Kim, Taehoon; Kim, Yong-Woo; Cho, Hunhee. (2020). Dynamic Production Scheduling Model Under Due Date Uncertainty in Precast Concrete Construction. Journal Of Cleaner Production, 257.
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Abstract
Precast concrete structures (PCs) are widely used in the construction industry to reduce project delivery times and improve quality. On-time delivery of PCs is critical for successful project completion because the processes involving precast concrete are the critical paths in most cases. However, existing models for scheduling PC production are not adequate for use in dynamic environments where construction projects have uncertain construction schedules because of various reasons such as poor labor productivity, inadequate equipment, and poor weather. This research proposes a dynamic model for PC production scheduling by adopting a discrete-time simulation method to respond to due date changes in real time and by using a new dispatching rule that considers the uncertainty of the due dates to minimize tardiness. The model is validated by simulation experiments based on various scenarios with different levels of tightness and due date uncertainty. The results of this research will contribute to construction project productivity with a reliable and economic precast concrete supply chain. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords
Multiple Production; Demand Variability; Supply Chain; Shop; Management; Minimize; Lines; Precast Concrete Production; Dynamic Simulation; Uncertainty; Production Scheduling; Dispatching Rule
Besser, Lilah M.; Lovasi, Gina S.; Michael, Yvonne L.; Garg, Parveen; Hirsch, Jana A.; Siscovick, David; Hurvitz, Phil; Biggs, Mary L.; Galvin, James E.; Bartz, Traci M.; Longstreth, W. T. (2021). Associations between Neighborhood Greenspace and Brain Imaging Measures in Non-Demented Older Adults: The Cardiovascular Health Study. Social Psychiatry And Psychiatric Epidemiology, 56(9), 1575 – 1585.
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Abstract
Purpose Greater neighborhood greenspace has been associated with brain health, including better cognition and lower odds of Alzheimer's disease in older adults. We investigated associations between neighborhood greenspace and brain-based magnetic resonance imaging (MRI) measures and potential effect modification by sex or apolipoprotein E genotype (APOE), a risk factor for Alzheimer's disease. Methods We obtained a sample of non-demented participants 65 years or older (n = 1125) from the longitudinal, population-based Cardiovascular Health Study (CHS). Greenspace data were derived from the National Land Cover Dataset. Adjusted multivariable linear regression estimated associations between neighborhood greenspace five years prior to the MRI and left and right hippocampal volume and 10-point grades of ventricular size and burden of white matter hyperintensity. Interaction terms tested effect modification by APOE genotype and sex. CHS data (1989-1999) were obtained/analyzed in 2020. Results Participants were on average 79 years old [standard deviation (SD) = 4], 58% were female, and 11% were non-white race. Mean neighborhood greenspace was 38% (SD = 28%). Greater proportion of greenspace in the neighborhood five years before MRI was borderline associated with lower ventricle grade (estimate: - 0.30; 95% confidence interval: - 0.61, 0.00). We observed no associations between greenspace and the other MRI outcome measures and no evidence of effect modification by APOE genotype and sex. Conclusion This study suggests a possible association between greater greenspace and less ventricular enlargement, a measure reflecting global brain atrophy. If confirmed in other longitudinal cohort studies, interventions and policies to improve community greenspaces may help to maintain brain health in older age.
Keywords
Mild Cognitive Impairment; Ventricular Enlargement; Residential Greenness; Hippocampal Atrophy; Volume; Disease; Environment; Progression; Symptoms; Dementia; Neighborhood; Green Space; Mri; Brain Volume; Hippocampal; White Matter
Chen, Cindy X.; Pierobon, Francesca; Jones, Susan; Maples, Ian; Gong, Yingchun; Ganguly, Indroneil. (2022). Comparative Life Cycle Assessment of Mass Timber and Concrete Residential Buildings: A Case Study in China. Sustainability, 14(1).
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Abstract
As the population continues to grow in China's urban settings, the building sector contributes to increasing levels of greenhouse gas (GHG) emissions. Concrete and steel are the two most common construction materials used in China and account for 60% of the carbon emissions among all building components. Mass timber is recognized as an alternative building material to concrete and steel, characterized by better environmental performance and unique structural features. Nonetheless, research associated with mass timber buildings is still lacking in China. Quantifying the emission mitigation potentials of using mass timber in new buildings can help accelerate associated policy development and provide valuable references for developing more sustainable constructions in China. This study used a life cycle assessment (LCA) approach to compare the environmental impacts of a baseline concrete building and a functionally equivalent timber building that uses cross-laminated timber as the primary material. A cradle-to-gate LCA model was developed based on onsite interviews and surveys collected in China, existing publications, and geography-specific life cycle inventory data. The results show that the timber building achieved a 25% reduction in global warming potential compared to its concrete counterpart. The environmental performance of timber buildings can be further improved through local sourcing, enhanced logistics, and manufacturing optimizations.
