Lee, Yong-Cheol; Shariatfar, Moeid; Rashidi, Abbas; Lee, Hyun Woo. (2020). Evidence-Driven Sound Detection for Prenotification and Identification Of Construction Safety Hazards and Accidents. Automation In Construction, 113.
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Abstract
As the construction industry experiences a high rate of casualties and significant economic loss associated with accidents, safety has always been a primary concern. In response, several studies have attempted to develop new approaches and state-of-the-art technology for conducting autonomous safety surveillance of construction work zones such as vision-based monitoring. The current and proposed methods including human inspection, however, are limited to consistent and real-time monitoring and rapid event recognition of construction safety issues. In addition, the health and safety risks inherent in construction projects make it challenging for construction workers to be aware of possible safety risks and hazards according to daily planned work activities. To address the urgent demand of the industry to improve worker safety, this study involves the development of an audio-based event detection system to provide daily safety issues to laborers and through the rapid identification of construction accidents. As an evidence-driven approach, the proposed framework incorporates the occupational injury and illness manual data, consisting of historical construction accident data classified by types of sources and events, into an audio-based safety event detection framework. This evidence-driven framework integrated with a daily project schedule can automatically provide construction workers with prenotifications regarding safety hazards at a pertinent work zone as well as consistently contribute to enhanced construction safety monitoring by audio-based event detection. By using a machine learning algorithm, the framework can clearly categorize the narrowed-down sound training data according to a daily project schedule and dynamically restrict sound classification types in advance. The proposed framework is expected to contribute to an emerging knowledge base for integrating an automated safety surveillance system into occupational accident data, significantly improving the accuracy of audio-based event detection.
Keywords
Construction Projects; Occupational Hazards; Construction Workers; Construction; System Safety; Video Surveillance; Work-related Injuries; Audio-based Accident Recognition; Autonomous Safety Surveillance; Construction Safety; Evidence-driven Sound Event Detection; Accident Prevention; Accidents; Audio Acoustics; Classification (of Information); Construction Industry; Health Hazards; Health Risks; Knowledge Based Systems; Learning Algorithms; Losses; Machine Learning; Monitoring; Motion Compensation; Occupational Diseases; Steel Beams And Girders; Audio-based; Construction Accidents; Construction Work Zones; Historical Construction; Sound Event Detection; State-of-the-art Technology; Vision Based Monitoring; Algorithm; System
Shang, Luming; Lee, Hyun Woo; Dermisi, Sofia; Choe, Youngjun. (2020). Impact of Energy Benchmarking and Disclosure Policy on Office Buildings. Journal Of Cleaner Production, 250.
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Abstract
Building energy benchmarking policies require owners to publicly disclose their building's energy performance. In the US, the adoption of such policies is contributing to an increased awareness among tenants and buyers and is expected to motivate the owners of less efficient buildings to invest in energy efficiency improvements. However, there is a lack of studies specifically aimed at investigating the impact of such policies on office buildings among major cities through quantitative analyses. In response, this study evaluated the effectiveness of the benchmarking policy on energy efficiency improvements decision-making and on real estate performances, by applying two interrupted time series analyses to office buildings in downtown Chicago. The initial results indicate a lack of statistically strong evidence that the policy affected the annual vacancy trend of the energy efficient buildings (represented by ENERGY STAR labeled buildings). However, the use of interrupted time series in a more in-depth analysis shows that the policy is associated with a 6.7% decrease in vacancy among energy efficient buildings. The study proposed a method to quantitatively evaluate the impact of energy policies on the real estate performance of office buildings, and the result confirms the positive impact of energy-efficient retrofits on the real estate performance. The study findings support the reasoning behind the owners' decision in implementing energy efficiency improvements in their office buildings to remain competitive in the market. (C) 2019 Elsevier Ltd. All rights reserved.
