Poster Presentations

Zoysiagrasses are known to be a thick sod-producing turfgrass that once adequately established have minimal weed invasion. The challenge is that zoysiagrasses have traditionally been slow to establish from seed or sprig and excessively expensive to establish from sod. Thus, this research project was conducted to evaluate different varieties, methods and timings for establishment of zoysiagrass on roadsides in different climatic regions of NC. A secondary objective was to evaluate fall and spring zoysiagrass establishment using two large-scale sprigging units. In a third objective, experimental zoysiagrasses were tested against commercial cultivars for their ability to establish faster under minimal inputs. Our research found that zoysiagrass planted via seed was faster to establish with greater coverage compared to sprigging. Although establishment and coverage from sprigging material was not rapid, zoysiagrass sprigs were able to show comparatively similar coverage to seed plantings after about 12-18 months. The results further suggested that the limitation in large-scale sprigging equipment may be impacted more by limited available water than the type of equipment. Research suggested that experimental lines may offer advantages in establishment, which contributed to the recent release of Lobo zoysiagrass by NC State. Overall, results from this research suggest that zoysiagrass can be established on NC roadsides with minimal inputs.

The fatal or incapacitating injury caused by overturn crashes involving Sport Utility Vehicles (SUVs) is irreparable. The purpose of this study is to identify potential factors that affect the injury severity of overturn crashes involving SUVs and develop adequate preventive strategies. Given the unobserved heterogeneity existing in the dataset, crash data in North Carolina from Highway Safety Information System (HSIS) is analyzed and separated by Latent Class Clustering into six relatively homogeneous groups. To improve the model performance, random parameter logit models are developed for each cluster, and the impacts of significant factors are estimated with marginal effects. Variables (including female, people over 50 years old, improper or aggressive behavior, rural areas, high speed limit, curve roadway, rolling and mountainous terrain, adverse weather, and poor light condition) are associated with the injury severity of the overturn crashes involving SUVs. The findings of this study can further provide decision-makers with countermeasures to improve the transportation safety and mitigate the injuries of overturn crashes involving SUVs.

Traditional transit signal priority (TSP) control strategies generally have negative impacts on other traffic involved. In this study, two typical signal control strategies under connected vehicle (CV) environment that give priority to transit vehicles at signalized intersections are investigated, i.e., actuated TSP with CV and optimized TSP with CV. The optimization algorithm used in this study is genetic algorithm (GA) and the goal of the optimization is to minimize the total person delay. A real-world intersection is modeled in microsimulation environment to evaluate the performance. The results are compared with fully actuated signal control strategies with and without TSP. Results show that the proposed optimization control strategy can reduce the average bus delay by 24.5% during peak hours while minimizing the negative impact on conflicting traffic. Under the low traffic demand condition, the actuated control with TSP using CV has a better performance in terms of average delay.

Highways are considered a major source of pollution to stormwater and its runoff can introduce various contaminants including nutrients, Indicator bacteria, heavy metals, chloride, and phosphorus compounds, which can have negative impacts on receiving waters. This study assessed the ability of biochar to contaminants removal and improve water retention. Ten commercially available biochar has been strategically selected. Lab scale batch testing was done at 3% and 6% by the weight of the soil to find the preliminary estimate of contaminants removal along with hydraulic conductivities, and water retention capacity. Furthermore, from the above-conducted studies, six best-performing candidates with an application rate of 6% has been selected for the column studies.  The soil biochar mixture was filled in 3in assembled columns up to a fixed height of 30in based on hydraulic conductivity. Total of eight column experiments have been conducted for nutrient, heavy metal, and indicator bacteria analysis over a period of one year, which includes a drying as well as a deicing period. The saturated hydraulic conductivity was greatly improved which is attributed to the high porosity of the biochar soil mixture. The column effluents were examined and the data demonstrate that most of the biochar successfully removed up to three logs for the indicator bacteria and up to 90% nutrients removal efficiency has been found with three biochar. These results demonstrate that biochar could be efficiently applied with clay soil to improve the soil’s hydraulic characteristics as well as remove the pollutants from stormwater runoff.

