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"The static elastic moduli of pavement layers can be considered to be among the most controversial physical properties in pavement engineering. In addition, pavement analysis using the static elastic moduli of the constituting layers is widely known and accepted by engineers and practitioners due to its simplicity. Nondestructive tests are commonly performed on existing pavements to measure the surface deflections, which in turn are used to backcalculate the elastic moduli of the pavement layers. However, the accuracy of the backcalculated moduli is dependent on the backcalculation procedure and the associated seed moduli. None of the existing classical backcalculation methods can find the 'actual' pavement moduli due to the theoretical limitations of the existing methods. These limitations include the convergence to local optima due to the use of seed moduli, which in turn lead to erroneous pavement moduli. The genetic algorithms can be used to optimize the search domain of the backcalculated moduli to avoid the premature convergence to local optima. The use of genetic algorithms in pavement engineering is new and no guidelines or thorough investigations have been carried out to address all the aspects and challenges associated with the backcalculation procedure using the genetic algorithms. This study can be considered as the first comprehensive work that deals with all aspects of both pavement and genetic algorithms and how to merge them. In addition, this work can be considered as the first state of the art work on the backcalculation of pavement moduli using genetic algorithms. In this study, the use of genetic algorithms has been studied thoroughly to address all the important parameters and operators that affect the backcalculation process. In addition, recommendations and findings regarding all the details needed to carry out the backcalculation process were identified and discussed thoroughly. New novel methods to study the interaction between the genetic operators and parameters and their effect on the backcalculation process were developed. Recommendations regarding the genetic operators as well as the genetic parameters were presented throughout the work. In addition, the AASHTO recommended ranges of pavement moduli were modified based on the study results to suit the GAs backcalculation process. On the other hand, a new novel method was developed to automate the backcalculation process. The automation of the backcalculation process was aimed at reducing the number of inputs needed to carry out the backcalculation process and to make it more appealing to be used in practice. A new Dynamic Parameterless Genetic Algorithm (DPGA) was developed as part of this work. The new DPGA can be extended to many other applications of genetic algorithms including robotics and optimizations. A new program (BackGenetic3d) was developed based on the novel MultiSmart3D program developed by the Computation and Simulation Group at the University of Akron. The new program is the first in the world that can backcalculate the pavement moduli of pavement systems with any arbitrary number of layers, loading conditions, and loading configurations. Existing classical programs use backcalculation procedures that lead to local optima and limited to a maximum of 5 pavement layers and one loading circle with uniform pressure."--Abstract.
The importance of a backcalculation method in the analysis of elastic modulus in pavement engineering has been known for decades. Despite many backcalculation programs employing different backcalculation procedures and algorithms, accurate inverse of the pavement layer moduli is still very challenging. In this work, a detailed study on the backcalculation of pavement layer elastic modulus and thickness using genetic algorithm is presented. Falling weight deflectometer (FWD) data is generated by applying a load to the pavement and measuring pavement deflection at various fixed distances from the load center. The measurement errors in FWD data are simulated by perturbing the theoretical deflections. Based on these data, backcalculation technique is performed using an improved genetic algorithm (GA). Besides root mean square (RMS), another objective function called area value with correction factor (AVCF) is proposed for accurate backcalculation of pavement modulus and thickness. The proposed backcalculation method utilizes the efficient and accurate program MultiSmart3D for the forward calculation and it can backcalculate the modulus and thickness simultaneously for any number of pavement layers. A simple, user-friendly, and comprehensive program called BackGenetic3D is developed using this new backcalculation method which can be utilized for any layered structures in science and engineering.
Bituminous Mixtures and Pavements contains 113 accepted papers from the 6th International ConferenceBituminous Mixtures and Pavements (6th ICONFBMP, Thessaloniki, Greece, 10-12 June 2015). The 6th ICONFBMP is organized every four years by the Highway Engineering Laboratory of the Aristotle University of Thessaloniki, Greece, in conjunction with
Modern highway engineering reflects an integrated view of a road system's entire lifecycle, including any potential environmental impacts, and seeks to develop a sustainable infrastructure through careful planning and active management. This trend is not limited to developed nations, but is recognized across the globe. Edited by renowned authority
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.
Bituminous Mixtures and Pavements VIII contains 114 papers as presented at the 8th International Conference ‘Bituminous Mixtures and Pavements’ (8th ICONFBMP, 12-14 June 2024, Thessaloniki, Greece). The contributions reflect the research and practical experience of academics and practicing engineers from thirty-four (34) different countries, and cover a wide range of topics: Session I: Bitumen, Modified binders, Aggregates, and Subgrade Session II: Bituminous mixtures (Design, Construction, Testing, Performance) Session III: Pavements (Design, Construction, Maintenance, Sustainability, Energy and Environmental consideration) Session IV: Pavement management and Geosynthetics Session V: Pavement recycling Session VI: Pavement surface characteristics, Pavement performance monitoring, Safety Session VII: Biomaterials in pavement engineering Session VIII: Prediction models of pavement performance Bituminous Mixtures and Pavements VIII covers recent advances in highway materials technology and pavement engineering, and will be of interest to scientists and professionals involved or interested in these areas. The ICONFBMP-conferences have been organized every four years since 1992. This 8th conference was jointly organized by: Laboratory of Highway Engineering, Aristotle University of Thessaloniki, Greece; Built Environment Research Institute (BERI), University of Ulster, UK; University of Texas San Antonio (UTSA), USA; Laboratory for Advanced Construction Technology (LACT), Technological Institute of Iowa, USA; Technological University of Delft (TUDelft), The Netherlands, and University of Antwerp, (UA), Belgium.
This volume presents selected papers presented during the 4th International Conference on Transportation Geotechnics. The papers address the geotechnical challenges in design, construction, maintenance, monitoring, and upgrading of roads, railways, airfields, and harbor facilities and other ground transportation infrastructure with the goal of providing safe, economic, environmental, reliable and sustainable infrastructures. This volume will be of interest to postgraduate students, academics, researchers, and consultants working in the field of civil and transport infrastructure.