Journal ArticleDOI
Next-Generation Models for Evaluation of the Flow Number of Asphalt Mixtures
TLDR
In this article, the authors presented the development of next-generation prediction models for the flow number of dense asphalt-aggregate mixtures via an innovative machine learning approach, using linear genetic programming (LGP) and artificial neural network (ANN).Abstract:
This paper presents the development of next-generation prediction models for the flow number of dense asphalt–aggregate mixtures via an innovative machine learning approach. New nonlinear models were developed to predict the flow number using two robust machine learning techniques, called linear genetic programming (LGP) and artificial neural network (ANN). The flow number of Marshall specimens was formulated in terms of percentages of coarse aggregate, filler, bitumen, air voids, voids in mineral aggregate, and Marshall quotient. An experimental database containing 118 test results for Marshall specimens was used for the development of the models. Validity of the models was verified using parts of laboratory data that were not involved in the calibration process. The statistical measures of coefficient of determination, coefficient of efficiency, root-mean squared error, and mean absolute error were used to evaluate the performance of the models. Further, a multivariable least-squares regression ...read more
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Journal ArticleDOI
Probability and statistics in Civil Engineering: by G.N. Smith, Nichols Publishing Company, New York, NY, 1986, 244 pp.
Journal ArticleDOI
Prediction of UCS and CBR of microsilica-lime stabilized sulfate silty sand using ANN and EPR models; application to the deep soil mixing
TL;DR: In this article, the results of 90 Unconfined Compressive Strength (UCS) and California Bearing Ratio (CBR) tests on sulfate silty sand stabilized with different lime and microsilica percentages as the two main stabilizers.
Journal ArticleDOI
Prediction of permanent deformation in asphalt pavements using a novel symbiotic organisms search–least squares support vector regression
TL;DR: 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.
Journal ArticleDOI
Compressive strength analysis of soil reinforced with fiber extracted from water hyacinth
TL;DR: In this paper, the authors proposed an evolutionary approach of multi-gene genetic programming (MGGP) to formulate the functional relationships between unconfined compressive strength (UCS) of reinforced soil and four inputs (soil moisture, soil density, fiber content and unreinforced soil strength) of the silty sand.
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An eXtreme Gradient Boosting model for predicting dynamic modulus of asphalt concrete mixtures
TL;DR: In this article, an eXtreme Gradient Boosting (XGBoost) approach was proposed to predict the dynamic modulus (DM) of asphalt concrete (AC) mixtures.
References
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Approximation by superpositions of a sigmoidal function
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Beware of q2
TL;DR: It is argued that the high value of LOO q2 appears to be the necessary but not the sufficient condition for the model to have a high predictive power, which is the general property of QSAR models developed using LOO cross-validation.