Technology & Innovation Archives - Page 6 of 19

Immersive VR Versus BIM for AEC Team Collaboration in Remote 3D Coordination Processes

Asl, Bita Astaneh; Dossick, Carrie Sturts. (2022). Immersive VR Versus BIM for AEC Team Collaboration in Remote 3D Coordination Processes. Buildings, 12(10).

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Abstract

Building Information Modeling (BIM) and Virtual Reality (VR) are both tools for collaboration and communication, yet questions still exist as to how and in what ways these tools support technical communication and team decision-making. This paper presents the results of an experimental research study that examined multidisciplinary Architecture, Engineering, and Construction (AEC) team collaboration efficiency in remote asynchronous and synchronous communication methods for 3D coordination processes by comparing BIM and immersive VR both with markup tools. Team collaboration efficiency was measured by Shared Understanding, a psychological method based on Mental Models. The findings revealed that the immersive experience in VR and its markup tool capabilities, which enabled users to draw in a 360-degree environment, supported team communication more than the BIM markup tool features, which allowed only one user to draw on a shared 2D screenshot of the model. However, efficient team collaboration in VR required the members to properly guide each other in the 360-degree environment; otherwise, some members were not able to follow the conversations.

Keywords

Mental Models; Virtual-reality; Performance; Virtual Reality (vr); Building Information Modeling (bim); 3d Coordination; Clash Resolution; Remote Collaboration; Multidisciplinary Aec Team

Selection of Wearable Sensor Measurements for Monitoring and Managing Entry-level Construction Worker Fatigue: A Logistic Regression Approach

Lee, Wonil; Lin, Ken-yu; Johnson, Peter W.; Seto, Edmund Y.w. (2022). Selection of Wearable Sensor Measurements for Monitoring and Managing Entry-level Construction Worker Fatigue: A Logistic Regression Approach. Engineering Construction & Architectural Management (09699988), 29(8), 2905-2923.

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Abstract

Purpose: The identification of fatigue status and early intervention to mitigate fatigue can reduce the risk of workplace injuries. Off-the-shelf wearable sensors capable of assessing multiple parameters are available. However, using numerous variables in the fatigue prediction model can elicit data issues. This study aimed at identifying the most relevant variables for measuring occupational fatigue among entry-level construction workers by using common wearable sensor technologies, such as electrocardiogram and actigraphy sensors. Design/methodology/approach: Twenty-two individuals were assigned different task workloads in repeated sessions. Stepwise logistic regression was used to identify the most parsimonious fatigue prediction model. Heart rate variability measurements, standard deviation of NN intervals and power in the low-frequency range (LF) were considered for fatigue prediction. Fast Fourier transform and autoregressive (AR) analysis were employed as frequency domain analysis methods. Findings: The log-transformed LF obtained using AR analysis is preferred for daily fatigue management, whereas the standard deviation of normal-to-normal NN is useful in weekly fatigue management. Research limitations/implications: This study was conducted with entry-level construction workers who are involved in manual material handling activities. The findings of this study are applicable to this group. Originality/value: This is the first study to investigate all major measures obtainable through electrocardiogram and actigraphy among current mainstream wearables for monitoring occupational fatigue in the construction industry. It contributes knowledge on the use of wearable technology for managing occupational fatigue among entry-level construction workers engaged in material handling activities. [ABSTRACT FROM AUTHOR]; Copyright of Engineering Construction & Architectural Management (09699988) is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Keywords

Construction Workers; Wearable Technology; Logistic Regression Analysis; Fatigue (physiology); Frequency-domain Analysis; Heart Beat; Lifting & Carrying (human Mechanics); Construction Safety; Information And Communication Technology (ict) Applications; Management; Technology

Using Open Data and Open-source Software to Develop Spatial Indicators of Urban Design and Transport Features for Achieving Healthy and Sustainable Cities

Boeing, Geoff; Higgs, Carl; Liu, Shiqin; Giles-corti, Billie; Sallis, James F.; Cerin, Ester; Lowe, Melanie; Adlakha, Deepti; Hinckson, Erica; Moudon, Anne Vernez; Salvo, Deborah; Adams, Marc A.; Barrozo, Ligia, V; Bozovic, Tamara; Delclos-alio, Xavier; Dygryn, Jan; Ferguson, Sara; Gebel, Klaus; Thanh Phuong Ho; Lai, Poh-chin; Martori, Joan C.; Nitvimol, Kornsupha; Queralt, Ana; Roberts, Jennifer D.; Sambo, Garba H.; Schipperijn, Jasper; Vale, David; Van De Weghe, Nico; Vich, Guillem; Arundel, Jonathan. (2022). Using Open Data and Open-source Software to Develop Spatial Indicators of Urban Design and Transport Features for Achieving Healthy and Sustainable Cities. Lancet Global Health, 10(6), E907-E918.

