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A software program has been developed to predict the remaining life of flexible pavements using artificial neural network (ANN) technology. The remaining life due to either rutting or fatigue cracking can be predicted. The inputs to the software are the best estimate of the thickness of the layers, the deflection basin measured with a falling weight deflectometer (FWD), and optionally, the extent of damage at the time of the FWD test. The outputs are the best estimate of the remaining life and the pavement performance curve. If uncertainty in the thicknesses, FWD measurements and traffic exists, a probabilistic description of the remaining life is also provided. The main benefit of the proposed approach is that the backcalculation process for determining moduli is not necessary. The remaining lives or alternatively the critical stresses needed to calculate them are directly estimated. As such, the results seem to be more robust. In this paper, the overall procedure and the details of the methodology followed in developing the software are described. A case study is included to demonstrate the application of the methodology.
Most mechanistic-empirical methods for determining the integrity of an existing pavement rely on the use of deflection-based nondestructive evaluation devices to determine the integrity of a pavement section. To estimate the remaining life associated with two types of distress in a flexible pavement, namely fatigue cracking and rutting, the critical strains and stresses at the interfaces of the layers of the pavement should be known. After the critical strains are calculated, a number of models can be used to estimate the remaining life. This report presents a case study that shows the feasibility of using an algorithm based on artificial neural network technology (ANN) to estimate the remaining life of flexible pavements. The report includes, in detail, the development and results of a system of ANN models that have been developed to predict the critical strains for a wide range of three and four layer flexible pavement sections with variable depth to bedrock. The inputs to these ANN models are only the best estimates of the thickness of each layer and the surface deflections obtained from a Falling Weight Deflectometer (FWD).
As with the previous two symposia, the 32 papers from the June/July, 1999, Seattle symposium present advances in the nondestructive testing of pavements using conventional falling weight deflectometer techniques and other promising techniques such as ground penetrating radar, rolling weight deflecto
Most mechanistic-empirical methods for determining the remaining life of an existing pavement rely on the use of deflection-based nondestructive evaluation (NDE) devices. This report describes a methodology based on Artificial Neural Network (ANN) techniques to estimate the remaining life of flexible pavements given the occurrence of two possible failure modes: rutting and fatigue cracking. The ANN techniques are also used to develop models that predict the critical strains at the interfaces of the pavement. The inputs to all the models are the best estimates of the thickness of each layer and the surface deflections obtained from a Falling Weight Deflectometer test. Uncertainty in these variables is accounted for by the proposed methodology. The report also describes an approach to the production of pavement performance curves using the results of the ANN models.
The term “soft computing” applies to variants of and combinations under the four broad categories of evolutionary computing, neural networks, fuzzy logic, and Bayesian statistics. Although each one has its separate strengths, the complem- tary nature of these techniques when used in combination (hybrid) makes them a powerful alternative for solving complex problems where conventional mat- matical methods fail. The use of intelligent and soft computing techniques in the field of geo- chanical and pavement engineering has steadily increased over the past decade owing to their ability to admit approximate reasoning, imprecision, uncertainty and partial truth. Since real-life infrastructure engineering decisions are made in ambiguous environments that require human expertise, the application of soft computing techniques has been an attractive option in pavement and geomecha- cal modeling. The objective of this carefully edited book is to highlight key recent advances made in the application of soft computing techniques in pavement and geo- chanical systems. Soft computing techniques discussed in this book include, but are not limited to: neural networks, evolutionary computing, swarm intelligence, probabilistic modeling, kernel machines, knowledge discovery and data mining, neuro-fuzzy systems and hybrid approaches. Highlighted application areas include infrastructure materials modeling, pavement analysis and design, rapid interpre- tion of nondestructive testing results, porous asphalt concrete distress modeling, model parameter identification, pavement engineering inversion problems, s- grade soils characterization, and backcalculation of pavement layer thickness and moduli.
This book brings together scientific experts in different areas that contribute to the design, analysis, and performance of sustainable pavements. This book also contributes to transportation engineering challenges and solutions, evaluate the state of the art, identify the shortcomings and opportunities for research, and promote the interaction with the industry. In particular, scientific topics that are addressed in this book include the use of different waste and recycled materials to improve pavement performance, pavement maintenance and rehabilitation, urban heat island due to transportation infrastructure and its mitigation techniques, machine learning applications in the prediction of pavement distresses, and analysis of pavement overlay.
Inspired from the legacy of the previous four 3DFEM conferences held in Delft and Athens as well as the successful 2018 AM3P conference held in Doha, the 2020 AM3P conference continues the pavement mechanics theme including pavement models, experimental methods to estimate model parameters, and their implementation in predicting pavement performance. The AM3P conference is organized by the Standing International Advisory Committee (SIAC), at the time of this publication chaired by Professors Tom Scarpas, Eyad Masad, and Amit Bhasin. Advances in Materials and Pavement Performance Prediction II includes over 111 papers presented at the 2020 AM3P Conference. The technical topics covered include: - rigid pavements - pavement geotechnics - statistical and data tools in pavement engineering - pavement structures - asphalt mixtures - asphalt binders The book will be invaluable to academics and engineers involved or interested in pavement engineering, pavement models, experimental methods to estimate model parameters, and their implementation in predicting pavement performance.
This book is an outcome of the sixth conference on bearing capacity of roads and airfield held in Lisbon, Portugal. It focuses on railway tracks and covers following topics: bearing capacity policies, concepts, costs and condition surveys; analysis and modelling; design and environmental effects.
Infrastructure Risk Assessment & Management contains selected papers presented at both the 10th International Conference on Computer Simulation in Risk Analysis and Hazard Mitigation and the 14th International Conference on Structures under Shock and Impact, organized by the Wessex Institute. The papers cover a variety of topics, including impact and blast loading, response of buildings and other structures to blast and their dynamic behaviour. These are all areas of active research and general interest, focused on the survivability of physical facilities and the protection of people. It contains a series of research contributions, essential to deepen the knowledge of how structures and materials behave under a wide variety of dynamic load actions. Current events emphasise the importance of the analysis and management of risk to planners, civil authorities, law enforcement agencies, non-governmental organisations, information technology experts and many other researchers and practitioners throughout the world. This volume brings together the work of researchers and other professionals actively involved in finding new ways to cope with the increased demands for a more effective control of impact and blast effects as well as risk management and control.