Standing Committee on Design and Rehabilitation of Concrete Pavements sponsored a POSTER SESSION during the Transportation Research Board’s (TRB) 100th Annual Meeting – A Virtual Event – throughout January 2021.
Session included Topics Related to
Concrete Pavement Design:
Bonded & Unbonded Overlays
Reliability
RPAS
Pavement ME
Calibration of models
CTE
Cross-Tensioned of DOFs
M&R of Jointed Plain
IRI
Structural Synthetic Fibers
Thin & Ultra-thin
ABSTRACTS INCLUDE:
—Redevelopment of Artificial Neural Networks for Predicting the Response of Bonded Concrete Overlays of Asphalt for use in a Faulting Prediction Model
John DeSantis, University of Illinois-Urbana-Champaign
E-mail: jwd2309@gmail.com
Julie Vandenbossche, University of Pittsburgh
ABSTRACT: Transverse joint faulting is a common distress in bonded concrete overlays of asphalt pavements (BCOAs), also known as whitetopping. However, to date, there is no predictive faulting model available for these structures. Therefore, the intended research is to develop a predictive faulting model for BCOAs. In addition, it is important to be able to account for conditions unique to BCOA when characterizing the response in a faulting prediction model. Therefore, computational models were developed using a three-dimensional finite element program, ABAQUS, to accurately predict the response of these structures. These models account for different depths of joint activation, as well as full and partial bonding between the concrete overlay and existing asphalt pavement. These models were validated with falling weight deflectometer (FWD) data from existing field sections at the Minnesota Road Research Facility (MnROAD) as well as at the University of California Pavement Research Center (UCPRC). A fractional factorial analysis was executed using the computational models to generate a database to be used in the development of the predictive models. The predictive models, based on artificial neural networks (ANNs), are used to rapidly estimate the structural response at the joint in BCOA to environmental and traffic loads so that these responses can be incorporated into the design process. The structural response obtained using the ANNs is related to damage using the differential energy concept. Future work includes the implementation of the ANNs developed in this study into a faulting prediction model for designing BCOA.
—Concrete Pavement Reliability By Monte Carlo Simulation
Anastasios Ioannides, Hosei Daigaku Rikogakubu Daigakuin Rikogaku Kenkyuka
Jeb Tingle, U.S. Army Corps of Engineers (USACE)
ABSTRACT: Pavement reliability is a topic with which very few investigators are familiar enough to engage in a productive discussion about it, on a scientific, mathematical or philosophical level. Most engineers, however, are quite experienced in applying the concept of the safety factor, SF, in design. Monte Carlo Simulation is used in this paper to show that Reliability is related to SF for any given level of assumed variability in material properties and traffic. A range of SF between 1 and 5 is found to be adequate in describing R from 50 to 99.9%, for typical concrete pavement sections. The relationship between R and SF is much more sensitive to changes in the variability of material properties, than to changes in traffic variability. The methodology developed in this study is simple to follow and leads to practical values of the overall standard deviation, So required in AASHTO 86/93 designs. Findings corroborate the stipulation that for rigid pavements, So typically lies between 0.3 and 0.4, but this range depends on the assumed variability levels. The contributions of the Office of Engineering and of the Bureau of Statistics to the success or failure of a pavement are found to be essentially commensurate. The approach described herein addresses the debilitating weaknesses in the reliability methodology of AASHTO 86/93, which led to its abandonment in the NCHRP 1-26 and 2008 MEPDG efforts. It is argued that the AASHTO 86/93 reliability approach is mathematically and philosophically superior to its successors and deserves to be reinstated.
—Mechanistic-Empirical Faulting Prediction Model for Unbonded Concrete Overlays of Concrete
Charles Donnelly, University of Pittsburgh
John DeSantis, University of Illinois, Urbana-Champaign
Julie Vandenbossche, University of Pittsburgh
Steven Sachs, University of Pittsburgh
ABSTRACT: Transverse joint faulting is a distress that develops in unbonded concrete overlays (UBOL). Historically, faulting models used for predicting the performance of a UBOL have not accounted for the effects of the interlayer between the overlay and the existing pavement on the development of faulting. This is a significant limitation since characteristics of the interlayer play a primary role in the rate at which faulting develops in UBOLs. In order to develop a more robust faulting prediction model for UBOLs, enhancements were made to the current process to address this limitation. This includes the use of a structural response model that can account for the effects of the interlayer properties on the response of the UBOL. Additional enhancements include the use of a deflection basin of the overlay, in lieu of corner deflections of an equivalent slab system for accumulating differential energy, the incorporation of an erosion model that can account for the erodibility of the interlayer material, the adjustment of the incremental faulting equations to accommodate small slab sizes that are common in UBOLs, and a national calibration using faulting data from in service UBOLs. This enhanced faulting model has been implemented in the mechanistic-empirical design tool Pitt UBOL-ME.