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Abstract

Benchmarking and monitoring of urban design and transport features is crucial to achieving local and international health and sustainability goals. However, most urban indicator frameworks use coarse spatial scales that either only allow between-city comparisons, or require expensive, technical, local spatial analyses for within-city comparisons. This study developed a reusable, open-source urban indicator computational framework using open data to enable consistent local and global comparative analyses. We show this framework by calculating spatial indicators-for 25 diverse cities in 19 countries-of urban design and transport features that support health and sustainability. We link these indicators to cities' policy contexts, and identify populations living above and below critical thresholds for physical activity through walking. Efforts to broaden participation in crowdsourcing data and to calculate globally consistent indicators are essential for planning evidence-informed urban interventions, monitoring policy effects, and learning lessons from peer cities to achieve health, equity, and sustainability goals.

Keywords

Systems; Access; Care

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.

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

Efficiency Index for Fiber-Reinforced Concrete Lining at Ultimate Limit State

Fantilli, Alessandro P.; Nemati, Kamran M.; Chiaia, Bernardino. (2016). Efficiency Index for Fiber-Reinforced Concrete Lining at Ultimate Limit State. Sustainable And Resilient Infrastructure, 1(1-2), 84 – 91.

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Abstract

The fiber contribution to the ultimate limit state capacity of precast and cast-in situ tunnel linings is analytically investigated. By means of a numerical model, capable of computing the interaction curves of reinforced concrete cross sections subjected to combined compressive and bending actions, the mechanical performances of plain and fiber-reinforced concrete are compared. As a result, a new index is introduced to quantify the effectiveness of fiber addition. The higher the efficiency index, the higher the amount of steel reinforcing bar that can be removed from a plain concrete cross section. The application to real concrete linings, where shear resistance is ensured without shear reinforcement, shows that a large volume of rebar can be saved by the presence of steel fibers. This gives significant advantages in terms of durability and rapidity of tunnel construction.

Keywords

Fiber-reinforced Concrete; Efficiency Index; Ultimate Limit State; Cast-in Situ Concrete Lining; Precast Tunnel Segments

In Situ Measurement of Wind Pressure Loadings on Pedestal Style Rooftop Photovoltaic Panels

Bender, W.; Waytuck, D.; Wang, S.; Reed, D. A. (2018). In Situ Measurement of Wind Pressure Loadings on Pedestal Style Rooftop Photovoltaic Panels. Engineering Structures, 163, 281 – 293.

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Abstract

The installation of rooftop photovoltaic (PV) arrays is increasing throughout the US. Until recently, pedestal type PV framing systems for rooftops were basically designed using procedures from the ASCE7-10 Components and Cladding Standard for rooftop equipment. The 2011 Japanese Standard Load design guide on structures for photovoltaic arrays was useful in characterizing the pressure coefficients on rooftops, but the Standard employs different wind speed and importance factors, making its use in the US quite limited, Even the updated 2017 version is written for a different audience. Because rooftop pressure loadings are high due to flow separation, SEAOC and other organizations contracted boundary layer wind tunnel tests of panels attached to rooftops to ascertain if the ASCE7-10 equipment loadings were appropriate. The investigations resulted in new standards for pedestal-style arrays that appear in Chapter 29 of ASCE7-16. However, the new standards are limited to simple geometries and orientations, and the dynamics of the simply-supported thin PV plates do not appear to be considered. Questions regarding the ability of the boundary tunnels to simulate accurately the turbulence at the scale required for the attached panels have been raised. In response, very limited full-scale investigations in large-scale tunnels and in situ have been undertaken to calibrate the tunnel results. The results of this paper represent one of these calibration investigations. Specifically, in situ full-scale net wind pressure loadings on a rooftop PV array in a pedestal-style framing system located on the three story Hogue Technology Building of Central Washington University (CWU) in Ellensburg, Washington were measured. The CWU campus has a rural setting in a region with steady winds: Ellensburg is located in the Kittitas Breezeway portion of the Northwest wind power region. Indeed, the Wild Horse Wind and Solar Farm is located on the outskirts of town. The data described here were collected from April through August 2014. The measured net pressure coefficient time series were similar to those for rooftop pressure loadings for low-rise buildings described in the literature such as the Wind Engineering Research Field Laboratory at Texas Tech University (Ham and Bienkiewicz, 1998 [1]; Levitan and Mehta, 1992 [2]). The analysis of the net pressure time series data included an examination of the minimum, maximum, mean, and RMS values. Preliminary results suggest that the range of the values is larger than assumed in the ASCE7 Standard, and that the magnitude of the loadings vary considerably spatially over the multiple panel array. The pressure loading measurements are ongoing.

