Liang, Huakang; Lin, Ken-yu; Zhang, Shoujian; Su, Yikun. (2018). The Impact Of Coworkers’ Safety Violations On An Individual Worker: A Social Contagion Effect Within The Construction Crew. International Journal Of Environmental Research And Public Health, 15(4).
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Abstract
This research developed and tested a model of the social contagion effect of coworkers' safety violations on individual workers within construction crews. Both situational and routine safety violations were considered in this model. Empirical data were collected from 345 construction workers in China using a detailed questionnaire. The results showed that both types of safety violations made by coworkers were significantly related to individuals' perceived social support and production pressure. Individuals' attitudinal ambivalence toward safety compliance mediated the relationships between perceived social support and production pressure and both types of individuals' safety violations. However, safety motivation only mediated the effects of perceived social support and production pressure on individuals' situational safety violations. Further, this research supported the differences between situational and routine safety violations. Specifically, we found that individuals were more likely to imitate coworkers' routine safety violations than their situational safety violations. Coworkers' situational safety violations had an indirect effect on individuals' situational safety violations mainly through perceived social support and safety motivation. By contrast, coworkers' routine safety violations had an indirect effect on individuals' routine safety violations mainly through perceived production pressure and attitudinal ambivalence. Finally, the theoretical and practical implications, research limitations, and future directions were discussed.
Keywords
Health-care Settings; Job Demands; Attitudinal Ambivalence; Industry Development; Workplace Safety; Behavior; Climate; Model; Risk; Employee; Social Contagion; Situational Safety Violations; Routine Safety Violations; Social Learning; Social Information Processing
Kim, Taehoon; Kim, Yong-woo; Lee, Dongmin; Kim, Minju. (2022). Reinforcement Learning Approach To Scheduling Of Precast Concrete Production. Journal Of Cleaner Production, 336.
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Abstract
The production scheduling of precast concrete (PC) is essential for successfully completing PC construction projects. The dispatching rules, widely used in practice, have the limitation that the best rule differs according to the shop conditions. In addition, mathematical programming and the metaheuristic approach, which would improve performance, entail more computational time with increasing problem size, let alone its models being revised as the problem size changes. This study proposes a PC production scheduling model based on a reinforcement learning approach, which has the advantages of a general capacity to solve various problem conditions with fast computation time and good performance in real-time. The experimental study shows that the proposed model outperformed other methods by 4-12% of the total tardiness and showed an average winning rate of 77.0%. The proposed model could contribute to the successful completion of off-site construction projects by supporting the stable progress of PC construction.
Keywords
Precast Concrete; Reinforcement Learning; Deep Q -network; Production Scheduling; Minimize; Model
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
Stewart, Orion T.; Moudon, Anne Vernez; Littman, Alyson J.; Seto, Edmund; Saelens, Brian E. (2018). The Association between Park Facilities and Duration of Physical Activity During Active Park Visits. Journal Of Urban Health, 95(6), 869 – 880.
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Abstract
Public parks provide places for urban residents to obtain physical activity (PA), which is associated with numerous health benefits. Adding facilities to existing parks could be a cost-effective approach to increase the duration of PA that occurs during park visits. Using objectively measured PA and comprehensively measured park visit data among an urban community-dwelling sample of adults, we tested the association between the variety of park facilities that directly support PA and the duration of PA during park visits where any PA occurred. Cross-classified multilevel models were used to account for the clustering of park visits (n=1553) within individuals (n=372) and parks (n=233). Each additional different PA facility at a park was independently associated with a 6.8% longer duration of PA bouts that included light-intensity activity, and an 8.7% longer duration of moderate to vigorous PA time. Findings from this study are consistent with the hypothesis that more PA facilities increase the amount of PA that visitors obtain while already active at a park.
Keywords
Park Facilities; Physical Activity; Park Use; Recreation; Built Environment; Global Positioning System; Accelerometer; Gis; Gps; Accelerometer Data; United-states; Adults; Proximity; Features; Walking; Size; Attractiveness; Improvements; Environment; Parks & Recreation Areas; Parks; Luminous Intensity; Clustering; Urban Areas
Launching the Inspire Fund: An early step for CBE’s Office of Research “For a small college, CBE has a broad range of research paradigms, from history and arts, to social science and engineering.” — Carrie Sturts Dossick, Associate Dean of Research Upon taking on the role of Associate Dean of Research, Carrie Sturts Dossick, professor in the Department of Construction Management, undertook listening sessions to learn about the research needs of faculty, staff and students across the College of Built…
I am interested in developing analysis methods and metrics for accurate daylight analysis. More concretely, I would like to work on developing color accurate sky models through analyzing HDR photographs, and to integrate it to annual daylight simulation method. Additionally, I am also interested in integration of daylight simulation in environmental design.
Julie Kriegh, researcher with the Carbon Leadership Forum and other CBE research centers, and owner of Kriegh Architecture Studios, collaborated with other CBE faculty and external partners to lead a UW CBE studio course in collaboration with Google that developed and delivered a design proposal for a sustainable data center. CBE collaborators included Hyun Woo “Chris” Lee, P.D. Koon Professorship in Construction Management; Jan Whittington, Associate Professor of the Department of Urban Design and Planning, and Director of the Urban…
Sustainable transportation, travel behavior, GIS, geospatial big data
Urban systems, system complexity, big data, artificial intelligence, smart cities, communities, and coupled human-built-environmental systems
Emerging transportation technologies, shared mobility and land use, interaction between human mobility based on shared vehicles and urban land uses. Spatio-temporal analysis and big data. Smart visualization methods