Keywords
Mass Timber; Embodied Carbon; Climate Change; Carbon Reduction; Building Footprint; Built Environment; Forest Products; Life Cycle Analysis; Environmental Impacts; Wood Laminates; Geography; Concrete; Flooring; Manufacturing; Global Warming; Concrete Construction; Construction Materials; Emissions Trading; Greenhouse Gases; Residential Areas; Energy Consumption; Life Cycle Assessment; Greenhouse Effect; Life Cycles; Construction Industry; Logistics; Floor Coverings; Urbanization; Timber; Urban Environments; Building Components; Emissions; Residential Buildings; Carbon Footprint; Urban Areas; Environmental Impact; Building Construction; Case Studies; Wood Products; Mitigation; Buildings; Timber (structural); United States--us; China
Ochsner, Jeffrey Karl; Rash, David A. (2012). The Emergence of Naramore, Bain, Brady & Johanson and the Search for Modern Architecture in Seattle, 1945-1950. Pacific Northwest Quarterly, 103(3), 123 – 141.
Van Den Wymelenberg, Kevin; Brown, G. Z.; Burpee, Heather; Djunaedy, Ery; Gladics, Gunnar; Kline, Jeff; Loveland, Joel; Meek, Christopher; Thimmanna, Harshana. (2013). Evaluating Direct Energy Savings and Market Transformation Effects: A Decade of Technical Design Assistance in the Northwestern USA. Energy Policy, 52, 342 – 353.
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Abstract
This paper documents the direct energy savings and energy efficiency market transformation impacts of a multi-state design assistance program in the northwestern US. The paper addresses four specific aims. (1) It provides a conservative and justified estimate of the direct energy savings associated with design assistance activities of a market transformation program from 2001 to 2010. (2) It provides a rigorous methodology to evaluate direct energy savings associated with design assistance market transformation programs. (3) It provides a low-cost replicable method to predict energy savings in new buildings by evaluating the integrated design process. (4) It provides quantitative indicators useful for estimating indirect energy savings from market transformation. Applying the recommended analysis method and assuming a 12-year measure life, the direct energy savings of the population (626 buildings; 51,262,000 ft(2)) is estimated as 453 aMW (average megawatts) (electric), and 265,738.089 therms (non-electric). If the entire program budget were divided into the electric savings only, the Lab Network cost per kWh saved ranged from $0.0016 to $0.003 using the recommended method and $0.0092/kWh using the most conservative method. These figures do not isolate contextual influences or represent total resource cost. Statistically significant correlations (r(2)=0.1-0.3) between integrated design scores and energy savings are reported. (C) 2012 Elsevier Ltd. All rights reserved.
Keywords
Programs; Sweden; Energy Efficiency; Market Transformation; Evaluation
Migliaccio, G. C.; Bogus, Susan M.; Cordova-Alvidrez, A. A. (2014). Continuous Quality Improvement Techniques for Data Collection in Asset Management Systems. Journal Of Construction Engineering And Management, 140(4).
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Abstract
Transportation infrastructure assets are among the largest investments made by governmental agencies. These agencies use data on asset conditions to make decisions regarding the timing of maintenance activities, the type of treatment, and the resources to employ. To collect and record these data, agencies often utilize trained evaluators who assess the asset either on site or by analyzing photos and/or videos. These visual assessments are widely used to evaluate conditions of various assets, including pavement surface distresses. This paper describes a Data Quality Assessment & Improvement Framework (DQAIF) to measure and improve the performance of multiple evaluators of pavement distresses by controlling for subjective judgment by the individual evaluators. The DQAIF is based on a continuous quality improvement cyclic process that is based on the following main components: (1)assessment of the consistency over timeperformed using linear regression analysis; (2)assessment of the agreement between evaluatorsperformed using inter-rater agreement analysis; and (3)implementation of management practices to improve the results shown by the assessments. A large and comprehensive case study was employed to describe, refine, and validate the framework. When the DQAIF is applied to pavement distress data collected on site by different evaluators, the results show that it is an effective method for quickly identifying and solving data collection issues. The benefit of this framework is that the analyses employed produce performance measures during the data collection process, thus minimizing the risk of subjectivity and suggesting timely corrective actions. The DQAIF can be used as part of an asset management program, or in any engineering program in which the data collected are subjected to the judgment of the individuals performing the evaluation. The process could also be adapted for assessing performance of automated distress data acquisition systems.
