2017
DOI: 10.1016/j.conbuildmat.2016.12.186
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An ANN model to correlate roughness and structural performance in asphalt pavements

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Cited by 111 publications
(36 citation statements)
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“…Given the advances of deep learning, there has been significant research using these techniques for Pavement Engineering applications [21][22][23][24]. These applications can be assigned to the following areas: Pavement condition and performance predictions [25][26][27][28], Pavement management systems [29][30][31], pavement performance forecasting [32][33][34], structural evaluations [35][36][37], modelling pavement materials [38][39][40] and pavement image analysis and classification [22,[41][42][43][44]. Pavement Image analysis and classification is the most researched area, where the focus has been split between image classifications, where images are classified based on the distress occurring in the image; and object detection, where distresses are located within bounding boxes or masks within the image.…”
Section: The Use Of Deep Learning In Pavement Engineeringmentioning
confidence: 99%
“…Given the advances of deep learning, there has been significant research using these techniques for Pavement Engineering applications [21][22][23][24]. These applications can be assigned to the following areas: Pavement condition and performance predictions [25][26][27][28], Pavement management systems [29][30][31], pavement performance forecasting [32][33][34], structural evaluations [35][36][37], modelling pavement materials [38][39][40] and pavement image analysis and classification [22,[41][42][43][44]. Pavement Image analysis and classification is the most researched area, where the focus has been split between image classifications, where images are classified based on the distress occurring in the image; and object detection, where distresses are located within bounding boxes or masks within the image.…”
Section: The Use Of Deep Learning In Pavement Engineeringmentioning
confidence: 99%
“…Soft computing methods such as neuro-fuzzy models and ANN have been used to predict shear capacity and compressive strength of concrete structures with high accuracy [2,3]. A literature review indicates examples of application of the use of ANNs in a variety of cases for pavement engineering such as for predicting moduli from falling weight deflectometer (FWD) testing [4][5][6], spectral analysis of surface waves [7], prediction of laboratory permeability of hot mix asphalt (HMA) [8] and pavement performance [9][10][11], estimation of laboratory dynamic modulus of HMA [12,13] and pavement temperatures [14], prediction of non-linear material response [15], in-place layer moduli [16], foaming qualities of mixers [17], moisture damage of modified binders [18], field permeability of asphalt pavements [19] and in pavement management [20]. Ceylan et al [21] has summarized the application of ANN in pavement engineering in the following areas: "(1) prediction of pavement condition and performance, (2) pavement management and maintenance strategies, (3) pavement distress forecasting, (4) structural evaluation of pavement systems, (5) pavement image analysis and classification, (6) pavement materials modeling, and (7) other miscellaneous transportation infrastructure applications. "…”
Section: Introductionmentioning
confidence: 99%
“…Meanwhile, this study also briefly discussed the relationships among potholes, map cracking, longitudinal cracking, bleeding, and raveling. Furthermore, Sollazzo et al [19] probed the relationships between roughness and the pavement structural condition with an artificial neural network model, which is built and trained based on a huge number of data from the LTPP program. is model can give many clues for road engineers to determine maintenance measures at the project level, in which the pavement structure strength is very important for selecting maintenance measures.…”
Section: Introductionmentioning
confidence: 99%