Incorporation of GPR data into genetic algorithms for assessing recycled pavements

Plati, Christina; Georgouli, Konstantina; Cliatt, Brad; Loizos, Andreas

doi:10.1016/j.conbuildmat.2017.06.109

Cited by 19 publications

(9 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the density prediction of asphalt concrete layer, the four prediction models chosen from literature-the Birefringence, Boettcher, Linearity indicator, and Rayleigh models-are presented in Equations ( 7)- (10), respectively, which can calculate the bulk density of the asphalt concrete layer using the dielectric constant [20][21][22][23][24].…”

Section: Calculation Methods Of Densitymentioning

confidence: 99%

See 1 more Smart Citation

Homogeneity Assessment of Asphalt Concrete Base in Terms of a Three-Dimensional Air-Launched Ground Penetrating Radar

et al. 2021

View full text Add to dashboard Cite

Obtaining the required homogeneity, including uniform thickness and density, is very crucial for controlling the quality of flexible asphalt layers. Although non-destructive testing (NDT) methods are time-saving and less labor-intensive, they only provide indirect measurement data under testing area conditions and strongly depend on the explanations by prediction models. In this study, in terms of the three-dimensional air-launched Ground Penetrating Radar (GPR) technique, the dielectric constant of asphalt concrete base with dry conditions in pavements was detected and calculated by different methods (the Coring Method, Reflection Amplitudes Method and Common Mid-Point Method). According to the calculated dielectric constant, the thickness and density of asphalt concrete base were further calculated and assessed. Comparing with the Coring Method, the Common Mid-Point Method was recommended to calculate dielectric constants in order to obtain reliable thickness of asphalt pavement base. Among the Birefringence, Boettcher, Linearity indicator, and Rayleigh models, the Rayleigh model was suggested to predict the density, and the predicted density exhibited a good correlation coefficient with the measured one. Furthermore, by choosing these proper calculation methods, an assessment was successfully conducted to evaluate homogeneity of a constructed field pavement in practice.

show abstract

Section: Calculation Methods Of Densitymentioning

confidence: 99%

“…Previous studies thus showed that GPR can be used to assess the thickness and density of asphalt concrete layers [20][21][22][23][24][25]. The information from the GPR should be explained and then used to calculate and predict dielectric constant, thickness, and density by using prediction models.…”

Section: Introductionmentioning

confidence: 99%

Homogeneity Assessment of Asphalt Concrete Base in Terms of a Three-Dimensional Air-Launched Ground Penetrating Radar

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In this work, GA is employed to calculate the optimal parameter values of C and s 2 because of its perfect performance in solving complicated optimization algorithms. 36,37 The main optimization procedure can be concluded as follows:…”

Section: Parameter Selection Of Svm Classifiermentioning

confidence: 99%

Wavelet packet energy–based damage identification of wood utility poles using support vector machine multi-classifier and evidence theory

Dackermann

Liu

et al. 2018

Structural Health Monitoring

View full text Add to dashboard Cite

This article presents a novel assessment framework to identify the health condition of wood utility poles. The innovative approach is based on the integration of data mining and machine learning methods and combines advanced signal processing, multi-sensor data fusion and decision ensembles to classify different damage condition types of wood poles. In the proposed framework, wavelet packet analysis is employed to transform captured multi-channel stress wave signals into energy information, which is consequently compressed by principal component analysis to extract a feature vector. Furthermore, support vector machine multi-classifier, optimized by genetic algorithm, is designed to identify the pole condition type. Finally, evidence theory is applied to fuse different assessment results from different sensors for a final decision. For validation of the proposed approach, the wood pole specimens with three common damage condition types are tested using a novel multi-sensor narrow-band frequency-excitation non-destructive testing system in the laboratory. The final experimental analysis results confirm that the proposed approach is capable of making full use of multi-sensor information and providing an effective and accurate identification on types of conditions in wood poles.

show abstract

“…New algorithms are further developed to assist with the fast convergence of the deflection basins and to reduce the effect of the seed variables selected on the latter. Genetic algorithm technique (GA) is recently presented as an advanced robust method and a highly effective optimization tool, capable of conveying various parameters and features that have an impact on the backcalculation procedure (Plati et al 2017). Instead of having a seed value, the optimizedunknown in GA can have a certain range.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Instead of having a seed value, the optimizedunknown in GA can have a certain range. Thus, making the quest for an optimal solution tends to be more potent than other approaches as the overall solution can be attained and the divergence is proscribed (Plati et al 2017). Additionally, Li and Wang (2019) combined GA with Artificial…”

Section: Literature Reviewmentioning

confidence: 99%

Development of Seed Variables Prediction Models for Use in Dynamic Backcalculation of FWD Data. (c2022)

Nasr¹

View full text Add to dashboard Cite

Understanding the material properties of a pavement structure is crucial for evaluating the pavement’s performance and assessing its damage level. Generally, the backcalculation process is extensively used to analyze the Falling Weight Deflectometer (FWD)-data for estimating the layer-moduli of a pavement structure. It is mainly an iterative process that starts with a set of seed (initial) variables, calculates the theoretical pavement surface deflections, and compares them to the measured deflections. Yet, this process is most likely unstable and is prone to numerous errors including the selection of relevant seed variables. The selected seed-variables hold significant consequences on the-final backcalculated-results. This research project aims-to-develop models through classification analysis to predict the seed variables. This involves (1) calculating theoretical surface deflections through a finite element model that simulates different pavement structures and properties, (2) calculating FWD parameters and indices for each structure and (3) using those parameters to build Random Forest models that predict the seed variables with low OOB-error and high accuracy. The dynamic approach is adopted to perform the analysis on 3-layered rigid and flexible pavements. The AC layer is modeled as an LVE/material while the PCC and the unbound layers are modeled as linear/elastic materials with damping. The OOB-Estimate of error rate and the overall accuracy values obtained dictate that the predictor variables selected to build the RF models are efficiently trained and generate accurate predictions for all seed variables except for the Rayleigh Damping Parameter of the PCC layer “𝛼𝑅𝑃𝐶𝐶”. The developed models can be considered as an effective guidance for pavement engineers to select the seed variables that are closer to the actual values to initiate the backcalculation process.

show abstract

Incorporation of GPR data into genetic algorithms for assessing recycled pavements

Cited by 19 publications

References 13 publications

Homogeneity Assessment of Asphalt Concrete Base in Terms of a Three-Dimensional Air-Launched Ground Penetrating Radar

Homogeneity Assessment of Asphalt Concrete Base in Terms of a Three-Dimensional Air-Launched Ground Penetrating Radar

Wavelet packet energy–based damage identification of wood utility poles using support vector machine multi-classifier and evidence theory

Development of Seed Variables Prediction Models for Use in Dynamic Backcalculation of FWD Data. (c2022)

Contact Info

Product

Resources

About