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

机译：使用新型共生生物的沥青路面中永久性变形预测 - 最小二乘支持向量回归

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摘要

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.
机译：在节省能源和金钱时，沥青表现的预测可能非常重要。在本研究中，已经提出了一种新的混合人工智能（AI）系统SOS-LSSVR，以预测沥青路面混合物的永久变形电位。 SOS-LSSVR利用共生生物搜索（SOS）和最小二乘支持向量回归（LSSVR），其被视为互补系统。可以从LSSVR的所有输入和输出数据对建立预测模型，而SOS优化系统的调谐参数。为避免采样偏置并将数据集分区为测试和培训，选择了交叉验证技术。结果可以与先前的研究和其他预测方法进行比较。通过使用四个误差指示器，在预测沥青混合料的永久变形行为时验证了SOS-LSSVR精度。本研究表明，所提出的AI系统是道路设计师的有价值的决策工具。另外，建立精确预测模型的SOS-LSSVR的成功表明，所提出的自我优化预测框架已经发现了当前数据库中的底层图案，因此可能在各种学科中实现。

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