Sunghwan Kim

Fabrication of Polytetrafluoroethylene-coated Asphalt Concrete Biomimetic Surfaces: A Nanomaterials-based Pavement Winter Maintenance Approach

Application of super ice/water-repellent materials on pavement surfaces has increasingly drawn attention as a smart and cost-effective method for ice and snow removal. This study focused on fabricating and evaluating icephobic coatings on asphalt concrete pavements for obtaining ice- and snow-free biomimetic icephobic surfaces. After a statistical design, the layer by layer (LBL) method was used for spray depositing the polytetrafluoroethylene (PTFE) on the asphalt concrete at variable dosages of PTFE and different spray times. The degree of hydrophobicity/icephobicity of the coated asphalt concrete was characterized through measuring the water contact angles. Statistical analysis proved the significance of design variables. In addition, skid resistance measurements on the PTFE coated surfaces were compared with those from uncoated surfaces to evaluate the effect of PTFE coating on pavement slipperiness of asphalt surface. A slight decrease in skid resistance of the PTFE coated surface was negligible in comparison to the drastic reduction in skid resistance caused by presence of ice/snow.

Integration of a prototype wireless communication system with micro-electromechanical temperature and humidity sensor for concrete pavement health monitoring

Abstract In recent years, structural health monitoring and management (SHMM) has become a popular approach and is considered essential for achieving well-performing, long-lasting, sustainable transportation infrastructure systems. Key requirements in ideal SHMM of road infrastructure include long-term, continuous, and real-time monitoring of pavement response and performance under various pavement geometry-materials-loading configurations and environmental conditions. With advancements in wireless technologies, integration of wireless communications into sensing device is considered an alternate and superior solution to existing time- and labor-intensive wired sensing systems in meeting the requirements of an ideal SHMM. This study explored the development and integration of a wireless communications sub-system into a commercial off-the-shelf micro-electromechanical sensor-based concrete pavement monitoring system. A success-rate test was performed after the wireless transmission system was buried in the concrete slab, and the test results indicated that the system was able to provide reliable communications at a distance of more than 46 m (150 feet). This will be a useful feature for highway engineers performing routine pavement scans from the pavement shoulder without the need for traffic control or road closure.

Implementing Advanced Wireless Sensing System for Airfield Pavement Condition Monitoring

Real-time monitoring of pavement conditions is beneficial not only to more accurate predictions of airport pavement performance but also to more efficient pavement management activities. Although wireless-based sensing system have shown some advantages compared to wired sensing system, there are just few studies that describe instrumenting wireless sensors in the field for pavement condition monitoring. To bridge this gap, this paper presents case studies describing use of advanced wireless sensing system for airport pavement conditions monitoring at three test sites: the Des Moines International Airport (DSM), the Ohio State University (OSU) Airport, and the Texas A&M University (TAMU) Airport. The wireless sensors communicate through a gateway to collect sensor data and then upload it to the cloud. The wireless sensor instrumentation strategy and design plans, based on the field instrumentation experience, are summarized. The key requirements and potential restrictions for implementing wireless sensing systems in airfield pavement are also discussed. Disciplines Civil and Environmental Engineering | Civil Engineering | Transportation Engineering Comments This is a manuscript of an proceeding published as Yang, Shuo, Halil Ceylan, Sunghwan Kim, and Hesham Abdulla. Implementing Advanced Wireless Sensing System for Airfield Pavement Condition Monitoring. In International Conference on Transportation and Development (2018): 25. doi: 10.1061/9780784481554004. Posted with permission. This conference proceeding is available at Iowa State University Digital Repository: https://lib.dr.iastate.edu/ccee_conf/94 1 Implementing Advanced Wireless Sensing System for Airfield Pavement Condition Monitoring Shuo Yang1, Halil Ceylan2, Sunghwan Kim3 and Hesham Abdulla4 1Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA; PH (515) 294-8051; FAX (515) 294-8216; email: shuoy@iastate.edu 2 Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA; PH (515) 294-8051; FAX (515) 294-8216; email: hceylan@iastate.edu (Corresponding Author) 3Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA; PH (515) 294-8051; FAX (515) 294-8216; email: sunghwan@iastate.edu 4Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA; PH (515) 294-8051; FAX (515) 294-8216; email: abdualla@iastate.edu ABSTRACT Real-time monitoring of pavement conditions is beneficial not only to more accurate predictions of airport pavement performance but also to more efficient pavement management activities. Although wireless-based sensing system have shown some advantages compared to wired sensing system, there are just few studies that describe instrumenting wireless sensors in the field for pavement condition monitoring. To bridge this gap, this paper presents case studies describing use of advanced wireless sensing system for airport pavement conditions monitoring at three test sites: the Des Moines International Airport (DSM), the Ohio State University (OSU) Airport, and the Texas A&M University (TAMU) Airport. The wireless sensors communicate through a gateway to collect sensor data and then upload it to the cloud. The wireless sensor instrumentation strategy and design plans, based on the field instrumentation experience, are summarized. The key requirements and potential restrictions for implementing wireless sensing systems in airfield pavement are also discussed.Real-time monitoring of pavement conditions is beneficial not only to more accurate predictions of airport pavement performance but also to more efficient pavement management activities. Although wireless-based sensing system have shown some advantages compared to wired sensing system, there are just few studies that describe instrumenting wireless sensors in the field for pavement condition monitoring. To bridge this gap, this paper presents case studies describing use of advanced wireless sensing system for airport pavement conditions monitoring at three test sites: the Des Moines International Airport (DSM), the Ohio State University (OSU) Airport, and the Texas A&M University (TAMU) Airport. The wireless sensors communicate through a gateway to collect sensor data and then upload it to the cloud. The wireless sensor instrumentation strategy and design plans, based on the field instrumentation experience, are summarized. The key requirements and potential restrictions for implementing wireless sensing systems in airfield pavement are also discussed.

