Choi, Kunhee, Bae, Junseo, Yin, Yangtian, and Lee, Hyun Woo. (2014). ACT²: Time–Cost Tradeoffs from Alternative Contracting Methods. Journal of Management in Engineering, 37(1).
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Abstract
Incentive/disincentive (I/D) and cost-plus-time (A+B) are two of the most widely used alternative contracting methods (ACMs) for accelerating the construction of highway infrastructure improvement projects. However, little is known about the effects of trade-offs in terms of project schedule and cost performance. This study addresses this problem by creating and testing a stochastic decision support model called accelerated alternative contracting cost-time trade-off (ACT2). This model was developed by a second-order polynomial regression analysis and validated by the predicted error sum of square statistic and paired comparison tests. The results of a descriptive trend analysis based on a rich set of high-confidence project data show that I/D is effective at reducing project duration but results in higher cost compared to pure A+B and conventional methods. This cost-time trade-off effect was confirmed by the ACT2 model, which determines the level of cost-time trade-off for different ACMs. This study will help state transportation agencies promote more effective application of ACMs by providing data-driven performance benchmarking results when evaluating competing acceleration strategies and techniques.
Keywords
Errors (statistics), Project management, Benefit cost ratios, Regression analysis, Construction costs, Infrastructure construction, Contracts and subcontracts, Construction methods
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
Ruoniu (Vince) Wang is an Assistant Professor in the Runstad Department of Real Estate in the College of Built Environments at the University of Washington. He studies spatial justice and inclusive communities, including their impacts reflected in the built environment, human behaviors, and policy interventions. Vince joined the University of Washington after serving six years as the research manager and director in a national non-profit organization Grounded Solutions Network. He has designed and conducted a U.S. Census of inclusionary housing policies, a U.S. census of community land trusts, and a national performance evaluation of shared equity homeownership programs. His research expands to policy evaluation for the two largest federal assisted housing rental programs in the U.S.: the Low-Income Housing Tax Credit program and the Housing Choice Voucher program. Vince grounds his research with applied tools to democratize data for low-income communities.
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.
Celina Balderas Guzmán, PhD, is Assistant Professor in the Department of Landscape Architecture. Dr. Balderas’ research spans environmental planning, design, and science and focuses on climate adaptation to sea level rise on the coast and urban stormwater inland. On the coast, her work demonstrates specific ways that the climate adaptation actions of humans and adaptation of ecosystems are interdependent. Her work explores how these interdependencies can be maladaptive by shifting vulnerabilities to other humans or non-humans, or synergistic. Using ecological modeling, she has explored these interdependencies focusing on coastal wetlands as nature-based solutions. Her work informs cross-sectoral adaptation planning at a regional scale.
Inland, Dr. Balderas studies urban stormwater through a social-ecological lens. Using data science and case studies, her work investigates the relationship between stormwater pollution and the social, urban form, and land cover characteristics of watersheds. In past research, she developed new typologies of stormwater wetlands based on lab testing in collaboration with environmental engineers. The designs closely integrated hydraulic performance, ecological potential, and recreational opportunities into one form.
Her research has been funded by major institutions such as the National Science Foundation, National Socio-Environmental Synthesis Center, UC Berkeley, and the MIT Abdul Latif Jameel Water and Food Systems Lab. She has a PhD in the Department of Landscape Architecture and Environmental Planning from the University of California, Berkeley. Previously, she obtained masters degrees in urban planning and urban design, as well as an undergraduate degree in architecture all from MIT.
Lee, Namhun; Dossick, Carrie S.; Foley, Sean P. (2013). Guideline for Building Information Modeling in Construction Engineering and Management Education. Journal Of Professional Issues In Engineering Education And Practice, 139(4), 266 – 274.
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Keywords
Buildings (structures); Computer Aided Instruction; Construction Industry; Educational Courses; Management Education; Structural Engineering Computing; Building Information Modeling; Construction Engineering And Management Education; Cem Education; Bim; Cem Curriculum
Drewnowski, A.; Buszkiewicz, J.; Aggarwal, A.; Cook, A.; Moudon, A. V. (2018). A New Method to Visualize Obesity Prevalence in Seattle-King County at the Census Block Level. Obesity Science & Practice, 4(1), 14 – 19.
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Abstract
Objective The aim of this study is to map obesity prevalence in Seattle King County at the census block level. Methods Data for 1,632 adult men and women came from the Seattle Obesity Study I. Demographic, socioeconomic and anthropometric data were collected via telephone survey. Home addresses were geocoded, and tax parcel residential property values were obtained from the King County tax assessor. Multiple logistic regression tested associations between house prices and obesity rates. House prices aggregated to census blocks and split into deciles were used to generate obesity heat maps. Results Deciles of property values for Seattle Obesity Study participants corresponded to county-wide deciles. Low residential property values were associated with high obesity rates (odds ratio, OR: 0.36; 95% confidence interval, CI [0.25, 0.51] in tertile 3 vs. tertile 1), adjusting for age, gender, race, home ownership, education, and incomes. Heat maps of obesity by census block captured differences by geographic area. Conclusion Residential property values, an objective measure of individual and area socioeconomic status, are a useful tool for visualizing socioeconomic disparities in diet quality and health.
