Neural Networks Modeling of Stress Growth in Asphalt Overlays due to Load and Thermal Effects during Reflection Cracking

Although several techniques have been introduced to reduce reflective cracking, one of the primary forms of distress in hot-mix asphalt (HMA) overlays of flexible and rigid pavements, the underlying mechanism and causes of reflective cracking are not yet well understood. Fracture mechanics is used to understand the stable and progressive crack growth that often occurs in engineering components under varying applied stress. The stress intensity factor (SIF) is its basis and describes the stress state at the crack tip. This can be used with the appropriate material properties to calculate the rate at which the crack will propagate in a linear elastic manner. Unfortunately, the SIF is difficult to compute or measure, particularly if the crack is situated in a complex three-dimensional (3D) geometry or subjected to a non-simple stress state. In this study, the neural networks (NN) methodology is successfully used to model the SIF as cracks grow upward through a HMA overlay as a result of both load and thermal effects with and without reinforcing interlayers. Nearly 100,000 runs of a finite-element program were conducted to calculate the SIFs at the tip of the reflection crack for a wide variety of crack lengths and pavement structures. The coefficient of determination (R2) of all the developed NN models except one was above 0.99. Owing to the rapid prediction of SIFs using developed NN models, the overall computer run time for a 20-year reflection cracking prediction of a typical overlay was significantly reduced

Neural networks based models for mechanistic-empirical design of rubblized concrete pavements

Rubblization is an in-place rehabilitation technique that involves breaking the concrete pavement into pieces. This process results in a structurally sound, rut resistant base layer which prevents reflective cracking (by obliterating the existing concrete pavement distresses and joints) that can then be overlaid with Hot-Mix Asphalt (HMA). The design of the structural overlay thickness for rubblized projects is difficult as the resulting structure is neither a true rigid pavement nor a true flexible pavement. The HMA overlay thickness design methodology currently used in the state of Iowa is purely empirical. In the Mechanistic-Empirical (M-E) design approach developed for the analysis and design of rubblized concrete pavements in Iowa, the tensile strain at the bottom of the HMA layer (εt) is used to predict fatigue life using an HMA fatigue design algorithm and the vertical compressive strain on top of the subgrade layer (εc) is used to consider subgrade rutting. In the current study, the use of Artificial Neural Networks (ANN)-based structural models for predicting the critical strains based on FWD deflection data, is successfully demonstrated. The ANN-based structural models were validated by comparing the ANN-based strain predictions with the field-measured strains from an instrumented trial project at highway IA-141 located in Polk County, Iowa

A Framework to Incorporate a Structural Capacity Indicator into the State of Louisiana Pavement Management System

Non-structural factors such as surface distresses and ride quality have been commonly used as the main indicators of in-service pavement conditions. In the last decade, the concept of implementing a structural condition index in Pavement Management System (PMS) to complement functional condition indices has become an important goal for many highway agencies. The Rolling Wheel Deflectometer (RWD) provides the ability to measure pavement deflection while operating at the posted speed limits causing no user delays. The objective of this study was two-fold. First, this study developed a model to predict pavement structural capacity at a length interval of 0.16 km (0.1 mi.) based on RWD measurements and assessed its effectiveness in identifying structurally deficient pavement sections. Second, this study introduced a framework, along with the required implementation tools, for incorporating pavement structural conditions into the Louisiana PMS decision matrix at the network level. The proposed framework aims at filling the gap between network level and project level decisions and eventually, allowing more accurate budget estimation. To achieve these objectives, RWD data collected from 153 road sections (more than 1,600 km) in District 05 of Louisiana were utilized in this study. The predicted Structural Number (SNRWD0.1) showed an acceptable accuracy with a Root Mean Square Error (RMSE) of 0.8 and coefficient of determination (R2) of 0.80 in the validation stage. Core samples showed that sections that were predicted to be structurally-deficient suffered from asphalt stripping and material deterioration distresses. Results support that the developed model is a valuable tool that could be used in PMS at the network level to predict pavement structural condition with an acceptable level of accuracy. With respect to the implementation of RWD in Louisiana PMS, two enhanced decision trees, for collectors and arterials, were developed, such that both functional and structural pavement conditions are considered in the decision-making process. Implementation of RWD in the decision-making process is demonstrated and is expected to improve the overall performance of the pavement network. Furthermore, the enhanced decision trees are expected to reduce the total maintenance and rehabilitation (M&R) construction costs if applied to relatively high volume roads (e.g., Interstates, Arterials, and Major Collectors). Based on the results of this study, a one-step enhanced decision-making tool, which considers both structural and functional pavement conditions in treatment selection, was developed. In the developed tool, the predicted structural number based on RWD measurements was utilized to calculate a pavement structural health indicator known as the Structural Condition Index (SCI). Finally, an Artificial Neural Network (ANN)-based pattern recognition system was trained and validated using pavement condition data and RWD measurements-based SN to arrive at the most optimum maintenance and rehabilitation (M&R) decisions

Rehabilitation of concrete pavements utilizing rubblization: A mechanistic based approach to HMA overlay thickness design