Keywords
Office Buildings; Building Failures; Time Series Analysis; Real Property; Energy Consumption; Metropolis; Building Performance; Chicago (ill.); Building Energy Benchmarking And Disclosure Policies; Building Energy Efficiency; Time Series Modeling; Energy Star (program); Building Management Systems; Buildings (structures); Decision Making; Energy Conservation; Maintenance Engineering; Time Series; Disclosure Policy; Energy Benchmarking Policies; Building; Benchmarking Policy; Energy Efficiency Improvements Decision-making; Estate Performance; Energy Efficient Buildings; Energy Star; Energy Policies; Energy-efficient Retrofits; Interrupted Time-series; Regression; Behavior; Designs; Building Energy Benchmarking And; Disclosure Policies; Buildings; Cities; Energy Efficiency; Energy Policy; Markets; Quantitative Analysis; United States
Chen, Chen; Lindell, Michael K.; Wang, Haizhong. (2021). Tsunami Preparedness and Resilience in the Cascadia Subduction Zone: A Multistage Model of Expected Evacuation Decisions and Mode Choice. International Journal Of Disaster Risk Reduction, 59.
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Abstract
Physical scientists have estimated that the Cascadia Subduction Zone (CSZ) has as much as a 25% chance to produce a M9.0 earthquake and tsunami in the next 50 years, but few studies have used survey data to assess household risk perceptions, emergency preparedness, and evacuation intentions. To understand these phenomena, this study conducted a mail-based household questionnaire using the Protective Action Decision Model (PADM) as a guide to collect 483 responses from two coastal communities in the CSZ: Crescent City, CA and Coos Bay, OR. We applied multistage regression models to assess the effects of critical PADM variables. The results showed that three psychological variables (risk perception, perceived hazard knowledge, and evacuation mode efficacy) were associated with some demographic variables and experience variables. Evacuation intention and evacuation mode choice are associated with those psychological variables but not with demographic variables. Contrary to previous studies, location and experience had no direct impact on evacuation intention or mode choice. We also analyzed expected evacuation mode compliance and the potential of using micro-mobility during tsunami response. This study provides empirical evidence of tsunami preparedness and intentions to support interdisciplinary evacuation modeling, tsunami hazard education, community disaster preparedness, and resilience plans.
Keywords
False Discovery Rate; American-samoa; Earthquake; Washington; Behavior; Oregon; Wellington; Responses; Disaster; Tsunami Evacuation; Cascadia Subduction Zone; Risk Perception
Lin, Lin; Chen, Xueming (Jimmy); Moudon, Anne Vernez. (2021). Measuring the Urban Forms of Shanghai’s City Center and Its New Districts: A Neighborhood-Level Comparative Analysis. Sustainability, 13(15).
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Abstract
Rapid urban expansion has radically transformed the city centers and the new districts of Chinese cities. Both areas have undergone unique redevelopment and development over the past decades, generating unique urban forms worthy of study. To date, few studies have investigated development patterns and land use intensities at the neighborhood level. The present study aims to fill the gap and compare the densities of different types of developments and the spatial compositions of different commercial uses at the neighborhood level. We captured the attributes of their built environment that support instrumental activities of daily living of 710 neighborhoods centered on the public elementary schools of the entire Shanghai municipality using application programming interfaces provided in Baidu Map services. The 200 m neighborhood provided the best fit to capture the variations of the built environment. Overall, city center neighborhoods had significantly higher residential densities and housed more daily routine destinations than their counterparts in the new districts. Unexpectedly, however, the total length of streets was considerably smaller in city-center neighborhoods, likely reflecting the prominence of the wide multilane vehicular roads surrounding large center city redevelopment projects. The findings point to convergence between the city center's urban forms and that of the new districts.
Keywords
Quantifying Spatiotemporal Patterns; Fast-food Restaurants; Instrumental Activities; Physical-activity; Chinese Cities; Land; Schools; Redevelopment; Expansion; Transformation; Built Environment; Planning; Neighborhood; Urban Form; Shanghai
Zou, Tianqi; Aemmer, Zack; Mackenzie, Don; Laberteaux, Ken. (2022). A Framework for Estimating Commute Accessibility and Adoption of Ridehailing Services Under Functional Improvements from Vehicle Automation. Journal Of Transport Geography, 102.
