Control; Construction Costs; Project Management; Machine Translating; Activity And Task Analysis; Construction Worker; Data Fusion; Health; Location Tracking; Productivity; Safety; Sensors; Thoracic Posture Data; Workforce; Construction Industry; Costing; Labour Resources; Sensor Fusion; Real-time Productivity Assessment; Automatic Work Sampling; Worker Activity Type; Spatio-temporal Data; Labor Activity Assessment; Construction Operations; Spatial Information; Productivity Information; Day-to-day Project Management; Claim Evaluation; Cost Estimating; Task Level Productivity; Industry Levels; Project Levels; Construction Project; Workforce Activity; Workforce Productivity; Worker Thoracic Posture Data; Real Time Location Sensors Fusion; Automated Task-level Activity Analysis; Construction-industry Productivity

An Exploratory Study of the Relationship between Construction Workforce Physical Strain and Task Level Productivity

Gatti, Umberto C.; Migliaccio, Giovanni C.; Bogus, Susan M.; Schneider, Suzanne(3). (2014). An Exploratory Study of the Relationship between Construction Workforce Physical Strain and Task Level Productivity. Construction Management And Economics, 32(6), 548 – 564.

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Abstract

The monitoring of construction workforce physical strain can be a valuable management strategy in improving workforce productivity, safety, health, and quality of work. Nevertheless, clear relationships between workforce performance and physical strain have yet to be established. An exploratory investigation of the relationship between task level productivity and physical strain was conducted. Nine participants individually performed a four-hour simulated construction task while a wearable physiological status monitor continuously assessed their physiological condition. Heart rate, relative heart rate, and breathing rate were utilized as predictors of physical strain, and task level-single factor productivity was used as an index of productivity. Numerous regression models were generated using the collected data. This investigation initially unsuccessfully attempted to establish a relationship between physiological condition and productivity at the individual worker level. However, an analysis of the regression models showed that there is a relationship between productivity and either heart rate or relative heart rate at the group level, and that this relationship is parabolic. Breathing rate was proved to not be a significant predictor of productivity. Research results significantly improve understanding of the relationship between work physiology and task productivity. Researchers and practitioners may use the tested monitoring devices, analysis methods, and results to design further applied studies and to improve workforce productivity. © 2013 © 2013 Taylor & Francis.

Keywords

Heart; Industrial Hygiene; Occupational Risks; Personnel; Regression Analysis; Construction Workforces; Management Strategies; Occupational Health And Safety; Operations Management; Physiological Condition; Physiological Status Monitors; Work Physiology; Workforce