Rapid developments in Light Detection and Ranging (LiDAR) technology and capabilities, especially miniaturization and mounting on UAS platforms, have revolutionized the collection of 3-dimensional data using active remote sensing sensors. Depth measurements of the resources below the water surface involve significant time, cost, labor and technology-intensive tasks over various spatial scales that have variable success across the remote, dangerous, turbid, and tannic waters of the state. Our study will demonstrate the unique and cutting-edge capabilities of topo bathymetric LiDAR sensors mounted on a commercial off-the-shelf UAS platform as a means of filling some of the technology and capability gaps outlined above. Using topo bathymetric LiDAR, our study will examine a spectrum of clear, tannic and turbid waters of varying depths to fill gaps and improve data quality and collection capabilities for activities that include planning, monitoring and inspections. This study will lead to time and cost savings through increased inspection capabilities, improved mapping and models of water-logged and water-covered areas, and more robust measurements of drainage system capacities for a variety of monitoring and planning applications.

This project developed elaborate driving simulator models of three types intersections including: Standard, Grade Separated Quadrant, and Grade Separated Contraflow. The models were used to assess driver behavior and performance effects of the presence and placement of signage at the virtual intersections. In a designed experiment, driver behavior was measured in terms of situation awareness and cognitive workload responses as well as operational measures. Experiment results revealed the quadrant grade separated intersection design to produce driver behaviors and performance comparable to standard intersections with or without lane assignment signs and when using side-mounted decision point signs and providing lane information on a junction sign. Based on these findings, it was recommended that signing engineers develop novel junction sign content or provide additional guidance signs upstream of the decision point for intersections with non-traditional movements (quadrant design) to support driver situation awareness and reduce cognitive demands associated with timely lane changes. Opposite to the experiment findings on the quadrant design, the contraflow (or displaced left turn) intersection design led to lower driver situation and more cases of drivers failing to complete lane change maneuvers in a timely manner. For the contraflow intersection design, drivers may require additional guidance or lane assignment information beyond that provided at standard intersections. In conclusion, this study provided some guidance for highway systems engineers on the need for novel signage designs to ensure effective driver information processing under unique highway configurations to achieve performance comparable to standard intersections.

As corridor construction or improvement projects continue to utilize Alternative Intersection and Interchange (AII) designs, there is a concern that drivers might be confused on how to safely navigate a corridor when adjacent intersections handle movements in different ways. This research investigated challenges for corridor-level deployment of AII designs through a literature review and driving simulator experiment. The objectives of the research were to: 1) identify potential combinations of AII designs, 2) collect data on driver’s understanding of AII designs, and 3) assess driver performance when navigating corridors with various AIIs. The simulator study presented intersections with a virtual spacing of 1⁄4 mile. Results revealed corridors with predominantly traditional, U-turn-based or mixed intersection types to produce similar driver performance. For specific intersection designs, minor left turns at Median U- turn intersections produced the greatest number of failures to complete movements as compared to the Quadrant and Traditional major left turns, which produced superior performance. Other intersection and movement combinations did not produce increased movement failures as compared to Traditional intersections. Hard braking events were observed less often for MUT and RCUT intersection, specifically when they occurred in corridors without predominantly traditional intersections. Drivers also produced decreased approach speeds and more movement failures in their first simulator trial. This effect did not vary by corridor type or test intersection type. Across all intersection and movement types, there was a significant increase in movement failures when the preceding intersection was an RCUT.

Connected and autonomous vehicle (CAV) technologies have advanced at an accelerated rate in the last few years. This accelerated advancement increases the possibility of full-scale CAV public deployment. Before a full-scale public deployment of CAVs, it is important to assess whether existing and upcoming roadway infrastructures would be able to handle the CAV traffic. This investigates whether the operational benefits of a mini-roundabout would be retained by CAVs. Traffic volume data, from a small-town pre-timed signalized intersection, were collected. Volume data for each movement and both peak hour and off-peak hours were collected. Microsimulation platform VISSIM was used for analysis and the vehicle delays were used as the metric for comparisons. Delays for hundred percent human-driven vehicles (HDV) traffic were compared to those for hundred percent CAVs. The results indicate that for both HDV and CAV traffic, switching the pre-timed signals with a mini-roundabout reduces the delays by 80 percent approximately. For off-peak conditions, CAV delay for the mini-roundabout was about 5 percent shorter than that for HDVs. For peak hour conditions, CAV delay for the mini-roundabout however was about 1 percent longer than that for HDV. In general, the results indicate that CAVs would be able to retain the operational benefits of the mini-roundabout. Under off-peak conditions, the CAVs can be expected to further reduce the delay whereas, for peak conditions, the delay can be expected to be close to that for HDVs.

