Identifying and Quantifying Construction Safety Risks at the Attribute Level

The number of injuries and fatalities is disproportionally high when compared with other industries. In addition to physical pain and emotional suffering experienced by the victims and their families, these incidents have staggering societal costs. Therefore, investing in construction safety and developing innovations that improve safety is critical. The dissertation includes five manuscripts. The first explores the diffusion patterns of traditional injury prevention practices using common innovation diffusion models. The implications of the findings are that the construction industry has now reached saturation with respect to traditional injury prevention strategies and new safety innovations are needed. One of the most recent advancement in the preconstruction safety management strategies, that is proved to be highly effective, is to integrate safety risk data in to the schedule of project. Therefore, the second and third papers identify safety risks of common highway construction work tasks and their temporal and spatial interactions using the Delphi method and integrate them into a decision support system to produce predictive plots of safety risk over time based on the temporal and spatial interactions among concurrent activities. While, the results indicate that integrating safety risk data with schedule of project is highly effective, using the current methods to quantify safety risks for every individual task that can be experienced is infeasible with current risk modeling and data collection approaches. To address this limitation, the forth paper presents an attribute-based risk identification and analysis method that helps safety managers to identify and model the safety risk independently of specific activities or trades. The fundamental attributes that cause accidents are identified and their associated risks quantified by conducting reliable content analysis on 1771 accident reports from the National databases. The last paper uses the attribute-based risk management concept and proposes several safety predictive models to determine the outcome of possible injuries in early phases of a project. This research yield robust data and mathematical forecasting models that can be to objectively, accurately, and reliably predict hazardous conditions based on the identifiable attributes that characterize the workplace. It is expected that the findings of this research will transform the current risk analysis techniques and the created database have the potential to be applied to information models and emerging construction technologies

The Influence of National Culture on Effectiveness of Safety Trainings During Postdisaster Reconstruction

Non-English speaking workers constitute a disproportionately high number of workers involved in postdisaster reconstruction. Additionally, the rate of fatality among these workers is higher than the industry average. Research shows this population is more prone to unsafe behaviors in the working environment, conceivably because many of these workers are sent into the field prior to any formalized training. Recent studies show that the native culture of construction workers can impact risk-taking behavior. While numerous researchers have attempted to develop training materials for Hispanic workers, the number of studies that consider the impact of native culture on safety behavior is minimal. To answer this emerging knowledge gap, this paper develops a framework that will help to discern the influence of native culture, as well as other socioeconomic characteristics, on the effectiveness of safety trainings for non-English speaking workers. The formulation of this framework will pave the way for an enhanced understanding of the impact native culture plays on unsafe behaviors within a diverse workforce. Foreseeably, this understanding will play a significant role in developing culturally sensitive training materials in the future

The Influence of National Culture on Effectiveness of Safety Trainings During Postdisaster Reconstruction

Non-English speaking workers constitute a disproportionately high number of workers involved in postdisaster reconstruction. Additionally, the rate of fatality among these workers is higher than the industry average. Research shows this population is more prone to unsafe behaviors in the working environment, conceivably because many of these workers are sent into the field prior to any formalized training. Recent studies show that the native culture of construction workers can impact risk-taking behavior. While numerous researchers have attempted to develop training materials for Hispanic workers, the number of studies that consider the impact of native culture on safety behavior is minimal. To answer this emerging knowledge gap, this paper develops a framework that will help to discern the influence of native culture, as well as other socioeconomic characteristics, on the effectiveness of safety trainings for non-English speaking workers. The formulation of this framework will pave the way for an enhanced understanding of the impact native culture plays on unsafe behaviors within a diverse workforce. Foreseeably, this understanding will play a significant role in developing culturally sensitive training materials in the future

Inclusion of an Introduction to Infrastructure Course in a Civil and Environmental Engineering Curriculum

Civil infrastructure refers to the built environment (sometimes referred to as public works) and consists of roads, bridges, buildings, dams, levees, drinking water treatment facilities, wastewater treatment facilities, power generation and transmission facilities, communications, solid waste facilities, hazardous waste facilities, and other sectors. Although there is a need to train engineers who have a holistic view of infrastructure, there is evidence that civil and environmental engineering (CEE) programs have not fully addressed this increasingly recognized need. One effective approach to address this educational gap is to incorporate a course related to infrastructure into the curriculum for first-year or second-year civil and environmental engineering students. Therefore, this study assesses the current status of teaching such courses in the United States and identifies the incentives for, and the barriers against, incorporating an introduction to infrastructure course into schools’ current CEE curricula. Two distinct activities enabled these objectives. First, a questionnaire was distributed to CEE programs across the United States, to which 33 responses were received. The results indicated that although the majority of participants believe that offering such a course will benefit students by increasing the breadth of the curriculum and by providing a holistic view of CEE, barriers such as the maximum allowable credits for graduation, the lack of motivation within a department—either because such a course did not have a champion or because the department had no plans to revise their curriculum—and a lack of expertise among faculty members inhibited inclusion of the course in curricula. Second, three case studies demonstrating successful inclusion of an introduction to infrastructure course into the CEE curriculum were evaluated. Cases were collected from Marquette University, University of Wisconsin-Platteville, and West Point CEE programs, and it was found that the key to success in including such a course is a motivated team of faculty members who are committed to educating students about different aspects of infrastructure. The results of the study can be used as a road map to help universities successfully incorporate an introduction to infrastructure course in their CEE programs

