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Prediction of permanent deformation in asphalt pavements using a novel symbiotic organisms search–least squares support vector regression

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Abstract

The prediction of asphalt performance can be very important in terms of increasing service life and performance while saving energy and money. In this study, a new hybrid artificial intelligence (AI) system, SOS–LSSVR, has been proposed to predict the permanent deformation potential of asphalt pavement mixtures. SOS–LSSVR utilizes the symbiotic organisms search (SOS) and the least squares support vector regression (LSSVR), which are seen as a complementary system. The prediction model can be established from all input and output data pairs for LSSVR, while SOS optimizes the system’s tuning parameters. To avoid sampling bias and to partition the dataset into testing and training, a cross-validation technique was chosen. The results can be compared to those of previous studies and other predictive methods. Through the use of four error indicators, SOS–LSSVR accuracy was verified in predicting the permanent deformation behavior of an asphalt mixture. The present study demonstrates that the proposed AI system is a valuable decision-making tool for road designers. Additionally, the success of SOS–LSSVR in building an accurate prediction model suggests that the proposed self-optimized prediction framework has found an underlying pattern in the current database and thus can potentially be implemented in various disciplines.

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Authors and Affiliations

  1. Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Rd., Taipei, 106, Taiwan, ROC

    Min-Yuan Cheng & Yu-Wei Wu

  2. Department of Civil Engineering, Petra Christian University, Jalan Siwalankerto 121-131, Surabaya, 60236, Indonesia

    Doddy Prayogo

Authors
  1. Min-Yuan Cheng
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Correspondence to Doddy Prayogo.

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Cheng, MY., Prayogo, D. & Wu, YW. Prediction of permanent deformation in asphalt pavements using a novel symbiotic organisms search–least squares support vector regression. Neural Comput & Applic 31, 6261–6273 (2019). https://doi.org/10.1007/s00521-018-3426-0

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