Stormwater runoff continues to be a source of pollution in urban areas, due to rapid urban developments and everchanging climatic patterns. To counter these challenges, green infrastructure (GI) such as bioswales and other biofilter systems have been shown to improve soil retention and mitigate erosion caused by runoff at various scales. Furthermore, GI has additional unintended benefits to society and the environment. GIs are instrumental in improving air quality and biodiversity in urban areas, which translates into improved ecosystem services for inhabitants. These GIs can be enhanced with the addition of carbon-based supplements such as biochar to improve the soil’s hydraulic properties and contaminant removal. Biochar has also been found to be a cost-effective soil amendment in GI applications and has shown promise in improving urban habitats through contaminant removal and carbon sequestration. Additionally, biochar has been shown to improve nutrient release and cycling in depleted soils for plant growth and development. For this study, the embedded benefits of biochar amended GI sites will be evaluated using a triple bottom line (TBL) framework and model. We will model the economic, environmental, and social co-benefits associated with biochar amended GI systems. The resulting model will be used in a case study to provide insight into biochar’s embedded benefits for stormwater management applications. We will also identify limitations and areas where future research will be needed. Future integration of this TBL model can be incorporated into the optimization of storm water best management practices (BMPs).

Recycling agents (RAs) are added to asphalt binder blends containing recycled materials to mitigate the negative effects of adding oxidized material to asphalt mixtures.  Several methods exist to select a particular RA dosage to restore a specific Superpave performance parameter.  However, the rheological similarities between the RA-modified systems and typical virgin binders across the complete range of temperatures remain to be ascertained. In this study, recycled binder blends prepared with a RA dosage selection procedure aimed to restore high-temperature performance grade were evaluated using statistical analysis. Quality assurance dataset from the North Carolina Department of Transportation (NCDOT) was used to benchmark the typical virgin binder properties, and 7 recycled binder blends using constituent materials from North Carolina. Both univariate and bivariate statistical analyses were applied to contrast the rheological properties of these blends with the benchmarked virgin binder properties.  Next, a statistical similarity analysis was done using the Mahalanobis distance to evaluate the rheological similarity of these blends with those of the reference binders.  Finally, a tentative framework was proposed to use the present statistical analysis to select an RA dosage that yields a rheological fingerprint more similar to that of virgin binder systems.

The vast majority of asphalt mixtures produced in North Carolina contain reclaimed asphalt pavement (RAP) due to the associated environmental and economic benefits. Recycled binder availability refers to the proportion of total recycled binder that is available to blend with virgin asphalt and is considered an inherent property of a given RAP source. Binder that is inside agglomerated particles that do not separate during the mixing process is ‘unavailable’. Only available recycled binder should be given credit when designing and evaluating mixtures as unavailable binder acts as ‘black rock’. Inaccurately giving full credit to recycled binder leads to a lower effective binder content, and therefore, lower Voids in Mineral Aggregate (VMA) than what may be calculated. Consequently, mixtures designed under current procedures may have insufficient virgin asphalt and lack durability. This poster will present a practical method to quantify RAP binder availability using comparative sieve analysis of RAP and recovered RAP aggregate has been developed through NCDOT RP 2019-21 and NCHRP IDEA Project 236. The method is implementable by adding a washed sieve analysis of RAP to current practices for characterizing RAP materials. The method is validated by quantifying the recycled binder contribution in asphalt mixtures using tracer-based microscopy using a diverse set of surface asphalt mixtures from four states.