Development of the Nebraska Department of Transportation Winter Severity Index

Adverse weather conditions are responsible for millions of vehicular crashes, thousands of vehicular deaths and billions of dollars in economic and congestion costs. Many transportation agencies utilize a performance or mobility metric to assess how well they are maintaining road access. This research focuses on the development of a winter severity index for the State of Nebraska (NEWINS). NEWINS is an event-driven index that was derived for the Nebraska Department of Transportation (NDOT) and its districts across the state. The NEWINS framework includes a categorical storm classification framework and climatological aspect to capture atmospheric conditions more accurately across the diverse spatial regions of Nebraska. A ten-year (2006-2016) winter season database of meteorological variables for Nebraska was obtained from the National Centers for Environmental Information. Meteorological parameters were grouped into categories that subsequently provided a storm classification database. The NEWINS is based on a weighted linear combination to the collected database to measure severity statewide and across NDOT individual districts. The NEWINS results were compared to other meteorological variables, many used in other agencies’ winter severity indices. This comparison verified the NEWINS robustness for the observed events for the ten year period. For example, an assessment of the difference between days with observed snowfall versus days with accumulated snowfall revealed a 39% average reduction in days. Furthermore, the NEWINS results highlight the greater number of events during the 2009-2010 winter season, and the lack of events during the 2011-2012 drought year. The NEWINS also shows strong differences monthly and among NDOT districts across the state with a general decrease in events from the western to eastern NDOT districts. In addition, NEWINS storm classifications were compared to NDOT winter maintenance operations performance data for a sample winter season. Last, the 2016-17 winter season was computed to provide a testbed for the NEWINS procedure. It is expected that the NEWINS could help transportation personnel to efficiently allocate resources during adverse weather events, while balancing safety, mobility, and available budget. Further, the theoretical and practical contributions provided by the NEWINS can be used by other agencies to assess their weather sensitivity

Development of the Nebraska Department of Transportation Winter Severity Index

Adverse weather conditions are responsible for millions of vehicular crashes, thousands of vehicular deaths and billions of dollars in economic and congestion costs. Many transportation agencies utilize a performance or mobility metric to assess how well they are maintaining road access. This research focuses on the development of a winter severity index for the State of Nebraska (NEWINS). NEWINS is an event-driven index that was derived for the Nebraska Department of Transportation (NDOT) and its districts across the state. The NEWINS framework includes a categorical storm classification framework and climatological aspect to capture atmospheric conditions more accurately across the diverse spatial regions of Nebraska. A ten-year (2006-2016) winter season database of meteorological variables for Nebraska was obtained from the National Centers for Environmental Information. Meteorological parameters were grouped into categories that subsequently provided a storm classification database. The NEWINS is based on a weighted linear combination to the collected database to measure severity statewide and across NDOT individual districts. The NEWINS results were compared to other meteorological variables, many used in other agencies’ winter severity indices. This comparison verified the NEWINS robustness for the observed events for the ten year period. For example, an assessment of the difference between days with observed snowfall versus days with accumulated snowfall revealed a 39% average reduction in days. Furthermore, the NEWINS results highlight the greater number of events during the 2009-2010 winter season, and the lack of events during the 2011-2012 drought year. The NEWINS also shows strong differences monthly and among NDOT districts across the state with a general decrease in events from the western to eastern NDOT districts. In addition, NEWINS storm classifications were compared to NDOT winter maintenance operations performance data for a sample winter season. Last, the 2016-17 winter season was computed to provide a testbed for the NEWINS procedure. It is expected that the NEWINS could help transportation personnel to efficiently allocate resources during adverse weather events, while balancing safety, mobility, and available budget. Further, the theoretical and practical contributions provided by the NEWINS can be used by other agencies to assess their weather sensitivity

An efficient data driven-based model for prediction of the total sediment load in rivers

Sediment load in fluvial systems is one of the critical factors shaping the river geomorphological and hydraulic characteristics. A detailed understanding of the total sediment load (TSL) is required for the protection of physical, environmental, and ecological functions of rivers. This study develops a robust methodological approach based on multiple linear regression (MLR) and support vector regression (SVR) models modified by principal component analysis (PCA) to predict the TSL in rivers. A database of sediment measurement from large-scale physical modelling tests with 4759 datapoints were used to develop the predictive model. A dimensional analysis was performed based on the literature, and ten dimensionless parameters were identified as the key drivers of the TSL in rivers. These drivers were converted to uncorrelated principal components to feed the MLR and SVR models (PCA-based MLR and PCA-based SVR models) developed within this study. A stepwise PCA-based MLR and a 10-fold PCA-based SVR model with different kernel-type functions were tuned to derive an accurate TSL predictive model. Our findings suggest that the PCA-based SVR model with the kernel-type radial basis function has the best predictive performance in terms of statistical error measures including the root-mean-square error normalized with the standard deviation (RMSE/StD) and the Nash–Sutcliffe coefficient of efficiency (NSE), for the estimation of the TSL in rivers. The PCA-based MLR and PCA-based SVR models, with an overall RMSE/StD of 0.45 and 0.35, respectively, outperform the existing well-established empirical formulae for TSL estimation. The analysis of the results confirms the robustness of the proposed PCA-based SVR model for prediction of the cases with high concentration of sediments (NSE = 0.68), where the existing sediment estimation models usually have poor performance