Keywords
Asset Management; Civil Engineering Computing; Data Acquisition; Decision Making; Inspection; Maintenance Engineering; Quality Control; Regression Analysis; Roads; Transportation; Continuous Quality Improvement Techniques; Asset Management System; Governmental Agencies; Transportation Infrastructure Assets; Maintenance Activities; Visual Assessment; Pavement Surface Distresses; Data Quality Assessment & Improvement Framework; Dqaif; Linear Regression Analysis; Interrater Agreement Analysis; Data Collection Process; Automated Distress Data Acquisition System; Manual Pavement Distress; Pavement Management; Quantitative Analysis; Data Collection; Assets; Reliability; Case Studies
Choi, Kunhee; Lee, Hyun Woo. (2016). Deconstructing the Construction Industry: A Spatiotemporal Clustering Approach to Profitability Modeling. Journal Of Construction Engineering And Management, 142(10).
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Abstract
In spite of the strong influence of the construction industry on the national health of the United States' economy, very little research has specifically aimed at evaluating the key performance parameters and trends (KPPT) of the industry. Due to this knowledge gap, concerns have been constantly raised over lack of accurate measures of KPPT. To circumvent these challenges, this study investigates and models the macroeconomic KPPT of the industry through spatiotemporal clustering modeling. This study specifically aims to analyze the industry in 14 of its subsectors and subsequently, by 51 geographic spatial areas at a 15-year temporal scale. KPPT and their interdependence were firstly examined by utilizing the interpolated comprehensive U.S. economic census data. A hierarchical spatiotemporal clustering analysis was then performed to create predictive models that can reliably determine firm's profitability as a function of the key parameters. Lastly, the robustness of the predictive models was tested by a cross-validation technique called the predicted error sum of square. This study yields a notable conclusion that three key performance parameterslabor productivity, gross margin, and labor wageshave steadily improved over the study period from 1992 to 2007. This study also reveals that labor productivity is the most critical factor; the states and subsectors with the highest productivity are the most profitable. This study should be of value to decision-makers when plotting a roadmap for future growth and rendering a strategic business decisions.
Keywords
Construction Industry; Decision Making; Knowledge Management; Labour Resources; Macroeconomics; Organisational Aspects; Productivity; Profitability; Salaries; Statistical Analysis; Strategic Planning; Hierarchical Spatiotemporal Clustering Approach; National Health; Macroeconomic Kppt; Knowledge Gap; Spatiotemporal Clustering Modeling; Interpolated Comprehensive U.s. Economic Census Data; Parameters-labor Productivity; Gross Margin; Labor Wages; Strategic Business Decisions; Deconstructing; Key Performance Parameters And Trends; Firms Profitability; Error Sum Of Square; Labor Productivity; Projects; Firms; Performance; Performance Measurement; Cluster Analysis; Economic Census; Project Planning And Design
Van Den Wymelenberg, Kevin; Inanici, Mehlika. (2016). Evaluating a New Suite of Luminance-Based Design Metrics for Predicting Human Visual Comfort in Offices with Daylight. Leukos, 12(3), 113 – 138.
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Abstract
A new suite of visual comfort metrics is proposed and evaluated for their ability to explain the variability in subjective human responses in a mock private office environment with daylight. Participants (n = 48) rated visual comfort and preference factors, including 1488 discreet appraisals, and these subjective results were correlated against more than 2000 unique luminance-based metrics that were captured using high dynamic range photography techniques. Importantly, luminance-based metrics were more capable than illuminance-based metrics for fitting the range of human subjective responses to data from visual preference questionnaire items. No metrics based upon the entire scene ranked in the top 20 squared correlation coefficients, nor did any based upon illuminance or irradiance data, nor did any of the studied glare indices, luminance ratios, or contrast ratios. The standard deviation of window luminance was the metric that best fit human subjective responses to visual preference on seven of 12 questionnaire items (with r(2) = 0.43). Luminance metrics calculated using the horizontal 40. band (a scene-independent mask) and the window area (a scene-dependent mask) represented the majority of the top 20 squared correlation coefficients for almost all subjective visual preference questionnaire items. The strongest multiple regression model was for the semantic differential rating (too dim-too bright) of the window wall (R-adj(2) = 0.49) and was built upon three variables; standard deviation of window luminance, the 50th percentile luminance value from the lower view window, and mean luminance of the 40. horizontal band.
Keywords
Discomfort Glare; Controls; Daylighting; Visual Perception
Zhu, Panyu; Gilbride, Michael; Yan, Da; Sun, Hongshan; Meek, Christopher. (2017). Lighting Energy Consumption in Ultra-Low Energy Buildings: Using a Simulation and Measurement Methodology to Model Occupant Behavior and Lighting Controls. Building Simulation, 10(6), 799 – 810.
