Joint Faulting Research Articles

Quantifying the Effects of Material Input Levels on Jointed Plain Concrete Pavement (JPCP) Performance and Slab Thickness

The mechanistic-empirical pavement design guide (MEPDG) is a commonly accepted design principles guide that aids in jointed plain concrete pavement (JPCP) design and performance analysis. The MEPDG uses three different design parameter input levels, referred to as level one, level two, and level three, providing increasing confidence in the analysis at the lower numbered levels, which use more locally relevant (level two) or project-specific (level one) data. The state-of-the-art pavement ME software (version 2.6.2) uses MEPDG design principles to predict pavement performance. The three performance indicators for JPCP systems (international roughness index (IRI), joint faulting, and transverse cracking) experience significant changes when simulating under a different input level. The IRI and faulting indicator changed by 78 percent when using inputs varying from level one to level three, with the cracking indicator change being more severe at 87 percent. To accommodate the change in performance indicator values between input level one and input level three, increasing the concrete slab thickness is necessary to achieve comparable pavement performance. An increase in the Portland cement concrete (PCC) layer from one inch to two inches is required when input level three simulations are performed, demonstrating the economic and sustainability benefits of using project-specific level one inputs. Understanding the impact of simulation input levels will help to meet design and sustainability goals and improve the lifecycle performance of JPCP systems.

Expansion Joints Risk Prediction System Based on IoT Displacement Device

Damage to bridge expansion joints arises from a variety of causes such as increasingly deteriorated bridges, abnormal temperatures, and increased traffic. To detect anomalies in the expansion joints, this study proposes an Artificial Intelligence (AI)-model-based diagnosis method of analyzing the vibration of the bridge bearing that supports the upper structure of a bridge. The proposed system establishes big data with the measured displacement of a bridge bearing and makes an AI-based prediction about the risk of bridge expansion joints. Replacing a bridge bearing makes it possible to manage the bridge displacement before and after construction and helps improve safety inspections and diagnosis methods. It is necessary to prepare a bridge with anomalies for the AI model training. For this reason, a bridge with a bridge bearing was simulated. In addition, a vehicle suitable for the bridge was simulated. The displacement data in normal and abnormal situations were collected, cleaned, and applied to the AI analysis model. The system was found to have over 90% accuracy of prediction about expansion joint faulting and damage.

Applications of Artificial Intelligence Enhanced Drones in Distress Pavement, Pothole Detection, and Healthcare Monitoring with Service Delivery

Artificial Intelligence (AI) has fascinated the present study assigned to multiple areas such as distress detection on the pavement, pothole detection, and healthcare. The distress detection on pavement and roads and delivering healthcare and medical services need to be monitored through state-of-the-art technology, i.e., drone technology. Improvement in construction sites and healthcare delivery are of serious concern. Nowadays, computer vision techniques are commonly used in this area utilizing images and videos of construction sites. Due to confined data, researchers are using Unmanned Aerial Vehicles (UAVs) or Drone to get maximum information through 360° monitoring. This review article presents the useful monitoring techniques using AI-enabled drones for scholars around the world. In this comprehensive review, initially, the image acquisition equipment along with the perks and limitations has been presented. Second, the main constraints related to different computer vision techniques are highlighted for detecting distress in the pavement. Then, the possible research solution to some of the distress issues such that detection of pavement texture, cracks or potholes, joint faulting, temperature segregation, and rutting issues are predicted. Finally, the application of AI-enhanced drones in the healthcare field is elucidated which showed their significance. Moreover, in this research, the comparative image analysis of pavement and path hole detection was presented for the collection of detailed information and accurate detection. In the future, the work can also be enhanced to monitor the live pavement distress detection, especially for busy roads and highways. Moreover, an analysis to determine and reduce the costs in the healthcare sectors and organizations is required for future work.