—Calibration of the Rigid Pavement Analysis System (RPAS) Using Field Test Data
Abbas TaghaviGhalesari, The Transtec Group, Inc.
E-mail: abbas@thetranstecgroup.com
Nancy Beltran, Cement Council of Texas
Cesar Carrasco, University of Texas, El Paso
ABSTRACT: The accurate analysis of rigid pavements requires a reliable modeling procedure based on integrating mechanistic analysis methods and empirical observations. Researchers from the University of Texas at El Paso developed the software Rigid Pavement Analysis System (RPAS) to analyze comprehensively the responses of concrete pavements under different geometric configurations, foundation models, temperature profiles and traffic loads by using finite element method. Although multiple comparative studies have already been conducted documenting its accuracy, RPAS still needs improvement through a well-established calibration process. Therefore, this paper documents the results of research aimed to calibrate RPAS by comparing and reconciling the analytical solutions and field measurements. A series of studies that included verification and validation were conducted on a variety of pavement sections under different loading conditions. A multi-objective optimization algorithm was used to obtain a list of calibration factors that applied to a number of parameters that significantly impact the responses and have large variability. The calibration factors obtained ranged from 0.75 to 1.60 with most being close to unity. Given the high variability in the estimated foundation moduli, RPAS can predict the pavement responses reasonably accurately. The accuracy of RPAS after applying the calibration factors was assessed using a reliability metric. The results of this reliability assessment indicate a successful calibration process that brings the results produced by RPAS within engineering expected thresholds. Ultimately, this research provides transportation agencies and pavement design engineers with a more reliable tool for the analysis of concrete pavements in comparison with the existing analysis tools.
—Local Calibration of Pavement ME Faulting Reliability Using Pavement Management Data
Lucio De Salles, University of Pittsburgh E-mail: lus49@pitt.edu
Lev Khazanovich, University of Pittsburgh
ABSTRACT: Pavement ME transverse joint faulting model incorporates mechanistic theories that predict joint faulting development in jointed plain concrete pavements (JPCP). The model is calibrated using the Long-Term Pavement Performance (LTPP) database. However, the Mechanistic-Empirical Pavement Design Guide (MEPDG) encourages transportation agencies, such as state departments of transportation, to perform local calibrations of the faulting model included in Pavement ME. Model calibration is a complicated and effort-intensive process that requires high-quality pavement design and performance data. The quality of pavement management data – which is collected regularly and in large amounts – may be lower than is desired for faulting performance model calibration. The MEPDG performance prediction models predict pavement distresses with 50 percent reliability. JPCP are usually designed for high levels of faulting reliability to reduce likelihood of excessive faulting. For design, improving the faulting reliability model is as important as improving the faulting prediction model. This paper proposes a calibration of the Pavement ME reliability model using pavement management system (PMS) data. It illustrates the proposed approach using Pennsylvania Department of Transportation (PennDOT) PMS data. Results show an increase in accuracy for faulting predictions using the new reliability model with various design characteristics. Moreover, the new reliability model allows design of JPCP considering higher levels of traffic.
—Pavement ME Transverse Cracking Model Calibration Using California’s Pavement Management System
Ashkan Saboori, University of California, Davis
Jeremy Lea, University of California, Davis
Angel Mateos, University of California, Berkeley
Rongzong Wu, University of California, Davis
John Harvey, University of California, Davis
ABSTRACT: This paper presents a new approach for calibration of mechanistic-empirical (ME) pavement simulation models. It uses network level performance data from the pavement management system (PMS), and was applied for the local calibration of the Pavement ME transverse cracking models in California, using 30,155 pavement sections with a combined length of approximately 4,400 lane-miles built on 446 lane replacement projects completed between 1947 and 2017. This represents two orders of magnitude more observations, sections, and length of pavement than are typically used when following the traditional ME calibration approach for all pavement types. The new approach does not require sampling and testing of materials from all sections in the network, but rather uses the median values from a representative sample of materials across the network (state-wide median). This approach is appropriate because pavement designers only know pavement material property specification limits in a design-bid-build delivery process. Variability of performance and reliability of design (probability that the design will meet or exceed the design life) are accounted for through separate consideration of within-project, between-contractor, and between-project variability. The calibration against PMS performance data reduced the significant prediction bias and standard error (13.3 percent and 23.03 percent cracked slabs, respectively) that would result from use of the nationally calibrated cracking models in California, with the bias and standard error reduced to 0.039 percent and 5.69 percent, respectively.