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Abstract
As building owners, designers, and operators aim to achieve significant reductions in overall energy consumption, understanding and evaluating the probable impacts of occupant behavior becomes a critical component of a holistic energy conservation strategy. This becomes significantly more pronounced in ultra-efficient buildings, where system loads such as heating, cooling, lighting, and ventilation are reduced or eliminated through high-performance building design and where occupant behavior-driven impacts reflect a large portion of end-use energy. Further, variation in behavior patterns can substantially impact the persistence of any performance gains. This paper describes a methodology of building occupant behavior modeling using simulation methods developed by the Building Energy Research Center (BERC) at Tsinghua University using measured energy consumption data collected by the University of Washington Integrated Design Lab (UW IDL). The Bullitt Center, a six-story 4831 m(2) (52,000 ft(2)) net-positive-energy urban office building in Seattle, WA, USA, is one of the most energy-efficient buildings in the world (2013 WAN Sustainable Building of the Year Winner). Its measured energy consumption in 2015 was approximately 34.8 kWh/(m(2)a (TM) yr) (11 kBtu/(ft(2)a (TM) yr)). Occupant behavior exerts an out-sized influence on the energy performance of the building. Nearly 33% of the end-use energy consumption at the Bullitt Center consists of unregulated miscellaneous electrical loads (plug-loads), which are directly attributable to occupant behavior and equipment procurement choices. Approximately 16% of end-use energy is attributable to electric lighting which is also largely determined by occupant behavior. Key to the building's energy efficiency is employment of lighting controls and daylighting strategies to minimize the lighting load. This paper uses measured energy use in a 330 m(2) (3550 ft(2)) open office space in this building to inform occupant profiles that are then modified to create four scenarios to model the impact of behavior on lighting use. By using measured energy consumption and an energy model to simulate the energy performance of this space, this paper evaluates the potential energy savings based on different occupant behavior. This paper describes occupant behavior simulation methods and evaluates them using a robust dataset of 15 minute interval sub-metered energy consumption data. Lighting control strategies are compared via simulation results, in order to achieve the best match between occupant schedules, controls, and energy savings. Using these findings, we propose a simulation methodology that incorporates measured energy use data to generate occupant schedules and control schemes with the ultimate aim of using simulation results to evaluate energy saving measures that target occupant behavior.
Keywords
Control-systems; Patterns; Offices; Lighting Control; Ultra-low Energy Building; Occupant Behavior; Building Simulation; Energy Consumption
Taylor, John E.; Alin, Pauli; Anderson, Anne; Çomu, Semra; Dossick, Carrie Sturts; Hartmann, Timo; Iorio, Josh; Mahalingam, Ashwin; Mohammadi, Neda. (2018). Cybergrid: A Virtual Workspace for Architecture, Engineering, and Construction. Transforming Engineering Education: Innovative, Computer-mediated Learning Technologies, 291-321.
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Abstract
Projects in the architecture, engineering and construction (AEC) industry frequently involve a large number of firms that increasingly span national boundaries. National boundary spanning by AEC firms engaged in complex, interdependent work introduces coordination challenges because stakeholders may not share the same language, culture or work practices. These types of firms have begun to explore the use of technologies that can meaningfully create productive work connections between the distributed participants 47 and help improve work coordination and execution. In this chapter, we describe the CyberGRID (Cyber-enabled Global Research Infrastructure for Design); a virtual workspace designed to support geographically distributed AEC work coordination and execution. The CyberGRID was created as a research tool to both enable and study virtual AEC teamwork. We summarize findings from multiple experiments over the jive year history of CyberGRID research and development. These findings help to improve our understanding of interactional dynamics among virtual teams in complex sociotechnical systems like the CyberGRID. We then discuss the challenges faced in developing the CyberGRID and in achieving widespread adoption of such tools in the industry. We close the chapter with a discussion of future research opportunities to develop improved sociotechnical systems to better support the execution of AEC projects. Our goal with this chapter is to argue that sociotechnical systems like the CyberGRID can fundamentally and positively transform the interactional dynamics of AEC project stakeholders to create more efficient global virtual work practices.
Keywords
Civil Engineering Computing; Construction Industry; Data Visualisation; Groupware; Project Management; Team Working; Virtual Reality; Cybergrid; Virtual Workspace; Construction; Engineering; National Boundaries; National Boundary Spanning; Aec Firms; Complex Work; Interdependent Work; Coordination Challenges; Culture; Productive Work Connections; Chapter; Global Research Infrastructure; Geographically Distributed Aec Work Coordination; Research Tool; Virtual Aec Teamwork; Virtual Teams; Complex Sociotechnical Systems; Future Research Opportunities; Improved Sociotechnical Systems; Aec Projects; Aec Project Stakeholders; Efficient Global Virtual Work Practices