Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil.

The present research employs new boosting-based ensemble machine learning models i.e., gradient boosting (GB) and adaptive boosting (AdaBoost) to predict the unconfined compressive strength (UCS) of geopolymer stabilized clayey soil. The GB and AdaBoost models were developed and validated using 270 clayey soil samples stabilized with geopolymer, with ground-granulated blast-furnace slag and fly ash as source materials and sodium hydroxide solution as alkali activator. The database was randomly divided into training (80%) and testing (20%) sets for model development and validation. Several performance metrics, including coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE), were utilized to assess the accuracy and reliability of the developed models. The statistical results of this research showed that the GB and AdaBoost are reliable models based on the obtained values of R2 (= 0.980, 0.975), MAE (= 0.585, 0.655), RMSE (= 0.969, 1.088), and MSE (= 0.940, 1.185) for the testing dataset, respectively compared to the widely used artificial neural network, random forest, extreme gradient boosting, multivariable regression, and multi-gen genetic programming based models. Furthermore, the sensitivity analysis result shows that ground-granulated blast-furnace slag content was the key parameter affecting the UCS.

Predicting California bearing ratio of HARHA-treated expansive soils using Gaussian process regression.

The California bearing ratio (CBR) is one of the basic subgrade strength characterization properties in road pavement design for evaluating the bearing capacity of pavement subgrade materials. In this research, a new model based on the Gaussian process regression (GPR) computing technique was trained and developed to predict CBR value of hydrated lime-activated rice husk ash (HARHA) treated soil. An experimental database containing 121 data points have been used. The dataset contains input parameters namely HARHA-a hybrid geometrical binder, liquid limit, plastic limit, plastic index, optimum moisture content, activity and maximum dry density while the output parameter for the model is CBR. The performance of the GPR model is assessed using statistical parameters, including the coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), Relative Root Mean Square Error (RRMSE), and performance indicator (ρ). The obtained results through GPR model yield higher accuracy as compare to recently establish artificial neural network (ANN) and gene expression programming (GEP) models in the literature. The analysis of the R2 together with MAE, RMSE, RRMSE, and ρ values for the CBR demonstrates that the GPR achieved a better prediction performance in training phase with (R2 = 0.9999, MAE = 0.0920, RMSE = 0.13907, RRMSE = 0.0078 and ρ = 0.00391) succeeded by the ANN model with (R2 = 0.9998, MAE = 0.0962, RMSE = 4.98, RRMSE = 0.20, and ρ = 0.100) and GEP model with (R2 = 0.9972, MAE = 0.5, RMSE = 4.94, RRMSE = 0.202, and ρ = 0.101). Furthermore, the sensitivity analysis result shows that HARHA was the key parameter affecting the CBR.

Investigating the Mechanical Properties and Durability of Asphalt Mixture Modified with Epoxidized Natural Rubber (ENR) under Short and Long-Term Aging Conditions.

Modifiers such as fibers, fillers, natural and synthetic polymer extenders, oxidants and anti-oxidants, and anti-stripping agents are added to produce modified asphalt. However, polymers are the most widely utilized modifiers to enhance the function of asphalt mixtures. The objective of this research was to evaluate the mechanical properties and durability of epoxidized natural rubber (ENR)-modified asphalt mix under short- and long-term aging conditions. The physical and rheological characteristics of the base asphalt and ENR-modified asphalt (ENRMA) were tested. In order to evaluate the mechanical properties and durability of the modified mixtures, the resilient modulus of the ENR-asphalt mixtures under unaged, and short- and long-term aging conditions at various temperatures and frequencies was obtained. Furthermore, the resistance to moisture damage of asphalt mixtures was investigated. The findings showed that the stiffness of the ENR-asphalt mixes increased because of the mutual influence of short- and long-term aging on the mixes. In addition, ENR reduced the susceptibility to moisture damage. The stiffness of the mixes was influenced by the temperature and frequencies. By using mathematical modelling via the multivariable power least squares method, it was found that temperature was the dominant factor among all other factors. The results suggested that the durability of asphalt pavements is improved by using ENR.