Reclaimed asphalt pavement (RAP) and recycled asphalt shingles (RAS) are incorporated into some asphalt mixtures produced today. The use of recycled asphalt materials (RAM) in asphalt mixtures encompasses engineering challenges about uncertainty in the proportion of the recycled binder that is in contact and blends with the virgin binder in the asphalt mixture, termed recycled binder contribution (RBC) herein. Recent research shows that agglomerations of RAM particles are the main cause of partial RBC. These agglomerations prevent recycled binders from interacting with the virgin binder. However, current practices generally assume complete RBC and a detailed understanding of the role of asphalt mixture constituents on RBC does not presently exist. The use of softer binders, extenders, and recycling agents has been proposed in high RAM content mixtures to restore recycled binder stiffness and embrittlement. However, the effects of asphalt binder variables as well as RAM type, source, and content on RBC are presently poorly understood. This study aims to investigate the effects of asphalt mixtures constituents on the RBC in asphalt mixtures using Energy Dispersive x-ray Spectroscopy (EDS) tracer-based microscopy analysis. It was found that the virgin binder, RAM type, and source significantly influence recycled binder contribution in asphalt mixtures. However, RAP age level and content as well as additives were found to have only marginal impacts on RBC.

Traffic signals are an element of traffic control that traffic engineers use to ensure the safety of drivers and the efficiency of the transportation system.  Playing such a vital role, a thorough understanding and optimization of the installation, operation, repair, and replacement of signals is key to their use.  An important but as of yet unanswered question regarding signals is “how long do LED modules last and what is an optimal replacement process for them?” This poster focuses on the latter. In order to effectively optimize NCDOT’s LED signal repair and replacement process, it was first necessary to understand the procedures that are currently in place.  Our research team has analyzed both qualitative and quantitative data from various sources at NCDOT to determine current processes. A series of informational interviews with NCDOT employees across the divisions were conducted.  Current repair and replacement procedures were identified and were determined to be similar across the state, a somewhat surprising finding given that there are currently no guidelines in place defining such procedures.  An analysis of the signal data in the NCDOT Division of Technical Services (DTS) system was conducted.  Data stored in DTS includes signal type, location, and a record of any technician-performed events.  Results from both the interviews and the data are presented in this poster.

Landslides are a common natural hazard in the United States and globally, causing significant damage to property, infrastructure, as well road corridors.  It has affected around 4.8 million people and caused more than 18,000 deaths in the last 25 years. A landslide is a geological phenomenon in which earth, rock, or debris slides down a slope because of gravity. This movement can happen fast (as a rockfall or debris flow) or gradually over time. The Landslide assessment to provide a clear picture of what measures need to be taken based on the evaluation of the causes and types of landslides has triggered many observational studies performed by universities and government agencies. Remote sensing methods can provide critical information to detect and monitor landslide activities and provide insight into disaster risk assessment and management. Before determining the landslide risk assessment methods, several influencing factors and a map of the known landslides should be developed since it is assumed that conditions that led to previous landslides are more likely to cause a new landslide. This research work provides a comprehensive literature review to understand landslides, and their relationships to other environmental or intrinsic factors (such as slope, geology, aspect, …) and extrinsic factors (such as rainfalls, and climate change). The study also used a statistical method - frequency ratio (FR) technique to experiment with landslide susceptibility mapping. This material is based on work supported by the North Carolina Department of Transportation. Project # RP 2023-04.

Environmental changes have a significant impact on the design, delivery, and management of asphalt pavements. Depending on the directionality of the shift in environmental conditions, pavements may deteriorate faster or slower than original intended design. It is imperative that pavement management systems account for this type of uncertainty so that the long-term performance of the pavement network is not negatively impacted. However, in light of changing climate patterns and the increasing frequency and severity of extreme events, it is extremely critical to incorporate these dynamic changes into pavement performance models. This study aims to understand these issues by evaluating the viability of using pavement management data to identify and quantify damage on pavement sections that were impacted by extreme events, given the uncertainty associated with pavement performance. To identify variations in pavement deterioration caused by extreme events, a Bayesian beta regression model was developed and sections of I-40 that were affected by Hurricane Florence in 2018 were examined. The Pavement condition rating (PCR) values of the pavement sections are plotted on a probability family curve. Plotting these PCR values against a family curve reveals that there are typically three distinct patterns of PCR degradation. With its PCR value exceeding the uncertainty band, the eastbound pavements have experienced rapid deterioration, while westbound pavements have experienced a change in PCR value from an overperforming 96th percentile value to an underperforming 40th percentile value. 85% of the time, the Bayesian beta regression model was successful in identifying sections affected by extreme events.