Joint performance and faulting of ultra-thin and thin structural-fiber reinforced concrete pavements

ABSTRACT Structural fibers contribute to the overall performance of concrete pavements by mitigating various distresses. To understand the influence of structural fibers on the pavement structural responses and distresses, the Minnesota Department of Transportation (MnDOT) constructed two ultra-thin (3 and 4 in. thick) and three thin (5 and 6 in. thick) fiber reinforced concrete cells along with one plan concrete control test cell at the Minnesota Road Research (MnROAD) facility in 2017. These test cells have 6 ft x 6 ft concrete slabs and were constructed on the granular base layer. While the structural designs of these cells were intended for roads with low to moderate traffic volume, the cells were subjected to accelerated loading to complete the study in a short period of time. This paper summarizes the design and construction of the six test cells and provides a comprehensive discussion on the joint performance behavior and transverse joint faulting trends, emphasizing the influence of the fibers on the abovementioned. It was found that fibers can improve joint performance and decrease faulting in thin concrete pavements. Regarding the ultra-thin pavements, no conclusion was possible to be drawn on the contribution of the fibers.

Mechanistic-empirical model to predict transverse joint faulting of bonded concrete overlays of asphalt

Transverse joint faulting is a distress observed in bonded concrete overlays of asphalt pavements (BCOAs). However, to date, there is no predictive faulting model for BCOAs. Therefore, the objective of this research is to develop such a model. First, models were developed to predict the structural response of BCOAs due to environmental and traffic loads. Previously-developed artificial neural networks that rapidly estimate the structural response of BCOAs at the joint due to these loads was used to relate the structural response to the damage using the differential energy (DE) concept. Next, DE was related to faulting through an incremental analysis considering traffic, climate, and joint deterioration. Finally, a calibration using performance data from existing BCOAs throughout the continental United States and an extensive sensitivity analysis on the model’s prediction capabilities was performed. This faulting prediction model has been incorporated into the BCOA-ME design guide developed by the University of Pittsburgh.

Mechanistic-Empirical Faulting Prediction Model for Unbonded Concrete Overlays of Concrete

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. 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 [DE]), 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.

Improved artificial neural networks for predicting the response of unbonded concrete overlays in a faulting prediction model

ABSTRACT Transverse joint faulting is a common distress that develops in unbonded concrete overlays (UBOLs). In the previous predictive faulting model for UBOLs, artificial neural networks (ANNs) were trained to predict the critical response (deflections) resulting from traffic and environmental loads. These ANNs were trained using an extensive factorial of finite element runs of a field-validated structural model. While the ANNs were developed to predict the critical response of UBOLs with asphalt and fabric interlayers, the predictive models were not able to differentiate the critical response as a function of interlayer type. In this study, the ANNs were improved to address this prior limitation. A separate set of ANNs was produced for UBOLs with asphalt interlayers and UBOLs with fabric interlayers. Several enhancements were also performed, including modification of the ANN architecture and the inclusion of an extensive sensitivity analysis in the validation process. The newly developed ANNs are incorporated into the Pitt UBOL-ME faulting prediction model and design guide.

Local Calibration of Pavement Mechanistic-Empirical Faulting Reliability using Pavement Management Data

The Pavement ME transverse joint faulting model incorporates mechanistic theories that predict development of joint faulting in jointed plain concrete pavements (JPCP). The model is calibrated using the Long-Term Pavement Performance 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. Pavement management data—which is collected regularly and in large amounts—may present higher variability than is desired for faulting performance model calibration. The MEPDG performance prediction models predict pavement distresses with 50% 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 PMS data from Pennsylvania Department of Transportation. 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 because of the less conservative predictions.

Redevelopment of Artificial Neural Networks for Predicting the Response of Bonded Concrete Overlays of Asphalt for use in a Faulting Prediction Model

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. To address this, 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. The 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.

Effects of Concrete Age on Coefficient of Thermal Expansion of Paving Mixes and Its Significance in Unbonded Overlay Design

