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The analysis results demonstrate that assuming full bonding, instead of actual interface conditions of asphalt-stabilized base layer, moduli of stabilized base layers is underestimated, up to 2.5 times smaller than the original values. Finally, the backcalculated layer moduli and the original moduli values were compared. Secondly, different friction coefficient values between asphalt layer and stabilized base layer were introduced into dynamic finite element model, accordingly, deflection basin parameters were calculated and layer moduli are determined based on the established regression functions. Then, the regression functions between deflections basin parameters and layer moduli were created and used to backcalculate layer moduli. Firstly, according to the full bonding hypothesis, a dynamic finite element analysis model was established to generate a synthetic surface deflection database of the semi-rigid asphalt pavements. The objective of this study was to analyze the errors in layer moduli backcalculation due to modeling of layer interface condition.
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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.To make appropriate rehabilitation decisions, determining structural layer moduli of the existing semi-rigid asphalt pavements is a crucial task for highway engineers. 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. 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.
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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 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. 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.
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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. Intelligent and Soft Computing in Infrastructure Systems Engineering Book Description : Intelligent and Soft Computing in Infrastructure Systems Engineering Papers were selected from the 5th GeoChina International Conference 2018 on Civil Infrastructures Confronting Severe Weathers and Climate Changes Conference, held on July 23 to 25, 2018 in HangZhou, China. The scope of this volume is the application of findings for solving problems in geotechnical, pavement, and transportation engineering using emerging techniques. The articles presented in this volume focus on fundamental investigations on various aspects of civil engineering materials and structures. This book presents challenges in transportation engineering, recent developments and advancements in technologies, and design and construction using sustainable materials. Solving Pavement and Construction Materials Problems with Innovative and Cutting edge Technologies Book Description : Solving Pavement and Construction Materials Problems with Innovative and Cutting edge Technologies