Keywords
Residential Property-values; Socioeconomic-status; Health; Environment; Adults; Census Block; Geographic Information Systems; Mapping Obesity; Ses Measures
Zhang, Zhenyu; Lin, Ken-yu; Lin, Jia-hua. (2022). 2safe: A Health Belief Model-integrated Framework For Participatory Ergonomics. Theoretical Issues In Ergonomics Science, 1 – 18.
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Abstract
Abstract Initiating ergonomics interventions in a business environment requires changes in the behaviour of relevant actors. When participating in an intervention, researchers need to collect and share information with practitioners to help them make better behaviour-related decisions. This paper describes the five-step 2SAFE (Surveillance, Screening, Assessment, Framing, and Evaluation) planning framework, which can be used to guide research-practice collaboration in participatory ergonomics programmes. This framework combines the understanding of work-related musculoskeletal disorders with the principles of the health belief model. This theoretical synthesis empowers the framework to address the following critical challenges: (1) how to make data collection processes attuned to the nature of ergonomic injuries; and (2) how to transform the data collected into immediately usable information for practitioners to change their behaviours. The framework is interdisciplinary and can facilitate transfer of knowledge between ergonomics and health behaviour science. The framework can enhance the ability of researchers to collaborate with practitioners and bring participatory ergonomics programmes closer to success. In the long term, we hope that this framework can lead to more high-quality interventions that are able to prevent work-related musculoskeletal disorders in various industrial settings. [ABSTRACT FROM AUTHOR]; Copyright of Theoretical Issues in Ergonomics Science is the property of Taylor & Francis Ltd 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
Health Belief Model; Intervention Programme; Participatory Ergonomics; Planning Framework; Work-related Musculoskeletal Disorders
Migliaccio, Giovanni C.; Guindani, Michele; D’Incognito, Maria; Zhang, Linlin. (2013). Empirical Assessment of Spatial Prediction Methods for Location Cost-Adjustment Factors. Journal Of Construction Engineering & Management, 139(7), 858 – 869.
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Abstract
In the feasibility stage of a project, location cost-adjustment factors (LCAFs) are commonly used to perform quick order-of-magnitude estimates. Nowadays, numerous LCAF data sets are available in North America, but they do not include all locations. Hence, LCAFs for unsampled locations need to be inferred through spatial interpolation or prediction methods. Using a commonly used set of LCAFs, this paper aims to test the accuracy of various spatial prediction methods and spatial interpolation methods in estimating LCAF values for unsampled locations. Between the two regression-based prediction models selected for the study, geographically weighted regression analysis (GWR) resulted the most appropriate way to model the city cost index as a function of multiple covariates. As a direct consequence of its spatial nonstationarity, the influence of each single covariate differed from state to state. In addition, this paper includes a first attempt to determine if the observed variability in cost index values could be at least partially explained by independent socioeconomic variables. (C) 2013 American Society of Civil Engineers.
Keywords
Construction Industry; Interpolation; Regression Analysis; Socio-economic Effects; Spatial Prediction Methods; Location Cost-adjustment Factors; Empirical Assessment; Lcaf; Order-of-magnitude Estimates; North America; Unsampled Locations; Spatial Interpolation Methods; Geographically Weighted Regression Analysis; Gwr; Independent Socioeconomic Variables; Inflation; Indexes; Estimation; Geostatistics; Construction Costs; Planning; Budgeting
Kim, Jonghyeob; Lee, Hyun Woo; Bender, William; Hyun, Chang-taek. (2018). Model for Collecting Replacement Cycles of Building Components: Hybrid Approach of Indirect and Direct Estimations. Journal Of Computing In Civil Engineering, 32(6).
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Abstract
Building maintenance, repair, and replacement (MR&R) costs are estimated to be two to three times larger than initial construction costs. Thus, it is important to accurately estimate and manage MR&R costs in the planning phase and/or design phase of a construction project based on life cycle cost analysis (LCCA). However, the nature of LCCA requires making necessary assumptions for the prediction and analysis of MR&R costs, and the reliability of the assumptions greatly impacts LCCA results. In particular, determining reasonable replacement cycles is especially important given that each replacement typically involves a significant amount of capital. However, conventional approaches largely focus on either collecting component-specific replacement cases or surveying expert opinions, both of which reduce the usability and reliability of replacement cycle data at an early stage. To overcome these limitations, this study aims to develop a replacement cycle collection model that can expedite the data collection by combining indirect estimations with direct estimations. The development of the model involves collecting replacement cases, developing replacement cycle and index estimation methods, and developing an algorithm to implement the suggested model. As a validation, the applicability and effectiveness of the model were illustrated and tested by using simulated cases based on 21 real-world facilities. This study makes a theoretical contribution to the body of knowledge by developing a replacement cycle data collection model based on long-term and macro perspectives. The developed model will also be of value to practitioners when they try to improve the reliability of their LCCA.
Keywords
Buildings (structures); Life Cycle Costing; Maintenance Engineering; Structural Engineering; Building Components; Building Maintenance; Planning Phase; Design Phase; Construction Project; Life Cycle Cost Analysis; Replacement Cycle Data Collection Model; Construction Costs; Lcca; Maintenance Repair And Replacement Cost; Service Life Prediction; Repair; Replacement; Replacement Cycles; Replacement Index; Database; Indirect Estimations