Abstract With the advancement in rigid pavement design and advent of AASHTOWare Pavement Mechanistic-Empirical (ME) Design (American Association of State Highway and Transportation Officials, Washington, DC) as the latest design tool, increasing emphasis is being laid on the coefficient of thermal expansion (CTE) of concrete. The CTE affects the performance of rigid pavements in a significant manner, with a higher CTE corresponding to greater curling and resulting in greater pavement distresses. Prior research has conflicting views regarding CTE variation with age, and there has been very little to no research on the effects of CTE on the performance of unbonded concrete overlays (UBCOs). This study is performed to investigate the variation of CTE with concrete age through laboratory testing and further quantify the effects of CTE variation on UBCO design. To facilitate, four concrete paving mixes with different types of coarse aggregates and different mix properties from different districts of New Mexico were collected. The cast specimens from each of the mixes were tested for CTE as per the AASHTO T-336, Coefficient of Thermal Expansion of Hydraulic Cement Concrete, protocol, at the ages of 7, 14, 28, 60, 90, 120, and 180 days, respectively. The test results were analyzed, and it was observed that there is an increase in CTE over the range of 3.49 % to 9.77 %, between 28 days and 180 days. Further analysis was conducted with simulations in Pavement ME Design Version 2.3® that indicated a significantly deteriorated performance of the UBCO with increased CTE. The impact on transverse cracking and joint faulting is the most significant, with up to 13.3 % increase in cracking and up to 19.7 % increase in faulting distress. An unbonded overlay designed with 28 days CTE value may not perform for the design life because of the increase in CTE with age progression.

An advanced local calibration method for mechanistic‐empirical pavement design

AbstractModel calibration and validation is an important step of empirical modeling. The current local calibration (LC) method suggested by the AASHTOWare mechanistic‐empirical pavement design guide (MEPDG) is defective and can result in a completely distorted prediction of pavement reliability in design. This paper proposes an advanced LC method that integrates a jackknife sampling procedure and an iteratively weighted least squares technique into one coherent LC process. The jackknife sampling method, which has been recommended by AASHTO for model validation, is now used for identifying outliers of the data in order to ensure a homogeneous population. Meanwhile, an iteratively weighted least squares method is proposed to simultaneously estimate the LC coefficients and the standard deviation functions. Two LC case studies for Ontario roads are presented: one for the bottom‐up fatigue cracking models of asphalt concrete pavements and the other for the joint faulting models of Portland cement concrete pavements. The comparison with the traditional split‐data method has shown the effectiveness and efficiency of the proposed method. With little modification, the method can be introduced as a general calibration‐validation process for all empirical and mechanistic‐empirical models in engineering disciplines.

A hybrid wavelet-optimally-pruned extreme learning machine model for the estimation of international roughness index of rigid pavements

ABSTRACT International Roughness Index (IRI) is a key parameter in pavement performance evaluation. This study investigates developing a reliable prediction model that can be used to estimate IRI of rigid pavements using innovative machine learning techniques. Optimally Pruned Extreme Learning Machine (OP-ELM) and Wavelet analysis are integrated to improve the OP-ELM results and design a novel hybrid Wavelet-OPELM (WOPELM) model for the IRI prediction. The proposed model is compared statistically to the OP-ELM and conventional feed-forward Artificial Neural Network (ANN) as well as regression model with respect to their efficiency to predict IRI of jointed plain concrete pavement (JPCP) sections in USA. The relevant data was collected from the Long-Term Pavement Performance (LTPP) database. Eight input variables, initial IRI, pavement age, transverse cracks, percent joints spalled, flexible and rigid patching areas, total joint faulting, freeze index, and percent subgrade passing No. 200 U.S. sieve, are assessed and used to predict the IRI. The results show that the initial IRI, total joint faulting, and freezing index are the most significant parameters for IRI prediction. The WOPELM is found to be a robust and more accurate modelling technique compared to OP-ELM, ANN, and regression for IRI prediction with only a 7% prediction error.

Evaluation of effectiveness of geotextile in reducing subgrade migration in rigid pavement

Pumping in rigid pavements is defined as the migration of subgrade soil into the overlying layers, redistribution of materials under the slabs, and ejection of materials through joints. Pumping can compromise pavement performance. This study evaluated geotextiles as separation and filtration solutions to mitigate pumping and reduce the resulting pavement joint faulting. A one-third scale Model Mobile Load Simulator (MMLS3) was used to simulate cyclic loading on a scaled rigid highway pavement that has experienced some loss of load transfer. The results from four tests were compared to assess the effectiveness of geotextile in reducing pumping. The four experiments had identical configurations, except that a geotextile was placed at the subgrade-subbase interface in two tests. Non-plastic saturated silt and partially saturated aggregate were used as the subgrade and subbase, respectively. Using a geotextile at the subgrade-subbase interface substantially reduced pumping. More fines accumulated in the subbase beneath the approach slab than the leave slab, which resulted in faulting of the slabs. However, the magnitude of this faulting was more pronounced for the cases without geotextile. Reductions of 71% and 52% occurred in the magnitude of subgrade migration and faulting, respectively, when using geotextile. To conclude, geotextile can be effective in mitigating pumping, leading to longer-lasting pavement systems.