—An Insight into Moisture-Dependence of Concrete Coefficient of Thermal Expansion in Concrete Pavements
Angel Mateos, University of California, Berkeley
John Harvey, University of California, Davis
Dulce Feldman, California Department of Transportation (CALTRANS)
David Lim, California Department of Transportation (CALTRANS)
Rongzong Wu, University of California, Davis
Julio Paniagua, University of California, Davis
ABSTRACT: The mechanistic-empirical design of concrete pavements traditionally assumes that concrete coefficient of thermal expansion (CTE) is a constant, however, experimental evidence shows that CTE can increase up to 60% as concrete dries. The research presented in this paper provides insight into the mechanisms that lie behind this phenomenon, by establishing a link between the diurnal variations of concrete internal relative humidity (RH) and the moisture-dependence of concrete CTE. The experimental data come from four concrete pavements, built with different high early strength mixes, that were instrumented with sensors to measure slab deformations and concrete internal temperature and RH. RH measurements in the four concrete mixes indicate that—depending on concrete moisture content—the RH of the air in concrete pores may experience considerable diurnal variations that are mainly driven by changes in temperature. These RH changes take place in opposite direction compared to what typically occurs in the open air. In the concrete pores, the RH of the air increases as temperature increases and decreases as temperature decreases. These temperature-dependent changes in concrete internal RH are indication of temperature-dependent changes in concrete pore water suction which, at the same time, result in changes in drying shrinkage deformation. Since this deformation occurs as temperature changes, it is interpreted as thermal deformation. The consequence is that the CTE of the concrete increases. As shown in this paper, the magnitudes of the temperature-dependent changes in concrete internal RH are sufficient to explain by themselves the increase in CTE that concrete experiences as it dries.
—Prestress Force Monitoring in Cross-Tensioned Concrete Pavement Using Distributed Optical Sensing Technology
Ke Cheng, Tongji University
Mengyuan Zeng, Tongji University
Hui Chen, Tongji University E-mail: huihui9166@outlook.com
Jianming Ling, Tongji University
Hongduo Zhao, Tongji University
Difei Wu, Tongji University
ABSTRACT: This paper presents a novel method to monitor prestress force in cross-tensioned concrete pavement (CTCP) using distributed optical fiber sensing (DOFS) technology. A DOFS-based sensor was designed to monitor the prestress force in the CTCP and free fiber was set to eliminate the thermal effect on the sensor. A prototype system that consisted of multiple connected sensors and an interrogator was developed as well. Through finite element analysis and laboratory tests, the design of the sensor was optimized, and the monitoring accuracy was demonstrated. To study the performance of the prototype system and reveal the thermal effect on the prestress force, a full-scale test with the prototype system employed was conducted for over six months. Based on the monitored prestress force extracted from the load cell and the prototype system, it is found that the shrinkage of prestress strand and concrete will both cause significant loss of prestress force and the loss is different among prestress strands. Furthermore, the temperature has a considerable effect on the prestress force. The thermal effect is not related to the magnitude of prestress force but influenced by the position of temperature monitoring points. Temperature measured from a deeper position, as well as a closer position from the prestress monitoring area, provide a better correlation with the prestress force. Overall, these findings verify the practicality of the DOFS-based sensor to be utilized in prestress monitoring and suggest the prototype system has a potential to investigate the complicated effect of temperature and load on prestress force.
