Estimation of soil erodibility in Peninsular Malaysia: A case study using multiple linear regression and artificial neural networks.

Soil erodibility (K) is an essential component in estimating soil loss indicating the soil's susceptibility to detach and transport. Data Computing and processing methods, such as artificial neural networks (ANNs) and multiple linear regression (MLR), have proven to be helpful in the development of predictive models for natural hazards. The present case study aims to assess the efficiency of MLR and ANN models to forecast soil erodibility in Peninsular Malaysia. A total of 103 samples were collected from various sites and K values were calculated using the Tew equation developed for Malaysian soil. From several extracted parameters, the outcomes of correlation and principal component analysis (PCA) revealed the influencing factors to be used in the development of ANN and MLR models. Based on the correlation and PCA results, two sets of influencing factors were employed to develop predictive models. Two MLR (MLR-1 and MLR-2) models and four neural networks (NN-1, NN-2, NN-3, and NN-4) optimized using Levenberg-Marquardt (LM) and scaled conjugate gradient (SCG) were developed and evaluated. The model performance validation was conducted using the coefficient of determination (R2), mean squared error (MSE), root mean squared error (RMSE), and Nash-Sutcliffe efficiency coefficient (NSE). The analysis showed that ANN models outperformed MLR models. The R2 values of 0.446 (MLR-1), 0.430 (MLR-2), 0.894 (NN-1), 0.855 (NN-2), 0.940 (NN-3), and 0.826 (NN-4); MSE values of 0.0000306 (MLR-1), 0.0000315 (MLR-2), 0.0000158 (NN-1), 0.0000261 (NN-2), 0.0000318 (NN-3), and 0.0000216 (NN-4) suggested the higher accuracy and lower modelling error of ANN models as compared with MLR. This study could provide an empirical basis and methodological support for K factor estimation in the region.

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.

The relationship between the demographic, personal, and social factors of Malaysian motorcyclists and risk taking behavior at signalized intersections.

In the context of road safety, risk-taking is undoubtedly one of the main contributory factors in road accidents. The actual forces which influence individuals to take such risks, nevertheless, are still not fully understood. To address this, this study was therefore conducted to investigate the relationship of the demographic, personal, and social factors of motorcyclists, with a specific focus on their risk-taking behavior at signalized intersections in Malaysia. This study adopted the quantitative method using cross-sectional questionnaire surveys and involved 251 respondents. The demographic factors were analyzed using the t-test and an ANOVA Scheffe Post-Hoc test, while the motorcyclists' personal and social characteristics were analyzed with multiple linear regression. The findings indicate that the individuals who were greater risk takers at signalized intersections were teenage motorcyclists (16-25 years old) who had finished their education before taking their high school diploma, and who also received a lower than average monthly income from private sector firms. The actual experience of accidents was also shown to be positively related to this risk-taking behavior. In addition, in term of personal and social factors, results showed that, for these individuals, there was a significant difference between the strength of peer influence and that of parental and spouse guidance. However, there was no significant difference in the risk-taking behavior of Malaysian motorcyclists riding at signalized intersections for the following factors: between genders, in terms of accident involvement, in terms of enforcement of traffic regulations, and prevention steps and confidence level after being involved in an accident.