How Are the Subsurface Drainage Outlets in Iowa Roadways with Recycled Concrete Aggregate Base Performing

This paper discusses subdrain outlet condition and performance in Iowa roadways. A forensic test plan was developed and executed during wet summer conditions covering over 230 and 120 drain outlet locations of the newly constructed jointed plain concrete pavement (JPCP) and the hot mix asphalt (HMA) over JPCP sites, respectively. The tufa formation (i.e., calcium carbonate deposits) is the primary cause of drainage outlet blockage in newly constructed JPCP sites using recycled portland cement concrete (RPCC) as a subbase material. Lesser tufa formation from the use of RPCC base in JPCP was observed with the: (a) use of plastic outlet pipe without the gate screen type rodent guard, (b) use of blended RPCC and virgin aggregate materials. The observations of moisture-related surface distresses nearby blocked drainage outlet locations in HMA over JPCP indicates that compromised drainage outlet performance could accelerate the development of moisture-related distresses in HMA over JPCP.

Alternative Approaches to the Local Calibration of AASHTOWare Pavement ME Design Jointed Plain Concrete Pavement (JPCP) Smoothness Models

Pavement smoothness is one of the performance measures utilized for pavement maintenance and rehabilitation strategies. The International Roughness Index (IRI), a widely used indicator of pavement smoothness, is required for the Federal Highway Administration’s (FHWA’s) Highway Performance Monitoring System (HPMS). It has also been adapted as a functional performance characteristic in Mechanistic-Empirical Pavement Design Guide (MEPDG), now rebranded as the AASHTOWare Pavement ME Design. The local calibration of IRI prediction model for jointed plain concrete pavements (JPCP) in AASHTOWare Pavement ME Design software requires actual cracking, faulting and spalling measurements, which are not always collected or available with the state highway agencies (SHAs). The primary objective of this study is to examine if the JPCP IRI model can be locally calibrated without doing a local calibration of the primary distress prediction models associated with the IRI model. In this study, the local calibration of IRI model for JPCP pavement systems is performed using two approaches: the coefficients of IRI model are calibrated using (1) locally-calibrated and (2) nationally-calibrated faulting and transverse cracking model predictions. The accuracy of IRI model predictions using both approaches is evaluated and discussed with a focus on achieving reliable smoothness predictions in a cost-effective manner. Comparable accuracy improvement was achieved when Approach 1 was employed in the local calibration of IRI model compared to Approach 2.

Economic Assessment of Heated Pavements for the Minneapolis–St. Paul International Airport

In this study, the Minneapolis–St. Paul International airport (MSP) in Minnesota, a representative large-hub airport (1% of total U.S. enplanements), was selected to assess the relative economic benefits of using heated pavement systems (HPS) for snow removal. Two approaches were adopted for defining the boundary conditions for economic assessments: (1) installation of HPS only in apron and ramp areas which represent the busiest part of the airside operations where ground vehicles and airplanes share the same space, and (2) considering the entire airport as one entity for installation of HPS. Based on the available data collected during a site visit to MSP, interviews and email surveys, the economic feasibility of HPS at MSP was assessed by employing two economic analysis techniques, including the net present value (NPV) and benefit cost ratio (BCR). The analysis procedure and results are discussed highlighting the economic benefits and tradeoffs of installing heated pavements over conventional ice and snow removal strategies.

I-BACK: Iowa's Intelligent Pavement Backcalculation Software

The Iowa Department of Transportation (DOT) has been collecting the Falling Weight Deflectometer (FWD) data on regular basis. However, the pavement layer moduli backcalculation techniques used so far have been cumbersome and time consuming. More efficient and faster methods in FWD test data analysis were demanded and deemed necessary for routine analysis. Researchers at Iowa State University (ISU) have developed a suite of advanced pavement layer moduli backcalculation models using the Artificial Neural Networks (ANN) methodology for flexible, rigid, and composite pavements. The current study aims to develop a fully- automated backcalculation software system, referred to as I-BACK, with improved accuracy and usability of Iowa FWD data. Evolutionary optimization/nonlinear optimization algorithms were implemented with the developed ANN models to improve the accuracy of predictions.