In Iowa, there are many portland cement concrete (PCC) type highway pavements. These pavements deteriorate over time due to materials, traffic and environmental related distresses and they are commonly rehabilitated by providing a hot-mix asphalt (HMA) overlay. To mitigate reflection cracking, a frequently observed distress on HMA overlaid PCC pavements, various fractured slab techniques are used, of which rubblization is considered to be the most utilized and effective technique. This paper describes the development of a mechanistic-empirical (M-E) thickness design system for HMA overlaid rubblized PCC pavements. In this computerized design procedure, HMA fatigue and subgrade rutting failure are considered using appropriate transfer functions. The design system strain predictions were validated using field results from an instrumented trial section in Polk county, Iowa

Development and Evaluation of Performance Models for Asphalt, Concrete, and Composite Pavements using Machine Learning

Transportation infrastructures account for a considerable portion of public investments, which serve as the backbone of a country’s economy by providing essential services to businesses and people. In the United States, public investments in transportation infrastructure assets represent trillions of dollars. The U.S road network consists of about 4 million miles, being the world’s largest, longest, and biggest transportation system. Paved roads account for 2.6 million miles, and 93% of them are surfaced with asphalt. However, a portion of the paved roads consists of asphalt overlaid concrete pavements, also known as composite pavements. When concrete pavements start to fail, they are overlaid with Hot Mix Asphalt (HMA). Compared to flexible or rigid pavements, this offers better performance measures both structurally and functionally, and accordingly, it can be considered a cost-effective alternative.Several performance indicators have been used to assess pavement surface conditions, but the Pavement Condition Rating (PCR) and the International Roughness Index (IRI) are the most widely used and well-recognized pavement performance indicators. Transportation agencies use these indexes to evaluate and classify the conditions for the road networks in the long term. If maintenance and rehabilitation (M&R) interventions are not performed timely, the pavement damage caused by environmental impacts and traffic repetitions can lead the roads to early deterioration. Billions of dollars are spent every year on M&R. However, a shortage in federal and state funds led roads and bridges to poor conditions since M&R interventions were not carried out timely. Therefore, there is a need to develop pavement performance prediction models that can support and allow decision-makers to prioritize M&R actions due to the limited budget allocation and estimate the rate of pavement deterioration. Traditionally, linear, non-linear, multiple linear regression analysis, Markov chains, mechanistic-empirical relations, survivor curves, semi-Markov, and Bayesian models have been used for predicting pavement performance. However, simple statistical approaches do not account for the complex relations among input variables and pavement performance. A growing body of literature is exploring the use of more advanced modeling techniques for pavement performance prediction. Among these techniques, the Artificial Neural Networks (ANNs) approach has shown the most significant improvements with consistent and reliable results. However, most performance models did not consider M&R history in the model development. This doctoral research presents new pavement performance models incorporating the M&R history and activities for composite pavements of the LTPP database. Additionally, a more comprehensive approach was developed for flexible, rigid, and composite pavements of the Mississippi Department of Transportation (MDOT) database, accounting for the influence of M&R history. This dissertation successfully utilized the ANNs modeling technique to obtain accurate and promising prediction results for pavement performance. Furthermore, the development of a simple, low-cost, and easy-access graphical user interface (GUI) tool brings a significant contribution to the enhancement of agencies\u27 pavement management system (PMS) by predicting future pavement conditions, identifying rehabilitation needs, and allowing a better budget allocation for critical pavement sections without the need of distress data

Predictive Analytics for Roadway Maintenance: A Review of Current Models, Challenges, and Opportunities

With the pressing need to improve the poorly rated transportation infrastructure, asset managers leverage predictive maintenance strategies to lower the life cycle costs while maximizing or maintaining the performance of highways. Hence, the limitations of prediction models can highly impact prioritizing maintenance tasks and allocating budget. This study aims to investigate the potential of different predictive models in reaching an effective and efficient maintenance plan. This paper reviews the literature on predictive analytics for a set of highway assets. It also highlights the gaps and limitations of the current methodologies, such as subjective assumptions and simplifications applied in deterministic and probabilistic approaches. This article additionally discusses how these shortcomings impact the application and accuracy of the methods, and how advanced predictive analytics can mitigate the challenges. In this review, we discuss how advancements in technologies coupled with ever-increasing computing power are creating opportunities for a paradigm shift in predictive analytics. We also propose new research directions including the application of advanced machine learning to develop extensible and scalable prediction models and leveraging emerging sensing technologies for collecting, storing and analyzing the data. Finally, we addressed future directions of predictive analysis associated with the data-rich era that will potentially help transportation agencies to become information-rich

Analysis of jointed plain concrete pavement systems with nondestructive test results using artificial neural networks