Joint faulting behaviour of innovative short concrete slabs

Pavements are one of the largest assets of a city and their functional condition (ride quality) is priority for their clients. In jointed plain concrete pavements (JPCPs), the presence of joint faulting (JF) reduces the ride quality. Today, short slabs are available as a cost-effective JPCP innovation. The objective of this paper is to analyse the JF behaviour of JPCPs with short slabs. For this, a deterioration model to predict it and trends of JF observed in short slabs of Chile and the United States are considered. The HDM-4 model always yields lower JF per joint in short slabs than in traditional ones. However, real-world short slabs show not only lower JF per joint (that the modelled JF), but also that more joints do not necessarily mean more JF per length of pavement that affect the ride quality. One of the relevant explanatory factors for it is the radical reduction of crack width at joints, which produces a fundamental increase of the load transfer efficiency. To maintain favourable behaviour observed in the field it is recommended to assure joint activation and to provide adequate stiffness of the layers below the short slabs.

Artificial Neural Networks for Predicting the Response of Unbonded Concrete Overlays in a Faulting Prediction Model

Transverse joint faulting is a common distress in unbonded concrete overlays (UBOLs). However, the current faulting model in Pavement mechanistic-empirical (ME) is not suitable for accurately predicting the response of UBOLs. Therefore, to develop a more accurate faulting prediction model for UBOLs, the first step was to develop a predictive model that would be able to predict the response (deflections) of these structures. To account for the conditions unique to UBOLs, a computational model was developed using the pavement-specific finite element program ISLAB, to predict the response of these structures. The model was validated using falling weight deflectometer (FWD) data from existing field sections at the Minnesota Road Research Facility (MnROAD) as well as sections in Michigan. A factorial design was performed using ISLAB to efficiently populate a database of fictitious UBOLs and their responses. The database was then used to develop predictive models, based on artificial neural networks (ANNs), to rapidly estimate the structural response of UBOLs to environmental and traffic loads. The structural response can be related to damage through the differential energy concept. Future work will include implementation of the ANNs developed in this study into a faulting prediction model for designing UBOLs.

Quantifying the impact of coefficient of thermal expansion of overlay concrete on unbonded concrete overlay performance

With the advancement in pavement design and performance analysis procedures, the coefficient of thermal expansion (CTE) of concrete has emerged as a significant design input with a direct impact on concrete pavement performance parameters including transverse cracking, joint faulting, and pavement roughness. CTE is the measure of change in concrete volume with temperature change and the resulting curling of concrete pavement slab is directly proportional to CTE. Un-Bonded Concrete Overlay (UBCO) is a cost-effective and sustainable rehabilitation technique on Jointed Plain Concrete Pavements (JPCP) to improve the performance of deteriorated concrete pavements. This study examines the effects of variability of CTE on the performance of unbonded JPCP overlays for two different climatic regions. Simulations were conducted using AASHTO pavement ME design software with varying CTE values in the range of 6.8–10.8 micro-strain/°C and keeping all other design variables as constant. The performance predictions were evaluated for different values of CTE and the results indicated that with an increase in CTE value, the performance of UBCO is adversely affected by the increase in pavement distresses. Amongst all the performance parameters, transverse cracking is the most significantly affected parameter with the change in CTE. The impact of geometric properties of overlay pavement including transverse joint spacing and slab thickness on the pavement performance was also analyzed which indicated that these have a direct impact on the performance parameters. The overlay performance can be improved by increased overlay slab thickness or reduced joint spacing and with these modifications, the adverse effects of higher CTE can be compensated. Field performance data of UBCO extracted from the LTPP database showed that the pavement ME design software can accurately predict the performance of UBCO pavement systems.