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Abstract
This paper develops an analytical framework to estimate commute accessibility and adoption of various ridehailing service concepts across the US by synthesizing individual commute trips using national Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics (LODES) data. Focusing on potential improvements in cost and time that could be enabled by vehicle automation, we use this modeling framework to simulate a lower-price autonomous service (e.g., 50% or 75% lower) with variable wait times and implementation levels (solo, pooled, and first/last mile transit connections services, alone or in combination) to determine how they might affect adoption rates. These results are compared across metrics of accessibility and trip density, as well as socioeconomic factors such as household income. We find - unsurprisingly - that major cities (e.g. New York, Los Angeles, and Chicago) support the highest adoption rates for ridehailing services. Decreases in price tend to increase market share and accessibility. The effect of a decrease in price is more drastic for lower income groups. The proposed method for synthesizing trips using the LODES contributes to current travel demand forecasting methods and the proposed analytic framework can be flexibly implemented with any other mode choice model, extended to non-commute trips, or applied to different levels of geographic aggregation.
Keywords
Choice Of Transportation; Demand Forecasting; Poor People; Adoption; Price Cutting; Metropolis; Employment Statistics; Los Angeles (calif.); New York (state); Chicago (ill.); Accessibility; Autonomous Vehicles; New Mobility Services; Ridehailing; Travel Demand; Preferences
El-Anwar, Omar; El-Rayes, Khaled; Elnashai, Amr. (2010). Maximizing Temporary Housing Safety after Natural Disasters. Journal Of Infrastructure Systems, 16(2), 138 – 148.
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Abstract
In the aftermath of large-scale natural disasters, emergency management organizations are expected to provide safe temporary housing for a large number of displaced families and to ensure that these housing arrangements are not located in hazardous areas. Potential postdisaster hazards can take many forms such as earthquake aftershocks, landslides, postearthquake soil liquefaction, flooding, hazardous material releases, etc. This paper presents the development of a multiobjective optimization methodology to support decision-makers in emergency management organizations in optimizing postdisaster temporary housing arrangements. The developed methodology incorporates (1) a safety model to measure and quantify temporary housing safety in the presence of multiple potential postdisaster hazards; (2) a cost model to minimize total public expenditures on temporary housing; and (3) a multiobjective optimization model to simultaneously maximize temporary housing safety and minimize public expenditures on temporary housing. An application to a large region is presented to illustrate the use of the models and demonstrate their capabilities in optimizing postdisaster temporary housing arrangements.
Keywords
Earthquake; Landslides; Optimization; Temporary Housing; Postdisaster Hazards; Housing Safety; Postdisaster Recovery
Hutyra, Lucy R.; Yoon, Byungman; Hepinstall-Cymerman, Jeffrey; Alberti, Marina. (2011). Carbon Consequences of Land Cover Change and Expansion of Urban Lands: A Case Study in the Seattle Metropolitan Region. Landscape And Urban Planning, 103(1), 83 – 93.
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Abstract
Understanding the role humans play in modifying ecosystems through changing land cover is central to addressing our current and emerging environmental challenges. In particular, the consequences of urban growth and land cover change on terrestrial carbon budgets is a growing issue for our rapidly urbanizing planet. Using the lowland Seattle Statistical Metropolitan Area (MSA) region as a case study, this paper explores the consequences of the past land cover changes on vegetative carbon stocks with a combination of direct field measurements and a time series of remote sensing data. Between 1986 and 2007, the amount of urban land cover within the lowland Seattle MSA more than doubled, from 1316 km(2) to 2798 km(2), respectively. Virtually all of the urban expansion was at the expense of forests with the forested area declining from 4472 km(2) in 1986 to 2878 km(2) in 2007. The annual mean rate of urban land cover expansion was 1 +/- 0.6% year(-1). We estimate that the impact of these regional land cover changes on aboveground carbon stocks was an average loss of 1.2 Mg C ha(-1) yr(-1) in vegetative carbon stocks. These carbon losses from urban expansion correspond to nearly 15% of the lowland regional fossil fuel emissions making it an important, albeit typically overlooked, term in regional carbon emissions budgets. As we plan for future urban growth and strive for more ecologically sustainable cities, it is critical that we understand the past patterns and consequences of urban development to inform future land development and conservation strategies. (C) 2011 Elsevier B.V. All rights reserved.
Keywords
Sprawl; Growth; Carbon Cycle; Emissions; Land Cover; Urbanization; Seattle; Vegetation; Carbon; Carbon Sinks; Case Studies; Cities; Ecosystems; Forests; Fossil Fuels; Humans; Land Use; Planning; Remote Sensing; Time Series Analysis
Lin, K. Y.; Levan, A.; Dossick, C. S. (2012). Teaching Life-Cycle Thinking in Construction Materials and Methods: Evaluation of and Deployment Strategies for Life-Cycle Assessment in Construction Engineering and Management Education. Journal Of Professional Issues In Engineering Education And Practice, 138(3), 163 – 170.
