Because natural coarse aggregates were depleting rapidly, concrete industry has been trended toward substitute aggregates from industrial by-products or waste. One of the waste materials is oil palm ash (OPS), which is widely generated in the processing of palm oil in the tropics. Concretes made with OPS to estimate the compressive strength (CS) is cost and time consuming. This study aims to propose novel hybrid models by concepts of extreme gradient boosting (XGB) model optimized with different optimization algorithms such as sinecosine algorithm, multiverse optimization algorithm (MVO), and particle swarm optimization for predicting the uniaxial CS (UCS) of oil palm shell lightweight aggregate concrete (OPS). Also, the multivariate adaptive regression spline model is also developed to present a meaningful relationship between input and output variables. To this aim, a data set containing data samples for concrete made with OPS was gathered from the published literature. Results show that all models have acceptable performance in predicting the UCS, representing the admissible correlation between observed and predicted values and models' robustness. In the training step, the value of R 2 , the root mean square error, and the variance accounted factor for MVO-XGB are 0.9713, 1.5777, and 97.129. These values in testing phase are 0.9019, 2.6786, and 89.158. Therefore, the MVO-XGB model outperforms others, and the results demonstrate the ability of the MVO algorithm to determine the optimal value of XGB parameters.
The Bauschinger effect (BE) of X80 pipeline steel was investigated in this paper. Axial loading tests were carried out, both forward and reverse stress-strain curves were measured, and the constitutive relations with BE have been presented for X80 steel. Bending experiments were performed and the stress in the specimens was calculated. The reverse bending Strength of the steel must decrease much with the increase of pre-deformation. The BE of X80 steel is noticeable.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.