Effects of existing concrete pavement condition on performance of unbonded jointed plain concrete overlay

Abstract Unbonded concrete overlay (UBCO) is the most used pavement rehabilitation technique across United States with an overall usage of 47% amongst all the rehabilitation methods. It is aimed to improve the performance of any deteriorated/cracked jointed plain concrete pavement (JPCP). Unbonded JPCP overlay involves placement of a separation layer of hot mix asphalt (HMA), which acts as a stress relief layer between the existing concrete pavement and the overlay. There are numerous factors that affect the design and performance of UBCOs and out of these, existing pavement condition or the severity of damage of existing pavement is a prime factor. The severity of damage is described by the distressed elastic modulus thus, accurate determination of the distressed elastic modulus of the existing concrete pavement is essential for predicting the accurate performance of the unbonded overlay. This study focuses on analyzing the impact of distressed modulus by conducting simulations in the AASHTOWare pavement ME design software version 2.3 and evaluating the predicted performance of JPCP overlay for two different climatic regions. The results indicated that the distressed modulus of existing concrete pavement affects the performance of the overlay with regards to transverse cracking, joint faulting and pavement roughness. Transverse cracking is the most affected performance parameter with a change of 0.27%–2.31% with a unit change in distressed modulus. The impact of climatic conditions on the performance of unbonded overlay was also observed. The adverse effects of distressed modulus can be minimized by reducing the joint spacing or increasing the overlay slab thickness.

Development of Artificial Neural Networks for Predicting the Response of Bonded Concrete Overlays of Asphalt for Use in a Faulting Prediction Model

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. To account for conditions unique to BCOA, a computational model was developed using a three-dimensional finite element program, ABAQUS, to predict the response of these structures. The model was 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 large database of analyses was then developed using a fractional factorial design. The database is used to develop predictive models, based on artificial neural networks (ANNs), to rapidly estimate the structural response at the joint in BCOA to environmental and traffic loads. The structural response will be related to damage using the differential energy concept. Future work includes the implementation of the developed ANNs in this study into a faulting prediction model for designing BCOA.

Unsealed joints in urban concrete pavements for buses

Urban pavements for buses need to resist high traffic demands without regular invasive maintenance interventions that affect the pavement clients. Although jointed plain concrete pavements can provide these requirements, sealing the joints and keeping them sealed for 10 years costs up to 45% more than unsealed joints. The objective of this paper is to evaluate the performance of unsealed transverse joints in urban pavements for buses. The field measurements made after 8 years in-service (average) in 270 unsealed joints in Chile, 150 on bus corridors and 120 on their adjacent lanes, show that only ten joints of the bus corridors were affected by very low spalling (30 mm) due to the construction process and not due to the performance of the unsealed joints. The adjacent lanes have zero joints affected by spalling. Furthermore, no pumping or measurable joint faulting was detected even after 5 million accumulated equivalent single-axle loads. These results, the 50 years of Wisconsin's experience in the USA and the deficiencies of joint seals place unsealed joints as a cost-effective alternative to optimise urban pavements for buses, considering the needs of the pavement clients – that is, users and municipal transportation agencies.

Preparation of Joint Faulting Data for the Local Calibration of AASHTO Pavement Mechanistic–Empirical Design in Louisiana

Louisiana utilized performance data from the pavement management system (PMS) to evaluate and calibrate the AASHTO Pavement Mechanistic–Empirical (ME) Design. Analysis of the PMS faulting data revealed that there were no records between 0 and 0.2 in. (5 mm); others over 0.2 in. (5 mm) appeared to be much greater than would be expected based on engineering experience. Therefore, several tasks were completed to validate the PMS faulting data and prepare them for local calibration. This paper presents details of the problem, approach, results, and lessons learned. First, faulting data from the PMS and Long-Term Pavement Performance database were analyzed to have an overview of the common range of joint faulting. To validate the PMS faulting data, 43 representative projects across Louisiana were selected for further analysis. Longitudinal profiles were collected with high-speed profilers and analyzed with the AASHTO R36 automated faulting measurement (AFM) algorithms. Manual measurements were also conducted during site visits. The comparison of faulting from different methods showed that the PMS data extremely overestimated faulting compared with the AFM estimation or the manual measurement. Results from the AFM algorithm were much closer (in the same magnitude) to the manual measurements. Therefore, faulting data from the AFM algorithm were used, and the faulting model was successfully calibrated. It is recommended to evaluate PMS faulting data carefully before applying them to calibrate the AASHTO Pavement ME Design software. Automated faulting measurement based on high-speed profiles is a feasible approach.