Effect of Carbon Nanofibers on Physical, Adhesion and Rheological Properties of Liquid Epoxidized Natural Rubber Modified Asphalt.

This study aimed to evaluate the effects of carbon nanofibers (CNFs) on the performance of liquid epoxidized natural rubber (LENR)-modified asphalt. The physical, adhesion and rheological properties were determined by several tests such as penetration, elastic recovery, ring and ball softening point, Brookfield rotational viscometer, AFM and dynamic shear rheometer. LENR was used at concentrations of 3, 6, and 9%, while CNFs were used at contents of 0.3, 0.4, and 0.5% by weight of asphalt. Conventional test results showed that the increases in LENR and LENR/CNFs composite contents in binder leads to an increase in the hardness and consistency and a reduction in the temperature susceptibility of base asphalt. Adhesion results revealed that the addition of CNFs significantly increases the adhesion and bonding properties of base and rubberized binders. Rheological properties analysis exhibited that LENR improved the viscoelastic properties and permanent deformation resistance of asphalt at different temperatures and frequencies. On the other hand, it was found that the addition of CNFs significantly improves the stiffness, elasticity, and hardness of LENR-modified binders. The 6% LENR and 0.4% CNFs were found to be the optimum to enhance the physical, adhesion, and rheological properties of asphalt in this study. Thus, it can be stated that the addition of CNFs is promising to improve the performance of rubberized binders for high temperature applications.

Prediction of Rockfill Materials' Shear Strength Using Various Kernel Function-Based Regression Models-A Comparative Perspective.

The mechanical behavior of the rockfill materials (RFMs) used in a dam's shell must be evaluated for the safe and cost-effective design of embankment dams. However, the characterization of RFMs with specific reference to shear strength is challenging and costly, as the materials may contain particles larger than 500 mm in diameter. This study explores the potential of various kernel function-based Gaussian process regression (GPR) models to predict the shear strength of RFMs. A total of 165 datasets compiled from the literature were selected to train and test the proposed models. Comparing the developed models based on the GPR method shows that the superlative model was the Pearson universal kernel (PUK) model with an R-squared (R2) of 0.9806, a correlation coefficient (r) of 0.9903, a mean absolute error (MAE) of 0.0646 MPa, a root mean square error (RMSE) of 0.0965 MPa, a relative absolute error (RAE) of 13.0776%, and a root relative squared error (RRSE) of 14.6311% in the training phase, while it performed equally well in the testing phase, with R2 = 0.9455, r = 0.9724, MAE = 0.1048 MPa, RMSE = 0.1443 MPa, RAE = 21.8554%, and RRSE = 23.6865%. The prediction results of the GPR-PUK model are found to be more accurate and are in good agreement with the actual shear strength of RFMs, thus verifying the feasibility and effectiveness of the model.

Engineering characterisation of epoxidized natural rubber-modified hot-mix asphalt.

Road distress results in high maintenance costs. However, increased understandings of asphalt behaviour and properties coupled with technological developments have allowed paving technologists to examine the benefits of introducing additives and modifiers. As a result, polymers have become extremely popular as modifiers to improve the performance of the asphalt mix. This study investigates the performance characteristics of epoxidized natural rubber (ENR)-modified hot-mix asphalt. Tests were conducted using ENR-asphalt mixes prepared using the wet process. Mechanical testing on the ENR-asphalt mixes showed that the resilient modulus of the mixes was greatly affected by testing temperature and frequency. On the other hand, although rutting performance decreased at high temperatures because of the increased elasticity of the ENR-asphalt mixes, fatigue performance improved at intermediate temperatures as compared to the base mix. However, durability tests indicated that the ENR-asphalt mixes were slightly susceptible to the presence of moisture. In conclusion, the performance of asphalt pavement can be enhanced by incorporating ENR as a modifier to counter major road distress.