Feezan Ahmad¹, Xiaowei Tang¹, Jilei Hu^2,*, Mahmood Ahmad^3,4, Behrouz Gordan⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 455-487, 2023, DOI:10.32604/cmes.2023.025993

Abstract Slope stability prediction plays a significant role in landslide disaster prevention and mitigation. This paper’s reduced error pruning (REP) tree and random tree (RT) models are developed for slope stability evaluation and meeting the high precision and rapidity requirements in slope engineering. The data set of this study includes five parameters, namely slope height, slope angle, cohesion, internal friction angle, and peak ground acceleration. The available data is split into two categories: training (75%) and test (25%) sets. The output of the RT and REP tree models is evaluated using performance measures including accuracy (Acc), Matthews correlation coefficient (Mcc), precision… More >

E. Akshay¹, V. Sugumaran^1,*, M. Elangovan²

Structural Durability & Health Monitoring, Vol.16, No.3, pp. 255-270, 2022, DOI:10.32604/sdhm.2022.0271

Abstract Tool wear is inevitable in daily machining process since metal cutting process involves the chip rubbing the tool surface after it has been cut by the tool edge. Tool wear dominantly influences the deterioration of surface finish, geometric and dimensional tolerances of the workpiece. Moreover, for complete utilization of cutting tools and reduction of machine downtime during the machining process, it becomes necessary to understand the development of tool wear and predict its status before happening. In this study, tool condition monitoring system was used to monitor the behavior of a single point cutting tool to predict flank wear. A… More >