—International Roughness Index Model for Jointed Plain Concrete Highway Pavements: An Artificial Neural Network Application
Salma Sultana, University of Mississippi
Hakan Yasarer, University of Mississippi
Waheed Uddin, University of Mississippi
Rulian Barros, University of Mississippi
ABSTRACT:An efficient and safe road network secures the nation’s economy and prosperity by providing public mobility and freight transport. Maintenance and rehabilitation of the road network cost billions of dollars annually. If maintenance and rehabilitation are not done promptly, the damages to the road caused by heavy traffic and extreme climate may lead to life-threatening conditions for road users. The literature review to date indicates that the Maintenance and Rehabilitation (M&R) history was not considered in the concrete pavement condition models. The hypothesis testing in this study demonstrated that it is imperative to use M&R history of the pavement in the development of the International Roughness Index (IRI) prediction model. Therefore, this study utilized Construction Number (CN) for developing IRI prediction models for Jointed Plain Concrete Pavements (JPCP). The models were developed using Long Term Pavement Performance (LTPP) database. A total of 590 data points was used to develop the IRI prediction using Artificial Neural Network (ANN) modeling technique. Three ANN models were developed using variables such as initial IRI, pavement age, concrete pavement thickness, ESAL, climatic region, and CN. The best model was found to be the one with an R 2 value of 0.87. The IRI prediction model developed in this study can successfully estimate the increase of IRI values with time and decrease of IRI value after maintenance and rehabilitation. The developed IRI models can be utilized by the local and state agencies to prepare M&R programs and budget allocations.
—Roughness Modeling for Asphalt Overlay on Concrete Pavements Using Neural Networks
Rulian Barros, University of Mississippi
Hakan Yasarer, University of Mississippi
Waheed Uddin, University of Mississippi
Salma Sultana, University of Mississippi
ABSTRACT: Pavement performance modeling is an important part of pavement management systems (PMS). The International Roughness Index (IRI) is accepted as an important indicator of pavement performance and used as the standard for the pavement roughness. The objective of this study is to develop pavement roughness models for asphalt overlay on concrete pavements using the Long-Term Performance Pavement (LTPP) database. A feed-forward Artificial Neural Networks (ANNs) approach with backpropagation learning algorithm was used in this study. A total of 592 data points from 52 pavement sections were analyzed. Five models were developed, and the best performing model was selected based on the lowest average square error (ASE), lowest mean absolute relative error (MARE), and highest coefficient of determination (R²). The best performing model utilizes 14 input variables (i.e. Initial IRIMean, Age, Wet-Freeze, Wet Non-Freeze, Dry-Freeze, Dry Non-Freeze, Asphalt Thickness, Concrete Thickness, CN Code, ESAL, Annual Air Temperature, Freeze Index, Freeze-Thaw, and Precipitation) and one output variable (IRIMean). Literature review indicated that roughness prediction models did not consider maintenance and rehabilitation (M&R) history as an independent variable. The use of Construction Number (CN) in the model development resulted in more realistic models considering that M&R actions affect the future condition of the pavement. Furthermore, the developed models predict future IRI values without using distress data. Therefore, the developed ANN roughness model allows local and state agencies to save time in data collection and processing, accordingly, reducing costs by providing a tool for better condition assessment and effective M&R scheduling.
—Influence of Structural Synthetic Fibers on the Transverse Joint Behavior and Faulting of Ultra-thin and Thin Concrete Pavements
Manik Barman, University of Minnesota-Duluth
Souvik Roy, University of Minnesota-Duluth
Amarjeet Tiwari, University of Minnesota-Duluth
Thomas Burnham, Minnesota Department of Transportation
ABSTRACT: Application of structural fiber-reinforced concrete in ultra-thin and thin concrete overlays or pavements are quite common in the U.S. While it is evident that structural fibers contribute to the overall performance of the pavements by mitigating some critical distresses, their contribution in mitigating faulting is neither properly quantified nor accounted for in any currently available concrete overlay design procedures. To this end, the Minnesota Department of Transportation (MnDOT) constructed two ultra-thin (3 and 4 inches thick) and four thin (5 and 6 inches thick) concrete pavement test cells at the Minnesota Road Research (MnROAD) facility in the summer and fall of 2017. These test cells have 6 ft x 6 ft concrete slabs and were constructed on a granular base layer. This paper summarizes the design and construction of these six test cells and provides a comprehensive discussion on the joint performance behavior and faulting trends of these cells emphasizing the influence of the fibers on the abovementioned. It was found that fibers can potentially improve the joint performance and decrease faulting in thin concrete pavements.
For the TRB webpage of concrete abstracts and links, please go to: https://annualmeeting.mytrb.org/OnlineProgram/Details/15562
Home photo: FHWA, AASHTO gauge real-time concrete pavement quality, June 4, 2014: http://concreteproducts.com/index.php/2014/06/04/fhwa-aashto-gauge-real-time-concrete-pavement-quality-2/