A study on Artificial Neural Networks – Genetic Algorithm model and its application on back-calculation of road pavement moduli

Le, Dinh-Viet; Phan, Cao-Tho

doi:10.1109/atigb50996.2021.9423109

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…All recent methods continue using GA, for instance, together with Multi-Layer Elastic Theory (MLET) for investigating the performance of the foamed asphalt base [32]. New hybridized methods use GAs for the optimization of moduli back-calculation together with artificial neural networks (ANN) for the prediction of pavement deflection basin [33,34]. For instance, ANN-GA methods are developed considering the viscoelastic properties of the asphalt layer and nonlinear parameters of unbound layers [35][36][37].…”

Section: Back-calculation Through the Use Of A Gamentioning

confidence: 99%

Using Genetic Algorithms to Improve Airport Pavement Structural Condition Assessment: Code Development and Case Study

Donato

Carfì

2023

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In this paper, we propose a new method of optimization based on genetic algorithms using the MATLAB toolbox “Global Optimization”. The algorithm finds layers moduli of a flexible pavement through the measurement of pavement surface deflections under assigned load conditions. First, the algorithm for the forward calculation is validated, then the algorithm for the back-calculation is proposed, and the results are compared, in the case of airport pavements, with other software using different back-calculation techniques. The goodness of the procedure and the way of managing the algorithm operator is demonstrated by means of positive feedback obtained from the comparison of the results of ELMOD and BackGenetic3D. Moreover, the findings of the analysis prove that, in such an optimization procedure by GA, the best solution is always reached with a low number of generations, generally less than 10, allowing a reduction in the time of calculation and choosing a population big enough to select with good probability, in the initial population, solutions close to the real ones. The code is made available in such a way that the reader can easily apply it to other flexible pavements in the case of fully bonded layers (both for roads and airports). In particular, interested readers can easily modify the algorithm parameters (population number, stop criteria, probability of mutation, cross-over, and reproduction) and the type of fitness function to minimize, together with the geometric and load characteristics (number and thickness of the layers and the range of module variation). The possibility to change the algorithm parameters and the fitness function allows for exploring different scenarios in order to find the best solution in terms of fitness values. It is also possible to intervene in the time of calculation by managing the algorithm’s stopping criteria.

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Section: Back-calculation Through the Use Of A Gamentioning

confidence: 99%

Using Genetic Algorithms to Improve Airport Pavement Structural Condition Assessment: Code Development and Case Study

Donato

Carfì

2023

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“…The most effective method of nondestructive testing, used to determine the mechanical characteristics of structural layers in road pavement, is the method of determining the modulus of elasticity of the layers, based on solving the inverse problem of restoring the required parameters by maximum vertical displacements (bowl of deflections) recorded experimentally under impact loading. Recent years have witnessed fundamentally new approaches to solving this class of problems: with the use of artificial neural networks (Han et al, 2021;Saltan et al, 2013;Vyas et al, 2021;Wang et al, 2021), genetic algorithms to adjust the theoretical and experimental fields of vertical displacements (Fwa et al, 1997;Le and Phan, 2021;Park et al, 2010;Tsai et al, 2004;Varma et al, 2013;Wang et al, 2019;Zhang et al, 2021), new approaches to dynamic deformation analysis (Bazi and Assi, 2022;Booshehrian and Khazanovich, 2018;Cao et al, 2020;Lee et al, 2018;Zhang et al, 2019;Zhao et al, 2015), consideration for the wave nature of deformation (Al-Adhami and Gucunski, 2021;Chatti et al, 2017;Marchant and Papagiannakis, 2010;Quan et al, 2022;Zaabar et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Determining Viscoelastic Characteristics of the Elements of Multi-Layer Structures Based on Energy Dissipation Analysis

Tiraturyan

Uglova²,

Akulov³

2022

A&J

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The deteriorating operating conditions of roads is one of the most important problems facing specialists in the road industry. It is mainly associated with a reduction in the rigidity of road pavements made as extended multi-layer structures. To identify the causes of rigidity reduction, non-destructive testing is used, which is based on solving the inverse coefficient problem of restoring the elastic constants based on the response on the surface. Purpose of the study: We aimed to provide a rationale for a new pavement condition indicator that would take into account the history of deformation and loading from a source on the surface, based on which it would be possible to solve the inverse problem of restoring the elastic and viscous characteristics of pavement layers. Methods: To do that, we performed mathematical modeling of the stress-strain state of a multi-layer medium based on the solution of a system of dynamic Lame equations. Viscosity is taken into account by introducing tangents of the angles of wave energy losses in the materials of layers. Results: The results obtained in modeling for the first time made it possible to establish the relationship between changes in the modulus of elasticity as well as tangents of the angles of energy losses in pavement layers and the amount of energy dissipation in the structure. Discussion: It should be noted that it is possible to switch from the bowl of maximum dynamic deflections as the main pavement condition indicator to the analysis of hysteresis loops on the road structure surface, recorded at different distances from the point of load application and being an analogue of the full bowl of dynamic deflections showing the history of the test object loading.

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Modulus backcalculation methodology based on full-scale testing road and its rationality and feasibility analysis

Xiong

Zhang

et al. 2022

International Journal of Pavement Engineering

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A study on Artificial Neural Networks – Genetic Algorithm model and its application on back-calculation of road pavement moduli

Cited by 3 publications

References 6 publications

Using Genetic Algorithms to Improve Airport Pavement Structural Condition Assessment: Code Development and Case Study

Using Genetic Algorithms to Improve Airport Pavement Structural Condition Assessment: Code Development and Case Study

Determining Viscoelastic Characteristics of the Elements of Multi-Layer Structures Based on Energy Dissipation Analysis

Modulus backcalculation methodology based on full-scale testing road and its rationality and feasibility analysis

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