Keywords

Building Integrated Photovoltaics; Buildings (structures); Calibration; Design Engineering; Pressure Measurement; Roofs; Solar Cell Arrays; Standards; Time Series; Turbulence; Wind Tunnels; Japanese Standard Load Design Guide; Wind Pressure Loading Measurements; Asce7-10 Components-cladding Standard; Tunnel Calibration; Texas Tech University; Kittitas Breezeway Portion; Wild Horse Wind And Solar Farm; Ellensburg; Central Washington University; Hogue Technology Building; Boundary Layer Wind Tunnel Test; Flow Separation; Multiple Panel Array; Wind Engineering Research Field Laboratory; Net Pressure Coefficient Time Series; Northwest Wind Power Region; Pedestal-style Framing System; Pv Plates; Rooftop Equipment; Pedestal Style Rooftop Photovoltaic Panels; Solar-arrays; Loads; Simulation; Wind Engineering; Photovoltaic Modules; Solar Energy; Full-scale Measurements; Wind Loadings; Photovoltaic Cells; Roofing; Wind Power; Structural Engineering; Boundary Layers; Cladding; Wind Tunnel Testing; Solar Cells; In Situ Measurement; Framing; Photovoltaics; Engineering Research; Wind Measurement; Pressure; Panels; Wind Pressure; Design Standards; Fluid Dynamics; Low Rise Buildings; Colleges & Universities; Wind Speed; United States--us

Urban Systems Design: A Conceptual Framework for Planning Smart Communities

Tobey, Michael B.; Binder, Robert B.; Chang, Soowon; Yoshida, Takahiro; Yamagata, Yoshiki; Yang, Perry P. J. (2019). Urban Systems Design: A Conceptual Framework for Planning Smart Communities. Smart Cities, 2(4), 522 – 537.

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Abstract

Urban systems design arises from disparate current planning approaches (urban design, Planning Support Systems, and community engagement), compounded by the reemergence of rational planning methods from new technology (Internet of Things (IoT), metric based analysis, and big data). The proposed methods join social considerations (Human Well-Being), environmental needs (Sustainability), climate change and disaster mitigation (Resilience), and prosperity (Economics) as the four foundational pillars. Urban systems design integrates planning methodologies to systematically tackle urban challenges, using IoT and rational methods, while human beings form the core of all analysis and objectives. Our approach utilizes an iterative three-phase development loop to contextualize, evaluate, plan and design scenarios for the specific needs of communities. An equal emphasis is placed on feedback loops through analysis and design, to achieve the end goal of building smart communities.

Keywords

Urban Design; Planning Support System; Resilience; Sustainability; Economics; Human Factors; Big Data

Toward a Cross-Platform Framework: Assessing the Comprehensiveness of Online Rental Listings

Costa, Ana; Sass, Victoria; Kennedy, Ian; Roy, Roshni; Walter, Rebecca J.; Acolin, Arthur; Crowder, Kyle; Hess, Chris; Ramiller, Alex; Chasins, Sarah. (2021). Toward a Cross-Platform Framework: Assessing the Comprehensiveness of Online Rental Listings. Cityscape, 23(2), 327 – 339.

Abstract

Research on rental housing markets in the United States has traditionally relied on national or local housing surveys. Those sources lack temporal and spatial specificity, limiting their use for tracking short-term changes in local markets. As rental housing ads have transitioned to digital spaces, a growing body of literature has utilized web scraping to analyze listing practices and variations in rental market dynamics. Those studies have primarily relied on one platform, Craigslist, as a source of data. Despite Craigslist's popularity, the authors contend that rental listings from various websites, rather than from individual ones, provide a more comprehensive picture. Using a mixed-methods approach to study listings across various platforms in five metropolitan areas, this article demonstrates considerable variation in both the types of rental units advertised and the features provided across those platforms. The article begins with an account of the birth and consolidation of online rental platforms and emergent characteristics of several selected websites, including the criteria for posting, search parameters, search results priority, and first-page search results. Visualizations are used to compare features such as the 40th percentile of rent, rent distribution, and bedroom size based on scraped data from six online platforms (Padmapper, Forrent.com , Trulia, Zillow, Craigslist, and GoSection8), 2020 Fair Market Rents, and 2019 American Community Survey data. The analyses indicate that online listing platforms target different audiences and offer distinct information on units within those market segments, resulting in markedly different estimates of local rental costs and unit size distribution depending on the platform.