The State of Michigan in the United States often encounters weak soil subgrades during its road construction and maintenance activities. Undercutting has been the usual solution, while a very few attempts of in-situ soil stabilization with cement or lime have been made. Compared to the large volume of weak soils that require improvement and the cost incurred on an annual basis, some locally available industrial byproducts present the potential to become effective soil subgrade stabilizers and a better solution from the sustainability perspective as well. The candidate industrial byproducts are Cement Kiln Dust (CKD), Lime Kiln Dust (LKD), and Fly Ash (FA), out of which only a fraction is currently used for any other secondary purposes while the rest is disposed of in Michigan landfills. This manuscript describes a laboratory investigation conducted on above industrial byproducts and/or their combinations to assess their suitability to be used as soil subgrade stabilizers in three selected weak soil types often found in Michigan. Results reveal that CKD or a combination of FA/LKD can be recommended for the long-term soil subgrade stabilization of all three soil types tested, while FA and LKD can be used in some soil types as a short-term soil stabilizer (for construction facilitation). A brief discussion is also presented at the end on the potential positive impact that can be made by the upcycling of CKD/LKD/FA on sustainability.

Freeway crashes represent a significant and persistent threat to road safety, resulting in both loss of life and extensive property damage. Effectively addressing this critical issue requires a comprehensive understanding of the factors contributing to these incidents and the ability to accurately predict crash severity under different traffic conditions. This study aims to improve the accuracy of crash classification by incorporating key traffic-related variables such as braking, weather conditions, and speed. To validate the effectiveness of proposed model, we utilize real-world crash data from Flint, Michigan. To achieve the objective, we employ an innovative Boosting Ensemble Learning approach, leveraging five advanced ensemble learning models: Gradient Boosting, Cat Boost, XGBoost, LightGBM, and SGD. Through the application of hyperparameter optimization techniques, we further enhance the performance of these models, improving their predictive capabilities. Our evaluation results demonstrated the effectiveness of our approach, with Gradient Boosting algorithms achieving an accuracy rate of up to 96% in crash classification. This research provides valuable insights into the potential of using Boosting Ensemble Learning as a tool for accurately and efficiently classifying freeway crashes across a spectrum of traffic conditions. Additionally, it sheds light on the nuanced variations in crash mechanisms observed when employing diverse ensemble learning models. The findings of this study underscore the significance of hyperparameter optimization as a critical factor in elevating the predictive precision of freeway crashes.

Remove-and-replace with suitable material has been the primary solution used for improving subgrades in Michigan, USA, when weak subgrades are encountered in road construction. Considering the large extent of silty and clayey soils found in southeastern Michigan, where much of the population and the roads are located within the state, the earthwork associated with this solution is massive and expensive. The use of cement kiln dust (CKD) or lime kiln dust (LKD) as a subgrade stabilizer can be a cost-effective solution if there is sufficient evidence to prove that such stabilization is suitable for the soils and the climate in southeastern Michigan. This became the subject of a field and laboratory investigation carried out in Michigan and sponsored by the Michigan Department of Transportation. The findings from the laboratory portion of this research (which were published in a separate manuscript) proved CKD’s suitability for long-term stabilization and LKD’s capacity for being a stabilizer for short-term modifications of clayey soils found in southeastern Michigan. This study covers the field testing portion of this investigation. Two CKD-stabilized and another two LKD-stabilized subgrades, which were already in use for 4–6 years, were tested for strength, using dynamic cone penetration (DCP) tests. The California bearing ratios estimated from the DCP tests showed that the CKD-stabilized and LKD-stabilized subgrades could offer strength gains as high as 200–515% and 149–257% compared to in situ soils, respectively, even after 4–6 years in use.

The safety of construction and maintenance work zones has been highlighted as a crucial aspect of construction management that requires special attention due to the increasing number of fatal and non-fatal injuries in recent years. Temporary traffic control (TTC) is required by the Occupational Safety and Health Administration (OSHA) to improve overall safety performance during road construction and maintenance projects. The fact that speeding and distracted drivers may overlook TTC warning signs and directions has been reported as one of the leading causes of work zone incidents. This study aimed to examine both the impact of temporary portable rumble strips (TPRSs) on traffic speeds and the response of different vehicle types in road work zones, including trucks and cars. Accordingly, field experiments were conducted in collaboration with the Road Commission for Oakland County (RCOC) in Michigan. The findings indicate that TPRSs have a statistically significant impact on the driving speed of light vehicle drivers but not on medium and heavy vehicles, such as trucks. This study contributes to the existing literature by quantifying the safety benefits of TPRS use, providing valuable data for policymakers and construction professionals. By demonstrating the effectiveness of TPRSs in reducing the speed of light vehicles, this research supports the implementation of these systems as a practical measure for enhancing safety within road construction work zones. Additionally, this study highlights the need for tailored approaches to address the limited impact on larger vehicles, underscoring the importance of developing complementary strategies to ensure comprehensive safety improvements across all vehicle types.

The Enhanced Integrated Climate Model (EICM) in the Mechanistic- Empirical (ME) Pavement Design software is an integral part of the ME pavement design process. EICM has a national weather database with hourly data from approximately 800 weather stations for at least 60 months. EICM uses the included historical hourly weather data to model future climatic conditions for accurate prediction of pavement distresses. Recently, many state departments of transportation started to enhance these weather station data by adding more weather stations and/or expanding the number of months of available data. The goal of these efforts is to utilize a weather station very close to the project site and include more historical weather data. In the ME Pavement Design software virtual weather stations can be created by selecting up to six weather stations to model climate change in the vicinity of the project site. However, without having extensive knowledge on regional climatic patterns, the pavement designer may not select the most appropriate weather stations to represent site specific climate conditions. Many states have microclimates due to different geographical or environmental conditions. This paper examines the use of Geographical Information System (GIS) to create a local climate zone map to aid pavement designers to select the most appropriate weather stations for pavement designs. Four climate zone maps were developed using the average low temperatures in January, average high temperatures in July, average precipitation in January and average precipitation in July for the I-94 corridor in Michigan as a case study. Further, the combined effects of these variables were examined by combining above individual climate zone maps into a single composite map using Inverse Distance Weighted (IDW) techniques. The maps show localized climate zones even within the same county highlighting the need of using appropriate weather stations to correctly model the climatic effects in pavement performance. The use of the developed methodology was demonstrated by comparing pavement performance results for a same pavement structure located in different climate zones using MEPDG software.

A multiuser driving simulator is used in this study to explore the relationship between road and weather conditions, vehicle speed, and crash potential due to driver’s behavior. In this study, ten driving simulator scenarios were conducted. A total of 10 participants were recruited from different age groups to use the driving simulator. This study aims toward finding the relationship between the change in speed limit in certain weather condition and the number of crashes that are likely to occur as a sequence to this change. The data obtained from the driving simulator was used to estimate the crash modification factors (CMFs). The expected CMF gives the notion on whether a treatment or a change in speed limit in a certain weather condition will lead to change in the number of crashes and consequently a change in the safety level. The resultant CMFs from this study are considered a new dimension that will increase road safety and reduce the rate of road traffic crashes.