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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.
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.
When FWD tests are performed on broken or cracked pavements (of which information is crucial in making rehabilitation and overlay decisions), the multi-layered elastic theory-based backcalulation programs assume that the effect of these discontinuities in a cracked layer on deflection basins would be accounted for by the reduction of the elastic modulus for that layer. However, it has been concluded and confirmed by researchers and practitioners that the backcalculation algorithms based on the multi-layered elastic theory produce large variation in the algorithms based on the multi-layered elastic theory produce large variation in the 'effective' moduli of the cracked layers. Studies have also shown that significant errors in the backcalculated pavement moduli can accrue from performing a static analysis of what is inherently a dynamic test. Unfortunately, dynamic analysis usually involves complex calculations and requires significant computation time, thus making it impracticable for routine applications. This study presents a methology based on deflection basin parameters.
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
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.
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.