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Keywords
Sustainability; Design
El-Anwar, Omar. (2013). Advancing Optimization of Hybrid Housing Development Plans Following Disasters: Achieving Computational Robustness, Effectiveness, and Efficiency. Journal Of Computing In Civil Engineering, 27(4), 358 – 369.
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Abstract
Following disasters, displaced families often face significant challenges to move from temporary to permanent housing. The Federal Emergency Management Agency is exploring alternative housing pilot programs to evaluate the possibility of providing quickly deployable, affordable housing that can serve both as temporary and permanent housing. Because of the complexities and costs associated with these programs, it is impractical to assume that accelerated permanent housing can fully replace the need for traditional temporary housing, especially in cases of large-scale displacements. A novel methodology was developed to evaluate the socioeconomic benefits of candidate configurations of hybrid housing plans, which incorporates both temporary and accelerated permanent housing developments. This paper presents the computational implementation and performance analysis of this novel methodology to offer a practical decision-support tool to emergency planners. To this end, genetic algorithms and integer-programming optimization models are formulated, and their performances are analyzed based on their effectiveness, efficiency, and robustness. In lieu of developing the integer-programming model, the paper also presents a linear formulation that overcomes the need to use logical operations to model fixed and variable cost components for developing housing projects. Results show the superior performance of integer programming, whereas genetic algorithms offer higher modeling flexibility.
Keywords
Decision Support Systems; Emergency Management; Genetic Algorithms; Integer Programming; Advancing Optimization; Hybrid Housing Development Plans Following Disasters; Achieving Computational Robustness; Achieving Computational Effectiveness; Achieving Computational Efficiency; Federal Emergency Management Agency; Housing Pilot Programs; Temporary Housing; Permanent Housing Developments; Decision-support Tool; Emergency Planners; Integer-programming Optimization Models; Logical Operations; Optimization; Disasters; Housing; Social Factors; Economic Factors; Computation; Hybrid Methods; Disaster Recovery; Accelerated Permanent Housing; Socioeconomic Welfare; Robustness; Effectiveness; Computational Efficiency; 0
Aggarwal, Anju; Cook, Andrea J.; Jiao, Junfeng; Seguin, Rebecca A.; Moudon, Anne Vernez; Hurvitz, Philip M.; Drewnowski, Adam. (2014). Access to Supermarkets and Fruit and Vegetable Consumption. American Journal Of Public Health, 104(5), 917 – 923.
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Abstract
Objectives. We examined whether supermarket choice, conceptualized as a proxy for underlying personal factors, would better predict access to supermarkets and fruit and vegetable consumption than mere physical proximity. Methods. The Seattle Obesity Study geocoded respondents' home addresses and locations of their primary supermarkets. Primary supermarkets were stratified into low, medium, and high cost according to the market basket cost of 100 foods. Data on fruit and vegetable consumption were obtained during telephone surveys. Linear regressions examined associations between physical proximity to primary supermarkets, supermarket choice, and fruit and vegetable consumption. Descriptive analyses examined whether supermarket choice outweighed physical proximity among lower-income and vulnerable groups. Results. Only one third of the respondents shopped at their nearest supermarket for their primary food supply. Those who shopped at low-cost supermarkets were more likely to travel beyond their nearest supermarket. Fruit and vegetable consumption was not associated with physical distance but, with supermarket choice, after adjusting for covariates. Conclusions. Mere physical distance may not be the most salient variable to reflect access to supermarkets, particularly among those who shop by car. Studies on food environments need to focus beyond neighborhood geographic boundaries to capture actual food shopping behaviors.
Keywords
Confidence Intervals; Correlation (statistics); Fruit; Geographic Information Systems; Ingestion; Multivariate Analysis; Population Geography; Questionnaires; Regression Analysis; Research Funding; Sales Personnel; Shopping; Travel; Vegetables; Predictive Validity; Cross-sectional Method; Statistical Models; Descriptive Statistics; Null Hypothesis; Washington (state); Local Food Environment; Diet Quality; Socioeconomic Position; Atherosclerosis Risk; Stores; Associations; Obesity; Adults; Availability; Communities