The COVID-19 pandemic had wide-reaching implications across most, if not all, aspects of society. In this research, the effect of traffic reductions resulting from COVID-19 related restrictions on the number of collisions in North Carolina’s primary highway network was studied. First, Monthly Average Daily Traffic (MADT) counts from cellphone-based data were collected and validated against ground station counts. It was found that the cellphone data produces accurate MADT counts for roads with an average annual daily traffic greater than 6,000 vehicles per day. Then, macro, and micro-level crash analysis was conducted. For the former, crash counts on 12 major routes in North Carolina were evaluated. For the latter, 147 sites were selected based on their construction history and surface type. Crash counts and MADT observations were used to quantify traffic disruptions and to assess the impact of these reductions on crash rates, i.e., the number of collisions per million-vehicle-mile. The macro level crash analysis indicated that the highest reduction in crashes per mile is on average 32% and 21% on Interstates and US-Routes, respectively. It also showed that the age and type of the surface play an important role on crash trends and should be accounted for when evaluating the effect of traffic disruptions on crash numbers. The micro-level crash analysis showed that at the beginning of the stay-at-home orders, crash rates increased for 60% of sites, but then these gradually normalized, and after a year were lower than they were before the start of the pandemic.

Drones are becoming a growing new source of environmental noise pollution. The objective of this study is to experimentally determine the sound levels from various drones and propellor designs in common use today. The study will benefit the community and researchers in categorizing the acceptability limits in terms of types and height of their operation and also to devise new techniques for noise abatement. The study will be completed in three stages. In the first stage, a comprehensive literature survey will be carried out to gather relevant information. The second stage of study will be focused on establishing an experimental method of measuring the sound level emitted from flying UAS. Data will be collected,  processed, and analyzed with aims to categorize drone noise characteristics based on various parameters; these include height of operation, weight, number of motors, propeller type, blade count, etc. The result will provide recommendations of which parameters most greatly impact noise levels. In the third stage, study will focus on both passive and active noise abatement techniques and a new passive technique will be suggested. Two approaches will be taken. The first approach will use noise absorbing liner material applied at appropriate locations on aircraft to reduce noise traveling to the ground underneath the drone. The second approach will explore active noise-cancellation methods. The study will be carried out both theoretically and experimentally. While a quieter drone will be very useful in almost all applications, this study will be most beneficial to UAS users in sound-critical applications.

Recent technological advancements in wearable and Augmented Reality technologies have accelerated designing innovative real-time solutions for increasing the safety of labor-intensive occupations such as workforce in highway work zones. However, the transition from today’s practice to future technologies requires extensive user research that enables designing usable and human-centered technologies. Meanwhile, conducting usability tests and user experience research in highway work zones is costly and complicated, which could hinder designing inclusive technologies from end-users’ perspective. To overcome this challenge, Virtual Reality has been considered as a prominent alternative for simulating the context and conducting user research in some disciplines such as manufacturing. With this, this research provides a preview of the application of Virtual Reality for simulating how this technology can be leveraged in conducting usability research for highway work zones. To this end, we used a case study from a novel safety system that utilizes augmented reality for short-duration highway work zones and simulated the end-to-end functionality of this AR-oriented safety system in a high-fidelity VR environment. We then used this simulation to benchmark the usability of the virtual augmented reality and compared the results with that of a similar usability test conducted on a real-world prototype of the AR system. The outcomes of this research provide a roadmap for broader application of virtual reality in conducting context-aware user research for safety critical systems, and including domain-specific inputs into future user-centered technologies for broader highway workforce community.

Recent hurricanes and other extreme events have caused more than $450 million in damage to the State’s transportation infrastructure. Based on NCDOT records more than 3,000 disruptions resulted from Hurricane Florence. The damage in some of these locations during Hurricane Matthew was different between the two events, suggesting that DOT strategies were effective. However, detailed quantification of the performance differences and effectiveness of various strategies has not been completed. With respect to this need, the research presented in this poster seeks to achieve three objectives; 1) evaluate the design process for roadway infrastructure that was repaired following Hurricanes Matthew and Florence, 2) identify the specific elements of the new infrastructure that positively contributed to improved performance during Hurricane Florence, and 3) develop recommendations on design elements that improve the resilience of NCDOT roadways. The research team and NCDOT summer interns collected data on total of 59 sites in six counties. The initial analyses include: 1) assessing the sites by categorizing the case studies based on their level of damage, 2) establishing a vulnerability scoring system based on influential parameters (e.g., presence of headwall, rip rap, beaver dam, swamp, type of material, type of structure, etc.), 3) analyzing flow models by HDS-5 and HY-8 and 4) developing correlation between damage level/scores and flow related parameters such as drainage area, HW/D, or rainfall intensity. The preliminary results show that there is a correlation between damage levels and drainage areas. Also, certain elements in design and maintenance contribute to failure pathways.