The primary goal of this research was to show that artificial neural network (ANN) models could be developed to perform rapid and accurate predictions of jointed plain concrete pavement system (JPCP) parameters which will enable pavement engineers to incorporate the state-of-the-art finite element (FE) solutions into routine practical design. The ISLAB2000 finite element program has been used as an advanced structural model for solving the responses of the concrete pavement systems and generating a large knowledge database.;Totally, fifty-six ANN-based backcalculation and forward calculation models were developed as part of this research for the analysis of JPCP systems under traffic and temperature loading combinations to predict the concrete pavement parameters and critical pavement responses. In this research, BCM stands for the ANN-based backcalculation model and FCM stands for the ANN-based forward calculation model. BCM-EPCC, BCM-kS, BCMTELTD, FCM-RRS, and FCM-sigma MAX models were developed for the prediction of elastic modulus of Portland cement concrete (PCC) layer (EPCC), coefficient of subgrade reaction (kS) of the pavement foundation, total effective linear temperature difference (TELTD) between top and bottom of the PCC layer, radius of relative stiffness (RRS) of the pavement system, and maximum tensile stresses at the bottom of the Portland cement concrete layer (sigmaMAX), respectively. These ANN-based models gave average errors less than 1% for synthetic database. In order to develop more robust networks that can tolerate the noisy or inaccurate pavement deflection patterns collected from the Falling Weight Deflectometer (FWD) field tests, several network architectures were also trained with varying levels of noise in them.;One of the most important advantages of the presented ANN approach is that the use of the ANN-based models resulted in a drastic reduction in computation time. Rapid prediction ability of the ANN-based models (capable of analyzing 100,000 FWD deflection profiles in one second) provides a tremendous advantage to the pavement engineers by allowing them to nondestructively assess the condition of the transportation infrastructure in real time while the FWD testing takes place in the field. In the developed approach, there is also no need a seed moduli or iteration process of the solution in order to predict the JPCP system parameters. The prediction of temperature difference (TELTD) in PCC layer which causes the slab curling and warping in concrete pavements is another tremendous advantage of the developed approach over the other methods since no other method does not take into account this parameter in the analyses. Finally, it can be concluded that ANN-based analysis models can provide pavement engineers and designers with state-of-the-art solutions, without the need for a high degree of expertise in the input and output of the problem, to rapidly analyze a large number of concrete pavement deflection basins needed for project specific and network level pavement testing and evaluation

Identification of Top-down, Bottom-up, and Cement-Treated Reflective Cracks Using Convolutional Neural Network and Artificial Neural Network

The objective of this study was to formulate a Convolutional Neural Networks (CNN) model and to develop a decision-making tool using Artificial Neural Networks (ANN) to identify top-down, bottom-up, and cement treated (CT) reflective cracking in in-service flexible pavements. The CNN’s architecture consisted of five convolutional layers with three max-pooling layers and three fully connected layers. Input variables for the ANN model were pavement age, asphalt concrete (AC) thickness, annual average daily traffic (AADT), type of base, crack orientation, and crack location. The ANN network architecture consisted of an input layer of six neurons, a hidden layer of ten neurons, and a target layer of three neurons. The developed CNN model was found to achieve an accuracy of 93.8% and 91.0% in the testing and validation phases, respectively. The ANN based decision-making tool achieved an overall accuracy of 92% indicating its effectiveness in crack identification and classification. In the second phase of the study, the flexible pavement responses under a dual tire assembly were analyzed to identify the critical stress mechanisms for bottom-up and top-down cracking. Higher tensile strains were observed to occur underneath the tire ribs than away from them supporting the argument that both surface initiated and bottom-up fatigue cracking develop in or near the wheel paths. The incorporation of surface transverse tangential stresses increased the surface tensile strains near the tire ribs by approximately 68%, 63%, and 53% respectively for low, medium, and high volume flexible pavements indicating an increased potential for the initiation and development of top-down cracking when tangential stresses are considered. In contrast, this effect was observed to be minimal for the tensile strains at the bottom of the asphalt layer, which are the main pavement responses used in the prediction of fatigue cracking. Shrinkage cracking in cement treated base (CTB) was also modeled in finite element using displacement boundary conditions. The tensile stresses due to shrinkage strains in the cement treated base were observed to be comparable to the tensile strength of CTB at 7 days and higher at 56 days indicating the potential development of shrinkage cracks

Overlay of flexible pavements: an ANN approach

The main problem in flexible pavement is deterioration due to traffic loading, material related factors and adverse climatic conditions. In order to avoid and mitigate such difficulties, a maintenance program should be carried out rather going for reconstruction. Most common method adopted in India is the use of an asphalt overlay on the old surface to increase the serviceability of the existing road, but designing an overlay is challenging given restricted boundary conditions that must be observed and designed for. Although, there is provided design code but difficulties in solving process such as accurate field data, error prone design curve reading, less accurate conversion formula for temperature variation, time consuming calculations make it complex and dull to be used for everyday purpose. Unavailability of design software leads to manual calculation which is prone to errors. This study presents an attempt to apply artificial neural network to recommend asphalt overlay thickness (HMA). Though noted common methods need time, reliable and some essential data to be able to start designing process but artificial intelligence especially artificial neural network is a method based on learning process which can find possible relation between input and output sample data and is able to predict the output without any time with founded relation quickly. Results of this study reveal that artificial neural network is appropriate for implementation in calculating flexible overlay thickness