Optimal design of uptime-guarantee contracts under IGFR valuations and convex costs

An uptime-guarantee contract commits a service provider to maintain the functionality of a customer’s equipment at least for certain fraction of working time during a contracted period. This paper addresses the optimal design of uptime-guarantee contracts for the service provider when the customer’s valuation of a contract with a given guaranteed uptime level has an Increasing Generalized Failure Rate (IGFR) distribution. We first consider the case where the service provider proposes only one contract and characterize the optimal contract in terms of price as well as guaranteed uptime level assuming that the service provider’s cost function is convex. In the second part, the case where the service provider offers a menu of contracts is considered. Given the guaranteed uptime levels of different contracts in the menu, we calculate the corresponding optimal prices. We also give the necessary and sufficient conditions for the existence of optimal contract menus with positive expected profits

Population and fertility by age and sex for 195 countries and territories, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017

Background: Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. Methods: We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10–54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10–14 years and 50–54 years was estimated from data on fertility in women aged 15–19 years and 45–49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-component method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories. Findings: From 1950 to 2017, TFRs decreased by 49\ub74% (95% uncertainty interval [UI] 46\ub74–52\ub70). The TFR decreased from 4\ub77 livebirths (4\ub75–4\ub79) to 2\ub74 livebirths (2\ub72–2\ub75), and the ASFR of mothers aged 10–19 years decreased from 37 livebirths (34–40) to 22 livebirths (19–24) per 1000 women. Despite reductions in the TFR, the global population has been increasing by an average of 83\ub78 million people per year since 1985. The global population increased by 197\ub72% (193\ub73–200\ub78) since 1950, from 2\ub76 billion (2\ub75–2\ub76) to 7\ub76 billion (7\ub74–7\ub79) people in 2017; much of this increase was in the proportion of the global population in south Asia and sub-Saharan Africa. The global annual rate of population growth increased between 1950 and 1964, when it peaked at 2\ub70%; this rate then remained nearly constant until 1970 and then decreased to 1\ub71% in 2017. Population growth rates in the southeast Asia, east Asia, and Oceania GBD super-region decreased from 2\ub75% in 1963 to 0\ub77% in 2017, whereas in sub-Saharan Africa, population growth rates were almost at the highest reported levels ever in 2017, when they were at 2\ub77%. The global average age increased from 26\ub76 years in 1950 to 32\ub71 years in 2017, and the proportion of the population that is of working age (age 15–64 years) increased from 59\ub79% to 65\ub73%. At the national level, the TFR decreased in all countries and territories between 1950 and 2017; in 2017, TFRs ranged from a low of 1\ub70 livebirths (95% UI 0\ub79–1\ub72) in Cyprus to a high of 7\ub71 livebirths (6\ub78–7\ub74) in Niger. The TFR under age 25 years (TFU25; number of livebirths expected by age 25 years for a hypothetical woman who survived the age group and was exposed to current ASFRs) in 2017 ranged from 0\ub708 livebirths (0\ub707–0\ub709) in South Korea to 2\ub74 livebirths (2\ub72–2\ub76) in Niger, and the TFR over age 30 years (TFO30; number of livebirths expected for a hypothetical woman ageing from 30 to 54 years who survived the age group and was exposed to current ASFRs) ranged from a low of 0\ub73 livebirths (0\ub73–0\ub74) in Puerto Rico to a high of 3\ub71 livebirths (3\ub70–3\ub72) in Niger. TFO30 was higher than TFU25 in 145 countries and territories in 2017. 33 countries had a negative population growth rate from 2010 to 2017, most of which were located in central, eastern, and western Europe, whereas population growth rates of more than 2\ub70% were seen in 33 of 46 countries in sub-Saharan Africa. In 2017, less than 65% of the national population was of working age in 12 of 34 high-income countries, and less than 50% of the national population was of working age in Mali, Chad, and Niger. Interpretation: Population trends create demographic dividends and headwinds (ie, economic benefits and detriments) that affect national economies and determine national planning needs. Although TFRs are decreasing, the global population continues to grow as mortality declines, with diverse patterns at the national level and across age groups. To our knowledge, this is the first study to provide transparent and replicable estimates of population and fertility, which can be used to inform decision making and to monitor progress. Funding: Bill & Melinda Gates Foundation