Heaving and thawing results in pavement crack, deflection, and potholes. According to the Federal Highway Administration (FHWA), this leads to recurrent annual maintenance costs estimated at over 2 billion dollars. Studies identify three basic requirements for frost action; freezing temperatures, availability of water, and frost susceptible soils. While advances have been made in the design for freezing temperatures and providing for groundwater separation, very little progress has been made in terms of in-situ soil improvement. This study proposes an innovative approach for mitigating frost action through Engineered Water Repellency (EWR). A frost-susceptible soil was collected from a test plot at the Charlotte Douglas International Airport and treated with a commercially available organosilane using a treatment mix ratio of 1:40 (OS to Soil), batched by weight. Two identical test setups were constructed within the university premises for performance testing of both untreated and treated samples. Both setups were subjected to occurring extreme weather conditions with snow cover, freezing, and thawing. Teros 12 and 21 sensors were placed in both setups at midways of the sample setup to obtain data on water content, soil temperature, suction, and electrical conductivity. Data showed that EWR was effective in limiting the infiltration and migration of water into the soil matrix when compared with the untreated soil. As such, engineered water repellency may be a viable solution for Airports and Departments of Transportation seeking methods to mitigate frost action.

Pavements are subjected to high-impact loads and conventional design wisdom prescribes pavement thicknesses utilizing the lowest modulus obtainable for subgrade soil over a given wet-dry cycle. This is due to variations in strain, stress, deformation, and reduction in strength when subjected to moisture changes. By engineering water repellency in pavement soils, moisture conditions can be kept fairly uniform, allowing for easier design, reducing speculation of performance, and saving material and construction costs. Temperature and water content measurements were obtained from soil obtained from the Charlotte Douglas Airport treated with OS and compared with untreated soil under varying moisture conditions. Results show a reduction in moisture content as well as variations in the treated soil sample compared to the untreated sample. Samples were exposed to two winter storm events, and a maximum heave of ~6mm was measured for the untreated soil, while the treated soil sample did not heave.

The accidental collision of overheight vehicles and objects with concrete bridges, during active construction and in service, presents engineers with the challenge of evaluating residual strength and serviceable life from field observations of visible damage. One of the overarching goals of RP2023-06 is to use parametric finite element simulations and statistical methods to develop guidance for timely estimation of the effects of impact damage on the strength and durability of prestressed concrete girder bridges. Finite element analysis will leverage advanced constitutive models capable of predicting the nonlinear response of concrete and associated damage mechanisms (e.g., spalling, cracking, and inelastic deformation). These capabilities will permit the finite element simulations to forecast the reductions in strength and serviceability as well as the expected extent of observable and internal damage resulting from accidental collisions. This poster presents validation of concrete constitutive models against published experimental data acquired from reinforced and prestressed concrete beams subject to impact. Experimental data from the published literature include measurements of time histories for impact force and midspan displacement as well as photographs of visible damage including cracking and spalling. In this study, the Continuous Surface Cap Model, Winfrith, Elasto-Plastic Damage Cap, and Karagozian and Case Concrete concrete models are evaluated with a primary focus on validating the ability to accurately predict residual deformation and observable damage. The results of the analysis are used to inform the development of representative AASHTO prestressed concrete girder models to investigate the influence of impact location and intensity on the resulting damage.

