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

2021 ◽  
pp. 036119812110288
Author(s):  
Charles Donnelly ◽  
John DeSantis ◽  
Julie Marie Vandenbossche ◽  
Steven G. Sachs
Keyword(s):  
Prediction Model ◽  
Structural Response ◽  
Design Tool ◽  
Primary Role ◽  
Response Model ◽  
Erosion Model ◽  
Concrete Overlays ◽  
Current Process ◽  
Transverse Joint ◽  
Empirical Design

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.

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

2019 ◽  
Vol 2673 (10) ◽  
pp. 762-769 ◽  
Author(s):  
John W. DeSantis ◽  
Julie M. Vandenbossche ◽  
Steven G. Sachs
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Structural Response ◽  
Finite Element Program ◽  
Concrete Overlays ◽  
Transverse Joint ◽  
Element Program ◽  
Artificial Neural ◽  
Future Work

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.

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

2018 ◽  
Vol 2672 (40) ◽  
pp. 360-370 ◽  
Author(s):  
John W. DeSantis ◽  
Julie M. Vandenbossche ◽  
Kevin Alland ◽  
John Harvey
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Three Dimensional ◽  
Structural Response ◽  
Fractional Factorial ◽  
Finite Element Program ◽  
Concrete Overlays ◽  
Element Program ◽  
Artificial Neural

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.

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

2021 ◽  
pp. 036119812110010
Author(s):  
John W. DeSantis ◽  
Julie M. Vandenbossche
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Predictive Models ◽  
Computational Models ◽  
Structural Response ◽  
Fractional Factorial ◽  
Finite Element Program ◽  
Concrete Overlays ◽  
Artificial Neural

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.

Dynamic Mechanical Response Validation of Pavement Structural under Complex Stress State

2010 ◽  
Vol 163-167 ◽  
pp. 1645-1650
Author(s):  
Guo Ping Qian ◽  
Shuai Li ◽  
Li Jun Jiang
Keyword(s):  
Stress State ◽  
Model Calculation ◽  
Mechanical Response ◽  
Structural Response ◽  
Complex Stress ◽  
Calculation Model ◽  
Dynamic Strain ◽  
Response Model ◽  
Dynamic Mechanical ◽  
Pavement Structure

Under the heavy traffic, the stress state of asphalt pavement structure has such a complex change that it is difficult for conventional pavement structural response calculation model to deal with. Therefore, "Pavement structure dynamic mechanical response model under complex stress condition" is established in this paper. Kinds of cases are calculated according to the characteristics of heavy vehicle. Then the FWD deflection test and dynamic strain test are carried out. Finally, the rationality of pavement structural response model calculation model is proved by comparing the test results with the theoretical model calculation results.

scholarly journals Erosion Prediction Model using Fractional Vegetation Cover

2020 ◽  
Vol 5 (1) ◽  
pp. 125-132
Author(s):  
Nursida Arif ◽  
Projo Danoedoro ◽  
Hartono Hartono ◽  
Andrew Mulabbi
Keyword(s):  
Prediction Model ◽  
Vegetation Cover ◽  
Input Data ◽  
Medium Density ◽  
Vegetation Density ◽  
Erosion Model ◽  
Fractional Vegetation Cover ◽  
Erosion Prediction ◽  
Digital Elevation ◽  
Elevation Model

The purpose of this study was to  create an erosion prediction model in Serang Watershed, Indonesia. The erosion model used two input data, namely the slope derivied from Digital Elevation Model (DEM) data, and Fractional Vegetation Cover (FVC) from SPOT images. Assessment of the model was carried out using questionnaires and interviews with several experts by presenting the results of the model and its supporting data. Based on the DEM data, the level of slope steepness in the study area is very varied namely; flat (52.77%), sloping (7.62%), and rather steep to very steep (39.59%). Vegetation density according to the FVC results is dominated by medium density. The results of the analysis of the two input models can provide predictions of the level of erosion with an accuracy of 67.92%. Evaluation of the model was done by experts with conclusions that the method was very flexible and can be adapted to similar watersheds elsewhere.

Analysis, Design, and Optimization of Thermal In-Plane Microactuator—Chevron Type

2011 ◽  
Vol 8 (3) ◽  
pp. 102-109
Author(s):  
K.B. Puneeth ◽  
K.N. Seetharamu
Keyword(s):  
Neural Network ◽  
Mechanical Response ◽  
Thermal Response ◽  
Structural Response ◽  
Design Tool ◽  
Thermal Actuator ◽  
Coupled Models ◽  
Element Analysis ◽  
Design And Optimization ◽  
Analysis Design

A predictive model of thermal actuator behavior has been developed and validated that can be used as a design tool to customize the performance of an actuator to a specific application. Modeling thermal actuator behavior requires the use of two sequentially or directly coupled models, the first to predict the temperature increase of the actuator due to the applied voltage and the second to model the mechanical response of the structure due to the increase in temperature. These models have been developed using ANSYS for both thermal response and structural response. Consolidation of FEA (finite element analysis) results has been carried out using an ANN (artificial neural network) in MATLAB. It is seen that an ANN can be successfully employed to interpolate and predict FEA results, thus avoiding necessity of running FEA code for every new case. Furtheroptimization of geometry for maximum actuation length has been carried out using a GA (genetic algorithm) in MATLAB. The results of the GA were verified against the ANN and FEA results.

scholarly journals Development of a Design Catalog for Bonded Concrete Overlay Pavements Considering the Regional Characteristics of Korea

2021 ◽  
Vol 13 (17) ◽  
pp. 9876
Author(s):  
Hae-Won Park ◽  
Jin-Seok Seo ◽  
Jae-Hoon Lee ◽  
Jin-Hoon Jeong
Keyword(s):  
Design Method ◽  
Empirical Method ◽  
Climatic Conditions ◽  
Traffic Volume ◽  
Concrete Pavement ◽  
Element Analysis ◽  
Concrete Overlays ◽  
Volume Elastic Modulus ◽  
Bonded Concrete Overlay ◽  
Empirical Design

The design of overlay pavement in Korea, using the American empirical method, does not consider the unique Korean climate, pavement material, and traffic conditions. Therefore, in this study, a mechanistic–empirical design catalog for bonded concrete overlays (BCO) that are appropriate for Korean pavement conditions was developed. First, the thickness of the new pavement slab was determined through the Korean pavement design method, which uses a mechanistic–empirical design program according to the traffic volume of the region with the worst climatic conditions in Korea. Then, finite element analysis models of new jointed concrete and BCO pavements were developed to determine the BCO thickness by adjusting it until the stress–strength ratio of an existing slab of BCO pavement was equal to that of a new concrete pavement slab. By repeating this procedure, a design catalog was developed for the sustainable management of concrete pavement according to the traffic volume, elastic modulus, and thickness of the existing slab after milling. The appropriateness of the BCO thickness predicted by the design catalog was verified by comparing it with that predicted by other design methods.

Sign in / Sign up

Export Citation Format

Share Document