Impact of Asphalt Concrete Properties on the Illinois Flexibility Index Cracking and Hamburg Wheel Tracking Test Rutting Potential

Rivera-Pérez, José J.; Al-Qadi, Imad L.

doi:10.1061/jpeodx.pveng-1276

Cited by 1 publication

(3 citation statements)

References 25 publications

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“…The AC content and aggregate gradation predictions were in acceptable ranges. For the aggregate percent passing 0.075 mm, Rivera-Pérez and Al-Qadi ( 20 ) found that the aggregate percent passing in this sieve was less likely to affect the FI and RD compared with other percentages. Therefore, a model based on FI and RD to predict the aggregate percent passing would struggle to predict the aggregate percent passing at the 0.075 mm sieve and was not considered in this study.…”

Section: Model Resultsmentioning

confidence: 93%

“…A total of 8,117 HWTT data sets were collected from 3,772 AC mix designs dating from 2010 to 2020. The database preparation was detailed in Rivera-Pérez and Al-Qadi ( 20 ). The database contained 26 mix parameters, summarized in Table 1.…”

Section: Database and Data Preprocessingmentioning

confidence: 99%

“…The HWTT data sets include the minimum number of passes required to reach the threshold in accordance with IDOT specifications ( 21 ). Table 1 summarizes the impact of FI and rut depth as reported in Rivera-Pérez and Al-Qadi ( 20 ). The AC aggregate gradation data were collected per ( 22 ).…”

Section: Database and Data Preprocessingmentioning

confidence: 99%

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Asphalt Concrete Mix Design Optimization Using Autoencoder Deep Neural Networks

Rivera-Perez

Al-Qadi

2023

Transportation Research Record: Journal of the Transportation R

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Asphalt concrete (AC) balanced mix design (BMD) is based on the selection of aggregate gradation, component volumetrics, and binder content to control pavement cracking and rutting potential. The Illinois Flexibility Index Test (I-FIT) and the Hamburg Wheel Tracking Test (HWTT) results, used to predict cracking and rutting potential, respectively, are used in the BMD approach. However, BMD generally relies on a trial-and-error process to identify the aggregate gradation and binder content needed to meet volumetrics and optimize I-FIT and HWTT results. Minimizing or eliminating the trial-and-error process would increase productivity and accuracy. Therefore, this study proposes an autoencoder deep neural network (ADNN) to develop optimized AC mix design alternatives that can meet a prescribed flexibility index (FI) and rut depth (RD). Autoencoders are a type of neural network designed for representation learning composed of an encoder and a decoder. The encoder detects a structured pattern in the original input data to create a compressed representation of the AC mix design. The decoder reconstructs the compressed representation. The proposed autoencoder is composed of an encoder of five hidden layers, a latent space of one node, and a five-hidden-layer decoder. Models were created from a database of 5,357 data sets that include mix properties, I-FIT FI, and HWTT RD (after data preprocessing was conducted). An autoencoder was then trained to predict the total binder content, and aggregate gradation based on a target mix type, FI, and RD.

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Section: Model Resultsmentioning

confidence: 93%

Section: Database and Data Preprocessingmentioning

confidence: 99%