As one of the largest industries in the world, the construction industry contains a vast variety of activities in the fields ranging from architecture, engineering, manufacturing, and fabrication to project management, inspection, and facility management. Despite being considered a technology-non-intensive industry, the construction industry has been increasingly adopting new techniques and methods to improve productivity and safety during the last decade.  Examples of this are the transition from 2D Computer-Aided Design (CAD) to 3D Building Information Modeling (BIM) and the change from using traditional manual operations to robotic-aided and automated workflows. The traditional construction surveying approaches are usually labor-intensive, time-consuming, and often costly, and conventional surveying with total stations and GPS is typically a manual and repetitive process with extensive time and labor efforts in the field. Also, certain field inspection tasks can expose construction workers to various types of risks and hazards that are related to site and weather conditions. The main goal of this research is to use UAS technologies to improve the efficiency of construction surveying and inspection activities. Different UAS flights were performed to verify the variety of measurements obtained from building plans. The comparison of volume calculations was obtained using photogrammetric and point cloud models. Field verification has been done by checking different measurements of a bridge’s typical structures using several point cloud models and photogrammetric models. The results showed discrepancies between the data obtained from the UAS flight data versus the information obtained from building plans. This research is still in progress.

The high cost of road maintenance has persisted for a long time and several research works continue to emerge to reduce the significant cost including crack mitigation in road embankments. Cracks nucleation and propagation development within road embankments are known to be  one of the major contributors responsible for early pavement deterioration. Crack development in embankments has been largely attributed to the presence of clay, however, cracks formation in embankments with low clay percentage have been confirmed and the mechanisms behind the causes of  major initiators of cracks need to be investigated. To this end, it is required to investigate the extent to which different contributing factors such as construction materials, weather conditions, construction methods influence crack formation and propagation. To complement field investigations,  numerical approaches that help spatial prediction of cracks can provide crucial insights.  From this study, the major causes of crack development are assessed using these two approaches and mitigating factors are recommended for the design and construction process. Ultimately, a more precise design and build process is envisaged for a reduced construction and maintenance cost.

Computer vision technology has been used in traffic services to enhance safety and efficiency on roads. Object detection models like YOLO and Faster R-CNN have improved traffic monitoring systems' performance. However, high-resolution drone captured aerial images present challenges due to the large amount of data that needs to be processed. To address these difficulties, various approaches have been proposed and implemented. One popular approach is image resizing, but it can lead to the loss of important details and information. Another approach is to use background subtractors and feature-based algorithms as an object detector. These algorithms are less computationally demanding but not as robust as deep learning algorithms. In this research, a combination of background subtraction algorithms and deep learning algorithms is used to improve the performance of object detection in high-resolution aerial images.

Concrete is the most used construction material in the world. During the past 50 years, the population growth in the urban areas caused rapid growth in the scale of infrastructure and therefore, an increase in the construction materials used. Consequently, the increase in the demand for concrete construction resulted in an increase in this industry's energy consumption and CO2 emissions. As a result, researchers started developing new technologies to mitigate the CO2 emissions of the concrete industry. During the last decades, the use of sustainable and alternative materials with zero or low CO2 emissions for cement and concrete has gained a lot of attention. This work provides a comprehensive review of the research carried out so far on concrete with minimum to zero carbon emissions with the objective to present a research direction for future work on sustainable concrete.

In the transportation field, wireless power transfer (WPT) or inductive power transfer (IPT) technology for electric vehicles (EV) has been profoundly and widely studied for the past years. However, the applications of WPT for railway vehicles have attracted only a little attention, especially for dynamic WPT analysis. Although some articles talk about dynamic charging for EVs, dynamic analysis for railway applications is still necessary because of the difference between the two kinds of vehicles. To fill this gap, a prototype of a single-transmitter and single-receiver (STSR) WPT system for locomotives has been developed. The prototype adopts a "W-I" coupling and an LCL-S compensation topology and has been tested at 5 kW with a DC/DC system efficiency of 92.5% for static charging. A dynamic test is also practiced, which shows the system's dynamic performance. Furthermore, a multiple-transmitters and single-receiver (MTSR) simulation model is built to provide references for the next step MTSR dynamic test.

This project is aimed at carrying out study to evaluate the use of drones to support mass evacuation during disaster management. Mass evacuation during an emergency imposes many challenges ranging from accurate planning to its efficient execution. This study is focused on operational and tactical drone application in disaster management using a time-scaled separation of the application between pre-disaster activity and the activity during the occurrence of a disaster. The project will be completed in three stages.