Intelligent and Soft Computing in Infrastructure Systems by Imad N. Abdallah, Soheil Nazarian (auth.), Kasthurirangan

By Imad N. Abdallah, Soheil Nazarian (auth.), Kasthurirangan Gopalakrishnan, Halil Ceylan, Nii O. Attoh-Okine (eds.)

The use of clever and smooth computing thoughts within the box of geomechanical and pavement engineering has progressively elevated over the last decade due to their skill to confess approximate reasoning, imprecision, uncertainty and partial fact. due to the fact real-life infrastructure engineering judgements are made in ambiguous environments that require human services, the appliance of sentimental computing strategies has been an enticing alternative in pavement and geomechanical modeling. the target of this rigorously edited booklet is to focus on key fresh advances made within the software of sentimental computing ideas in pavement and geomechanical structures. tender computing suggestions mentioned during this e-book contain, yet usually are not restricted to: neural networks, evolutionary computing, swarm intelligence, probabilistic modeling, kernel machines, wisdom discovery and information mining, neuro-fuzzy platforms and hybrid methods. Highlighted software components comprise infrastructure fabrics modeling, pavement research and layout, fast interpretation of nondestructive checking out effects, porous asphalt concrete misery modeling, version parameter identity, pavement engineering inversion difficulties, subgrade soils characterization, and backcalculation of pavement layer thickness and moduli. Researchers and practitioners engaged in constructing and making use of gentle computing and clever platforms ideas to fixing real-world infrastructure engineering difficulties will locate this e-book very priceless. This ebook also will function a superb state of the art reference fabric for graduate and postgraduate scholars in transportation infrastructure engineering.

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This abstract space is termed the model space, and is denoted by M. Individual sets of parameters describing a specific model, m={m1,m2,…}, are basically points in the model space. In an inverse problem, the values of parameters m are of the main interest; however, they are not directly measurable. The goal of the inverse problem is to obtain information on the values of m by making a direct observation on another set of parameters denoted as dobs. Similar to the concept of a model space, an abstract idea of a data space D can be introduced, which is the space of all conceivable observed responses, such as d.

As shown, these results have much sharper peaks at the correct moduli values. Therefore, it can be concluded that using the complete deflection time histories as the data is a much more reliable approach in the FWD backcalculation. 1 Background Elastic waves carry substantial information about the characteristics of the media they propagate in. The seismic evaluation techniques use the information carried by elastic waves to infer information about the properties of the media. These techniques are generally nondestructive and are being used increasingly in engineering applications, such as in determination of stiffness and integrity of structural elements, evaluation of elastic moduli of soil deposits and pavement systems, void detection and sizing in geotechnical engineering, crack detection, etc.

To accurately model the wave propagation, several criteria were imposed during the modeling to ensure accuracy of the simulation. The element size was selected relatively small to capture short wavelengths, while the overall model was relatively large to allow propagation of large wavelengths and reduce the boundary effects. Infinite absorbing elements were used at the boundaries to further reduce the reflections from the boundaries. 42 R. Hadidi and N. Gucunski (a) (b) 20 80 100 120 40 60 Layer 1 Compressional Wave Velocity (m/s) Correct Result Marginal Probability Marginal Probability Correct Result 20 140 40 60 80 100 120 Layer 2 Compressional Wave Velocity (m/s) 140 (c) Marginal Probability Correct Result 20 120 40 60 80 100 Layer 3 Compressional Wave Velocity (m/s) (d) (e) Correct Result Marginal Probability Marginal Probability Correct Result 20 140 40 60 80 100 120 Layer 4 Compressional Wave Velocity (m/s) 140 20 40 60 80 100 120 Layer 5 Compressional Wave Velocity (m/s) 140 Fig.

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