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Sponsored by the Committee on Expert Systems and Artificial Intelligence of the Technical Council on Computer Practices of ASCE. This report illustrates advanced methods and new developments in the application of artificial neural networks to solve problems in civil engineering.Ø Topics include: Øevaluating new construction technologies; Øusing multi-layeredØartificial neural networkØarchitecture to overcome problems with conventional traffic signal control systems; Øincreasing the computational efficiency of an optimization model; Øpredicting carbonation depth in concrete structures; Ødetecting defects in concrete piles; Øanalyzing pavement systems; Øusing neural network hybrids to select the most appropriate bidders for a construction project; and Øpredicting the Energy Performance Index of residential buildings. ØMany of the ideas and techniques discussed in this book cross across disciplinary boundaries and, therefore, should be of interest to all civil engineers.
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
Innovations in Road, Railway and Airfield Bearing Capacity – Volume 1 comprises the first part of contributions to the 11th International Conference on Bearing Capacity of Roads, Railways and Airfields (2022). In anticipation of the event, it unveils state-of-the-art information and research on the latest policies, traffic loading measurements, in-situ measurements and condition surveys, functional testing, deflection measurement evaluation, structural performance prediction for pavements and tracks, new construction and rehabilitation design systems, frost affected areas, drainage and environmental effects, reinforcement, traditional and recycled materials, full scale testing and on case histories of road, railways and airfields. This edited work is intended for a global audience of road, railway and airfield engineers, researchers and consultants, as well as building and maintenance companies looking to further upgrade their practices in the field.
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.
The analysis of pavement responses is important for better understanding of pavement performance and accurate estimation of pavement service life. This dissertation aims to study flexible pavement responses using forward and inverse analysis. The first objective is development of axisymmetric finite element (FE) models that can simulate FWD loading on the pavement system. After that, the backcalculation of pavement layer moduli from FWD testing was studied by means of soft computing techniques such as Artificial Neural Networks (ANNs) and Genetic Algorithms (GA). The axisymmetric FE models were used to generate a synthetic database. The ANN-GA backcalculating program is developed to assess existing pavement condition after the training and verification using the synthetic database. The second objective of this dissertation is to investigate airfield flexible pavement responses under aircraft loading in consideration of the realistic aircraft tire-pavement interaction. An advanced three-dimensional (3-D) finite element (FE) model was developed to simulate heavy aircraft loading with high tire pressure. The aircraft loading was simulated as moving wheels having non-uniform contact stress distributions. Different tire rolling conditions caused by aircraft ground maneuvering were simulated, including free rolling, full-braking, and turning. The multi-wheel aircraft loading was modeled in two-wheel, four-wheel and six-wheel assembly. The analysis concludes that FWD deflections were affected by dynamic analysis, temperature gradient, bedrock depth, asphalt layer delamination, viscoelasticity, and unbound material nonlinearity. After validated with the field measurements in the long-term pavement performance program (LTPP) database, the developed ANN-GA program can be used to obtain damaged dynamic moduli of asphalt concrete and evaluate in-situ pavement conditions from structural point of view, which facilitates pavement overlay design procedure using Mechanistic-Empirical Pavement Design Guideline (MEPDG). The investigation on airfield flexible pavement emphasized the importance of considering non-uniform tire contact stresses and temperature profiles in airfield pavement analysis. For the aircraft ground maneuvering, aircraft braking or turning significantly increases shear failure potential in asphalt layer. The analysis of stress states would facilitate evaluation of the shear failure potential at airfield asphalt pavements. Finally, the investigation on multi-wheel aircraft loading indicates that the six-wheel gear configuration would cause more fatigue cracking and near-surface cracking potential than dual-wheel and four-wheel gears.
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 presents innovative and interdisciplinary applications of advanced technologies. It includes the scientific outcomes of the 9th DAYS OF BHAAAS (Bosnian-Herzegovinian American Academy of Arts and Sciences) held in Banja Vrućica, Teslić, Bosnia and Herzegovina on May 25–28, 2017. This unique book offers a comprehensive, multidisciplinary and interdisciplinary overview of the latest developments in a broad section of technologies and methodologies, viewed through the prism of applications in computing, networking, information technology, robotics, complex systems, communications, energy, mechanical engineering, economics and medicine, to name just a few.